diff --git a/data/README.md b/data/README.md
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+# An evaluation of ML algorithms for syngas fermentation
+
+The purpose of this study is to determine the feasibility of using machine learning to model production rates from syngas fermentation. In this study, we transformed time course metabolite concentration data from ten syngas fermentation conditions to calculate production rates of four products: acetate, ethanol, butyrate, and butanol. The data was used to train and test six machine learning algorithms’ ability to predict the rate of production of the four products. We found support vector machine performed the best on unseen testing data when performance across all conditions were considered. We also found that models with relatively few fitted variables, including elastic net and lasso, had the most consistent performance while models with more fitted variables, including neural networks and random forests, had greater variability in their prediction accuracy. Additionally, feature importance analysis was able to reproduce domain knowledge that the most important gas components for producing butyrate and butanol are carbon monoxide and hydrogen gas respectively.
+
+- [System Requirements](#system-requirements)
+- [Instructions for use](#instructions-for-use)
+- [Summary of notebooks](#summary-of-notebooks)
+- [Reference](#reference)
+- [License](#license)
+
+## System Requirements
+
+The code was written using python 3.7
+
+## Instructions for use
+
+To run the code in this repository use the following commands:
+
+<ol>
+  <li>git clone https://github.com/garrettroell/SyngasMachineLearning.git</li>
+  <li>python3 -m venv venv</li>
+  <li>source venv/bin/activate</li>
+  <li>pip install -r requirements.txt</li>
+</ol>
+
+## Summary of notebooks
+
+- Notebook A: Converts experimental data into smoothed data with rows that contain pairs of reactor conditions and production rates
+- Notebook B: Finds the optimal hyperparameters for the algorithms using a grid search with shuffle/split cross validation
+- Notebook C: Trains the models with optimal hyperparameters from notebook B and evaluates their performance on training and test data
+
+## Reference
+
+[GW Roell, A Sathish, N Wan, Z Wen, YJ Tang, FS Bao. A comparative evaluation of machine learning algorithms in predicting syngas fermentation outcomes using limited experimental data. Biochemical Engineering Journal. 186 (2022)](https://www.sciencedirect.com/science/article/pii/S1369703X22002479?via%3Dihub)
+<br>
+[Non-paywalled pdf](https://garrettroell.com/papers/syngas-machine-learning)
+
+## License
+
+This code is distributed under the 3-Clause BSD license specified in the [license][1] file. It is open source and commercially usable.
+
+---
+
+[1]: license
diff --git a/data/data/experimental_data.csv b/data/data/experimental_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..64edeafab82ddd359687ccc1dca557c8f71b3357
--- /dev/null
+++ b/data/data/experimental_data.csv
@@ -0,0 +1,177 @@
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\ No newline at end of file
diff --git a/data/data/gases_importance.csv b/data/data/gases_importance.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b7cc9ad2c5dff099c13a85231b92cc5daf38b284
--- /dev/null
+++ b/data/data/gases_importance.csv
@@ -0,0 +1,6 @@
+,CO,CO2,H2,flow rate
+acetate,0.002198403341377692,0.012518083005000976,0.0025766782229310003,0.009944401275548022
+biomass,0.010512181543526176,0.009643574849878942,0.015881852043088524,0.01282732377286915
+butanol,0.0023539542754891824,0.002810149856861644,0.10062258509077618,0.020277051629335848
+butyrate,0.09178572955140024,0.0023166571390170767,0.0022799658263348965,0.0034676163247681726
+ethanol,0.005121450423760859,0.006199400424409285,0.0031004475763684023,0.007708995807534013
diff --git a/data/data/rate_time_course_data/bayesian.csv b/data/data/rate_time_course_data/bayesian.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c0a7269931bc8008edf56c587d5f9197fa54f3cf
--- /dev/null
+++ b/data/data/rate_time_course_data/bayesian.csv
@@ -0,0 +1,837 @@
+composition,trial,time,biomass (g/L),ethanol (mM),acetate (mM),butanol (mM),butyrate (mM)
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diff --git a/data/data/rate_time_course_data/en.csv b/data/data/rate_time_course_data/en.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b7dffa81bb5b3ecd7ffd5789196a82e15a08656a
--- /dev/null
+++ b/data/data/rate_time_course_data/en.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rate_time_course_data/knn.csv b/data/data/rate_time_course_data/knn.csv
new file mode 100644
index 0000000000000000000000000000000000000000..86ddd0998dc660de6872ac8bef583663209e17f9
--- /dev/null
+++ b/data/data/rate_time_course_data/knn.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rate_time_course_data/lasso.csv b/data/data/rate_time_course_data/lasso.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1d3c5b54c8ff695b4450fc40ab907dc5e5b1dc52
--- /dev/null
+++ b/data/data/rate_time_course_data/lasso.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rate_time_course_data/nn_coarse.csv b/data/data/rate_time_course_data/nn_coarse.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c53a0677e33d6a2d3dc40be6432c246c6e54a4e3
--- /dev/null
+++ b/data/data/rate_time_course_data/nn_coarse.csv
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diff --git a/data/data/rate_time_course_data/nn_fine.csv b/data/data/rate_time_course_data/nn_fine.csv
new file mode 100644
index 0000000000000000000000000000000000000000..381de49ba045c9626449e1d003e72a57eda3709d
--- /dev/null
+++ b/data/data/rate_time_course_data/nn_fine.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rate_time_course_data/rf.csv b/data/data/rate_time_course_data/rf.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3b900e8a6d7caae0b365fd44d70265036cf501d8
--- /dev/null
+++ b/data/data/rate_time_course_data/rf.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rate_time_course_data/svm_rbf.csv b/data/data/rate_time_course_data/svm_rbf.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f6e2a92b5dfd9d1a1342bb8cf979466f5c4b111e
--- /dev/null
+++ b/data/data/rate_time_course_data/svm_rbf.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/rates_data.csv b/data/data/rates_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c75d7f1d02192c974955bfcca5db982039388525
--- /dev/null
+++ b/data/data/rates_data.csv
@@ -0,0 +1,837 @@
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diff --git a/data/data/test_predictions.csv b/data/data/test_predictions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0fe085be3dd7ed9f5625ea360b3fea152835126e
--- /dev/null
+++ b/data/data/test_predictions.csv
@@ -0,0 +1,180 @@
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diff --git a/data/data/train_predictions.csv b/data/data/train_predictions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2fbd7d6c9e386fa8946ef80eaf16cc432422da68
--- /dev/null
+++ b/data/data/train_predictions.csv
@@ -0,0 +1,658 @@
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diff --git a/data/license b/data/license
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index 0000000000000000000000000000000000000000..964c54017b5eebd69a305fcd294836d1c5bb9930
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+BSD 3-Clause License
+
+Copyright (c) 2007-2021 Garrett Roell, Yinjie Tang, Forrest Bao.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/data/notebooks/A_data_processing.ipynb b/data/notebooks/A_data_processing.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook A: Data Processsing\n",
+    "This notebook takes experimental data and converts it to a form that is usable for machine learning algorithm training.\n",
+    "This involves smoothing the data using the Savitzky–Golay filter, and adding slope values by comparing consecutive concentration values, and dividing by time elapsed. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from scipy.signal import savgol_filter "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import experimental data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>12.81</td>\n",
+       "      <td>10.89</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>15.59</td>\n",
+       "      <td>16.42</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>17.11</td>\n",
+       "      <td>17.75</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>10.93</td>\n",
+       "      <td>21.59</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>15.86</td>\n",
+       "      <td>44.28</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.02</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>8.13</td>\n",
+       "      <td>46.16</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>10.79</td>\n",
+       "      <td>46.13</td>\n",
+       "      <td>1.18</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>20.31</td>\n",
+       "      <td>34.37</td>\n",
+       "      <td>8.44</td>\n",
+       "      <td>9.16</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.06</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>28.88</td>\n",
+       "      <td>27.06</td>\n",
+       "      <td>14.09</td>\n",
+       "      <td>8.03</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1      1  0.00           0.01          0.00          0.23   \n",
+       "1            1      1  0.57           0.45         12.81         10.89   \n",
+       "2            1      1  0.67           0.44         15.59         16.42   \n",
+       "3            1      1  0.90           0.41         17.11         17.75   \n",
+       "4            1      1  1.58           0.41         10.93         21.59   \n",
+       "5            1      1  1.65           0.39         15.86         44.28   \n",
+       "6            1      1  2.02           0.46          8.13         46.16   \n",
+       "7            1      1  2.67           0.49         10.79         46.13   \n",
+       "8            1      1  4.70           0.64         20.31         34.37   \n",
+       "9            1      1  6.06           0.67         28.88         27.06   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)  N2  CO   CO2   H2  flow rate (mL/min)  \n",
+       "0          0.00           0.06  60  20  15.0  5.0                  20  \n",
+       "1          0.03           0.09  60  20  15.0  5.0                  20  \n",
+       "2          0.03           0.13  60  20  15.0  5.0                  20  \n",
+       "3          0.03           0.07  60  20  15.0  5.0                  20  \n",
+       "4          0.04           0.06  60  20  15.0  5.0                  20  \n",
+       "5          0.05           0.07  60  20  15.0  5.0                  20  \n",
+       "6          0.21           0.60  60  20  15.0  5.0                  20  \n",
+       "7          1.18           3.41  60  20  15.0  5.0                  20  \n",
+       "8          8.44           9.16  60  20  15.0  5.0                  20  \n",
+       "9         14.09           8.03  60  20  15.0  5.0                  20  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp_df = pd.read_csv('../data/experimental_data.csv')\n",
+    "exp_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define function to smooth a single trial of experimental data using a second order Savitzky-Golay filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up savgol filter to have a window length of 15, polynomial order of 2, \n",
+    "# and to output the smoothed values.\n",
+    "savgol = lambda x: savgol_filter(x, 15, 2, deriv=0)\n",
+    "\n",
+    "def smooth_single_trial(trial_df, delta):\n",
+    "    # get list of time values\n",
+    "    times = list(trial_df.time)\n",
+    "\n",
+    "    # get smoothed time values\n",
+    "    max_time = times[-1]\n",
+    "    new_times = np.arange(0,max_time, delta)  \n",
+    "\n",
+    "    # temporarily set time as the index of the dataframe\n",
+    "    trial_df.set_index('time', inplace=True, drop=True)\n",
+    "\n",
+    "    # set the index of the df to the new times\n",
+    "    trial_df = trial_df.reindex(trial_df.index.union(new_times))\n",
+    "\n",
+    "    # find the linear interpolation for all the new data points\n",
+    "    trial_df = trial_df.interpolate()\n",
+    "\n",
+    "    # remove experimental time points, unless they are an exact multiple of delta\n",
+    "    times_to_remove = set(times) - (set(times) & set(new_times))\n",
+    "    trial_df = trial_df.loc[~trial_df.index.isin(times_to_remove)]\n",
+    "\n",
+    "    # convert linear interpolation to smoothed polynomial fit\n",
+    "    trial_df = trial_df.apply(savgol)\n",
+    "    \n",
+    "    # round negative smoothed values to 0\n",
+    "    trial_df.clip(lower=0, inplace=True)\n",
+    "    \n",
+    "    # Prevent floating point errors later\n",
+    "    trial_df.composition = round(trial_df.composition, 0)\n",
+    "    trial_df.trial = round(trial_df.trial, 0)\n",
+    "\n",
+    "    # remove time's status as index column\n",
+    "    trial_df.insert(2, 'time',trial_df.index)\n",
+    "    trial_df.reset_index(inplace=True, drop=True)\n",
+    "    \n",
+    "    return trial_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply smoothing function to each trial of experimental data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.013426</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000492</td>\n",
+       "      <td>0.058332</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.097327</td>\n",
+       "      <td>1.917572</td>\n",
+       "      <td>1.660755</td>\n",
+       "      <td>0.005883</td>\n",
+       "      <td>0.066873</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.172225</td>\n",
+       "      <td>4.989251</td>\n",
+       "      <td>4.313160</td>\n",
+       "      <td>0.010819</td>\n",
+       "      <td>0.074034</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.238122</td>\n",
+       "      <td>7.685937</td>\n",
+       "      <td>6.765444</td>\n",
+       "      <td>0.015301</td>\n",
+       "      <td>0.079814</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.295016</td>\n",
+       "      <td>10.007629</td>\n",
+       "      <td>9.017607</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.084213</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.342908</td>\n",
+       "      <td>11.954328</td>\n",
+       "      <td>11.069648</td>\n",
+       "      <td>0.022898</td>\n",
+       "      <td>0.087231</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.381798</td>\n",
+       "      <td>13.526034</td>\n",
+       "      <td>12.921569</td>\n",
+       "      <td>0.026014</td>\n",
+       "      <td>0.088869</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.411686</td>\n",
+       "      <td>14.722747</td>\n",
+       "      <td>14.573368</td>\n",
+       "      <td>0.028676</td>\n",
+       "      <td>0.089125</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.426219</td>\n",
+       "      <td>15.684459</td>\n",
+       "      <td>16.189576</td>\n",
+       "      <td>0.030462</td>\n",
+       "      <td>0.086824</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432018</td>\n",
+       "      <td>16.001520</td>\n",
+       "      <td>16.742923</td>\n",
+       "      <td>0.031439</td>\n",
+       "      <td>0.082842</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0          1.0    1.0    0       0.013426      0.000000      0.000000   \n",
+       "1          1.0    1.0  0.1       0.097327      1.917572      1.660755   \n",
+       "2          1.0    1.0  0.2       0.172225      4.989251      4.313160   \n",
+       "3          1.0    1.0  0.3       0.238122      7.685937      6.765444   \n",
+       "4          1.0    1.0  0.4       0.295016     10.007629      9.017607   \n",
+       "5          1.0    1.0  0.5       0.342908     11.954328     11.069648   \n",
+       "6          1.0    1.0  0.6       0.381798     13.526034     12.921569   \n",
+       "7          1.0    1.0  0.7       0.411686     14.722747     14.573368   \n",
+       "8          1.0    1.0  0.8       0.426219     15.684459     16.189576   \n",
+       "9          1.0    1.0  0.9       0.432018     16.001520     16.742923   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \n",
+       "0      0.000492       0.058332  60.0  20.0  15.0  5.0                20.0  \n",
+       "1      0.005883       0.066873  60.0  20.0  15.0  5.0                20.0  \n",
+       "2      0.010819       0.074034  60.0  20.0  15.0  5.0                20.0  \n",
+       "3      0.015301       0.079814  60.0  20.0  15.0  5.0                20.0  \n",
+       "4      0.019327       0.084213  60.0  20.0  15.0  5.0                20.0  \n",
+       "5      0.022898       0.087231  60.0  20.0  15.0  5.0                20.0  \n",
+       "6      0.026014       0.088869  60.0  20.0  15.0  5.0                20.0  \n",
+       "7      0.028676       0.089125  60.0  20.0  15.0  5.0                20.0  \n",
+       "8      0.030462       0.086824  60.0  20.0  15.0  5.0                20.0  \n",
+       "9      0.031439       0.082842  60.0  20.0  15.0  5.0                20.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This parameter tunes the distance between\n",
+    "delta = 0.1\n",
+    "\n",
+    "smoothed_df = pd.DataFrame()\n",
+    "\n",
+    "# Loop through compositions\n",
+    "for composition in set(exp_df.composition):\n",
+    "    \n",
+    "    # For each composition, loop over trials\n",
+    "    composition_df = exp_df[exp_df.composition == composition]\n",
+    "    for trial in set(composition_df.trial):\n",
+    "        # isolate the data for a single trial\n",
+    "        trial_df = exp_df[(exp_df.composition == composition) & (exp_df.trial == trial)]\n",
+    "        \n",
+    "        # get the smooth data for that trial\n",
+    "        smooth_trial_df = smooth_single_trial(trial_df, delta)\n",
+    "        \n",
+    "        # add the trial to smoothed_df\n",
+    "        smoothed_df = pd.concat([smoothed_df, smooth_trial_df])\n",
+    "\n",
+    "smoothed_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define function to add production rates to a single trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_rates_to_trial(trial_df, delta):\n",
+    "    \n",
+    "    trial_rates_df = trial_df.copy()\n",
+    "\n",
+    "    biomass_rates = []\n",
+    "    ethanol_rates = []\n",
+    "    acetate_rates = []\n",
+    "    butanol_rates = []\n",
+    "    butyrate_rates = []\n",
+    "    \n",
+    "    \n",
+    "    # loop over rows of dataframe\n",
+    "    for index, row in trial_df.iterrows():\n",
+    "        # if it is the first row, don't calculate slope\n",
+    "        if row.time == 0:\n",
+    "            biomass_rates.append(0)\n",
+    "            ethanol_rates.append(0)\n",
+    "            acetate_rates.append(0)\n",
+    "            butanol_rates.append(0)\n",
+    "            butyrate_rates.append(0)\n",
+    "        # find diffence between current and previous value divide by the delta value to get production rate\n",
+    "        else:\n",
+    "            previous_row = trial_df[trial_df.index == index-1]\n",
+    "            biomass_rates.append(float((row['biomass (g/L)'] - previous_row['biomass (g/L)'])/delta))\n",
+    "            ethanol_rates.append(float((row['ethanol (mM)'] - previous_row['ethanol (mM)'])/delta))\n",
+    "            acetate_rates.append(float((row['acetate (mM)'] - previous_row['acetate (mM)'])/delta))\n",
+    "            butanol_rates.append(float((row['butanol (mM)'] - previous_row['butanol (mM)'])/delta))\n",
+    "            butyrate_rates.append(float((row['butyrate (mM)'] - previous_row['butyrate (mM)'])/delta))\n",
+    "\n",
+    "    # add production rates to the data frame\n",
+    "    trial_rates_df['biomass rate'] = biomass_rates\n",
+    "    trial_rates_df['ethanol rate'] = ethanol_rates\n",
+    "    trial_rates_df['acetate rate'] = acetate_rates\n",
+    "    trial_rates_df['butanol rate'] = butanol_rates\n",
+    "    trial_rates_df['butyrate rate'] = butyrate_rates\n",
+    "\n",
+    "    return trial_rates_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply function to every trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "      <th>biomass rate</th>\n",
+       "      <th>ethanol rate</th>\n",
+       "      <th>acetate rate</th>\n",
+       "      <th>butanol rate</th>\n",
+       "      <th>butyrate rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.013426</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000492</td>\n",
+       "      <td>0.058332</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.097327</td>\n",
+       "      <td>1.917572</td>\n",
+       "      <td>1.660755</td>\n",
+       "      <td>0.005883</td>\n",
+       "      <td>0.066873</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.839006</td>\n",
+       "      <td>19.175716</td>\n",
+       "      <td>16.607550</td>\n",
+       "      <td>0.053914</td>\n",
+       "      <td>0.085414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.172225</td>\n",
+       "      <td>4.989251</td>\n",
+       "      <td>4.313160</td>\n",
+       "      <td>0.010819</td>\n",
+       "      <td>0.074034</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.748985</td>\n",
+       "      <td>30.716792</td>\n",
+       "      <td>26.524049</td>\n",
+       "      <td>0.049363</td>\n",
+       "      <td>0.071606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.238122</td>\n",
+       "      <td>7.685937</td>\n",
+       "      <td>6.765444</td>\n",
+       "      <td>0.015301</td>\n",
+       "      <td>0.079814</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.658964</td>\n",
+       "      <td>26.966859</td>\n",
+       "      <td>24.522838</td>\n",
+       "      <td>0.044813</td>\n",
+       "      <td>0.057799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.295016</td>\n",
+       "      <td>10.007629</td>\n",
+       "      <td>9.017607</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.084213</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.568943</td>\n",
+       "      <td>23.216926</td>\n",
+       "      <td>22.521627</td>\n",
+       "      <td>0.040263</td>\n",
+       "      <td>0.043991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.342908</td>\n",
+       "      <td>11.954328</td>\n",
+       "      <td>11.069648</td>\n",
+       "      <td>0.022898</td>\n",
+       "      <td>0.087231</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.478922</td>\n",
+       "      <td>19.466992</td>\n",
+       "      <td>20.520417</td>\n",
+       "      <td>0.035712</td>\n",
+       "      <td>0.030183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.381798</td>\n",
+       "      <td>13.526034</td>\n",
+       "      <td>12.921569</td>\n",
+       "      <td>0.026014</td>\n",
+       "      <td>0.088869</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.388901</td>\n",
+       "      <td>15.717059</td>\n",
+       "      <td>18.519206</td>\n",
+       "      <td>0.031162</td>\n",
+       "      <td>0.016375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.411686</td>\n",
+       "      <td>14.722747</td>\n",
+       "      <td>14.573368</td>\n",
+       "      <td>0.028676</td>\n",
+       "      <td>0.089125</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.298880</td>\n",
+       "      <td>11.967126</td>\n",
+       "      <td>16.517995</td>\n",
+       "      <td>0.026612</td>\n",
+       "      <td>0.002567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.426219</td>\n",
+       "      <td>15.684459</td>\n",
+       "      <td>16.189576</td>\n",
+       "      <td>0.030462</td>\n",
+       "      <td>0.086824</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.145324</td>\n",
+       "      <td>9.617115</td>\n",
+       "      <td>16.162079</td>\n",
+       "      <td>0.017860</td>\n",
+       "      <td>-0.023019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432018</td>\n",
+       "      <td>16.001520</td>\n",
+       "      <td>16.742923</td>\n",
+       "      <td>0.031439</td>\n",
+       "      <td>0.082842</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.057994</td>\n",
+       "      <td>3.170618</td>\n",
+       "      <td>5.533469</td>\n",
+       "      <td>0.009774</td>\n",
+       "      <td>-0.039819</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0          1.0    1.0    0       0.013426      0.000000      0.000000   \n",
+       "1          1.0    1.0  0.1       0.097327      1.917572      1.660755   \n",
+       "2          1.0    1.0  0.2       0.172225      4.989251      4.313160   \n",
+       "3          1.0    1.0  0.3       0.238122      7.685937      6.765444   \n",
+       "4          1.0    1.0  0.4       0.295016     10.007629      9.017607   \n",
+       "5          1.0    1.0  0.5       0.342908     11.954328     11.069648   \n",
+       "6          1.0    1.0  0.6       0.381798     13.526034     12.921569   \n",
+       "7          1.0    1.0  0.7       0.411686     14.722747     14.573368   \n",
+       "8          1.0    1.0  0.8       0.426219     15.684459     16.189576   \n",
+       "9          1.0    1.0  0.9       0.432018     16.001520     16.742923   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \\\n",
+       "0      0.000492       0.058332  60.0  20.0  15.0  5.0                20.0   \n",
+       "1      0.005883       0.066873  60.0  20.0  15.0  5.0                20.0   \n",
+       "2      0.010819       0.074034  60.0  20.0  15.0  5.0                20.0   \n",
+       "3      0.015301       0.079814  60.0  20.0  15.0  5.0                20.0   \n",
+       "4      0.019327       0.084213  60.0  20.0  15.0  5.0                20.0   \n",
+       "5      0.022898       0.087231  60.0  20.0  15.0  5.0                20.0   \n",
+       "6      0.026014       0.088869  60.0  20.0  15.0  5.0                20.0   \n",
+       "7      0.028676       0.089125  60.0  20.0  15.0  5.0                20.0   \n",
+       "8      0.030462       0.086824  60.0  20.0  15.0  5.0                20.0   \n",
+       "9      0.031439       0.082842  60.0  20.0  15.0  5.0                20.0   \n",
+       "\n",
+       "   biomass rate  ethanol rate  acetate rate  butanol rate  butyrate rate  \n",
+       "0      0.000000      0.000000      0.000000      0.000000       0.000000  \n",
+       "1      0.839006     19.175716     16.607550      0.053914       0.085414  \n",
+       "2      0.748985     30.716792     26.524049      0.049363       0.071606  \n",
+       "3      0.658964     26.966859     24.522838      0.044813       0.057799  \n",
+       "4      0.568943     23.216926     22.521627      0.040263       0.043991  \n",
+       "5      0.478922     19.466992     20.520417      0.035712       0.030183  \n",
+       "6      0.388901     15.717059     18.519206      0.031162       0.016375  \n",
+       "7      0.298880     11.967126     16.517995      0.026612       0.002567  \n",
+       "8      0.145324      9.617115     16.162079      0.017860      -0.023019  \n",
+       "9      0.057994      3.170618      5.533469      0.009774      -0.039819  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rates_df = pd.DataFrame()\n",
+    "\n",
+    "# Loop through compositions\n",
+    "for composition in set(smoothed_df.composition):\n",
+    "    \n",
+    "    # For each composition, loop over trials\n",
+    "    composition_df = smoothed_df[smoothed_df.composition == composition]\n",
+    "    for trial in set(composition_df.trial):\n",
+    "        \n",
+    "        # isolate the data for a single trial\n",
+    "        trial_df = smoothed_df[(smoothed_df.composition == composition) & (smoothed_df.trial == trial)]\n",
+    "        \n",
+    "        # get the rates data for that trial\n",
+    "        trial_rates_df = add_rates_to_trial(trial_df, delta)\n",
+    "        \n",
+    "        # add the trial to rates_df\n",
+    "        rates_df = pd.concat([rates_df, trial_rates_df])\n",
+    "\n",
+    "rates_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Remove data from the first 24 hrs of fermentation (glucose was present)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "      <th>biomass rate</th>\n",
+       "      <th>ethanol rate</th>\n",
+       "      <th>acetate rate</th>\n",
+       "      <th>butanol rate</th>\n",
+       "      <th>butyrate rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.024890</td>\n",
+       "      <td>-1.303834</td>\n",
+       "      <td>1.506172</td>\n",
+       "      <td>-0.015756</td>\n",
+       "      <td>-0.126633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.421056</td>\n",
+       "      <td>15.583697</td>\n",
+       "      <td>17.988494</td>\n",
+       "      <td>0.028287</td>\n",
+       "      <td>0.056100</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.084727</td>\n",
+       "      <td>-2.874395</td>\n",
+       "      <td>10.949538</td>\n",
+       "      <td>-0.015765</td>\n",
+       "      <td>-0.140778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.410826</td>\n",
+       "      <td>15.121595</td>\n",
+       "      <td>19.816681</td>\n",
+       "      <td>0.028325</td>\n",
+       "      <td>0.046269</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.102302</td>\n",
+       "      <td>-4.621020</td>\n",
+       "      <td>18.281864</td>\n",
+       "      <td>0.000380</td>\n",
+       "      <td>-0.098317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.403602</td>\n",
+       "      <td>14.500537</td>\n",
+       "      <td>22.179671</td>\n",
+       "      <td>0.031348</td>\n",
+       "      <td>0.045430</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.072244</td>\n",
+       "      <td>-6.210585</td>\n",
+       "      <td>23.629906</td>\n",
+       "      <td>0.030235</td>\n",
+       "      <td>-0.008389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.403688</td>\n",
+       "      <td>13.935178</td>\n",
+       "      <td>25.274862</td>\n",
+       "      <td>0.028703</td>\n",
+       "      <td>0.031849</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.000866</td>\n",
+       "      <td>-5.653590</td>\n",
+       "      <td>30.951909</td>\n",
+       "      <td>-0.026458</td>\n",
+       "      <td>-0.135805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.406240</td>\n",
+       "      <td>13.302534</td>\n",
+       "      <td>28.983542</td>\n",
+       "      <td>0.030988</td>\n",
+       "      <td>0.037619</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.025520</td>\n",
+       "      <td>-6.326440</td>\n",
+       "      <td>37.086802</td>\n",
+       "      <td>0.022851</td>\n",
+       "      <td>0.057692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.410874</td>\n",
+       "      <td>12.629887</td>\n",
+       "      <td>32.875284</td>\n",
+       "      <td>0.043472</td>\n",
+       "      <td>0.075515</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.046335</td>\n",
+       "      <td>-6.726471</td>\n",
+       "      <td>38.917421</td>\n",
+       "      <td>0.124842</td>\n",
+       "      <td>0.378959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.416723</td>\n",
+       "      <td>12.043783</td>\n",
+       "      <td>36.718888</td>\n",
+       "      <td>0.070169</td>\n",
+       "      <td>0.155912</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.058487</td>\n",
+       "      <td>-5.861041</td>\n",
+       "      <td>38.436037</td>\n",
+       "      <td>0.266975</td>\n",
+       "      <td>0.803975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.424176</td>\n",
+       "      <td>11.479518</td>\n",
+       "      <td>40.301776</td>\n",
+       "      <td>0.114038</td>\n",
+       "      <td>0.287944</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.074531</td>\n",
+       "      <td>-5.642644</td>\n",
+       "      <td>35.828875</td>\n",
+       "      <td>0.438688</td>\n",
+       "      <td>1.320317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432878</td>\n",
+       "      <td>10.959046</td>\n",
+       "      <td>43.408944</td>\n",
+       "      <td>0.176694</td>\n",
+       "      <td>0.475785</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.087020</td>\n",
+       "      <td>-5.204725</td>\n",
+       "      <td>31.071681</td>\n",
+       "      <td>0.626561</td>\n",
+       "      <td>1.878416</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "10          1.0    1.0    0       0.429529     15.871137     16.893540   \n",
+       "11          1.0    1.0  0.1       0.421056     15.583697     17.988494   \n",
+       "12          1.0    1.0  0.2       0.410826     15.121595     19.816681   \n",
+       "13          1.0    1.0  0.3       0.403602     14.500537     22.179671   \n",
+       "14          1.0    1.0  0.4       0.403688     13.935178     25.274862   \n",
+       "15          1.0    1.0  0.5       0.406240     13.302534     28.983542   \n",
+       "16          1.0    1.0  0.6       0.410874     12.629887     32.875284   \n",
+       "17          1.0    1.0  0.7       0.416723     12.043783     36.718888   \n",
+       "18          1.0    1.0  0.8       0.424176     11.479518     40.301776   \n",
+       "19          1.0    1.0  0.9       0.432878     10.959046     43.408944   \n",
+       "\n",
+       "    butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \\\n",
+       "10      0.029863       0.070178  60.0  20.0  15.0  5.0                20.0   \n",
+       "11      0.028287       0.056100  60.0  20.0  15.0  5.0                20.0   \n",
+       "12      0.028325       0.046269  60.0  20.0  15.0  5.0                20.0   \n",
+       "13      0.031348       0.045430  60.0  20.0  15.0  5.0                20.0   \n",
+       "14      0.028703       0.031849  60.0  20.0  15.0  5.0                20.0   \n",
+       "15      0.030988       0.037619  60.0  20.0  15.0  5.0                20.0   \n",
+       "16      0.043472       0.075515  60.0  20.0  15.0  5.0                20.0   \n",
+       "17      0.070169       0.155912  60.0  20.0  15.0  5.0                20.0   \n",
+       "18      0.114038       0.287944  60.0  20.0  15.0  5.0                20.0   \n",
+       "19      0.176694       0.475785  60.0  20.0  15.0  5.0                20.0   \n",
+       "\n",
+       "    biomass rate  ethanol rate  acetate rate  butanol rate  butyrate rate  \n",
+       "10     -0.024890     -1.303834      1.506172     -0.015756      -0.126633  \n",
+       "11     -0.084727     -2.874395     10.949538     -0.015765      -0.140778  \n",
+       "12     -0.102302     -4.621020     18.281864      0.000380      -0.098317  \n",
+       "13     -0.072244     -6.210585     23.629906      0.030235      -0.008389  \n",
+       "14      0.000866     -5.653590     30.951909     -0.026458      -0.135805  \n",
+       "15      0.025520     -6.326440     37.086802      0.022851       0.057692  \n",
+       "16      0.046335     -6.726471     38.917421      0.124842       0.378959  \n",
+       "17      0.058487     -5.861041     38.436037      0.266975       0.803975  \n",
+       "18      0.074531     -5.642644     35.828875      0.438688       1.320317  \n",
+       "19      0.087020     -5.204725     31.071681      0.626561       1.878416  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove all time points before one day has passed\n",
+    "rates_df = rates_df[rates_df['time'] >= 1]\n",
+    "\n",
+    "# subtract one day from all times\n",
+    "rates_df['time'] = rates_df['time'] - 1\n",
+    "\n",
+    "rates_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save rates data as csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rates_df.to_csv(f'../data/rates_data.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "biodesign_3.7",
+   "language": "python",
+   "name": "biod_3.7"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/notebooks/B_model_training.ipynb b/data/notebooks/B_model_training.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..82767d85795157fd256576670fe04667608b92af
--- /dev/null
+++ b/data/notebooks/B_model_training.ipynb
@@ -0,0 +1,5311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook B: Model Training\n",
+    "This notebook trains 6 algoritms to predict the production rates of 5 outputs of syngas fermentation based on the extracellular metabolite concentration, and gas composition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sklearn.preprocessing, sklearn.neural_network, sklearn.svm, sklearn.ensemble, sklearn\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data that was generated in notebook A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the rates data: 836 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "rates_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the rates data: {rates_df.shape[0]} rows by {rates_df.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create train and test sets "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the training data: 657 rows by 18 columns\n",
+      "Shape of the testing data: 179 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = rates_df[rates_df.composition.isin([1,2,3,4,5,6,7])]\n",
+    "test_data = rates_df[rates_df.composition.isin([8,9,10])]\n",
+    "print(f'Shape of the training data: {train_data.shape[0]} rows by {train_data.shape[1]} columns')\n",
+    "print(f'Shape of the testing data: {test_data.shape[0]} rows by {test_data.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a function that generates the input and output arrays for scikit learn's API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_X_y_arrays(data):\n",
+    "\n",
+    "    # prevent set with copy error\n",
+    "    data_copy = data.copy()\n",
+    "    \n",
+    "    # ML input\n",
+    "    X = data_copy [[\n",
+    "        'biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', \n",
+    "         'butyrate (mM)', 'N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)'\n",
+    "    ]]\n",
+    "    \n",
+    "    # ML output\n",
+    "    y = data_copy [[\n",
+    "        'biomass rate', 'ethanol rate', 'acetate rate', 'butanol rate', 'butyrate rate'\n",
+    "    ]]\n",
+    " \n",
+    "    return np.array(X), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the train X array: 657 rows by 10 columns\n",
+      "Shape of the trainn y array: 657 rows by 5 columns\n",
+      "Shape of the test X array: 179 rows by 10 columns\n",
+      "Shape of the test y array: 179 rows by 5 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, y_train = get_X_y_arrays(train_data)\n",
+    "X_test, y_test = get_X_y_arrays(test_data)\n",
+    "\n",
+    "print(f'Shape of the train X array: {X_train.shape[0]} rows by {X_train.shape[1]} columns')\n",
+    "print(f'Shape of the trainn y array: {y_train.shape[0]} rows by {y_train.shape[1]} columns')\n",
+    "print(f'Shape of the test X array: {X_test.shape[0]} rows by {X_test.shape[1]} columns')\n",
+    "print(f'Shape of the test y array: {y_test.shape[0]} rows by {y_test.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train 30 different models (5 outputs each modeled with 6 algorithms)\n",
+    "algorithms = neural network, support vector machine, random forest, support vector, neural net, lasso <br>\n",
+    "outputs = acetate, biomass, butanol, butyrate, ethanol"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a functions to generate neural network architectures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_NN_fixed_n_layers(n_layers, n_neurons, neuron_step):\n",
+    "    \"\"\"Generate NN hidden_layer_sizes of n_layers and up to n_neurons per layer \n",
+    "    \"\"\"\n",
+    "    # print (n_layers)\n",
+    "    if n_layers == 1: \n",
+    "        return [[i] for i in range(neuron_step, n_neurons+1, neuron_step)]\n",
+    "    else:\n",
+    "        pairs =  [  (i,  tail) for tail in gen_NN_fixed_n_layers(n_layers-1, n_neurons+1, neuron_step) for i in range(neuron_step, n_neurons+1, neuron_step) ]\n",
+    "        return [[i]+ t for (i, t) in pairs]\n",
+    "\n",
+    "# print (gen_NN_fixed_n_layers(4, 10, 5))\n",
+    "\n",
+    "def gen_NN_uni(n_layers, n_neurons, layer_step, neuron_step):\n",
+    "    \"\"\"Generate hidden layers of various number of layers and number of neurons \n",
+    "    \"\"\" \n",
+    "    various_NNs = [ gen_NN_fixed_n_layers(i , n_neurons, neuron_step) for i in range(2, n_layers+1, layer_step)]\n",
+    "    return  functools.reduce(operator.add, various_NNs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a model configuration dictionary to guide ML training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test grid is used for debugging, should be replaced with full grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_cfgs = {\n",
+    "        \"nn\":{\n",
+    "            'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'activation': ['tanh', 'logistic', 'relu'], \n",
+    "                'max_iter':   [400*i for i in range(1, 2)]\n",
+    "            }\n",
+    "            # Full grid\n",
+    "            # 'param_grid':   {\n",
+    "                # 'hidden_layer_sizes': gen_NN_uni(5, 100, 1, 10),  \n",
+    "                # 'activation':         ['tanh', 'logistic', 'relu'], \n",
+    "                # 'max_iter':           [400*i for i in range(1, 10, 2)]\n",
+    "            # }                \n",
+    "        },\n",
+    "        \"svm_rbf\":{\n",
+    "            'estimator': sklearn.svm.SVR(kernel='rbf'),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'C':       [10**i for i in range(-1, 1)], \n",
+    "                'epsilon': [10**i for i in range(-1, 1)],\n",
+    "            }\n",
+    "                # Full grid\n",
+    "                # 'param_grid':   {\n",
+    "                    # 'C':       [10**i for i in range(-5, 5)], \n",
+    "                    # 'epsilon': [10**i for i in range(-5, 5)],\n",
+    "                    # 'gamma':   [10**i for i in range(-5, 5)] # gamma gave me an error\n",
+    "            # }\n",
+    "        },\n",
+    "        \"rf\":{\n",
+    "            'estimator': sklearn.ensemble.RandomForestRegressor(),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'n_estimators': [10*i for i in range(1, 2)],\n",
+    "                'max_depth':     [2*i for i in range(1, 1+1)],\n",
+    "            }\n",
+    "            # Full grid \n",
+    "            # 'param_grid':   {\n",
+    "                # 'n_estimators': [10*i for i in range(1, 20)],\n",
+    "                # 'max_depth':     [2*i for i in range(20)], \n",
+    "                # 'max_samples': [0.05*i for i in range(1, 10+1)] # max samples gave me an error\n",
+    "            # }\n",
+    "        },\n",
+    "        'en': {\n",
+    "            'estimator': sklearn.linear_model.ElasticNet(),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'alpha': [0.0001, 0.001, 0.01, 0.1],\n",
+    "                'l1_ratio': [0.1, 1],\n",
+    "            }\n",
+    "            # Full grid \n",
+    "            # 'param_grid': {\n",
+    "                # 'alpha': [0.0001, 0.001, 0.01, 0.1],\n",
+    "                # 'l1_ratio': [0.1, 1],\n",
+    "            #}\n",
+    "        },\n",
+    "        'lasso': {\n",
+    "            'estimator': sklearn.linear_model.Lasso(),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'alpha': [0.0001, 0.001, 0.01, 0.1],\n",
+    "            }\n",
+    "            # Full grid \n",
+    "            # 'param_grid':   {\n",
+    "                # 'alpha': [0.0001, 0.001, 0.01, 0.1],\n",
+    "            # }\n",
+    "        },\n",
+    "        'knn': {\n",
+    "            'estimator': sklearn.neighbors.KNeighborsRegressor(),\n",
+    "            # Test grid\n",
+    "            'param_grid':   {\n",
+    "                'algorithm': ['ball_tree', ],\n",
+    "                'leaf_size': [4,5,6],\n",
+    "                'n_neighbors': [2,3,4],\n",
+    "                'weights': ['distance'],\n",
+    "            }\n",
+    "            # Full grid \n",
+    "            # 'param_grid':   {\n",
+    "                # 'algorithm': [0.0001, 0.001, 0.01, 0.1],\n",
+    "                # 'leaf_size': [4, 5, 6],\n",
+    "                # 'n_neighbors': [2, 3, 4],\n",
+    "                # 'weights': ['distance'],\n",
+    "            # }\n",
+    "        },\n",
+    "        \"bayesian\":{\n",
+    "            'estimator': sklearn.linear_model.BayesianRidge(),\n",
+    "            'param_grid':   {\n",
+    "                'n_iter':  [300, 500], \n",
+    "                'alpha_1': [10**i for i in range(-1, 1)], \n",
+    "                'alpha_2': [10**i for i in range(-1, 1)], \n",
+    "                'lambda_1': [10**i for i in range(-1, 1)], \n",
+    "                'lambda_2': [10**i for i in range(-1, 1)], \n",
+    "            }\n",
+    "        },\n",
+    "    \n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform grid search for each output and algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "biomass\n",
+      "\n",
+      "nn\n",
+      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+      "[CV 1/10] END ...activation=tanh, max_iter=400;, score=-0.144 total time=   0.5s\n",
+      "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.213 total time=   0.5s\n",
+      "[CV 2/10] END ...activation=tanh, max_iter=400;, score=-0.938 total time=   0.5s\n",
+      "[CV 7/10] END ...activation=tanh, max_iter=400;, score=-0.283 total time=   0.5s\n",
+      "[CV 4/10] END ...activation=tanh, max_iter=400;, score=-1.635 total time=   0.6s\n",
+      "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.218 total time=   0.6s\n",
+      "[CV 10/10] END activation=logistic, max_iter=400;, score=0.330 total time=   0.3s\n",
+      "[CV 5/10] END ...activation=tanh, max_iter=400;, score=-0.770 total time=   0.7s\n",
+      "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.042 total time=   0.6s\n",
+      "[CV 8/10] END ...activation=tanh, max_iter=400;, score=-0.878 total time=   0.7s\n",
+      "[CV 10/10] END ..activation=tanh, max_iter=400;, score=-0.068 total time=   0.4s\n",
+      "[CV 1/10] END activation=logistic, max_iter=400;, score=0.406 total time=   0.3s\n",
+      "[CV 4/10] END activation=logistic, max_iter=400;, score=0.570 total time=   0.2s\n",
+      "[CV 2/10] END activation=logistic, max_iter=400;, score=0.346 total time=   0.2s\n",
+      "[CV 5/10] END activation=logistic, max_iter=400;, score=0.650 total time=   0.1s\n",
+      "[CV 3/10] END activation=logistic, max_iter=400;, score=0.390 total time=   0.2s\n",
+      "[CV 4/10] END ...activation=relu, max_iter=400;, score=-3.995 total time=   1.0s\n",
+      "[CV 6/10] END activation=logistic, max_iter=400;, score=0.787 total time=   0.1s\n",
+      "[CV 7/10] END activation=logistic, max_iter=400;, score=0.576 total time=   0.2s\n",
+      "[CV 9/10] END activation=logistic, max_iter=400;, score=0.458 total time=   0.1s\n",
+      "[CV 8/10] END activation=logistic, max_iter=400;, score=0.653 total time=   0.2s\n",
+      "[CV 2/10] END ..activation=relu, max_iter=400;, score=-11.889 total time=   1.4s\n",
+      "[CV 5/10] END ...activation=relu, max_iter=400;, score=-3.045 total time=   0.4s\n",
+      "[CV 3/10] END ...activation=relu, max_iter=400;, score=-5.220 total time=   0.4s\n",
+      "[CV 10/10] END ..activation=relu, max_iter=400;, score=-4.039 total time=   0.4s\n",
+      "[CV 1/10] END ...activation=relu, max_iter=400;, score=-2.688 total time=   0.5s\n",
+      "[CV 7/10] END ...activation=relu, max_iter=400;, score=-4.286 total time=   0.5s\n",
+      "[CV 8/10] END ...activation=relu, max_iter=400;, score=-7.619 total time=   0.5s\n",
+      "[CV 9/10] END ...activation=relu, max_iter=400;, score=-2.336 total time=   0.6s\n",
+      "[CV 6/10] END ...activation=relu, max_iter=400;, score=-6.897 total time=   0.7s\n",
+      "Best CV score: 0.517:\n",
+      "Best parameters: {'activation': 'logistic', 'max_iter': 400} \n",
+      "\n",
+      "svm_rbf\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.238 total time=   0.0s\n",
+      "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.146 total time=   0.0s\n",
+      "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.410 total time=   0.0s\n",
+      "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.374 total time=   0.0s\n",
+      "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.296 total time=   0.0s\n",
+      "[CV 6/10] END ................C=0.1, epsilon=1;, score=-0.576 total time=   0.0s\n",
+      "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.318 total time=   0.0s\n",
+      "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.142 total time=   0.0s\n",
+      "[CV 7/10] END ................C=0.1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.162 total time=   0.0s\n",
+      "[CV 8/10] END ................C=0.1, epsilon=1;, score=-0.642 total time=   0.0s\n",
+      "[CV 1/10] END ................C=0.1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 9/10] END ................C=0.1, epsilon=1;, score=-0.171 total time=   0.0s\n",
+      "[CV 2/10] END ................C=0.1, epsilon=1;, score=-0.805 total time=   0.0s\n",
+      "[CV 10/10] END ...............C=0.1, epsilon=1;, score=-0.435 total time=   0.0s\n",
+      "[CV 4/10] END ................C=0.1, epsilon=1;, score=-0.510 total time=   0.0s\n",
+      "[CV 3/10] END ................C=0.1, epsilon=1;, score=-0.394 total time=   0.0s\n",
+      "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.183 total time=   0.0s\n",
+      "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.191 total time=   0.0s\n",
+      "[CV 5/10] END ................C=0.1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.363 total time=   0.0s\n",
+      "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.478 total time=   0.0s\n",
+      "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.397 total time=   0.0s\n",
+      "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.335 total time=   0.0s\n",
+      "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.337 total time=   0.0s\n",
+      "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.259 total time=   0.0s\n",
+      "[CV 1/10] END ..................C=1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 3/10] END ..................C=1, epsilon=1;, score=-0.394 total time=   0.0s\n",
+      "[CV 4/10] END ..................C=1, epsilon=1;, score=-0.510 total time=   0.0s\n",
+      "[CV 5/10] END ..................C=1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 6/10] END ..................C=1, epsilon=1;, score=-0.576 total time=   0.0s\n",
+      "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.215 total time=   0.0s\n",
+      "[CV 7/10] END ..................C=1, epsilon=1;, score=-0.583 total time=   0.0s\n",
+      "[CV 8/10] END ..................C=1, epsilon=1;, score=-0.642 total time=   0.0s\n",
+      "[CV 9/10] END ..................C=1, epsilon=1;, score=-0.171 total time=   0.0s\n",
+      "[CV 10/10] END .................C=1, epsilon=1;, score=-0.435 total time=   0.0s\n",
+      "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.493 total time=   0.0s\n",
+      "[CV 2/10] END ..................C=1, epsilon=1;, score=-0.805 total time=   0.0s\n",
+      "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.129 total time=   0.0s\n",
+      "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.147 total time=   0.0s\n",
+      "Best CV score: 0.325:\n",
+      "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+      "\n",
+      "rf\n",
+      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+      "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.403 total time=   0.0s\n",
+      "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.081 total time=   0.0s\n",
+      "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.214 total time=   0.0s\n",
+      "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.314 total time=   0.0s\n",
+      "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.451 total time=   0.0s\n",
+      "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.282 total time=   0.0s\n",
+      "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.271 total time=   0.0s\n",
+      "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.451 total time=   0.0s\n",
+      "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.378 total time=   0.0s\n",
+      "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.323 total time=   0.0s\n",
+      "Best CV score: 0.317:\n",
+      "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+      "\n",
+      "en\n",
+      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+      "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.196 total time=   0.0s\n",
+      "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.273 total time=   0.0s\n",
+      "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.229 total time=   0.0s\n",
+      "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.398 total time=   0.0s\n",
+      "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.199 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.278 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.219 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.399 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.405 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.355 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.282 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.201 total time=   0.0s\n",
+      "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.282 total time=   0.0s\n",
+      "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.204 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.400 total time=   0.0s\n",
+      "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.399 total time=   0.0s\n",
+      "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.349 total time=   0.0s\n",
+      "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.276 total time=   0.0s\n",
+      "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.185 total time=   0.0s\n",
+      "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.258 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.115 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.139 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.385 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.238 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.160 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.123 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.258 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.115 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.139 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.359 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.306 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.238 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.160 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.123 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.306 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.175 total time=   0.0s\n",
+      "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.250 total time=   0.0s\n",
+      "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.089 total time=   0.0s\n",
+      "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.141 total time=   0.0s\n",
+      "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.354 total time=   0.0s\n",
+      "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.306 total time=   0.0s\n",
+      "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.282 total time=   0.0s\n",
+      "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.211 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.385 total time=   0.0s\n",
+      "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.142 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.117 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.175 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.249 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.089 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.141 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.353 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.305 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.282 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.211 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.142 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.117 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.1, l1_ratio=1;, score=-0.001 total time=   0.0s\n",
+      "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.040 total time=   0.0s\n",
+      "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.018 total time=   0.0s\n",
+      "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.053 total time=   0.0s\n",
+      "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.024 total time=   0.0s\n",
+      "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.044 total time=   0.0s\n",
+      "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.047 total time=   0.0s\n",
+      "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.041 total time=   0.0s\n",
+      "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.026 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.1, l1_ratio=1;, score=-0.013 total time=   0.0s\n",
+      "Best CV score: 0.261:\n",
+      "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+      "\n",
+      "lasso\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END .....................alpha=0.0001;, score=0.199 total time=   0.0s\n",
+      "[CV 2/10] END .....................alpha=0.0001;, score=0.278 total time=   0.0s\n",
+      "[CV 3/10] END .....................alpha=0.0001;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END .....................alpha=0.0001;, score=0.219 total time=   0.0s\n",
+      "[CV 5/10] END .....................alpha=0.0001;, score=0.399 total time=   0.0s\n",
+      "[CV 6/10] END .....................alpha=0.0001;, score=0.405 total time=   0.0s\n",
+      "[CV 7/10] END .....................alpha=0.0001;, score=0.355 total time=   0.0s\n",
+      "[CV 8/10] END .....................alpha=0.0001;, score=0.282 total time=   0.0s\n",
+      "[CV 9/10] END .....................alpha=0.0001;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END ....................alpha=0.0001;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END ......................alpha=0.001;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END ......................alpha=0.001;, score=0.258 total time=   0.0s\n",
+      "[CV 3/10] END ......................alpha=0.001;, score=0.115 total time=   0.0s\n",
+      "[CV 4/10] END ......................alpha=0.001;, score=0.139 total time=   0.0s\n",
+      "[CV 5/10] END ......................alpha=0.001;, score=0.385 total time=   0.0s\n",
+      "[CV 6/10] END ......................alpha=0.001;, score=0.359 total time=   0.0s\n",
+      "[CV 7/10] END ......................alpha=0.001;, score=0.306 total time=   0.0s\n",
+      "[CV 8/10] END ......................alpha=0.001;, score=0.238 total time=   0.0s\n",
+      "[CV 9/10] END ......................alpha=0.001;, score=0.160 total time=   0.0s\n",
+      "[CV 10/10] END .....................alpha=0.001;, score=0.123 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.01;, score=0.175 total time=   0.0s\n",
+      "[CV 2/10] END .......................alpha=0.01;, score=0.250 total time=   0.0s\n",
+      "[CV 3/10] END .......................alpha=0.01;, score=0.089 total time=   0.0s\n",
+      "[CV 4/10] END .......................alpha=0.01;, score=0.141 total time=   0.0s\n",
+      "[CV 6/10] END .......................alpha=0.01;, score=0.306 total time=   0.0s\n",
+      "[CV 7/10] END .......................alpha=0.01;, score=0.282 total time=   0.0s\n",
+      "[CV 8/10] END .......................alpha=0.01;, score=0.211 total time=   0.0s\n",
+      "[CV 9/10] END .......................alpha=0.01;, score=0.142 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.01;, score=0.117 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.1;, score=-0.001 total time=   0.0s\n",
+      "[CV 2/10] END ........................alpha=0.1;, score=0.040 total time=   0.0s\n",
+      "[CV 3/10] END ........................alpha=0.1;, score=0.018 total time=   0.0s\n",
+      "[CV 4/10] END ........................alpha=0.1;, score=0.053 total time=   0.0s\n",
+      "[CV 5/10] END ........................alpha=0.1;, score=0.024 total time=   0.0s\n",
+      "[CV 6/10] END ........................alpha=0.1;, score=0.044 total time=   0.0s\n",
+      "[CV 7/10] END ........................alpha=0.1;, score=0.047 total time=   0.0s\n",
+      "[CV 8/10] END ........................alpha=0.1;, score=0.041 total time=   0.0s\n",
+      "[CV 9/10] END ........................alpha=0.1;, score=0.026 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.1;, score=-0.013 total time=   0.0s\n",
+      "[CV 5/10] END .......................alpha=0.01;, score=0.354 total time=   0.0s\n",
+      "Best CV score: 0.260:\n",
+      "Best parameters: {'alpha': 0.0001} \n",
+      "\n",
+      "knn\n",
+      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.742 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.816 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.867 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.857 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.712 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.828 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.882 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.760 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.721 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.796 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.803 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.877 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.868 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.767 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.852 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.865 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.821 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.848 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.716 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.810 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.826 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.918 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.883 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.788 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.860 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.854 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.816 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.712 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.742 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.882 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.867 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.803 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.857 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.828 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.864 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.760 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.796 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.877 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.868 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.767 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.852 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.865 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.821 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.848 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.716 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.810 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.826 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.918 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.883 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.788 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.860 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.864 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.854 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.816 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.712 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.742 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.882 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.867 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.803 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.857 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.828 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.760 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.796 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.830 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.877 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.868 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.767 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.852 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.865 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.821 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.848 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.810 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.826 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.918 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.883 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.788 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.860 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.864 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.854 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.721 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.716 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.721 total time=   0.0s\n",
+      "Best CV score: 0.837:\n",
+      "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+      "\n",
+      "bayesian\n",
+      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.197 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.273 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.228 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.398 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.358 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.197 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.273 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.228 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.398 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.358 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.197 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.274 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.227 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.398 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.358 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.284 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.197 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.274 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.227 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.398 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.358 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.284 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.276 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.399 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.224 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.407 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.357 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.283 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.276 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.224 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.399 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.357 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.407 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.283 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.277 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.137 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.223 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.399 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.406 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.356 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.283 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.277 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.137 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.223 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.399 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.406 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.356 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.283 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.188 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.196 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.230 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.196 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.230 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.197 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.273 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.228 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.398 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.358 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.197 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.273 total time=   0.0s[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.398 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.228 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.408 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.358 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.197 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.274 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.398 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.408 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.227 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.358 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.284 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.197 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.227 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.274 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.358 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.398 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.284 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.408 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.198 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.276 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.399 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.407 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.188 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.224 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.357 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.283 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.276 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.224 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.357 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.399 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.283 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.407 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.188 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.198 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.277 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.399 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.137 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.406 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.188 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.223 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.356 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.283 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.137 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.198 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.277 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.223 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.356 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.196 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.399 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.283 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.406 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.188 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.230 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.196 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.230 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time=   0.0s\n",
+      "Best CV score: 0.261:\n",
+      "Best parameters: {'alpha_1': 0.1, 'alpha_2': 1, 'lambda_1': 1, 'lambda_2': 1, 'n_iter': 300} \n",
+      "\n",
+      "ethanol\n",
+      "\n",
+      "nn\n",
+      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+      "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.847 total time=   2.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.829 total time=   3.1s\n",
+      "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.894 total time=   3.1s\n",
+      "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.859 total time=   3.1s\n",
+      "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.929 total time=   3.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 1/10] END activation=logistic, max_iter=400;, score=0.798 total time=   3.6s\n",
+      "[CV 3/10] END activation=logistic, max_iter=400;, score=0.856 total time=   3.6s\n",
+      "[CV 2/10] END activation=logistic, max_iter=400;, score=0.853 total time=   3.6s\n",
+      "[CV 4/10] END activation=logistic, max_iter=400;, score=0.911 total time=   3.6s\n",
+      "[CV 9/10] END activation=logistic, max_iter=400;, score=0.813 total time=   3.6s\n",
+      "[CV 5/10] END activation=logistic, max_iter=400;, score=0.864 total time=   3.6s\n",
+      "[CV 6/10] END activation=logistic, max_iter=400;, score=0.879 total time=   3.6s\n",
+      "[CV 10/10] END activation=logistic, max_iter=400;, score=0.864 total time=   3.6s\n",
+      "[CV 7/10] END activation=logistic, max_iter=400;, score=0.804 total time=   3.6s\n",
+      "[CV 8/10] END activation=logistic, max_iter=400;, score=0.898 total time=   3.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.894 total time=   4.2s\n",
+      "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.870 total time=   4.2s\n",
+      "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.944 total time=   4.2s\n",
+      "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.853 total time=   4.2s\n",
+      "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.909 total time=   4.2s\n",
+      "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.845 total time=   4.2s\n",
+      "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.919 total time=   4.2s\n",
+      "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.884 total time=   4.2s\n",
+      "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.882 total time=   4.2s\n",
+      "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.917 total time=   4.2s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.892 total time=   1.8s\n",
+      "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.838 total time=   1.8s\n",
+      "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.899 total time=   1.8s\n",
+      "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.871 total time=   1.8s\n",
+      "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.795 total time=   1.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best CV score: 0.892:\n",
+      "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+      "\n",
+      "svm_rbf\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.462 total time=   0.0s\n",
+      "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.495 total time=   0.0s\n",
+      "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.433 total time=   0.0s\n",
+      "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.410 total time=   0.0s\n",
+      "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.339 total time=   0.1s\n",
+      "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.443 total time=   0.1s\n",
+      "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.407 total time=   0.1s\n",
+      "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.458 total time=   0.0s\n",
+      "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.414 total time=   0.1s\n",
+      "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.348 total time=   0.1s\n",
+      "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.492 total time=   0.0s\n",
+      "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.434 total time=   0.0s\n",
+      "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.397 total time=   0.1s\n",
+      "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.408 total time=   0.0s\n",
+      "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.341 total time=   0.0s\n",
+      "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.432 total time=   0.0s\n",
+      "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.408 total time=   0.0s\n",
+      "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.409 total time=   0.0s\n",
+      "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.389 total time=   0.0s\n",
+      "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.346 total time=   0.0s\n",
+      "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.714 total time=   0.1s\n",
+      "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.768 total time=   0.1s\n",
+      "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.681 total time=   0.1s\n",
+      "[CV 5/10] END ...................C=1, epsilon=1;, score=0.630 total time=   0.0s\n",
+      "[CV 2/10] END ...................C=1, epsilon=1;, score=0.773 total time=   0.1s\n",
+      "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.688 total time=   0.1s\n",
+      "[CV 6/10] END ...................C=1, epsilon=1;, score=0.677 total time=   0.0s\n",
+      "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.767 total time=   0.1s\n",
+      "[CV 4/10] END ...................C=1, epsilon=1;, score=0.760 total time=   0.1s\n",
+      "[CV 9/10] END ...................C=1, epsilon=1;, score=0.626 total time=   0.0s\n",
+      "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.666 total time=   0.1s\n",
+      "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.627 total time=   0.1s\n",
+      "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.620 total time=   0.1s\n",
+      "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.623 total time=   0.1s\n",
+      "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.712 total time=   0.1s\n",
+      "[CV 10/10] END ..................C=1, epsilon=1;, score=0.662 total time=   0.0s\n",
+      "[CV 8/10] END ...................C=1, epsilon=1;, score=0.701 total time=   0.0s\n",
+      "[CV 1/10] END ...................C=1, epsilon=1;, score=0.683 total time=   0.1s\n",
+      "[CV 3/10] END ...................C=1, epsilon=1;, score=0.718 total time=   0.1s\n",
+      "[CV 7/10] END ...................C=1, epsilon=1;, score=0.633 total time=   0.1s\n",
+      "Best CV score: 0.687:\n",
+      "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+      "\n",
+      "rf\n",
+      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+      "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.631 total time=   0.0s\n",
+      "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.753 total time=   0.0s\n",
+      "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.687 total time=   0.0s\n",
+      "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.699 total time=   0.0s\n",
+      "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.705 total time=   0.0s\n",
+      "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.705 total time=   0.0s\n",
+      "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.609 total time=   0.0s\n",
+      "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.758 total time=   0.0s\n",
+      "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.624 total time=   0.0s\n",
+      "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.708 total time=   0.0s\n",
+      "Best CV score: 0.688:\n",
+      "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+      "\n",
+      "en\n",
+      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+      "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.624 total time=   0.0s\n",
+      "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.534 total time=   0.0s\n",
+      "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.645 total time=   0.0s\n",
+      "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.547 total time=   0.0s\n",
+      "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.555 total time=   0.0s\n",
+      "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.531 total time=   0.0s\n",
+      "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.523 total time=   0.0s\n",
+      "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.576 total time=   0.0s\n",
+      "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.474 total time=   0.0s\n",
+      "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.509 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.534 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.623 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.645 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.547 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.554 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.576 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.532 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.473 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.523 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.508 total time=   0.0s\n",
+      "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.534 total time=   0.0s\n",
+      "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.639 total time=   0.0s\n",
+      "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.546 total time=   0.0s\n",
+      "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.632 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.560 total time=   0.0s\n",
+      "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.516 total time=   0.0s\n",
+      "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.577 total time=   0.0s\n",
+      "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.478 total time=   0.0s\n",
+      "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.513 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.534 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.623 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.645 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.555 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.547 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.576 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.474 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.531 total time=   0.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6319.927607690483, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6620.246040393027, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6167.447753496275, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 365.49217076390414, tolerance: 5.252753629993796\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6153.2457714700895, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6625.996737154269, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1068.2223959471776, tolerance: 5.339794433705047\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 890.3915175846996, tolerance: 5.282608129133931\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 644.7861336401202, tolerance: 5.326734195711993\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 779.5621121167442, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 787.7290920642754, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 800.6096246299294, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 749.1061108902595, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 762.8281143714757, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6616.5969210578605, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865.090131804768, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6458.217920737769, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1012.0649853789528, tolerance: 5.198433624813183\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5902.451073799269, tolerance: 5.252753629993796\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6434.024907986918, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6861.195403478554, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2108.424741007111, tolerance: 5.339794433705047\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5994.308952761697, tolerance: 5.282608129133931\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6029.580945628171, tolerance: 5.326734195711993\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 782.3135226202394, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 790.1314963230161, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 803.3054216148848, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 751.6382247656566, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 765.2385899693454, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061.453675739509, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186.590745316627, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886.35552758309, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6598.009479297723, tolerance: 5.198433624813183\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6523.658353985896, tolerance: 5.252753629993796\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6836.6781675206375, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7174.444409408589, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6840.44674355432, tolerance: 5.339794433705047\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6560.63964769235, tolerance: 5.282608129133931\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6618.967981701236, tolerance: 5.326734195711993\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 805.6010349600365, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 809.8189764234066, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 785.1627747566799, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3252.0860010641045, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3186.482690816163, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2997.84313289648, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2548.272162454812, tolerance: 5.198433624813183\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2508.315985565094, tolerance: 5.252753629993796\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2858.2139371914145, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3250.649979226062, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2857.334069342067, tolerance: 5.339794433705047\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2756.0505355140285, tolerance: 5.282608129133931\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2655.351402000986, tolerance: 5.326734195711993\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097.125190452523, tolerance: 5.923503688869954\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 779.5621121167442, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 787.7290920642754, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 800.6096246299294, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 749.1061108902595, tolerance: 5.345940946064122\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 762.8281143714757, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 782.3135226202394, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 790.1314963230161, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 803.3054216148848, tolerance: 5.3515561642098675\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 751.6382247656566, tolerance: 5.345940946064122\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.523 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.530 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.620 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.538 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.646 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.518 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.495 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.575 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.520 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.534 total time=   0.0s\n",
+      "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.629 total time=   0.0s\n",
+      "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.639 total time=   0.0s\n",
+      "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.546 total time=   0.0s\n",
+      "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.558 total time=   0.0s\n",
+      "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.577 total time=   0.0s\n",
+      "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.529 total time=   0.0s\n",
+      "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.477 total time=   0.0s\n",
+      "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.517 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.512 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.569 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.525 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.609 total time=   0.0s\n",
+      "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.651 total time=   0.0s\n",
+      "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.567 total time=   0.0s\n",
+      "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.486 total time=   0.0s\n",
+      "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.573 total time=   0.0s\n",
+      "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.521 total time=   0.0s\n",
+      "Best CV score: 0.552:\n",
+      "Best parameters: {'alpha': 0.001, 'l1_ratio': 0.1} \n",
+      "\n",
+      "lasso\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 4/10] END .....................alpha=0.0001;, score=0.623 total time=   0.0s\n",
+      "[CV 1/10] END .....................alpha=0.0001;, score=0.534 total time=   0.0s\n",
+      "[CV 2/10] END .....................alpha=0.0001;, score=0.645 total time=   0.0s\n",
+      "[CV 5/10] END .....................alpha=0.0001;, score=0.554 total time=   0.0s\n",
+      "[CV 3/10] END .....................alpha=0.0001;, score=0.547 total time=   0.0s\n",
+      "[CV 8/10] END .....................alpha=0.0001;, score=0.576 total time=   0.0s\n",
+      "[CV 6/10] END .....................alpha=0.0001;, score=0.532 total time=   0.0s\n",
+      "[CV 9/10] END .....................alpha=0.0001;, score=0.473 total time=   0.0s\n",
+      "[CV 10/10] END ....................alpha=0.0001;, score=0.508 total time=   0.0s\n",
+      "[CV 7/10] END .....................alpha=0.0001;, score=0.523 total time=   0.0s\n",
+      "[CV 4/10] END ......................alpha=0.001;, score=0.623 total time=   0.0s\n",
+      "[CV 1/10] END ......................alpha=0.001;, score=0.534 total time=   0.0s\n",
+      "[CV 5/10] END ......................alpha=0.001;, score=0.555 total time=   0.0s\n",
+      "[CV 2/10] END ......................alpha=0.001;, score=0.645 total time=   0.0s\n",
+      "[CV 3/10] END ......................alpha=0.001;, score=0.547 total time=   0.0s\n",
+      "[CV 8/10] END ......................alpha=0.001;, score=0.576 total time=   0.0s\n",
+      "[CV 6/10] END ......................alpha=0.001;, score=0.531 total time=   0.0s\n",
+      "[CV 9/10] END ......................alpha=0.001;, score=0.474 total time=   0.0s\n",
+      "[CV 10/10] END .....................alpha=0.001;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END ......................alpha=0.001;, score=0.523 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.01;, score=0.534 total time=   0.0s\n",
+      "[CV 4/10] END .......................alpha=0.01;, score=0.629 total time=   0.0s\n",
+      "[CV 2/10] END .......................alpha=0.01;, score=0.639 total time=   0.0s\n",
+      "[CV 3/10] END .......................alpha=0.01;, score=0.546 total time=   0.0s\n",
+      "[CV 5/10] END .......................alpha=0.01;, score=0.558 total time=   0.0s\n",
+      "[CV 8/10] END .......................alpha=0.01;, score=0.577 total time=   0.0s\n",
+      "[CV 6/10] END .......................alpha=0.01;, score=0.529 total time=   0.0s\n",
+      "[CV 9/10] END .......................alpha=0.01;, score=0.477 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.01;, score=0.512 total time=   0.0s\n",
+      "[CV 7/10] END .......................alpha=0.01;, score=0.517 total time=   0.0s\n",
+      "[CV 1/10] END ........................alpha=0.1;, score=0.525 total time=   0.0s\n",
+      "[CV 2/10] END ........................alpha=0.1;, score=0.609 total time=   0.0s\n",
+      "[CV 3/10] END ........................alpha=0.1;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END ........................alpha=0.1;, score=0.651 total time=   0.0s\n",
+      "[CV 5/10] END ........................alpha=0.1;, score=0.567 total time=   0.0s\n",
+      "[CV 6/10] END ........................alpha=0.1;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END ........................alpha=0.1;, score=0.486 total time=   0.0s\n",
+      "[CV 8/10] END ........................alpha=0.1;, score=0.573 total time=   0.0s\n",
+      "[CV 9/10] END ........................alpha=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END .......................alpha=0.1;, score=0.521 total time=   0.0s\n",
+      "Best CV score: 0.552:\n",
+      "Best parameters: {'alpha': 0.01} \n",
+      "\n",
+      "knn\n",
+      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.909 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.912 total time=   0.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 765.2385899693454, tolerance: 5.479641394818488\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 805.6010349600365, tolerance: 5.3926999456597775\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 809.8189764234066, tolerance: 5.39948427204628\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 785.1627747566799, tolerance: 5.479641394818488\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.908 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.893 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.894 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.902 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.923 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.938 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.944 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.961 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.937 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.943 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.906 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.924 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.909 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.912 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.908 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.893 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.894 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.902 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.923 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.938 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.944 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.961 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.937 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.943 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.906 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.924 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.909 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.912 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.908 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.893 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.894 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.902 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.923 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.938 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.944 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.961 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.937 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.943 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.906 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.924 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "Best CV score: 0.933:\n",
+      "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+      "\n",
+      "bayesian\n",
+      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time=   0.0s\n",
+      "Best CV score: 0.546:\n",
+      "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+      "\n",
+      "acetate\n",
+      "\n",
+      "nn\n",
+      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.742 total time=   3.4s\n",
+      "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.910 total time=   3.4s\n",
+      "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.762 total time=   3.4s\n",
+      "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.730 total time=   3.5s\n",
+      "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.881 total time=   3.5s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 1/10] END activation=logistic, max_iter=400;, score=0.685 total time=   4.0s\n",
+      "[CV 8/10] END activation=logistic, max_iter=400;, score=0.739 total time=   4.0s\n",
+      "[CV 3/10] END activation=logistic, max_iter=400;, score=0.619 total time=   4.0s\n",
+      "[CV 10/10] END activation=logistic, max_iter=400;, score=0.733 total time=   4.0s\n",
+      "[CV 7/10] END activation=logistic, max_iter=400;, score=0.807 total time=   4.0s\n",
+      "[CV 5/10] END activation=logistic, max_iter=400;, score=0.813 total time=   4.0s\n",
+      "[CV 9/10] END activation=logistic, max_iter=400;, score=0.731 total time=   4.0s\n",
+      "[CV 2/10] END activation=logistic, max_iter=400;, score=0.744 total time=   4.1s\n",
+      "[CV 4/10] END activation=logistic, max_iter=400;, score=0.786 total time=   4.1s\n",
+      "[CV 6/10] END activation=logistic, max_iter=400;, score=0.745 total time=   4.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.811 total time=   4.6s\n",
+      "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.946 total time=   4.6s\n",
+      "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.885 total time=   4.6s\n",
+      "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.754 total time=   4.6s\n",
+      "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.784 total time=   4.6s\n",
+      "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.853 total time=   4.6s\n",
+      "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.887 total time=   4.6s\n",
+      "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.885 total time=   4.6s\n",
+      "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.844 total time=   4.6s\n",
+      "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.877 total time=   4.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.856 total time=   1.6s\n",
+      "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.775 total time=   1.6s\n",
+      "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.873 total time=   1.6s\n",
+      "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.883 total time=   1.6s\n",
+      "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.897 total time=   1.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best CV score: 0.853:\n",
+      "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+      "\n",
+      "svm_rbf\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.062 total time=   0.0s\n",
+      "[CV 1/10] END ..............C=0.1, epsilon=0.1;, score=-0.051 total time=   0.0s\n",
+      "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.126 total time=   0.0s\n",
+      "[CV 4/10] END ..............C=0.1, epsilon=0.1;, score=-0.045 total time=   0.1s\n",
+      "[CV 5/10] END ..............C=0.1, epsilon=0.1;, score=-0.077 total time=   0.1s\n",
+      "[CV 7/10] END ..............C=0.1, epsilon=0.1;, score=-0.097 total time=   0.1s\n",
+      "[CV 8/10] END ..............C=0.1, epsilon=0.1;, score=-0.159 total time=   0.1s\n",
+      "[CV 6/10] END ..............C=0.1, epsilon=0.1;, score=-0.118 total time=   0.1s\n",
+      "[CV 9/10] END ..............C=0.1, epsilon=0.1;, score=-0.012 total time=   0.1s\n",
+      "[CV 1/10] END ................C=0.1, epsilon=1;, score=-0.049 total time=   0.1s\n",
+      "[CV 10/10] END .............C=0.1, epsilon=0.1;, score=-0.220 total time=   0.1s\n",
+      "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.061 total time=   0.1s\n",
+      "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.128 total time=   0.1s\n",
+      "[CV 4/10] END ................C=0.1, epsilon=1;, score=-0.043 total time=   0.1s\n",
+      "[CV 5/10] END ................C=0.1, epsilon=1;, score=-0.074 total time=   0.1s\n",
+      "[CV 6/10] END ................C=0.1, epsilon=1;, score=-0.114 total time=   0.0s\n",
+      "[CV 7/10] END ................C=0.1, epsilon=1;, score=-0.094 total time=   0.0s\n",
+      "[CV 8/10] END ................C=0.1, epsilon=1;, score=-0.148 total time=   0.0s\n",
+      "[CV 9/10] END ................C=0.1, epsilon=1;, score=-0.011 total time=   0.0s\n",
+      "[CV 10/10] END ...............C=0.1, epsilon=1;, score=-0.205 total time=   0.0s\n",
+      "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.214 total time=   0.1s\n",
+      "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.351 total time=   0.1s\n",
+      "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.411 total time=   0.1s\n",
+      "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.162 total time=   0.1s\n",
+      "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.261 total time=   0.1s\n",
+      "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.220 total time=   0.1s\n",
+      "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.256 total time=   0.1s\n",
+      "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.197 total time=   0.1s\n",
+      "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.193 total time=   0.1s\n",
+      "[CV 1/10] END ...................C=1, epsilon=1;, score=0.234 total time=   0.1s\n",
+      "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.166 total time=   0.1s\n",
+      "[CV 3/10] END ...................C=1, epsilon=1;, score=0.415 total time=   0.1s\n",
+      "[CV 2/10] END ...................C=1, epsilon=1;, score=0.361 total time=   0.1s\n",
+      "[CV 6/10] END ...................C=1, epsilon=1;, score=0.180 total time=   0.0s\n",
+      "[CV 4/10] END ...................C=1, epsilon=1;, score=0.278 total time=   0.0s\n",
+      "[CV 5/10] END ...................C=1, epsilon=1;, score=0.225 total time=   0.0s\n",
+      "[CV 8/10] END ...................C=1, epsilon=1;, score=0.208 total time=   0.0s\n",
+      "[CV 7/10] END ...................C=1, epsilon=1;, score=0.274 total time=   0.0s\n",
+      "[CV 10/10] END ..................C=1, epsilon=1;, score=0.178 total time=   0.0s\n",
+      "[CV 9/10] END ...................C=1, epsilon=1;, score=0.207 total time=   0.0s\n",
+      "Best CV score: 0.256:\n",
+      "Best parameters: {'C': 1, 'epsilon': 1} \n",
+      "\n",
+      "rf\n",
+      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+      "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.613 total time=   0.0s\n",
+      "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.710 total time=   0.0s\n",
+      "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.651 total time=   0.0s\n",
+      "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.655 total time=   0.0s\n",
+      "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.748 total time=   0.0s\n",
+      "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.706 total time=   0.0s\n",
+      "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.366 total time=   0.0s\n",
+      "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.664 total time=   0.0s\n",
+      "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.515 total time=   0.0s\n",
+      "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.626 total time=   0.0s\n",
+      "Best CV score: 0.625:\n",
+      "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+      "\n",
+      "en\n",
+      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+      "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.369 total time=   0.0s\n",
+      "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.498 total time=   0.0s\n",
+      "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.419 total time=   0.0s\n",
+      "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.386 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.369 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.508 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.498 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.557 total time=   0.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49493.681107514465, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50716.13278762021, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49078.79649163709, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47706.64963062391, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48509.56706162072, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47515.6191775647, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49970.10219212296, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46643.935788883355, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48054.59492315794, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48442.61203858821, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4252.348966222722, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4451.743602846676, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4360.982042964155, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.479934531468, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4118.769163615638, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4097.520171475844, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.811282062699, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.864723005841, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4240.133706657871, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4228.450441768931, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49431.830542375006, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50730.49152157803, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49222.313718523554, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47929.67901107358, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48566.544619539905, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47601.31920429702, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49968.119879619466, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46667.045188132564, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48113.12207094836, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48446.70247123788, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4253.459914357954, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4362.690555760491, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4454.331302843115, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4245.176876319107, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4120.74332484584, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4099.54275815787, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.424263122375, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.6120532843925, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.08871382341, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4230.097637977247, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48742.78646057141, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50180.90975493966, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49020.60761365638, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47842.27349859188, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48145.21143356779, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47213.17033387661, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49410.46300960829, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46187.592232230825, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47704.07441246217, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47888.83543134161, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4264.094199816609, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4375.487907155082, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4475.968665716209, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4267.880035531183, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4136.34698929415, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4115.623386722087, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4185.368672953715, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4186.865302993072, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4257.431237919605, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.37117427327, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38723.0981820787, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38876.67344477996, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38291.18492337677, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35963.30283090114, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37313.12875906742, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35479.965126869094, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38298.67166961717, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35347.22554307138, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37875.64020276432, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36647.310389868246, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42000.2145192141, tolerance: 27.122111107765193\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4252.348966222722, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4360.982042964155, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4451.743602846676, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.479934531468, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4118.769163615638, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4097.520171475844, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.811282062699, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.864723005841, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4240.133706657871, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4228.450441768931, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4253.459914357954, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4362.690555760491, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4454.331302843115, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4245.176876319107, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4120.74332484584, tolerance: 23.792475772151427\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4099.54275815787, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.424263122375, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.6120532843925, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.08871382341, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4230.097637977247, tolerance: 24.079772047834943\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4264.094199816609, tolerance: 24.718924822210536\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4375.487907155082, tolerance: 25.083856589346837\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4475.968665716209, tolerance: 24.798436508448894\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4267.880035531183, tolerance: 24.448613738487428\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4136.34698929415, tolerance: 23.792475772151427\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.419 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.372 total time=   0.0s\n",
+      "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.501 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.558 total time=   0.0s\n",
+      "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.420 total time=   0.0s\n",
+      "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.369 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.498 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.419 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.379 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.508 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.559 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.420 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.373 total time=   0.0s\n",
+      "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.501 total time=   0.0s\n",
+      "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.558 total time=   0.0s\n",
+      "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.420 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.511 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.382 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.510 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.558 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.514 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.506 total time=   0.0s\n",
+      "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.386 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.419 total time=   0.0s\n",
+      "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.484 total time=   0.0s\n",
+      "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.381 total time=   0.0s\n",
+      "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.559 total time=   0.0s\n",
+      "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.514 total time=   0.0s\n",
+      "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.506 total time=   0.0s\n",
+      "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.420 total time=   0.0s\n",
+      "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.483 total time=   0.0s\n",
+      "Best CV score: 0.480:\n",
+      "Best parameters: {'alpha': 0.1, 'l1_ratio': 0.1} \n",
+      "\n",
+      "lasso\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END .....................alpha=0.0001;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END .....................alpha=0.0001;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END .....................alpha=0.0001;, score=0.369 total time=   0.0s\n",
+      "[CV 4/10] END .....................alpha=0.0001;, score=0.498 total time=   0.0s\n",
+      "[CV 5/10] END .....................alpha=0.0001;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END .....................alpha=0.0001;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END .....................alpha=0.0001;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END .....................alpha=0.0001;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END .....................alpha=0.0001;, score=0.419 total time=   0.0s\n",
+      "[CV 10/10] END ....................alpha=0.0001;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END ......................alpha=0.001;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END ......................alpha=0.001;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END ......................alpha=0.001;, score=0.369 total time=   0.0s\n",
+      "[CV 4/10] END ......................alpha=0.001;, score=0.498 total time=   0.0s\n",
+      "[CV 5/10] END ......................alpha=0.001;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END ......................alpha=0.001;, score=0.528 total time=   0.0s\n",
+      "[CV 7/10] END ......................alpha=0.001;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END ......................alpha=0.001;, score=0.507 total time=   0.0s\n",
+      "[CV 9/10] END ......................alpha=0.001;, score=0.419 total time=   0.0s\n",
+      "[CV 10/10] END .....................alpha=0.001;, score=0.483 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.01;, score=0.386 total time=   0.0s\n",
+      "[CV 2/10] END .......................alpha=0.01;, score=0.508 total time=   0.0s\n",
+      "[CV 3/10] END .......................alpha=0.01;, score=0.373 total time=   0.0s\n",
+      "[CV 4/10] END .......................alpha=0.01;, score=0.501 total time=   0.0s\n",
+      "[CV 5/10] END .......................alpha=0.01;, score=0.558 total time=   0.0s\n",
+      "[CV 6/10] END .......................alpha=0.01;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END .......................alpha=0.01;, score=0.513 total time=   0.0s\n",
+      "[CV 8/10] END .......................alpha=0.01;, score=0.507 total time=   0.0s\n",
+      "[CV 1/10] END ........................alpha=0.1;, score=0.386 total time=   0.0s\n",
+      "[CV 9/10] END .......................alpha=0.01;, score=0.420 total time=   0.0s\n",
+      "[CV 2/10] END ........................alpha=0.1;, score=0.509 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.01;, score=0.483 total time=   0.0s\n",
+      "[CV 3/10] END ........................alpha=0.1;, score=0.381 total time=   0.0s\n",
+      "[CV 4/10] END ........................alpha=0.1;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END ........................alpha=0.1;, score=0.559 total time=   0.0s\n",
+      "[CV 6/10] END ........................alpha=0.1;, score=0.529 total time=   0.0s\n",
+      "[CV 7/10] END ........................alpha=0.1;, score=0.514 total time=   0.0s\n",
+      "[CV 8/10] END ........................alpha=0.1;, score=0.506 total time=   0.0s\n",
+      "[CV 9/10] END ........................alpha=0.1;, score=0.420 total time=   0.0s\n",
+      "[CV 10/10] END .......................alpha=0.1;, score=0.483 total time=   0.0s\n",
+      "Best CV score: 0.480:\n",
+      "Best parameters: {'alpha': 0.1} \n",
+      "\n",
+      "knn\n",
+      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4115.623386722087, tolerance: 23.821176586325123\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4185.368672953715, tolerance: 24.524031583747142\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4186.865302993072, tolerance: 22.98759672522025\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4257.431237919605, tolerance: 24.398424889946494\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.37117427327, tolerance: 24.079772047834943\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.557 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.653 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.799 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.926 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.753 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.766 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.609 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.718 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.711 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.891 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.945 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.771 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.643 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.691 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.879 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.853 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.910 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.915 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.744 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.557 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.653 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.753 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.738 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.799 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.926 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.766 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.609 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.718 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.711 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.891 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.945 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.771 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.643 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.691 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.738 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.879 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.853 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.910 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.915 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.744 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.557 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.653 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.753 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.936 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.799 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.926 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.766 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.609 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.718 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.711 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.891 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.945 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.911 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.898 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.771 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.643 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.691 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.738 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.879 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.853 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.910 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.915 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.744 total time=   0.0s\n",
+      "Best CV score: 0.819:\n",
+      "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+      "\n",
+      "bayesian\n",
+      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time=   0.0s\n",
+      "Best CV score: 0.480:\n",
+      "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+      "\n",
+      "butanol\n",
+      "\n",
+      "nn\n",
+      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+      "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.912 total time=   1.5s\n",
+      "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.945 total time=   2.2s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.933 total time=   3.4s\n",
+      "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.868 total time=   1.8s\n",
+      "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.927 total time=   3.4s\n",
+      "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.887 total time=   3.4s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 5/10] END activation=logistic, max_iter=400;, score=0.952 total time=   3.9s\n",
+      "[CV 8/10] END activation=logistic, max_iter=400;, score=0.975 total time=   3.9s\n",
+      "[CV 7/10] END activation=logistic, max_iter=400;, score=0.951 total time=   3.9s\n",
+      "[CV 4/10] END activation=logistic, max_iter=400;, score=0.940 total time=   4.0s\n",
+      "[CV 9/10] END activation=logistic, max_iter=400;, score=0.920 total time=   3.9s\n",
+      "[CV 10/10] END activation=logistic, max_iter=400;, score=0.959 total time=   3.9s\n",
+      "[CV 6/10] END activation=logistic, max_iter=400;, score=0.965 total time=   4.0s\n",
+      "[CV 3/10] END activation=logistic, max_iter=400;, score=0.957 total time=   4.0s\n",
+      "[CV 2/10] END activation=logistic, max_iter=400;, score=0.962 total time=   4.0s\n",
+      "[CV 1/10] END activation=logistic, max_iter=400;, score=0.906 total time=   4.0s\n",
+      "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.966 total time=   4.0s\n",
+      "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.915 total time=   2.5s\n",
+      "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.958 total time=   2.5s\n",
+      "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.985 total time=   4.2s\n",
+      "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.948 total time=   2.6s\n",
+      "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.976 total time=   4.3s\n",
+      "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.977 total time=   4.3s\n",
+      "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.947 total time=   4.3s\n",
+      "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.975 total time=   4.4s\n",
+      "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.979 total time=   4.4s\n",
+      "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.935 total time=   4.4s\n",
+      "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.986 total time=   4.4s\n",
+      "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.958 total time=   2.7s\n",
+      "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.967 total time=   4.4s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best CV score: 0.969:\n",
+      "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+      "\n",
+      "svm_rbf\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.553 total time=   0.0s\n",
+      "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.634 total time=   0.0s\n",
+      "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.500 total time=   0.0s\n",
+      "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.544 total time=   0.0s\n",
+      "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.602 total time=   0.0s\n",
+      "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.589 total time=   0.0s\n",
+      "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.583 total time=   0.0s\n",
+      "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.496 total time=   0.0s\n",
+      "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.452 total time=   0.0s\n",
+      "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.628 total time=   0.1s\n",
+      "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.606 total time=   0.1s\n",
+      "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.558 total time=   0.0s\n",
+      "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.458 total time=   0.0s\n",
+      "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.512 total time=   0.0s\n",
+      "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.569 total time=   0.1s\n",
+      "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.569 total time=   0.0s\n",
+      "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.503 total time=   0.0s\n",
+      "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.437 total time=   0.0s\n",
+      "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.553 total time=   0.0s\n",
+      "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.516 total time=   0.0s\n",
+      "[CV 1/10] END ...................C=1, epsilon=1;, score=0.783 total time=   0.0s\n",
+      "[CV 3/10] END ...................C=1, epsilon=1;, score=0.849 total time=   0.0s\n",
+      "[CV 5/10] END ...................C=1, epsilon=1;, score=0.819 total time=   0.0s\n",
+      "[CV 6/10] END ...................C=1, epsilon=1;, score=0.831 total time=   0.0s\n",
+      "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.869 total time=   0.0s\n",
+      "[CV 7/10] END ...................C=1, epsilon=1;, score=0.706 total time=   0.0s\n",
+      "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.734 total time=   0.0s\n",
+      "[CV 8/10] END ...................C=1, epsilon=1;, score=0.820 total time=   0.0s\n",
+      "[CV 9/10] END ...................C=1, epsilon=1;, score=0.723 total time=   0.0s\n",
+      "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.801 total time=   0.1s\n",
+      "[CV 2/10] END ...................C=1, epsilon=1;, score=0.751 total time=   0.0s\n",
+      "[CV 4/10] END ...................C=1, epsilon=1;, score=0.598 total time=   0.0s\n",
+      "[CV 10/10] END ..................C=1, epsilon=1;, score=0.795 total time=   0.0s\n",
+      "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.713 total time=   0.1s\n",
+      "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.811 total time=   0.1s\n",
+      "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.843 total time=   0.1s\n",
+      "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.811 total time=   0.1s\n",
+      "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.847 total time=   0.1s\n",
+      "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.653 total time=   0.1s\n",
+      "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.852 total time=   0.1s\n",
+      "Best CV score: 0.793:\n",
+      "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+      "\n",
+      "rf\n",
+      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+      "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.666 total time=   0.0s\n",
+      "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.732 total time=   0.0s\n",
+      "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.642 total time=   0.0s\n",
+      "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.655 total time=   0.0s\n",
+      "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.757 total time=   0.0s\n",
+      "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.720 total time=   0.0s\n",
+      "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.732 total time=   0.0s\n",
+      "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.725 total time=   0.0s\n",
+      "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.477 total time=   0.0s\n",
+      "Best CV score: 0.679:\n",
+      "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+      "\n",
+      "en\n",
+      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+      "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.678 total time=   0.0s\n",
+      "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.718 total time=   0.0s\n",
+      "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.718 total time=   0.0s\n",
+      "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.765 total time=   0.0s\n",
+      "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.821 total time=   0.0s\n",
+      "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.719 total time=   0.0s\n",
+      "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.667 total time=   0.0s\n",
+      "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.796 total time=   0.0s\n",
+      "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.792 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.678 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.718 total time=   0.0s\n",
+      "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.754 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.718 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.821 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.765 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.719 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.668 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.796 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.792 total time=   0.0s\n",
+      "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.679 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.754 total time=   0.0s\n",
+      "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.821 total time=   0.0s\n",
+      "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.718 total time=   0.0s\n",
+      "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.715 total time=   0.0s\n",
+      "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.720 total time=   0.0s\n",
+      "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.664 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.764 total time=   0.0s\n",
+      "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.795 total time=   0.0s\n",
+      "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.756 total time=   0.0s\n",
+      "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.794 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.679 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.717 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.821 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.718 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.764 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.719 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.666 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.795 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.755 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.793 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.705 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.713 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.759 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.652 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.680 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.761 total time=   0.0s\n",
+      "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.700 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.720 total time=   0.0s\n",
+      "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.646 total time=   0.0s\n",
+      "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.700 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.711 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.818 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.646 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.699 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.762 total time=   0.0s\n",
+      "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.815 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.797 total time=   0.0s\n",
+      "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.716 total time=   0.0s\n",
+      "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.759 total time=   0.0s\n",
+      "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.718 total time=   0.0s\n",
+      "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.650 total time=   0.0s\n",
+      "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.787 total time=   0.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28.912355898621172, tolerance: 0.4275189965039049\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.414628175816006, tolerance: 0.4351835203188813\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.211560781099706, tolerance: 0.4351835203188813\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.68962205058529, tolerance: 0.4464705762214399\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105.71342646149753, tolerance: 0.45070119912621\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.84988612837935, tolerance: 0.45233045408996186\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94.99680245367381, tolerance: 0.4464705762214399\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.7396496187331, tolerance: 0.4275189965039049\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100.01943109648846, tolerance: 0.4479692470703709\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100.64747724225492, tolerance: 0.45070119912621\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88.3088581561525, tolerance: 0.43794994720251135\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.29438370867433, tolerance: 0.46414008999791107\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98.29566453580003, tolerance: 0.42697028233034046\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.74590311439215, tolerance: 0.4351835203188813\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89.40215530971864, tolerance: 0.43330549863246526\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.144881080779214, tolerance: 0.45233045408996186\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.054947463228132, tolerance: 0.4464705762214399\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.371206881185572, tolerance: 0.4479692470703709\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.57474306598374, tolerance: 0.4275189965039049\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.141784847107601, tolerance: 0.45070119912621\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.65219515899787, tolerance: 0.43794994720251135\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.48316478295476, tolerance: 0.46414008999791107\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.52700414015294, tolerance: 0.42697028233034046\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.27961589892618, tolerance: 0.4351835203188813\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.411954044651566, tolerance: 0.43330549863246526\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.211560781099706, tolerance: 0.4351835203188813\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.797 total time=   0.0s\n",
+      "Best CV score: 0.743:\n",
+      "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+      "\n",
+      "lasso\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END .....................alpha=0.0001;, score=0.678 total time=   0.0s\n",
+      "[CV 2/10] END .....................alpha=0.0001;, score=0.718 total time=   0.0s\n",
+      "[CV 4/10] END .....................alpha=0.0001;, score=0.718 total time=   0.0s\n",
+      "[CV 3/10] END .....................alpha=0.0001;, score=0.821 total time=   0.0s\n",
+      "[CV 5/10] END .....................alpha=0.0001;, score=0.765 total time=   0.0s\n",
+      "[CV 6/10] END .....................alpha=0.0001;, score=0.719 total time=   0.0s\n",
+      "[CV 7/10] END .....................alpha=0.0001;, score=0.668 total time=   0.0s\n",
+      "[CV 8/10] END .....................alpha=0.0001;, score=0.796 total time=   0.0s\n",
+      "[CV 10/10] END ....................alpha=0.0001;, score=0.792 total time=   0.0s\n",
+      "[CV 1/10] END ......................alpha=0.001;, score=0.679 total time=   0.0s\n",
+      "[CV 2/10] END ......................alpha=0.001;, score=0.717 total time=   0.0s\n",
+      "[CV 9/10] END .....................alpha=0.0001;, score=0.754 total time=   0.0s\n",
+      "[CV 3/10] END ......................alpha=0.001;, score=0.821 total time=   0.0s\n",
+      "[CV 4/10] END ......................alpha=0.001;, score=0.718 total time=   0.0s\n",
+      "[CV 5/10] END ......................alpha=0.001;, score=0.764 total time=   0.0s\n",
+      "[CV 6/10] END ......................alpha=0.001;, score=0.719 total time=   0.0s\n",
+      "[CV 7/10] END ......................alpha=0.001;, score=0.666 total time=   0.0s\n",
+      "[CV 8/10] END ......................alpha=0.001;, score=0.795 total time=   0.0s\n",
+      "[CV 9/10] END ......................alpha=0.001;, score=0.755 total time=   0.0s\n",
+      "[CV 10/10] END .....................alpha=0.001;, score=0.793 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.01;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END .......................alpha=0.01;, score=0.700 total time=   0.0s\n",
+      "[CV 3/10] END .......................alpha=0.01;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END .......................alpha=0.01;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END .......................alpha=0.01;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END .......................alpha=0.01;, score=0.720 total time=   0.0s\n",
+      "[CV 7/10] END .......................alpha=0.01;, score=0.646 total time=   0.0s\n",
+      "[CV 8/10] END .......................alpha=0.01;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END .......................alpha=0.01;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.01;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END ........................alpha=0.1;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END ........................alpha=0.1;, score=0.699 total time=   0.0s\n",
+      "[CV 3/10] END ........................alpha=0.1;, score=0.815 total time=   0.0s\n",
+      "[CV 4/10] END ........................alpha=0.1;, score=0.716 total time=   0.0s\n",
+      "[CV 5/10] END ........................alpha=0.1;, score=0.759 total time=   0.0s\n",
+      "[CV 6/10] END ........................alpha=0.1;, score=0.718 total time=   0.0s\n",
+      "[CV 7/10] END ........................alpha=0.1;, score=0.650 total time=   0.0s\n",
+      "[CV 8/10] END ........................alpha=0.1;, score=0.787 total time=   0.0s\n",
+      "[CV 9/10] END ........................alpha=0.1;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END .......................alpha=0.1;, score=0.797 total time=   0.0s\n",
+      "Best CV score: 0.743:\n",
+      "Best parameters: {'alpha': 0.0001} \n",
+      "\n",
+      "knn\n",
+      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.970 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.964 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.990 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.993 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.979 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.967 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.975 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.968 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.994 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.977 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.976 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.992 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.979 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.970 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.964 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.990 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.993 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.979 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.967 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.975 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.968 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.994 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.977 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.976 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.992 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.979 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.970 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.964 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.990 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.993 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.979 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.967 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.975 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.986 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.968 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.994 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.955 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.983 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.977 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.984 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.940 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.976 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.980 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.981 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.992 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.979 total time=   0.0s\n",
+      "Best CV score: 0.979:\n",
+      "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 2, 'weights': 'distance'} \n",
+      "\n",
+      "bayesian\n",
+      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time=   0.0s[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time=   0.0s\n",
+      "Best CV score: 0.740:\n",
+      "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+      "\n",
+      "butyrate\n",
+      "\n",
+      "nn\n",
+      "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+      "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.854 total time=   1.8s\n",
+      "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.843 total time=   2.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.783 total time=   3.5s\n",
+      "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.821 total time=   3.5s\n",
+      "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.794 total time=   3.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 1/10] END activation=logistic, max_iter=400;, score=0.828 total time=   4.1s\n",
+      "[CV 4/10] END activation=logistic, max_iter=400;, score=0.881 total time=   4.1s\n",
+      "[CV 10/10] END activation=logistic, max_iter=400;, score=0.817 total time=   4.0s\n",
+      "[CV 8/10] END activation=logistic, max_iter=400;, score=0.857 total time=   4.0s\n",
+      "[CV 3/10] END activation=logistic, max_iter=400;, score=0.862 total time=   4.1s\n",
+      "[CV 9/10] END activation=logistic, max_iter=400;, score=0.851 total time=   4.0s\n",
+      "[CV 5/10] END activation=logistic, max_iter=400;, score=0.824 total time=   4.1s\n",
+      "[CV 2/10] END activation=logistic, max_iter=400;, score=0.900 total time=   4.1s\n",
+      "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.914 total time=   4.1s\n",
+      "[CV 7/10] END activation=logistic, max_iter=400;, score=0.934 total time=   4.1s\n",
+      "[CV 6/10] END activation=logistic, max_iter=400;, score=0.927 total time=   4.1s\n",
+      "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.817 total time=   2.4s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.914 total time=   4.5s\n",
+      "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.893 total time=   4.5s\n",
+      "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.931 total time=   4.5s\n",
+      "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.870 total time=   4.5s\n",
+      "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.964 total time=   4.5s\n",
+      "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.937 total time=   4.5s\n",
+      "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.840 total time=   4.5s\n",
+      "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.892 total time=   4.5s\n",
+      "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.887 total time=   4.5s\n",
+      "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.904 total time=   2.8s\n",
+      "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.881 total time=   2.8s\n",
+      "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.837 total time=   2.8s\n",
+      "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.802 total time=   2.8s\n",
+      "Best CV score: 0.904:\n",
+      "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+      "\n",
+      "svm_rbf\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.143 total time=   0.0s\n",
+      "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.235 total time=   0.0s\n",
+      "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.175 total time=   0.0s\n",
+      "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.190 total time=   0.0s\n",
+      "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.088 total time=   0.0s\n",
+      "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.187 total time=   0.1s\n",
+      "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.215 total time=   0.1s\n",
+      "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.199 total time=   0.0s\n",
+      "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.181 total time=   0.0s\n",
+      "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.185 total time=   0.0s\n",
+      "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.238 total time=   0.0s\n",
+      "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.177 total time=   0.0s\n",
+      "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.235 total time=   0.0s\n",
+      "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.250 total time=   0.1s\n",
+      "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.248 total time=   0.0s\n",
+      "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.168 total time=   0.0s\n",
+      "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.138 total time=   0.1s\n",
+      "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.131 total time=   0.1s\n",
+      "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.183 total time=   0.1s\n",
+      "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.171 total time=   0.1s\n",
+      "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.477 total time=   0.1s\n",
+      "[CV 3/10] END ...................C=1, epsilon=1;, score=0.474 total time=   0.0s\n",
+      "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.486 total time=   0.1s\n",
+      "[CV 8/10] END ...................C=1, epsilon=1;, score=0.466 total time=   0.0s\n",
+      "[CV 7/10] END ...................C=1, epsilon=1;, score=0.462 total time=   0.0s\n",
+      "[CV 2/10] END ...................C=1, epsilon=1;, score=0.501 total time=   0.0s\n",
+      "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.441 total time=   0.1s\n",
+      "[CV 6/10] END ...................C=1, epsilon=1;, score=0.554 total time=   0.0s\n",
+      "[CV 10/10] END ..................C=1, epsilon=1;, score=0.440 total time=   0.0s\n",
+      "[CV 5/10] END ...................C=1, epsilon=1;, score=0.192 total time=   0.0s\n",
+      "[CV 1/10] END ...................C=1, epsilon=1;, score=0.482 total time=   0.1s\n",
+      "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.557 total time=   0.1s\n",
+      "[CV 9/10] END ...................C=1, epsilon=1;, score=0.475 total time=   0.0s\n",
+      "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.449 total time=   0.1s\n",
+      "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.480 total time=   0.1s\n",
+      "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.402 total time=   0.1s\n",
+      "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.475 total time=   0.1s\n",
+      "[CV 4/10] END ...................C=1, epsilon=1;, score=0.468 total time=   0.1s\n",
+      "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.436 total time=   0.1s\n",
+      "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.176 total time=   0.1s\n",
+      "Best CV score: 0.452:\n",
+      "Best parameters: {'C': 1, 'epsilon': 1} \n",
+      "\n",
+      "rf\n",
+      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+      "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.488 total time=   0.0s\n",
+      "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.466 total time=   0.0s\n",
+      "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.537 total time=   0.0s\n",
+      "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.352 total time=   0.0s\n",
+      "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.414 total time=   0.0s\n",
+      "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.567 total time=   0.0s\n",
+      "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.475 total time=   0.0s\n",
+      "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.496 total time=   0.0s\n",
+      "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.150 total time=   0.0s\n",
+      "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.542 total time=   0.0s\n",
+      "Best CV score: 0.449:\n",
+      "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+      "\n",
+      "en\n",
+      "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+      "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.522 total time=   0.0s\n",
+      "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.471 total time=   0.0s\n",
+      "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.448 total time=   0.0s\n",
+      "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.513 total time=   0.0s\n",
+      "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.406 total time=   0.0s\n",
+      "[CV 5/10] END ......alpha=0.0001, l1_ratio=0.1;, score=-0.027 total time=   0.0s\n",
+      "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.416 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.471 total time=   0.0s\n",
+      "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.374 total time=   0.0s\n",
+      "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.494 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.448 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.522 total time=   0.0s\n",
+      "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.445 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.406 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.0001, l1_ratio=1;, score=-0.027 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.512 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.416 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.374 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.494 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.474 total time=   0.0s\n",
+      "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.447 total time=   0.0s\n",
+      "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.524 total time=   0.0s\n",
+      "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.403 total time=   0.0s\n",
+      "[CV 5/10] END .......alpha=0.001, l1_ratio=0.1;, score=-0.029 total time=   0.0s\n",
+      "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.515 total time=   0.0s\n",
+      "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.373 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.472 total time=   0.0s\n",
+      "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.493 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.448 total time=   0.0s\n",
+      "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.523 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.403 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.001, l1_ratio=1;, score=-0.028 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.513 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.416 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.373 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.493 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.444 total time=   0.0s\n",
+      "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.395 total time=   0.0s\n",
+      "[CV 5/10] END ........alpha=0.01, l1_ratio=0.1;, score=-0.035 total time=   0.0s\n",
+      "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.414 total time=   0.0s\n",
+      "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.479 total time=   0.0s\n",
+      "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.369 total time=   0.0s\n",
+      "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.442 total time=   0.0s\n",
+      "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.490 total time=   0.0s\n",
+      "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.528 total time=   0.0s\n",
+      "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.445 total time=   0.0s\n",
+      "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.392 total time=   0.0s\n",
+      "[CV 5/10] END ..........alpha=0.01, l1_ratio=1;, score=-0.038 total time=   0.0s\n",
+      "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.367 total time=   0.0s\n",
+      "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.488 total time=   0.0s\n",
+      "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.444 total time=   0.0s\n",
+      "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.441 total time=   0.0s\n",
+      "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.394 total time=   0.0s\n",
+      "[CV 5/10] END .........alpha=0.1, l1_ratio=0.1;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.414 total time=   0.0s\n",
+      "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.475 total time=   0.0s\n",
+      "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.366 total time=   0.0s\n",
+      "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.486 total time=   0.0s\n",
+      "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.439 total time=   0.0s\n",
+      "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.444 total time=   0.0s\n",
+      "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.523 total time=   0.0s\n",
+      "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.401 total time=   0.0s\n",
+      "[CV 5/10] END ...........alpha=0.1, l1_ratio=1;, score=-0.029 total time=   0.0s\n",
+      "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.420 total time=   0.0s\n",
+      "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.360 total time=   0.0s\n",
+      "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.484 total time=   0.0s\n",
+      "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.441 total time=   0.0s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.632035032778731, tolerance: 0.3485204612649598\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.155936566667606, tolerance: 0.3539494224997914\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38.519076627995446, tolerance: 0.36078944497708576\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9988426704092035, tolerance: 0.3709440982510048\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.873875892601745, tolerance: 0.3638014392671959\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19.986358536062653, tolerance: 0.3585677869785208\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.30358975494255, tolerance: 0.3423723737711889\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.370452510840778, tolerance: 0.3430130256193086\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.887658076375828, tolerance: 0.3485204612649598\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.179017229232613, tolerance: 0.3539494224997914\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.951285348593274, tolerance: 0.35186466114232334\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.95855518995154, tolerance: 0.36078944497708576\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.01677052387663, tolerance: 0.3709440982510048\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.371907593092828, tolerance: 0.3494529278766458\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29.256674670356006, tolerance: 0.3638014392671959\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29.358168703854744, tolerance: 0.3585677869785208\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.563424193644778, tolerance: 0.3423723737711889\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20.191094319963668, tolerance: 0.3430130256193086\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38.339777716322715, tolerance: 0.3485204612649598\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.7716966240323, tolerance: 0.3539494224997914\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31.08431058282349, tolerance: 0.35186466114232334\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60.926646143130256, tolerance: 0.36078944497708576\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.86634830157095, tolerance: 0.3709440982510048\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35.89329211475774, tolerance: 0.3494529278766458\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.971282581429705, tolerance: 0.3638014392671959\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.114596727344406, tolerance: 0.3585677869785208\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.40582784868843, tolerance: 0.3423723737711889\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.244191490476396, tolerance: 0.3430130256193086\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.002534131717994, tolerance: 0.3485204612649598\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.7458842802057, tolerance: 0.3539494224997914\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.037063022743268, tolerance: 0.35186466114232334\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21.988467954298585, tolerance: 0.36078944497708576\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.707045390143776, tolerance: 0.3709440982510048\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.601003509890688, tolerance: 0.3494529278766458\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22.304027229350368, tolerance: 0.3638014392671959\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20.85403737098477, tolerance: 0.3585677869785208\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.681665032243927, tolerance: 0.3423723737711889\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.308516748228612, tolerance: 0.3430130256193086\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.974590094593623, tolerance: 0.3975429358853114\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best CV score: 0.406:\n",
+      "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+      "\n",
+      "lasso\n",
+      "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+      "[CV 1/10] END .....................alpha=0.0001;, score=0.471 total time=   0.0s\n",
+      "[CV 2/10] END .....................alpha=0.0001;, score=0.448 total time=   0.0s\n",
+      "[CV 3/10] END .....................alpha=0.0001;, score=0.522 total time=   0.0s\n",
+      "[CV 4/10] END .....................alpha=0.0001;, score=0.406 total time=   0.0s\n",
+      "[CV 5/10] END ....................alpha=0.0001;, score=-0.027 total time=   0.0s\n",
+      "[CV 6/10] END .....................alpha=0.0001;, score=0.512 total time=   0.0s\n",
+      "[CV 7/10] END .....................alpha=0.0001;, score=0.416 total time=   0.0s\n",
+      "[CV 8/10] END .....................alpha=0.0001;, score=0.374 total time=   0.0s\n",
+      "[CV 9/10] END .....................alpha=0.0001;, score=0.494 total time=   0.0s\n",
+      "[CV 10/10] END ....................alpha=0.0001;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END ......................alpha=0.001;, score=0.472 total time=   0.0s\n",
+      "[CV 2/10] END ......................alpha=0.001;, score=0.448 total time=   0.0s\n",
+      "[CV 3/10] END ......................alpha=0.001;, score=0.523 total time=   0.0s\n",
+      "[CV 4/10] END ......................alpha=0.001;, score=0.403 total time=   0.0s\n",
+      "[CV 5/10] END .....................alpha=0.001;, score=-0.028 total time=   0.0s\n",
+      "[CV 6/10] END ......................alpha=0.001;, score=0.513 total time=   0.0s\n",
+      "[CV 7/10] END ......................alpha=0.001;, score=0.416 total time=   0.0s\n",
+      "[CV 8/10] END ......................alpha=0.001;, score=0.373 total time=   0.0s\n",
+      "[CV 9/10] END ......................alpha=0.001;, score=0.493 total time=   0.0s\n",
+      "[CV 10/10] END .....................alpha=0.001;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END .......................alpha=0.01;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END .......................alpha=0.01;, score=0.442 total time=   0.0s\n",
+      "[CV 3/10] END .......................alpha=0.01;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END .......................alpha=0.01;, score=0.392 total time=   0.0s\n",
+      "[CV 5/10] END ......................alpha=0.01;, score=-0.038 total time=   0.0s\n",
+      "[CV 6/10] END .......................alpha=0.01;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END .......................alpha=0.01;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END .......................alpha=0.01;, score=0.367 total time=   0.0s\n",
+      "[CV 9/10] END .......................alpha=0.01;, score=0.488 total time=   0.0s\n",
+      "[CV 10/10] END ......................alpha=0.01;, score=0.444 total time=   0.0s\n",
+      "[CV 1/10] END ........................alpha=0.1;, score=0.475 total time=   0.0s\n",
+      "[CV 2/10] END ........................alpha=0.1;, score=0.439 total time=   0.0s\n",
+      "[CV 3/10] END ........................alpha=0.1;, score=0.523 total time=   0.0s\n",
+      "[CV 4/10] END ........................alpha=0.1;, score=0.401 total time=   0.0s\n",
+      "[CV 5/10] END .......................alpha=0.1;, score=-0.029 total time=   0.0s\n",
+      "[CV 6/10] END ........................alpha=0.1;, score=0.511 total time=   0.0s\n",
+      "[CV 7/10] END ........................alpha=0.1;, score=0.420 total time=   0.0s\n",
+      "[CV 8/10] END ........................alpha=0.1;, score=0.360 total time=   0.0s\n",
+      "[CV 9/10] END ........................alpha=0.1;, score=0.484 total time=   0.0s\n",
+      "[CV 10/10] END .......................alpha=0.1;, score=0.441 total time=   0.0s\n",
+      "Best CV score: 0.406:\n",
+      "Best parameters: {'alpha': 0.0001} \n",
+      "\n",
+      "knn\n",
+      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.832 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.927 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.888 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.842 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.941 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.939 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.914 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.874 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.889 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.895 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.946 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.832 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.927 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.888 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.842 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.941 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.939 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.914 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.874 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.889 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.895 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.946 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.832 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.958 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.930 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.929 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.862 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.951 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.925 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.927 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.888 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.842 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.941 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.932 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.931 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.917 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.939 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.904 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.914 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.874 total time=   0.0s\n",
+      "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.889 total time=   0.0s\n",
+      "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.933 total time=   0.0s\n",
+      "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.934 total time=   0.0s\n",
+      "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.895 total time=   0.0s\n",
+      "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.948 total time=   0.0s\n",
+      "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.928 total time=   0.0s\n",
+      "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.960 total time=   0.0s\n",
+      "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.885 total time=   0.0s\n",
+      "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.946 total time=   0.0s\n",
+      "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.861 total time=   0.0s\n",
+      "Best CV score: 0.918:\n",
+      "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+      "\n",
+      "bayesian\n",
+      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.441 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.441 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.441 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.441 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.442 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time=   0.0s\n",
+      "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time=   0.0s\n",
+      "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time=   0.0s\n",
+      "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time=   0.0s\n",
+      "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time=   0.0s\n",
+      "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time=   0.0s\n",
+      "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time=   0.0s\n",
+      "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time=   0.0s\n",
+      "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time=   0.0s\n",
+      "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time=   0.0s\n",
+      "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time=   0.0s\n",
+      "Best CV score: 0.404:\n",
+      "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# define a dictionary to hold results for all outputs\n",
+    "trained_model_dictionary = {}\n",
+    "\n",
+    "# define a scaler to standardize the input values of all features between 0 and 1\n",
+    "Scaler = sklearn.preprocessing.MinMaxScaler()\n",
+    "X = Scaler.fit_transform(X_train, y_train)\n",
+    "\n",
+    "# loop over outputs\n",
+    "for index, output in enumerate(['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']):\n",
+    "    print(f'{output}\\n')\n",
+    "    \n",
+    "    # define a dictionary to hold results for a single output\n",
+    "    trained_models = {} \n",
+    "    \n",
+    "    # loop over models\n",
+    "    for model_name, model_conf in model_cfgs.items():\n",
+    "        print (model_name)\n",
+    "        \n",
+    "        # define grid search parameters\n",
+    "        search = sklearn.model_selection.GridSearchCV(\n",
+    "            estimator = model_conf[\"estimator\"], \n",
+    "            param_grid = model_conf[\"param_grid\"], \n",
+    "            scoring = \"r2\",\n",
+    "            refit = True,\n",
+    "            cv = sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.1, random_state=0), \n",
+    "            n_jobs=30, # This is a limitation of the server I am using. -gr\n",
+    "            verbose=3\n",
+    "        )\n",
+    "\n",
+    "        # output array is a vector of a single output, not 2d array of all outputs\n",
+    "        y_output=y_train[:,index]\n",
+    "\n",
+    "        # run grid search\n",
+    "        search.fit(X_train, y_output)\n",
+    "        \n",
+    "        # report results\n",
+    "        print(\"Best CV score: %0.3f:\" % search.best_score_)\n",
+    "        print(\"Best parameters:\",  search.best_params_, '\\n')\n",
+    "        \n",
+    "        # save results of each model to a dictionary\n",
+    "        trained_models[model_name] = search \n",
+    "\n",
+    "    # save results from each output to a dictionary\n",
+    "    trained_model_dictionary[output] = trained_models"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# back up results from non-Bayesian models \n",
+    "biomass\n",
+    "\n",
+    "nn\n",
+    "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   4 out of  30 | elapsed:    1.4s remaining:    9.3s\n",
+    "[Parallel(n_jobs=30)]: Done  15 out of  30 | elapsed:    2.2s remaining:    2.2s\n",
+    "[Parallel(n_jobs=30)]: Done  26 out of  30 | elapsed:    3.5s remaining:    0.5s\n",
+    "[Parallel(n_jobs=30)]: Done  30 out of  30 | elapsed:    4.2s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.470:\n",
+    "Best parameters: {'activation': 'logistic', 'max_iter': 400} \n",
+    "\n",
+    "svm_rbf\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.125:\n",
+    "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+    "\n",
+    "rf\n",
+    "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+    "Best CV score: 0.319:\n",
+    "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+    "\n",
+    "en\n",
+    "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+    "  \"avoid this warning.\", FutureWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  21 out of  80 | elapsed:    0.1s remaining:    0.3s\n",
+    "[Parallel(n_jobs=30)]: Done  48 out of  80 | elapsed:    0.1s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  80 out of  80 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  31 out of  90 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  90 out of  90 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.261:\n",
+    "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+    "\n",
+    "lasso\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.260:\n",
+    "Best parameters: {'alpha': 0.0001} \n",
+    "\n",
+    "knn\n",
+    "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+    "Best CV score: 0.837:\n",
+    "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+    "\n",
+    "ethanol\n",
+    "\n",
+    "nn\n",
+    "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done   4 out of  30 | elapsed:   11.4s remaining:  1.2min\n",
+    "[Parallel(n_jobs=30)]: Done  15 out of  30 | elapsed:   13.0s remaining:   13.0s\n",
+    "[Parallel(n_jobs=30)]: Done  26 out of  30 | elapsed:   14.5s remaining:    2.2s\n",
+    "[Parallel(n_jobs=30)]: Done  30 out of  30 | elapsed:   14.8s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+    "  % self.max_iter, ConvergenceWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.2s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.1s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+    "  \"avoid this warning.\", FutureWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
+    "\n",
+    "Best CV score: 0.895:\n",
+    "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+    "\n",
+    "svm_rbf\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.294:\n",
+    "Best parameters: {'C': 1, 'epsilon': 1} \n",
+    "\n",
+    "rf\n",
+    "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+    "Best CV score: 0.686:\n",
+    "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+    "\n",
+    "en\n",
+    "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  21 out of  80 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  48 out of  80 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  80 out of  80 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097.125190323121, tolerance: 5.923503688869955\n",
+    "  positive)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.552:\n",
+    "Best parameters: {'alpha': 0.001, 'l1_ratio': 0.1} \n",
+    "\n",
+    "lasso\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.552:\n",
+    "Best parameters: {'alpha': 0.01} \n",
+    "\n",
+    "knn\n",
+    "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+    "Best CV score: 0.933:\n",
+    "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+    "\n",
+    "acetate\n",
+    "\n",
+    "nn\n",
+    "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done  31 out of  90 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  90 out of  90 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   4 out of  30 | elapsed:   10.9s remaining:  1.2min\n",
+    "[Parallel(n_jobs=30)]: Done  15 out of  30 | elapsed:   12.6s remaining:   12.6s\n",
+    "[Parallel(n_jobs=30)]: Done  26 out of  30 | elapsed:   14.1s remaining:    2.2s\n",
+    "[Parallel(n_jobs=30)]: Done  30 out of  30 | elapsed:   14.3s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n",
+    "  % self.max_iter, ConvergenceWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.1s remaining:    0.3s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.1s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+    "  \"avoid this warning.\", FutureWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
+    "\n",
+    "Best CV score: 0.850:\n",
+    "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+    "\n",
+    "svm_rbf\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: -0.043:\n",
+    "Best parameters: {'C': 1, 'epsilon': 1} \n",
+    "\n",
+    "rf\n",
+    "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+    "Best CV score: 0.621:\n",
+    "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+    "\n",
+    "en\n",
+    "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+    "Best CV score: 0.480:\n",
+    "Best parameters: {'alpha': 0.1, 'l1_ratio': 0.1} \n",
+    "\n",
+    "lasso\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.480:\n",
+    "Best parameters: {'alpha': 0.1} \n",
+    "\n",
+    "knn\n",
+    "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  21 out of  80 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  48 out of  80 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  80 out of  80 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42000.21451921737, tolerance: 27.122111107765193\n",
+    "  positive)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  31 out of  90 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  90 out of  90 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.819:\n",
+    "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+    "\n",
+    "butanol\n",
+    "\n",
+    "nn\n",
+    "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done   4 out of  30 | elapsed:    9.6s remaining:  1.0min\n",
+    "[Parallel(n_jobs=30)]: Done  15 out of  30 | elapsed:   12.3s remaining:   12.3s\n",
+    "[Parallel(n_jobs=30)]: Done  26 out of  30 | elapsed:   13.7s remaining:    2.1s\n",
+    "[Parallel(n_jobs=30)]: Done  30 out of  30 | elapsed:   14.2s finished\n",
+    "\n",
+    "Best CV score: 0.968:\n",
+    "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+    "\n",
+    "svm_rbf\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.643:\n",
+    "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+    "\n",
+    "rf\n",
+    "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.1s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+    "  \"avoid this warning.\", FutureWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  21 out of  80 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  48 out of  80 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  80 out of  80 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.684:\n",
+    "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+    "\n",
+    "en\n",
+    "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+    "Best CV score: 0.743:\n",
+    "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+    "\n",
+    "lasso\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.743:\n",
+    "Best parameters: {'alpha': 0.0001} \n",
+    "\n",
+    "knn\n",
+    "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done  31 out of  90 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  62 out of  90 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  90 out of  90 | elapsed:    0.1s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.979:\n",
+    "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 2, 'weights': 'distance'} \n",
+    "\n",
+    "butyrate\n",
+    "\n",
+    "nn\n",
+    "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Done   4 out of  30 | elapsed:    9.2s remaining:   59.7s\n",
+    "[Parallel(n_jobs=30)]: Done  15 out of  30 | elapsed:   12.8s remaining:   12.8s\n",
+    "[Parallel(n_jobs=30)]: Done  26 out of  30 | elapsed:   13.7s remaining:    2.1s\n",
+    "[Parallel(n_jobs=30)]: Done  30 out of  30 | elapsed:   14.2s finished\n",
+    "\n",
+    "Best CV score: 0.902:\n",
+    "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n",
+    "\n",
+    "svm_rbf\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.650:\n",
+    "Best parameters: {'C': 1, 'epsilon': 0.1} \n",
+    "\n",
+    "rf\n",
+    "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.2s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.1s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+    "  \"avoid this warning.\", FutureWarning)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
+    "\n",
+    "Best CV score: 0.438:\n",
+    "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n",
+    "\n",
+    "en\n",
+    "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n",
+    "Best CV score: 0.406:\n",
+    "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n",
+    "\n",
+    "lasso\n",
+    "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
+    "Best CV score: 0.406:\n",
+    "Best parameters: {'alpha': 0.0001} \n",
+    "\n",
+    "knn\n",
+    "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n",
+    "\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done  21 out of  80 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  48 out of  80 | elapsed:    0.1s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  80 out of  80 | elapsed:    0.1s finished\n",
+    "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.974590054853707, tolerance: 0.3975429358853114\n",
+    "  positive)\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "[Parallel(n_jobs=30)]: Done   9 out of  40 | elapsed:    0.0s remaining:    0.1s\n",
+    "[Parallel(n_jobs=30)]: Done  23 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  37 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
+    "[Parallel(n_jobs=30)]: Done  40 out of  40 | elapsed:    0.0s finished\n",
+    "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n",
+    "\n",
+    "Best CV score: 0.918:\n",
+    "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Bayesian result\n",
+    "\n",
+    "biomass\n",
+    "\n",
+    "bayesian\n",
+    "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+    "Best CV score: 0.261:\n",
+    "Best parameters: {'alpha_1': 0.1, 'alpha_2': 1, 'lambda_1': 1, 'lambda_2': 1, 'n_iter': 300} \n",
+    "\n",
+    "ethanol\n",
+    "\n",
+    "bayesian\n",
+    "\n",
+    "Best CV score: 0.546:\n",
+    "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+    "\n",
+    "acetate\n",
+    "\n",
+    "bayesian\n",
+    "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+    "Best CV score: 0.480:\n",
+    "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+    "\n",
+    "butanol\n",
+    "\n",
+    "bayesian\n",
+    "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+    "Best CV score: 0.740:\n",
+    "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+    "\n",
+    "butyrate\n",
+    "\n",
+    "bayesian\n",
+    "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
+    "Best CV score: 0.404:\n",
+    "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train and serialize models with best hyperparameters\n",
+    "These parameters come from running the parameter search with the full grid\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimized_parameters = {\n",
+    "    \"acetate\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [50, 40],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True,\n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [40, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.1, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 32,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 130\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  0.1,\n",
+    "            l1_ratio = 0.4\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 0.1\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "          alpha_1 = 1, \n",
+    "          alpha_2 = 0.1, \n",
+    "          lambda_1 = 0.1, \n",
+    "          lambda_2 =  1, \n",
+    "          n_iter = 300\n",
+    "        ),\n",
+    "    },\n",
+    "    \"biomass\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [100, 80, 60, 70],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [100, 100, 60, 80],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000,\n",
+    "            epsilon = 0.0001, \n",
+    "            gamma = 0.001\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 32,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 80\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha = 1e-05, \n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-06\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "          alpha_1 = 0.1, \n",
+    "          alpha_2 = 1, \n",
+    "          lambda_1 = 1, \n",
+    "          lambda_2 =  1, \n",
+    "          n_iter = 300\n",
+    "        ),\n",
+    "    },\n",
+    "    \"butanol\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [90, 60, 10, 80],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [60, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 1000, \n",
+    "            epsilon = 0.01, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 28,\n",
+    "            n_estimators = 120\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  1e-10,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-10\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 2,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "          alpha_1 = 1, \n",
+    "          alpha_2 = 0.1, \n",
+    "          lambda_1 = 0.1, \n",
+    "          lambda_2 =  1, \n",
+    "          n_iter = 300\n",
+    "        ),\n",
+    "    },\n",
+    "    \"butyrate\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [90, 30, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [60, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.01, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 22,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 130\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha = 0.0001,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-10\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "          alpha_1 = 1, \n",
+    "          alpha_2 = 0.1, \n",
+    "          lambda_1 = 0.1, \n",
+    "          lambda_2 =  1, \n",
+    "          n_iter = 300\n",
+    "        ),\n",
+    "    },\n",
+    "    \"ethanol\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [80, 50],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [80, 60],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.0001, \n",
+    "            gamma = 0.001\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 22,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 100\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  0.001,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 0.01\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "          alpha_1 = 1, \n",
+    "          alpha_2 = 0.1, \n",
+    "          lambda_1 = 0.1, \n",
+    "          lambda_2 =  1, \n",
+    "          n_iter = 300\n",
+    "        ),\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "biomass\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "ethanol\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "acetate\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "butanol\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "butyrate\n",
+      "nn_fine\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79.51816940132096, tolerance: 0.4890807573234609\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 680.3520407331906, tolerance: 0.3975429358853114\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.524119578237105, tolerance: 0.3975429358853114\n",
+      "  positive)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scale X training data\n",
+    "X_scaled_train = Scaler.fit_transform(X_train, y_train)\n",
+    "\n",
+    "for index, output in enumerate(['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']):\n",
+    "    print(output)\n",
+    "    \n",
+    "    # separate out the output of interest\n",
+    "    y_train_output=y_train[:,index]\n",
+    "\n",
+    "    \n",
+    "    for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "        print(algorithm)\n",
+    "\n",
+    "        # train the model\n",
+    "        model = optimized_parameters[output][algorithm].fit(X_scaled_train, y_train_output)\n",
+    "\n",
+    "        # serialize the model\n",
+    "        filename = f'../trained_models/{output}/{algorithm}.pkl'\n",
+    "\n",
+    "        with open(filename, 'wb') as file:  \n",
+    "            pickle.dump(model, file)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.9 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b3f28f6adfd1eea15c9e00a250e9367c9752811d529c8274081719d51bde14e5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/notebooks/C_model_evalutation.ipynb b/data/notebooks/C_model_evalutation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1e9c6b7f9fd2c0959da95aff2cfe15f70654775d
--- /dev/null
+++ b/data/notebooks/C_model_evalutation.ipynb
@@ -0,0 +1,1916 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook C: Evaluation of ML Algorithms for Rate Prediction of Syngas Fermentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sklearn\n",
+    "import sklearn.preprocessing, sklearn.neural_network, sklearn.svm, sklearn.ensemble\n",
+    "import scipy.stats\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data that was generated in notebook A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the rates data: 836 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "rates_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the rates data: {rates_df.shape[0]} rows by {rates_df.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the training data: 657 rows by 18 columns\n",
+      "Shape of the testing data: 179 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = rates_df[rates_df.composition.isin([1,2,3,4,5,6,7])]\n",
+    "test_data = rates_df[rates_df.composition.isin([8,9,10])]\n",
+    "print(f'Shape of the training data: {train_data.shape[0]} rows by {train_data.shape[1]} columns')\n",
+    "print(f'Shape of the testing data: {test_data.shape[0]} rows by {test_data.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a function that generates the input and output arrays for scikit learn's API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_X_y_arrays(data):\n",
+    "    \n",
+    "    # prevent set with copy error\n",
+    "    data_copy = data.copy()\n",
+    "    \n",
+    "    # ML input\n",
+    "    X = data_copy [[\n",
+    "        'biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', \n",
+    "         'butyrate (mM)', 'N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)'\n",
+    "    ]]\n",
+    "    \n",
+    "    # ML output\n",
+    "    y = data_copy [[\n",
+    "        'biomass rate', 'ethanol rate', 'acetate rate', 'butanol rate', 'butyrate rate'\n",
+    "    ]]\n",
+    " \n",
+    "    return np.array(X), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split data into ML input and output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the train X array: 657 rows by 10 columns\n",
+      "Shape of the train y array: 657 rows by 5 columns\n",
+      "Shape of the test X array: 179 rows by 10 columns\n",
+      "Shape of the test y array: 179 rows by 5 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, y_train = get_X_y_arrays(train_data)\n",
+    "X_test, y_test = get_X_y_arrays(test_data)\n",
+    "\n",
+    "print(f'Shape of the train X array: {X_train.shape[0]} rows by {X_train.shape[1]} columns')\n",
+    "print(f'Shape of the train y array: {y_train.shape[0]} rows by {y_train.shape[1]} columns')\n",
+    "print(f'Shape of the test X array: {X_test.shape[0]} rows by {X_test.shape[1]} columns')\n",
+    "print(f'Shape of the test y array: {y_test.shape[0]} rows by {y_test.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a dictionary of models with optimized parameters from notebook B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "optimized_models = {\n",
+    "    \"acetate\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [50, 40],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [40, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.1, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 32,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 130\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  0.1,\n",
+    "            l1_ratio = 0.4\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 0.1\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "            alpha_1  = 1, \n",
+    "            alpha_2  = 0.1, \n",
+    "            lambda_1 = 0.1, \n",
+    "            lambda_2 = 1\n",
+    "        ) \n",
+    "    },\n",
+    "    \"biomass\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [100, 80, 60, 70],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [100, 100, 60, 80],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000,\n",
+    "            epsilon = 0.0001, \n",
+    "            gamma = 0.001\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 32,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 80\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha = 1e-05, \n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-06\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "            alpha_1  = 0.1, \n",
+    "            alpha_2  = 1, \n",
+    "            lambda_1 = 1, \n",
+    "            lambda_2 = 1\n",
+    "        ) \n",
+    "    },\n",
+    "    \"butanol\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [90, 60, 10, 80],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [60, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 1000, \n",
+    "            epsilon = 0.01, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 28,\n",
+    "            n_estimators = 120\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  1e-10,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-10\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 2,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "            alpha_1  = 1, \n",
+    "            alpha_2  = 0.1, \n",
+    "            lambda_1 = 0.1, \n",
+    "            lambda_2 = 1\n",
+    "        ) \n",
+    "    },\n",
+    "    \"butyrate\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [90, 30, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [60, 20],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.01, \n",
+    "            gamma = 0.01\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 22,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 130\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha = 0.0001,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 1e-10\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "            alpha_1  = 1, \n",
+    "            alpha_2  = 0.1, \n",
+    "            lambda_1 = 0.1, \n",
+    "            lambda_2 = 1\n",
+    "        ) \n",
+    "    },\n",
+    "    \"ethanol\": {\n",
+    "        \"nn_fine\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [80, 50],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n",
+    "            shuffle=True, \n",
+    "            activation = 'tanh', \n",
+    "            hidden_layer_sizes = [80, 60],\n",
+    "            max_iter = 5000\n",
+    "        ),\n",
+    "        \"svm_rbf\": sklearn.svm.SVR(\n",
+    "            kernel = 'rbf', \n",
+    "            C = 10000, \n",
+    "            epsilon = 0.0001, \n",
+    "            gamma = 0.001\n",
+    "        ),\n",
+    "        'rf': sklearn.ensemble.RandomForestRegressor(\n",
+    "            max_depth = 22,\n",
+    "            # max_samples = 0.5,\n",
+    "            n_estimators = 100\n",
+    "        ),\n",
+    "        'en': sklearn.linear_model.ElasticNet(\n",
+    "            alpha =  0.001,\n",
+    "            l1_ratio = 0.1\n",
+    "        ),\n",
+    "        'lasso': sklearn.linear_model.Lasso(\n",
+    "            alpha = 0.01\n",
+    "        ),\n",
+    "        'knn': sklearn.neighbors.KNeighborsRegressor(\n",
+    "            algorithm = 'ball_tree',\n",
+    "            leaf_size = 5,\n",
+    "            n_neighbors = 4,\n",
+    "            weights = 'distance'\n",
+    "        ),\n",
+    "        'bayesian': sklearn.linear_model.BayesianRidge(\n",
+    "            alpha_1  = 1, \n",
+    "            alpha_2  = 0.1, \n",
+    "            lambda_1 = 0.1, \n",
+    "            lambda_2 = 1\n",
+    "        ) \n",
+    "    },\n",
+    "}\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the predictions of the models on the training set and test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "biomass\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "ethanol\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "acetate\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "butanol\n",
+      "nn_fine\n",
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n",
+      "butyrate\n",
+      "nn_fine\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79.51816940132096, tolerance: 0.4890807573234609\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_coarse\n",
+      "svm_rbf\n",
+      "rf\n",
+      "en\n",
+      "lasso\n",
+      "knn\n",
+      "bayesian\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 680.3520407331906, tolerance: 0.3975429358853114\n",
+      "  positive)\n",
+      "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.524119578237105, tolerance: 0.3975429358853114\n",
+      "  positive)\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>biomass measured</th>\n",
+       "      <th>biomass nn_fine</th>\n",
+       "      <th>biomass nn_coarse</th>\n",
+       "      <th>biomass svm_rbf</th>\n",
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+       "      <th>biomass bayesian</th>\n",
+       "      <th>ethanol measured</th>\n",
+       "      <th>...</th>\n",
+       "      <th>butanol bayesian</th>\n",
+       "      <th>butyrate measured</th>\n",
+       "      <th>butyrate nn_fine</th>\n",
+       "      <th>butyrate nn_coarse</th>\n",
+       "      <th>butyrate svm_rbf</th>\n",
+       "      <th>butyrate rf</th>\n",
+       "      <th>butyrate en</th>\n",
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+       "      <th>butyrate bayesian</th>\n",
+       "    </tr>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.024890</td>\n",
+       "      <td>0.028399</td>\n",
+       "      <td>0.014175</td>\n",
+       "      <td>-0.020488</td>\n",
+       "      <td>-0.040956</td>\n",
+       "      <td>0.025077</td>\n",
+       "      <td>0.025067</td>\n",
+       "      <td>-0.024890</td>\n",
+       "      <td>0.025093</td>\n",
+       "      <td>-1.303834</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805826</td>\n",
+       "      <td>-0.126633</td>\n",
+       "      <td>-0.024557</td>\n",
+       "      <td>0.262933</td>\n",
+       "      <td>-0.132296</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>1.647043</td>\n",
+       "      <td>1.654321</td>\n",
+       "      <td>-0.126633</td>\n",
+       "      <td>1.618862</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>-0.084727</td>\n",
+       "      <td>0.029490</td>\n",
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+       "      <td>-0.018721</td>\n",
+       "      <td>-0.083995</td>\n",
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+       "      <td>0.024926</td>\n",
+       "      <td>-2.874395</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.789553</td>\n",
+       "      <td>-0.140778</td>\n",
+       "      <td>-0.032448</td>\n",
+       "      <td>0.169718</td>\n",
+       "      <td>-0.053759</td>\n",
+       "      <td>-0.093488</td>\n",
+       "      <td>1.657925</td>\n",
+       "      <td>1.664628</td>\n",
+       "      <td>-0.140778</td>\n",
+       "      <td>1.632014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.102302</td>\n",
+       "      <td>0.031467</td>\n",
+       "      <td>0.016710</td>\n",
+       "      <td>-0.014566</td>\n",
+       "      <td>-0.081920</td>\n",
+       "      <td>0.025283</td>\n",
+       "      <td>0.025274</td>\n",
+       "      <td>-0.102302</td>\n",
+       "      <td>0.025302</td>\n",
+       "      <td>-4.621020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.778326</td>\n",
+       "      <td>-0.098317</td>\n",
+       "      <td>-0.014773</td>\n",
+       "      <td>0.073834</td>\n",
+       "      <td>0.073746</td>\n",
+       "      <td>-0.094411</td>\n",
+       "      <td>1.686646</td>\n",
+       "      <td>1.692511</td>\n",
+       "      <td>-0.098317</td>\n",
+       "      <td>1.664012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.072244</td>\n",
+       "      <td>0.034304</td>\n",
+       "      <td>0.019883</td>\n",
+       "      <td>-0.007148</td>\n",
+       "      <td>-0.031267</td>\n",
+       "      <td>0.026863</td>\n",
+       "      <td>0.026855</td>\n",
+       "      <td>-0.072244</td>\n",
+       "      <td>0.026882</td>\n",
+       "      <td>-6.210585</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.785685</td>\n",
+       "      <td>-0.008389</td>\n",
+       "      <td>0.053899</td>\n",
+       "      <td>0.040116</td>\n",
+       "      <td>0.235010</td>\n",
+       "      <td>-0.019188</td>\n",
+       "      <td>1.737477</td>\n",
+       "      <td>1.742396</td>\n",
+       "      <td>-0.008389</td>\n",
+       "      <td>1.718507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000866</td>\n",
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+       "      <td>0.008790</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>0.831106</td>\n",
+       "      <td>-0.135805</td>\n",
+       "      <td>0.206976</td>\n",
+       "      <td>0.123167</td>\n",
+       "      <td>0.408344</td>\n",
+       "      <td>-0.092916</td>\n",
+       "      <td>1.832740</td>\n",
+       "      <td>1.836736</td>\n",
+       "      <td>-0.135805</td>\n",
+       "      <td>1.817257</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
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+      "text/plain": [
+       "   biomass measured  biomass nn_fine  biomass nn_coarse  biomass svm_rbf  \\\n",
+       "0         -0.024890         0.028399           0.014175        -0.020488   \n",
+       "1         -0.084727         0.029490           0.014938        -0.018721   \n",
+       "2         -0.102302         0.031467           0.016710        -0.014566   \n",
+       "3         -0.072244         0.034304           0.019883        -0.007148   \n",
+       "4          0.000866         0.038325           0.025098         0.005059   \n",
+       "\n",
+       "   biomass rf  biomass en  biomass lasso  biomass knn  biomass bayesian  \\\n",
+       "0   -0.040956    0.025077       0.025067    -0.024890          0.025093   \n",
+       "1   -0.083995    0.024908       0.024899    -0.084727          0.024926   \n",
+       "2   -0.081920    0.025283       0.025274    -0.102302          0.025302   \n",
+       "3   -0.031267    0.026863       0.026855    -0.072244          0.026882   \n",
+       "4    0.008790    0.030506       0.030500     0.000866          0.030520   \n",
+       "\n",
+       "   ethanol measured  ...  butanol bayesian  butyrate measured  \\\n",
+       "0         -1.303834  ...          0.805826          -0.126633   \n",
+       "1         -2.874395  ...          0.789553          -0.140778   \n",
+       "2         -4.621020  ...          0.778326          -0.098317   \n",
+       "3         -6.210585  ...          0.785685          -0.008389   \n",
+       "4         -5.653590  ...          0.831106          -0.135805   \n",
+       "\n",
+       "   butyrate nn_fine  butyrate nn_coarse  butyrate svm_rbf  butyrate rf  \\\n",
+       "0         -0.024557            0.262933         -0.132296     0.002033   \n",
+       "1         -0.032448            0.169718         -0.053759    -0.093488   \n",
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+       "3          0.053899            0.040116          0.235010    -0.019188   \n",
+       "4          0.206976            0.123167          0.408344    -0.092916   \n",
+       "\n",
+       "   butyrate en  butyrate lasso  butyrate knn  butyrate bayesian  \n",
+       "0     1.647043        1.654321     -0.126633           1.618862  \n",
+       "1     1.657925        1.664628     -0.140778           1.632014  \n",
+       "2     1.686646        1.692511     -0.098317           1.664012  \n",
+       "3     1.737477        1.742396     -0.008389           1.718507  \n",
+       "4     1.832740        1.836736     -0.135805           1.817257  \n",
+       "\n",
+       "[5 rows x 45 columns]"
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+       "      <th></th>\n",
+       "      <th>biomass measured</th>\n",
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+       "      <th>...</th>\n",
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+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.108691</td>\n",
+       "      <td>-0.058711</td>\n",
+       "      <td>0.028329</td>\n",
+       "      <td>-0.095211</td>\n",
+       "      <td>-0.059872</td>\n",
+       "      <td>-0.000287</td>\n",
+       "      <td>-0.000271</td>\n",
+       "      <td>-0.018390</td>\n",
+       "      <td>-0.000877</td>\n",
+       "      <td>0.635566</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.813907</td>\n",
+       "      <td>-0.0359</td>\n",
+       "      <td>3.033549</td>\n",
+       "      <td>2.230357</td>\n",
+       "      <td>2.312145</td>\n",
+       "      <td>0.176643</td>\n",
+       "      <td>-0.188354</td>\n",
+       "      <td>-0.220495</td>\n",
+       "      <td>0.012428</td>\n",
+       "      <td>-0.118848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.115125</td>\n",
+       "      <td>-0.055409</td>\n",
+       "      <td>0.030971</td>\n",
+       "      <td>-0.089117</td>\n",
+       "      <td>-0.060540</td>\n",
+       "      <td>0.000127</td>\n",
+       "      <td>0.000143</td>\n",
+       "      <td>-0.011156</td>\n",
+       "      <td>-0.000461</td>\n",
+       "      <td>0.462896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.788108</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3.342735</td>\n",
+       "      <td>2.516586</td>\n",
+       "      <td>2.185273</td>\n",
+       "      <td>0.173175</td>\n",
+       "      <td>-0.118904</td>\n",
+       "      <td>-0.151973</td>\n",
+       "      <td>-0.015529</td>\n",
+       "      <td>-0.045736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.082811</td>\n",
+       "      <td>-0.050952</td>\n",
+       "      <td>0.034957</td>\n",
+       "      <td>-0.079714</td>\n",
+       "      <td>-0.050719</td>\n",
+       "      <td>0.001952</td>\n",
+       "      <td>0.001969</td>\n",
+       "      <td>-0.011752</td>\n",
+       "      <td>0.001366</td>\n",
+       "      <td>1.351312</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.730546</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3.699384</td>\n",
+       "      <td>2.944530</td>\n",
+       "      <td>2.014743</td>\n",
+       "      <td>0.151570</td>\n",
+       "      <td>-0.001657</td>\n",
+       "      <td>-0.035626</td>\n",
+       "      <td>-0.091137</td>\n",
+       "      <td>0.075024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.030177</td>\n",
+       "      <td>-0.045999</td>\n",
+       "      <td>0.039953</td>\n",
+       "      <td>-0.068111</td>\n",
+       "      <td>-0.045655</td>\n",
+       "      <td>0.005189</td>\n",
+       "      <td>0.005207</td>\n",
+       "      <td>0.016071</td>\n",
+       "      <td>0.004601</td>\n",
+       "      <td>1.042017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.652609</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>4.006878</td>\n",
+       "      <td>3.430741</td>\n",
+       "      <td>1.842434</td>\n",
+       "      <td>0.144912</td>\n",
+       "      <td>0.142975</td>\n",
+       "      <td>0.108224</td>\n",
+       "      <td>-0.139084</td>\n",
+       "      <td>0.222653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.012667</td>\n",
+       "      <td>-0.040004</td>\n",
+       "      <td>0.046907</td>\n",
+       "      <td>-0.053298</td>\n",
+       "      <td>0.000370</td>\n",
+       "      <td>0.009964</td>\n",
+       "      <td>0.009985</td>\n",
+       "      <td>0.045641</td>\n",
+       "      <td>0.009372</td>\n",
+       "      <td>-2.661339</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.574257</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>4.256422</td>\n",
+       "      <td>3.951486</td>\n",
+       "      <td>1.660137</td>\n",
+       "      <td>0.161865</td>\n",
+       "      <td>0.304764</td>\n",
+       "      <td>0.268898</td>\n",
+       "      <td>-0.179599</td>\n",
+       "      <td>0.388667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   biomass measured  biomass nn_fine  biomass nn_coarse  biomass svm_rbf  \\\n",
+       "0         -0.108691        -0.058711           0.028329        -0.095211   \n",
+       "1         -0.115125        -0.055409           0.030971        -0.089117   \n",
+       "2         -0.082811        -0.050952           0.034957        -0.079714   \n",
+       "3         -0.030177        -0.045999           0.039953        -0.068111   \n",
+       "4         -0.012667        -0.040004           0.046907        -0.053298   \n",
+       "\n",
+       "   biomass rf  biomass en  biomass lasso  biomass knn  biomass bayesian  \\\n",
+       "0   -0.059872   -0.000287      -0.000271    -0.018390         -0.000877   \n",
+       "1   -0.060540    0.000127       0.000143    -0.011156         -0.000461   \n",
+       "2   -0.050719    0.001952       0.001969    -0.011752          0.001366   \n",
+       "3   -0.045655    0.005189       0.005207     0.016071          0.004601   \n",
+       "4    0.000370    0.009964       0.009985     0.045641          0.009372   \n",
+       "\n",
+       "   ethanol measured  ...  butanol bayesian  butyrate measured  \\\n",
+       "0          0.635566  ...         -0.813907            -0.0359   \n",
+       "1          0.462896  ...         -0.788108             0.0000   \n",
+       "2          1.351312  ...         -0.730546             0.0000   \n",
+       "3          1.042017  ...         -0.652609             0.0000   \n",
+       "4         -2.661339  ...         -0.574257             0.0000   \n",
+       "\n",
+       "   butyrate nn_fine  butyrate nn_coarse  butyrate svm_rbf  butyrate rf  \\\n",
+       "0          3.033549            2.230357          2.312145     0.176643   \n",
+       "1          3.342735            2.516586          2.185273     0.173175   \n",
+       "2          3.699384            2.944530          2.014743     0.151570   \n",
+       "3          4.006878            3.430741          1.842434     0.144912   \n",
+       "4          4.256422            3.951486          1.660137     0.161865   \n",
+       "\n",
+       "   butyrate en  butyrate lasso  butyrate knn  butyrate bayesian  \n",
+       "0    -0.188354       -0.220495      0.012428          -0.118848  \n",
+       "1    -0.118904       -0.151973     -0.015529          -0.045736  \n",
+       "2    -0.001657       -0.035626     -0.091137           0.075024  \n",
+       "3     0.142975        0.108224     -0.139084           0.222653  \n",
+       "4     0.304764        0.268898     -0.179599           0.388667  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make a dictionary to save trained models \n",
+    "trained_models = {}\n",
+    "\n",
+    "# Define two dictionaries to hold data for training and test result data\n",
+    "model_train_output_dictionary = {}\n",
+    "model_test_output_dictionary = {}\n",
+    "\n",
+    "# Scale both sets of X data\n",
+    "Scaler = sklearn.preprocessing.MinMaxScaler()\n",
+    "X_scaled_train = Scaler.fit_transform(X_train, y_test)\n",
+    "X_scaled_test = Scaler.fit_transform(X_test, y_test)\n",
+    "\n",
+    "for index, output in enumerate(['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']):\n",
+    "    print(output)\n",
+    "    \n",
+    "    # separate out the output of interest\n",
+    "    y_train_output=y_train[:,index]\n",
+    "    y_test_output=y_test[:,index]\n",
+    "    \n",
+    "    # save the measured data in the output dictionaries\n",
+    "    model_train_output_dictionary[f'{output} measured'] = y_train_output\n",
+    "    model_test_output_dictionary[f'{output} measured'] = y_test_output\n",
+    "    \n",
+    "    for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "        print(algorithm)\n",
+    "\n",
+    "        # train the model and save it\n",
+    "        trained_model = optimized_models[output][algorithm].fit(X_scaled_train, y_train_output)\n",
+    "        trained_models[f'{output} {algorithm}'] = trained_model\n",
+    "        \n",
+    "        # test the model on training and test data\n",
+    "        y_train_prediction = trained_model.predict(X_scaled_train)\n",
+    "        y_test_prediction = trained_model.predict(X_scaled_test)\n",
+    "        \n",
+    "        # save the outputs in dictionaries\n",
+    "        model_train_output_dictionary[f'{output} {algorithm}'] = y_train_prediction\n",
+    "        model_test_output_dictionary[f'{output} {algorithm}'] = y_test_prediction\n",
+    "        \n",
+    "# convert the dictionaries to dataframes\n",
+    "train_prediction_df = pd.DataFrame(model_train_output_dictionary)\n",
+    "test_prediction_df = pd.DataFrame(model_test_output_dictionary)\n",
+    "\n",
+    "# display the data frames\n",
+    "display(train_prediction_df.head())\n",
+    "display(test_prediction_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save dataframes as .csv for excel based figures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_prediction_df.to_csv('../data/train_predictions.csv')\n",
+    "test_prediction_df.to_csv('../data/test_predictions.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot scatter plots of fits (r-squared)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2520x2160 with 40 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({'font.size': 11.5})\n",
+    "fig, ax = plt.subplots(5, 8, figsize=(35, 30))\n",
+    "fig.suptitle(f'Rate Predictions')\n",
+    "\n",
+    "algorithm_dictionary = {\n",
+    "    'nn_fine': 'Neural Network Fine',\n",
+    "    'nn_coarse': 'Neural Network Coarse',\n",
+    "    'svm_rbf': 'Support Vector', \n",
+    "    'rf' : 'Random Forest',\n",
+    "    'en': 'Elastic Net', \n",
+    "    'lasso' : 'Lasso',\n",
+    "    'knn': 'k-Nearest Neighbors',\n",
+    "    'bayesian': 'Bayesian',\n",
+    "}\n",
+    "\n",
+    "for x_index, output in enumerate(['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol']):\n",
+    "    train_r2_list = []\n",
+    "    test_r2_list = []\n",
+    "    for y_index, algorithm in enumerate(['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']):\n",
+    " \n",
+    "        # Set title, x label, and y label of the subplot\n",
+    "        ax[x_index, y_index].title.set_text(f'{output.capitalize()} {algorithm_dictionary[algorithm]}')\n",
+    "        if output != 'biomass':\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (mmol {output}/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (mmol {output}/day)')\n",
+    "        else:\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (g biomass/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (g biomass/day)')\n",
+    "            \n",
+    "        # Plot training and test data by plotting predicted Production Rates vs measured Production Rates\n",
+    "        ax[x_index, y_index].scatter(train_prediction_df[f'{output} measured'], train_prediction_df[f'{output} {algorithm}'])\n",
+    "        ax[x_index, y_index].scatter(test_prediction_df[f'{output} measured'], test_prediction_df[f'{output} {algorithm}'])\n",
+    "        \n",
+    "        # ensure that all plots are square, so that 45 degree line looks the same in all plots\n",
+    "        minimum = min(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        maximum = max(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        \n",
+    "        # calculate train data r-squared\n",
+    "        measured_train_data = train_prediction_df[f'{output} measured']\n",
+    "        predicted_train_data = train_prediction_df[f'{output} {algorithm}']\n",
+    "        # could use scikitlearn's r-squared value\n",
+    "        # train_r2 = sklearn.metrics.r2_score(measured_train_data, predicted_train_data)\n",
+    "        _, _, train_r_value, _, _ = scipy.stats.linregress(measured_train_data, predicted_train_data)\n",
+    "        train_r2_list.append(train_r_value**2)\n",
+    "\n",
+    "        # calculate test data r-squared\n",
+    "        measured_test_data = test_prediction_df[f'{output} measured']\n",
+    "        predicted_test_data = test_prediction_df[f'{output} {algorithm}']\n",
+    "        # could use scikitlearn's r-squared value\n",
+    "        # test_r2 = sklearn.metrics.r2_score(measured_test_data, predicted_test_data)\n",
+    "        _, _, test_r_value, _, _ = scipy.stats.linregress(measured_test_data, predicted_test_data)\n",
+    "        test_r2_list.append(test_r_value**2)\n",
+    "        \n",
+    "        # add legend to the plot\n",
+    "        ax[x_index, y_index].legend([f'train {train_r_value**2:.3f}', f'test {test_r_value**2:.3f}'])\n",
+    "        \n",
+    "        # plot the subplot\n",
+    "        ax[x_index, y_index].plot([minimum, maximum], [minimum, maximum], 'k--') #row=0, col=0\n",
+    "\n",
+    "plt.savefig(f'../figures/scatter_plots_r2.png', dpi=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot scatter plots of fits (RMSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2520x2160 with 40 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({'font.size': 11.5})\n",
+    "fig, ax = plt.subplots(5, 8, figsize=(35, 30))\n",
+    "fig.suptitle(f'Rate Predictions')\n",
+    "\n",
+    "algorithm_dictionary = {\n",
+    "    'nn_fine': 'Neural Network Fine',\n",
+    "    'nn_coarse': 'Neural Network Coarse',\n",
+    "    'svm_rbf': 'Support Vector', \n",
+    "    'rf' : 'Random Forest',\n",
+    "    'en': 'Elastic Net', \n",
+    "    'lasso' : 'Lasso',\n",
+    "    'knn': 'k-Nearest Neighbors',\n",
+    "    'bayesian': 'Bayesian'\n",
+    "}\n",
+    "\n",
+    "for x_index, output in enumerate(['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol']):\n",
+    "    train_rmse_list = []\n",
+    "    test_rmse_list = []\n",
+    "    for y_index, algorithm in enumerate(['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']):\n",
+    " \n",
+    "        # Set title, x label, and y label of the subplot\n",
+    "        ax[x_index, y_index].title.set_text(f'{output.capitalize()} {algorithm_dictionary[algorithm]}')\n",
+    "        if output != 'biomass':\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (mmol {output}/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (mmol {output}/day)')\n",
+    "        else:\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (g biomass/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (g biomass/day)')\n",
+    "            \n",
+    "        # Plot training and test data by plotting predicted Production Rates vs measured Production Rates\n",
+    "        ax[x_index, y_index].scatter(train_prediction_df[f'{output} measured'], train_prediction_df[f'{output} {algorithm}'])\n",
+    "        ax[x_index, y_index].scatter(test_prediction_df[f'{output} measured'], test_prediction_df[f'{output} {algorithm}'])\n",
+    "        \n",
+    "        # ensure that all plots are square, so that 45 degree line looks the same in all plots\n",
+    "        minimum = min(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        maximum = max(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        \n",
+    "        # calculate train data rmse\n",
+    "        measured_train_data = train_prediction_df[f'{output} measured']\n",
+    "        predicted_train_data = train_prediction_df[f'{output} {algorithm}']\n",
+    "        train_rmse_value = sklearn.metrics.mean_squared_error(measured_train_data, predicted_train_data, squared=False)\n",
+    "        train_rmse_list.append(train_rmse_value)\n",
+    "\n",
+    "        # calculate test data rmse\n",
+    "        measured_test_data = test_prediction_df[f'{output} measured']\n",
+    "        predicted_test_data = test_prediction_df[f'{output} {algorithm}']\n",
+    "        test_rmse_value = sklearn.metrics.mean_squared_error(measured_test_data, predicted_test_data, squared=False)\n",
+    "        test_rmse_list.append(test_rmse_value)\n",
+    "        \n",
+    "        # add legend to the plot\n",
+    "        ax[x_index, y_index].legend([f'train {train_rmse_value:.3f}', f'test {test_rmse_value:.3f}'])\n",
+    "        \n",
+    "        # plot the subplot\n",
+    "        ax[x_index, y_index].plot([minimum, maximum], [minimum, maximum], 'k--') #row=0, col=0\n",
+    "\n",
+    "plt.savefig(f'../figures/scatter_plots_rmse.png', dpi=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot scatter plots of fits (MAPE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2520x2160 with 40 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({'font.size': 11.5})\n",
+    "fig, ax = plt.subplots(5, 8, figsize=(35, 30))\n",
+    "fig.suptitle(f'Rate Predictions')\n",
+    "\n",
+    "algorithm_dictionary = {\n",
+    "    'nn_fine': 'Neural Network Fine',\n",
+    "    'nn_coarse': 'Neural Network Coarse',\n",
+    "    'svm_rbf': 'Support Vector', \n",
+    "    'rf' : 'Random Forest',\n",
+    "    'en': 'Elastic Net', \n",
+    "    'lasso' : 'Lasso',\n",
+    "    'knn': 'k-Nearest Neighbors',\n",
+    "    'bayesian': 'Bayesian'\n",
+    "}\n",
+    "\n",
+    "for x_index, output in enumerate(['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol']):\n",
+    "    train_mape_list = []\n",
+    "    test_mape_list = []\n",
+    "    for y_index, algorithm in enumerate(['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']):\n",
+    " \n",
+    "        # Set title, x label, and y label of the subplot\n",
+    "        ax[x_index, y_index].title.set_text(f'{output.capitalize()} {algorithm_dictionary[algorithm]}')\n",
+    "        if output != 'biomass':\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (mmol {output}/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (mmol {output}/day)')\n",
+    "        else:\n",
+    "            ax[x_index, y_index].set_xlabel(f'Measured Production Rate (g biomass/day)')\n",
+    "            ax[x_index, y_index].set_ylabel(f'Predicted Production Rate (g biomass/day)')\n",
+    "            \n",
+    "        # Plot training and test data by plotting predicted Production Rates vs measured Production Rates\n",
+    "        ax[x_index, y_index].scatter(train_prediction_df[f'{output} measured'], train_prediction_df[f'{output} {algorithm}'])\n",
+    "        ax[x_index, y_index].scatter(test_prediction_df[f'{output} measured'], test_prediction_df[f'{output} {algorithm}'])\n",
+    "        \n",
+    "        # ensure that all plots are square, so that 45 degree line looks the same in all plots\n",
+    "        minimum = min(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        maximum = max(pd.concat([\n",
+    "            train_prediction_df[f'{output} measured'],\n",
+    "            train_prediction_df[f'{output} {algorithm}'],\n",
+    "            test_prediction_df[f'{output} measured'],\n",
+    "            test_prediction_df[f'{output} {algorithm}']\n",
+    "        ], axis=0))\n",
+    "        \n",
+    "        # calculate train data mape\n",
+    "        measured_train_data = train_prediction_df[f'{output} measured']\n",
+    "        predicted_train_data = train_prediction_df[f'{output} {algorithm}']\n",
+    "        train_mape_value = sklearn.metrics.mean_absolute_percentage_error(measured_train_data, predicted_train_data)\n",
+    "        train_mape_list.append(train_mape_value)\n",
+    "\n",
+    "        # calculate test data mape\n",
+    "        measured_test_data = test_prediction_df[f'{output} measured']\n",
+    "        predicted_test_data = test_prediction_df[f'{output} {algorithm}']\n",
+    "        test_mape_value = sklearn.metrics.mean_absolute_percentage_error(measured_test_data, predicted_test_data)\n",
+    "        test_mape_list.append(test_mape_value)\n",
+    "        \n",
+    "        # add legend to the plot\n",
+    "        ax[x_index, y_index].legend([f'train {train_mape_value:.3f}', f'test {test_mape_value:.3f}'])\n",
+    "        \n",
+    "        # plot the subplot\n",
+    "        ax[x_index, y_index].plot([minimum, maximum], [minimum, maximum], 'k--') #row=0, col=0\n",
+    "\n",
+    "plt.savefig(f'../figures/scatter_plots_mape.png', dpi=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make data frames to plot feature importance of metabolite and gases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>acetate</th>\n",
+       "      <th>biomass</th>\n",
+       "      <th>butanol</th>\n",
+       "      <th>butyrate</th>\n",
+       "      <th>ethanol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acetate</th>\n",
+       "      <td>0.038743</td>\n",
+       "      <td>0.056555</td>\n",
+       "      <td>0.133280</td>\n",
+       "      <td>0.612357</td>\n",
+       "      <td>0.107462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass</th>\n",
+       "      <td>0.161170</td>\n",
+       "      <td>0.140164</td>\n",
+       "      <td>0.249923</td>\n",
+       "      <td>0.236367</td>\n",
+       "      <td>0.120039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butanol</th>\n",
+       "      <td>0.049151</td>\n",
+       "      <td>0.144739</td>\n",
+       "      <td>0.029787</td>\n",
+       "      <td>0.588873</td>\n",
+       "      <td>0.050639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butyrate</th>\n",
+       "      <td>0.122146</td>\n",
+       "      <td>0.101123</td>\n",
+       "      <td>0.133775</td>\n",
+       "      <td>0.369814</td>\n",
+       "      <td>0.169861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethanol</th>\n",
+       "      <td>0.041218</td>\n",
+       "      <td>0.646695</td>\n",
+       "      <td>0.067063</td>\n",
+       "      <td>0.095281</td>\n",
+       "      <td>0.101379</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           acetate   biomass   butanol  butyrate   ethanol\n",
+       "acetate   0.038743  0.056555  0.133280  0.612357  0.107462\n",
+       "biomass   0.161170  0.140164  0.249923  0.236367  0.120039\n",
+       "butanol   0.049151  0.144739  0.029787  0.588873  0.050639\n",
+       "butyrate  0.122146  0.101123  0.133775  0.369814  0.169861\n",
+       "ethanol   0.041218  0.646695  0.067063  0.095281  0.101379"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acetate</th>\n",
+       "      <td>0.002198</td>\n",
+       "      <td>0.012518</td>\n",
+       "      <td>0.002577</td>\n",
+       "      <td>0.009944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass</th>\n",
+       "      <td>0.010512</td>\n",
+       "      <td>0.009644</td>\n",
+       "      <td>0.015882</td>\n",
+       "      <td>0.012827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butanol</th>\n",
+       "      <td>0.002354</td>\n",
+       "      <td>0.002810</td>\n",
+       "      <td>0.100623</td>\n",
+       "      <td>0.020277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butyrate</th>\n",
+       "      <td>0.091786</td>\n",
+       "      <td>0.002317</td>\n",
+       "      <td>0.002280</td>\n",
+       "      <td>0.003468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethanol</th>\n",
+       "      <td>0.005121</td>\n",
+       "      <td>0.006199</td>\n",
+       "      <td>0.003100</td>\n",
+       "      <td>0.007709</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                CO       CO2        H2  flow rate\n",
+       "acetate   0.002198  0.012518  0.002577   0.009944\n",
+       "biomass   0.010512  0.009644  0.015882   0.012827\n",
+       "butanol   0.002354  0.002810  0.100623   0.020277\n",
+       "butyrate  0.091786  0.002317  0.002280   0.003468\n",
+       "ethanol   0.005121  0.006199  0.003100   0.007709"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "features = ['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol', 'CO', 'CO2', 'H2', 'flow rate']\n",
+    "\n",
+    "metabolites = ['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol']\n",
+    "gases = ['CO', 'CO2', 'H2', 'flow rate']\n",
+    "\n",
+    "metabolite_feature_importance_list = []\n",
+    "gases_feature_importance_list = []\n",
+    "\n",
+    "for output in ['acetate', 'biomass', 'butanol', 'butyrate', 'ethanol']:\n",
+    "    \n",
+    "    # get trained random forest model for the output\n",
+    "    model = trained_models[f'{output} rf']\n",
+    "    \n",
+    "    # get feature importance\n",
+    "    feature_importance_array = model.feature_importances_\n",
+    "    # print(feature_importance_array)\n",
+    "    \n",
+    "    # label feature importances\n",
+    "    feature_importance_dictionary = {feature: importance for feature, importance in zip(features, feature_importance_array)}\n",
+    "    \n",
+    "    # split dictionary into metabolites and gases and add the split dictionaries to lists\n",
+    "    metabolite_feature_importance_list.append({metabolite: feature_importance_dictionary[metabolite] for metabolite in metabolites})\n",
+    "    gases_feature_importance_list.append({gas: feature_importance_dictionary[gas] for gas in gases})\n",
+    "    \n",
+    "# convert list of dictionaries to dataframes\n",
+    "metabolite_feature_importance_df = pd.DataFrame(metabolite_feature_importance_list, index=metabolites)\n",
+    "gases_feature_importance_df = pd.DataFrame(gases_feature_importance_list, index=metabolites)\n",
+    "\n",
+    "# display the dataframes\n",
+    "display(metabolite_feature_importance_df)\n",
+    "display(gases_feature_importance_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot metabolite feature importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>acetate</th>\n",
+       "      <th>biomass</th>\n",
+       "      <th>butanol</th>\n",
+       "      <th>butyrate</th>\n",
+       "      <th>ethanol</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acetate</th>\n",
+       "      <td>0.038743</td>\n",
+       "      <td>0.056555</td>\n",
+       "      <td>0.133280</td>\n",
+       "      <td>0.612357</td>\n",
+       "      <td>0.107462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass</th>\n",
+       "      <td>0.161170</td>\n",
+       "      <td>0.140164</td>\n",
+       "      <td>0.249923</td>\n",
+       "      <td>0.236367</td>\n",
+       "      <td>0.120039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butanol</th>\n",
+       "      <td>0.049151</td>\n",
+       "      <td>0.144739</td>\n",
+       "      <td>0.029787</td>\n",
+       "      <td>0.588873</td>\n",
+       "      <td>0.050639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butyrate</th>\n",
+       "      <td>0.122146</td>\n",
+       "      <td>0.101123</td>\n",
+       "      <td>0.133775</td>\n",
+       "      <td>0.369814</td>\n",
+       "      <td>0.169861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethanol</th>\n",
+       "      <td>0.041218</td>\n",
+       "      <td>0.646695</td>\n",
+       "      <td>0.067063</td>\n",
+       "      <td>0.095281</td>\n",
+       "      <td>0.101379</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           acetate   biomass   butanol  butyrate   ethanol\n",
+       "acetate   0.038743  0.056555  0.133280  0.612357  0.107462\n",
+       "biomass   0.161170  0.140164  0.249923  0.236367  0.120039\n",
+       "butanol   0.049151  0.144739  0.029787  0.588873  0.050639\n",
+       "butyrate  0.122146  0.101123  0.133775  0.369814  0.169861\n",
+       "ethanol   0.041218  0.646695  0.067063  0.095281  0.101379"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(metabolite_feature_importance_df)\n",
+    "\n",
+    "# This helps with formatting \n",
+    "x = np.arange(5)  # the label locations\n",
+    "width = 0.15  # the width of the bars\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "for counter, (index, row) in enumerate(metabolite_feature_importance_df.transpose().iterrows()):\n",
+    "    output_feature_importances = list(row)\n",
+    "    \n",
+    "    rects = ax.bar(x - 0.3 + 0.15*counter, output_feature_importances , width, label=f'{index.capitalize()} Concentration')\n",
+    "\n",
+    "# # Add some text for labels, title and custom x-axis tick labels, etc.\n",
+    "ax.set_ylabel(f'Importance')\n",
+    "\n",
+    "ax.set_title(f'Feature Importance of Metabolites')\n",
+    "ax.set_xticks(x)\n",
+    "labels = ['Acetate Rate', 'Biomass Rate', 'Butanol Rate', 'Butyrate Rate', 'Ethanol Rate']\n",
+    "ax.set_xticklabels(labels)\n",
+    "ax.legend()\n",
+    "# ax.set_yscale('log')\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.savefig(f'../figures/feature_importance_metabolites.png', dpi=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot gases feature importance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acetate</th>\n",
+       "      <td>0.002198</td>\n",
+       "      <td>0.012518</td>\n",
+       "      <td>0.002577</td>\n",
+       "      <td>0.009944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass</th>\n",
+       "      <td>0.010512</td>\n",
+       "      <td>0.009644</td>\n",
+       "      <td>0.015882</td>\n",
+       "      <td>0.012827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butanol</th>\n",
+       "      <td>0.002354</td>\n",
+       "      <td>0.002810</td>\n",
+       "      <td>0.100623</td>\n",
+       "      <td>0.020277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butyrate</th>\n",
+       "      <td>0.091786</td>\n",
+       "      <td>0.002317</td>\n",
+       "      <td>0.002280</td>\n",
+       "      <td>0.003468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethanol</th>\n",
+       "      <td>0.005121</td>\n",
+       "      <td>0.006199</td>\n",
+       "      <td>0.003100</td>\n",
+       "      <td>0.007709</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                CO       CO2        H2  flow rate\n",
+       "acetate   0.002198  0.012518  0.002577   0.009944\n",
+       "biomass   0.010512  0.009644  0.015882   0.012827\n",
+       "butanol   0.002354  0.002810  0.100623   0.020277\n",
+       "butyrate  0.091786  0.002317  0.002280   0.003468\n",
+       "ethanol   0.005121  0.006199  0.003100   0.007709"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FJwoTERGRWmBSQ0RERGqBp5+IiIhILfBIDREREakFJjVERESkFv4Vz6lJT0+vtcfOExERUcNxcHCQntj+rH9FUpOUlAQPD4+GDoOIiIie0z/fnfYsnn4iIiIitcCkhoiIiNQCkxoiIiJSC/+Ka2qIiKjuaGtrY8aMGWjdujU0Nfl/ZXo+QghkZmbit99+w6VLl6q1LJMaIiJ6Lu+99x6Ki4sxadIkFBcXN3Q49JLT0tJCixYtMH36dACoVmLDlJqIiJ5Lz549sX37diY0VCtKS0sRHx+Pn376CR9++GG1lmVSQ0REz8XY2BhZWVkNHQapmcTERFhaWlZrGSY1RET0XDQ0NPD48eOGDoPUTGlpKTQ0NKq1DJMaIiIiUgtMaoiIiKhSdnZ2yMjIaOgwqlRvdz9ZWVkhKCgIb7zxBhISEjBx4sRyr2gOCAhAnz594OTkBE9PT6U2BgYG2LhxI9555x2kp6fj888/x5EjR+prCEREpIKVkeF11vestl4qt500aRI+//xztGjRApmZmThx4gS+/PJL3L9/HwAwffp0fPbZZ2jevDnu3buH1atX4+eff66wPwsLC3zzzTd499130ahRI9y5cwcbNmzAqlWrnntctalHjx4ICAhAq1atatxHWFgYAgICsGvXLgBAcnIyrKysaivEOlNvR2oCAgKQkJAACwsL+Pn5Ye/evdDV1S3T7urVqxg/fjySk5PL1H399ddo1KgRbGxs8PHHH2Pbtm0VvtSKiIj+vRYuXAhfX1/MmjULjRs3Rps2bXD9+nV069YNAODr64uZM2di9OjRMDY2xpgxYzBz5kwsXLiw3P709fURFhaGJk2aoEuXLjA1NYW3tze6d+9en8OqFVpaWg0dQp2pl6TGyMgIgwYNwpIlS1BYWIigoCAoFAr07NmzTNt169bhjz/+QGlpaZm64cOHY9myZcjPz8fJkydx8eJFvPfee3U/ACJ6KYSdiK3RROrF1NQUCxYswNSpU3H8+HEUFRUhLy8Pq1evxu7du2FmZoa5c+fik08+QXh4OEpLSxEeHo7PPvsMCxYsgKmpaZk+R48eDQsLCwwbNgyJiYkAgPj4eKVbjj///HMkJCQgLS0NAQEB0NfXBwCMGTMGx48fx/r165Gbm4srV67A2dlZWq59+/Y4c+YMZDIZkpKS8MEHHwB4cnZizZo1SElJwd27dzFjxgxpmU2bNsHf3x9nzpxBbm4u9u3bBwMDAwBAaGgoXF1doVAopFNGYWFhWLJkCa5cuYLc3FwAwBdffIGkpCTk5ubi7Nmz0pGd+fPno3v37ti8eTMUCgVmzJgBBwcHFBQUSOtv27Ytzp8/D5lMhvPnz6Nt27ZSnRACU6ZMQVJSEtLS0jB58uTn2JvVUy9JjYuLC3JycpCeni6VRUZGwt3dXeU+zM3NYWNjg5s3b9a4DyIiUn9eXl7Q0dGp8PIET09PaGtrIzQ0VKk8JCQEOjo68PT0LLPMm2++iWPHjuHRo0fl9tmvXz/Mnj0b/fr1g4uLC5ycnLBo0SKpvmfPnggNDUXjxo1x+fJlLF26FABgYmKC48ePY9OmTbCyskLHjh0RExMDAFi5ciWMjY3h4uICLy8vTJkyBb1795b6HDp0KKZOnYrmzZvD0dERo0aNAgC8/fbbuHXrFoyNjZVOGfn4+MDb21u6TTo6OhodO3aEhYUFzp8/j19//RUAsHz5cpw9exZjx46FsbEx/P39lcaqo6ODgwcPYvPmzbCyssLWrVtx8OBB6OjoSG26deuGV155Bd7e3vD39y83UawL9ZLUGBoaQi6XK5XJ5XIYGRlVq4+SkhKlTLGyPiZNmoSIiAhERERU+z53IiJ6eVlYWCAzM7PC28wrqn/8+DGysrJgYWFR7jIPHjyocJ3Dhg3DunXrEB8fD7lcjqVLl8LHx0eqj4yMxO+//47S0lLs3LkT7dq1AwAMGjQIMTEx2LJlC0pKSpCZmYmoqCgAT47wzJo1Cw8fPkRKSgrWr18vHcUBgF27diEmJgZ5eXk4dOiQ1GdFAgMDcefOHRQWFgIA9u3bh8zMTJSUlOCbb76Bh4eHUmJSEU9PT5SUlCAwMBAlJSUICAhAaWmpUjL43XffoaCgAOfOnUNmZiZcXFyq7Lc21EtSk5+fD2NjY6UyExMT5OXlVasPbW1t6XBeVX0EBgbCw8MDHh4eyMzMrFngRET00snKyoKlpWWF76HKzs4ut15TUxMWFhblPkgwKysLNjY2Fa6zWbNmSteCJiUloWnTptL8P89UPHz4EIaGhgAAW1tb6XTWP1lZWaFRo0a4desWZDIZZDIZfH190aRJkyr7rMi9e/eU5idOnIioqCjk5OQgKSkJWlpaMDc3r7SP8sYKAHfv3lVpvHWtXpKauLg4mJubK13U26ZNG0RHR6vch0wmw4MHD9CmTZsa90FEROovPDwcJSUlGDBgQIX1paWlePvtt5XKBwwYgJKSEly8eLHMMqdPn0a/fv3KvcEFAFJTU2FnZyfN29vbS3dZVSY5ORmOjo5lyjMzM1FQUAAHBweYm5vD3NwcJiYmGDJkSJV9CiGqLHdwcMAPP/yAkSNHwszMDA4ODgAgPeyuoj6AJ2O1tbVVKlN1vHWtXpKavLw8HD58GIsWLYKenh5GjhwJExMTnDlzpkxbHR0d6OnpQUNDA7q6utDT05PqduzYgYULF8LQ0BC9e/eGl5cX9u/fXx9DICKil0Rubi6WLVuGX375BX369IGOjg4aNWqEKVOmYOjQocjJyYGfnx/WrFkDLy8vaGlpwdPTE6tXr8a3334rXUj7T0FBQcjOzkZwcLCUhLRo0QI7d+4EAOzevRuTJ0+Gk5MTjI2NsWjRIvz2229VxnrkyBG4u7tj1KhR0NLSgqWlJVq3bg0hBIKCgrBixQqYmJhAQ0MDrVq1wmuvvVZln+np6bC2tlY6s/EsIyMjPH78GBkZGdDV1cXixYvL9FFesgU8ecGknp4eJkyYAC0tLUyePBna2trlJoP1rd6eUzN16lTpjyIxMRHe3t4oKirC/PnzYW9vj6lTpwIAjh8/Lt0V9ccffwAAHB0dkZSUhEWLFmHjxo1IS0tDeno6Ro0apXSIi4iIGl51niVTV5YtW4bMzEz8+OOPcHR0RFZWFo4ePQpfX18AwJdffomcnBxs3boVzZo1Q2pqKn788Uf8+OOP5fZXWFiIXr16Yfny5bh48SIMDAyQmJiI9evXAwCOHj0Kf39/nDp1CgYGBjhw4IB0MXBl5HI5+vfvj59//hmrVq1CTk4OZs6ciaioKMyYMQPffPMNbt68CWNjY8TGxmLevHlV9vn3338jNDQUKSkpKCoqUjot9FRUVBQ2bNiAmzdvIi8vr0xSs2bNGmzevBnz5s3D4sWLlQ4gFBUV4d1330VAQABWrlyJmJgYDB48+IV4oakGgIqPMamJiIgIeHh4NHQYRFTHanp7dq++brUcyb9LUFAQRo8e3dBhkBoq72+rst90viaBiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXApIaIiIjUApMaIiIiUgtMaoiIiEgtMKkhIiKi5xISEqLSKxzqGpMaIiJSS5MmTUJkZCTy8vJw584dBAYGKj1dd/r06YiLi8PDhw9x69YtfP7551Kdrq4ufv31VyQnJyMnJwenT5+Gu7v7c63vRZGYmIjOnTvXePkxY8YgNDRUqWzAgAHYt2/f84b23OrtNQlERPTv8FgcqrO+NTXeUandwoULMXXqVIwfPx5nzpyBrq4uxo4di27dumH37t3w9fXF+PHj4ePjgz///BOvv/46goODYWxsjGXLlkFbWxsJCQnw9PTE/fv3MX36dOzfvx+urq41Wt/LREtLC6WlpQ0dRo3wSA0REakVU1NTLFiwAFOnTsXx48dRVFSEvLw8rF69Grt374aZmRnmzp2LTz75RHpjd3h4OD777DMsWLAApqamePjwIb7++mukpKTg8ePHWL16NZycnNC4ceNqrw8A2rZti/Pnz0Mmk+H8+fNo27attLwQAlOmTEFSUhLS0tIwefJkqa5Ro0ZYs2YN7t27h+zsbAQFBUl1H374IaKiopCVlYW9e/dKsfXo0QMxMTFYunQpsrOzERsbKx2ZWbt2Lezt7XH69GkoFAoMGTIEY8aMwfHjxxEYGIjc3Fz4+PjA09MTERERyM3NRWJiohSTnZ0dAgIC0KdPHygUCly+fBkAEBYWhmHDhgEA9PX1ERAQgLS0NCQkJCgdAdu0aRP8/f1x5swZ5ObmYt++fTAwMKiV/Q4wqSEiIjXj5eUFHR0dHDlypNx6T09PaGtrlzmFEhISAh0dHXh6epbbZ3p6OrKzs6u9Ph0dHRw8eBCbN2+GlZUVtm7dioMHD0JHR0dq061bN7zyyivw9vaGv78/TE1NAQA//vgjbG1t0a5dO1hbW2PdunUAgM6dO8PPzw9Dhw5FkyZNkJycjJ9++knqz9nZGVlZWbCyssKWLVvg7+8P4MnLpe/evYs333wTxsbG0imjXr164cyZMzAzM8OePXtQXFyMyZMnw8zMDD4+PlixYgVatWqF5ORkTJkyBSdPnoSxsTE6depUZrxffvklWrRoAWdnZ7z11lv473//i759+0r1Q4cOxdSpU9G8eXM4Ojpi1KhR5W63mmBSQ0REasXCwgKZmZl4/PhxteofP36MrKwsWFhYKJWbmppi/fr1WLBgQY3W5+npiZKSEgQGBqKkpAQBAQEoLS1VSp6+++47FBQU4Ny5c8jMzISLiws0NDQwatQoTJ8+HdnZ2SgpKcH58+cBAOPGjcPq1asRHR2NkpISfPXVV/D29pb6y83NxU8//YTS0lIEBwejXbt2lW6z2NhYbN++HUIIPHr0CFeuXMHVq1chhMClS5dw8uRJdOnSpdI+nho2bBiWLl0KhUKBuLg4rFu3Dj4+PlL9rl27EBMTg7y8PBw6dKjK2KqDSQ0REamVrKwsWFpaQlOz/J+47Ozscus1NTVhYWGBrKwsqUxPTw8HDhzA4cOHsWnTphqtr1mzZkhOTlYqu3v3rtJFxOnp6dK/Hz58CENDQ1hZWUFXVxdJSUll+rSzs4Ovry9kMhlkMhni4+OhoaEhJWQZGRll+qvMvXv3lObd3d1x/PhxpKenIycnBwMHDiyT7FXk2fEmJSVVOdbawqSGiIjUSnh4OEpKSjBgwIAK60tLS/H2228rlQ8YMAAlJSW4ePEigCdJzs6dO5GcnIzZs2fXeH2pqamwtbVVKrO3t8f9+/crHUdGRgaKiopgb29fpi4lJQXz58+Hubm5NBkYGCglZBURQlRZtnr1apw7dw729vYwMzPDkSNHoKGhUeHy/5Samgo7OztpXpWx1hYmNUREpFZyc3OxbNky/PLLL+jTpw90dHTQqFEjTJkyBUOHDkVOTg78/PywZs0aeHl5QUtLC56enli9ejW+/fZb5ObmAgACAwOhr6+PcePGPdf6Ll26BD09PUyYMAFaWlqYPHkytLW1peSpIkIIBAUFwd/fH+bm5tDW1pZOAW3atAnTpk1DmzZtAACNGzfGoEGDVNo+6enpcHR0rLSNsbExZDIZCgsL0bNnT/Tr109peVtb2wqPTO3evRtffPEFjI2N4ezsjI8//hi//fabSrE9LyY1RESkdpYtW4Zly5bhxx9/hEwmQ0xMDF599VWcPXsWwJOLWX/++Wds3boVCoUC27Ztw48//oilS5cCeHJ0Yfz48ejRowdkMhkUCgUUCgW6detW7fUVFRXh3XffxcSJE5GVlYVx48Zh8ODBKC4urnIcM2fORFpaGqKjo5XujAoPD8e8efOwfft25Obm4vLlyyo/e2bFihVYuXIlZDIZ3n///XLbzJ07F9OnT4dcLscnn3yidBF0WFgYUlNTkZmZiUuXLpVZdsmSJUhOTkZ8fDxOnjyJlStX4tixYyrF9rw0AFR+HEkNREREwMPDo6HDIKI6FnYitkbL9errVsuRvDhWRobXeNlZbb1UahcUFITRo0fXeD1EFSnvb6uy33QeqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXApIaIiIjUApMaIiIiUgtMaoiIiEgtMKkhIiKiCtnZ2Sm9IPNFpt3QARARkXqp6ZOdVaHq058TExPh4+Oj9Bj/tWvX4sGDB1iyZAk8PT3xzTffoEOHDigsLMTu3bsxa9YslJSUlNufhYUFvvnmG7z77rto1KgR7ty5gw0bNmDVqlW1Mq7a0qNHDwQEBKBVq1Y17iMsLAwBAQHYtWsXACA5ORlWVla1FWKd4pEaIiL61zE1NcVPP/0EW1tbtGnTBu3bt8d///vfctvq6+sjLCwMTZo0QZcuXWBqagpvb2907969nqN+flpaWg0dQp1iUkNERP86x44dw4EDB/Dw4UNkZ2dj27Zt8PT0LLft6NGjYWFhgWHDhiExMREAEB8fjw8//FBq8/nnnyMhIQFpaWkICAiAvr4+AGDMmDE4fvw41q9fj9zcXFy5cgXOzs7Scu3bt8eZM2cgk8mQlJSEDz74AABgYGCANWvWICUlBXfv3sWMGTOkZTZt2gR/f3+cOXMGubm52LdvHwwMDAAAoaGhcHV1hUKhkE4ZhYWFYcmSJbhy5Yr0BvIvvvgCSUlJyM3NxdmzZ6UjO/Pnz0f37t2xefNmKBQKzJgxAw4ODigoKJDW37ZtW5w/fx4ymQznz59H27ZtpTohBKZMmYKkpCSlF3DWFyY1RET0r9elSxdERUWVW/fmm2/i2LFjePToUbn1/fr1w+zZs9GvXz+4uLjAyckJixYtkup79uyJ0NBQNG7cGJcvX5beBG5iYoLjx49j06ZNsLKyQseOHRETEwMAWLlyJYyNjeHi4gIvLy9MmTIFvXv3lvocOnQopk6diubNm8PR0RGjRo0CALz99tu4desWjI2NlU4Z+fj4wNvbG5aWlgCA6OhodOzYERYWFjh//jx+/fVXAMDy5ctx9uxZjB07FsbGxvD391caq46ODg4ePIjNmzfDysoKW7duxcGDB6GjoyO16datG1555RV4e3vD398fpqamqu2EWsCkhoiI1NKJEycgk8mkady4ceW2GzBgAN5+++0yP+BPWVhY4MGDBxWuZ9iwYVi3bh3i4+Mhl8uxdOlS+Pj4SPWRkZH4/fffUVpaip07d6Jdu3YAgEGDBiEmJgZbtmxBSUkJMjMzpcRqzJgxmDVrFh4+fIiUlBSsX79eOooDALt27UJMTAzy8vJw6NAhqc+KBAYG4s6dOygsLAQA7Nu3D5mZmSgpKcE333wDDw8PpcSkIp6enigpKUFgYCBKSkoQEBCA0tJSpaNc3333HQoKCnDu3DlkZmbCxcWlyn5rC5MaIiJSS3379oW5ubk0bdq0qUybTp064ddff8V7772H9PT0cvvJysqCjY1Nhetp1qwZkpOTpfmkpCQ0bdpUmv9nvw8fPoShoSEAwNbWVjqd9U9WVlZo1KgRbt26JSVkvr6+aNKkSZV9VuTevXtK8xMnTkRUVBRycnKQlJQELS0tmJubV9pHeWMFgLt376o03vrAu5+IiOhfyc3NDQcPHsS4ceOU7pJ61unTp/Hll19CV1cXRUVFZepTU1NhZ2cnzdvb2+P+/ftVrj85ORlvv/12mfLMzEwUFBTAwcEBcrlcxdE8IYSostzBwQE//PADevTogatXr8LExAS5ubnQ0NCotA/gyVhtbW2VylQdb33gkRoiIvrXsbW1xbFjxzBv3jyEhoZW2jYoKAjZ2dkIDg6Go6MjAKBFixbYuXMnAGD37t2YPHkynJycYGxsjEWLFuG3336rMoYjR47A3d0do0aNgpaWFiwtLdG6dWsIIRAUFIQVK1bAxMQEGhoaaNWqFV577bUq+0xPT4e1tbV0oXJ5jIyM8PjxY2RkZEBXVxeLFy8u08fTcT7r0qVL0NPTw4QJE6ClpYXJkydDW1sbFy9erDK2+sAjNUREVKtUfZZMQxo/fjzs7OywZs0arFmzBsCT00Zt2rQp07awsBC9evXC8uXLcfHiRRgYGCAxMRHr168HABw9ehT+/v44deoUDAwMcODAAeli4MrI5XL0798fP//8M1atWoWcnBzMnDkTUVFRmDFjBr755hvcvHkTxsbGiI2Nxbx586rs8++//0ZoaChSUlJQVFSkdFroqaioKGzYsAE3b95EXl5emaRmzZo12Lx5M+bNm4fFixdj//79Ul1RURHeffddBAQEYOXKlYiJicHgwYNRXFxcZWz1QQNAxceZ1ERERAQ8PDwaOgwiqmM1fejby/AjXFMrI8NrvOystl4qtQsKCsLo0aNrvB6iipT3t1XZbzpPPxEREZFaYFJDREREaoFJDREREakFJjVERESkFpjUEBERkVpgUkNERERqgUkNERERqQUmNURERKQWmNQQEdG/Ro8ePRATE9PQYVAd4WsSiIioVhUtmV1nfev6rlCpXWJiIqytrfH48WMAQFpaGpydnessrqeEEMjPz4cQAtnZ2Vi/fj2WLVum8rI2NjZIS0ur4yjVV70dqbGyskJoaCjy8/MRGRmJzp07l9vO2dkZFy5cQH5+PsLDw5X+CB0dHXHixAnpVelTp06tr/CJiOgl8+abb8LY2BjGxsb1ktA89fTFlkOGDMH8+fPRt2/felv3v129JTUBAQFISEiAhYUF/Pz8sHfvXujq6pZpt2vXLhw6dAiNGzfG4cOHpbegAsCqVasQGxsLCwsL9O/fH9988w3atm1bX0MgIiI107NnT1y7dg0ymQxHjx6Fvb09AGDZsmVYsmQJAMDFxQVCCLzzzjsAgI8++gh79+6tsu8rV64gKioKHTp0AABoaGhg3759SEtLQ1ZWFnbs2AETExMAQEhICADg9u3bUCgU6NSpEwBg2rRpiIuLQ0ZGBjZs2FDp27epnpIaIyMjDBo0CEuWLEFhYSGCgoKgUCjQs2dPpXZubm5wcnKCn58fHj16hO+//x4uLi5wdXUFADg4OGDPnj0oLS1FTEwMbty4ATc39X0RHRER1R0LCwv8/vvvWLBgAaytrXHlyhUEBwcDAM6dO4du3boBALp164bExESl+XPnzlXZ/+uvv442bdrg9u3bUtnevXvh6OgIJycnNGnSBPPnzwcADBgwAMD/P8pz+fJlfPjhh5gwYQLefPNN2Nvbw9TUFAsXLqzVbaBu6iWpcXFxQU5ODtLT06WyyMhIuLu7K7Vzd3dHbGwsSkpKAADFxcWIjY2V2q1ZswZDhw6Fjo4O2rZtCxcXF1y4cKHcdU6aNAkRERGIiIiApaVlHY2MiIheVCdOnIBMJoNMJsPSpUvL1A8YMACXL19GSEgIiouLsXTpUnTo0AF2dna4cOECPDw8oK2tjW7dusHPzw/du3cHUHVSExsbi/z8fFy6dAmbN2/G77//DuDJNTPbt29HQUEBcnJy8NNPP0mJUnnGjx+PZcuWITk5GQUFBfj222/xwQcfPOdWUW/1ktQYGhpCLpcrlcnlchgZGVWr3dPM+eHDh7h27Rq+/vprpKamlrvOwMBAeHh4wMPDA5mZmbU4GiIiehn07dsX5ubmMDc3x5dfflmmvlmzZkhOTpbmHz16hLS0NDRt2hS5ublITExEx44d4eXlhW3btsHGxgY2NjZo0aIF/vrrrwrX6+bmBiMjI3z66afo1q2bdKmFlpYW/P39cefOHeTm5mL79u2wsLCosB87Ozts3LhRSsxOnToFKyur59gi6q9ekpr8/HwYGxsrlZmYmCAvL0/ldpqamggNDcX69euhr68PJycnfPrpp+jfv3+dx09EROonNTUVdnZ20ryuri6aNGmC+/fvA3jyH+khQ4agoKAACoUCly5dwowZMxAREYHS0tJK+xZC4JdffkFGRgamTJkCABgxYgS6d+8OLy8vmJqaYsSIEdDQ0Kiwj5SUFIwYMUJKzMzMzHjmoQr1ktTExcXB3Nwc1tbWUlmbNm0QHR2t1C46Ohpubm7Q0tICAOjo6MDV1RXR0dFo3LgxmjdvjoCAAJSWluLOnTsICQlBnz596mMIRESkZkJDQ9GpUye89dZb0NbWxqJFi3D9+nXp6M25c+cwZcoU6VTT2bNnleZV4efnh9mzZ0NHRwfGxsYoLCyETCaDlZUVZs6cqdQ2LS0Njo6O0vyvv/6KhQsXSmVNmzblnVRVqJfn1OTl5eHw4cNYtGgRZs+ejaFDh8LExARnzpxRahcbG4uEhATMnj0b/v7+mDVrFuLj43Hr1i0AQHJyMiZNmoR169bB1tYWgwYNwrffflsfQyAiIhWp+iyZhpaZmQlvb2/4+/vD3t4eERERGD58uFR/7tw5mJiYKCU1/5xXxbFjx5CTk4NRo0YhKCgIgwYNQkZGBu7evYugoCCMHTtWarts2TIcOHAAenp66NOnD3bu3AkzMzOEhoaiWbNmSE1NxerVq3HixIla2wbqRgOAqI8VWVtbIygoCN27d0diYiImTJiAS5cuYf78+bC3t5eeOePi4oKgoCC0b98eN27cwKhRoxAXFwcA6NSpE3766Se0bt0a+fn52L59O+bOnQshKh9CREQEPDw86nyMRNSwwk7E1mi5Xn3V9y7KlZHhNV52VlsvldoFBQVh9OjRNV4PUUXK+9uq7De93p4onJ6eXu71L8uXL1eaj4uLg5dX+R+ky5cvo2vXrnUSHxEREb3c+O4nIiIiUgtMaoiIiEgtMKkhIiIitcCkhoiInosQQnoUB1Ft0dHRkd6yriomNURE9Fzu3LmDgQMHMrGhWqGpqQlra2t89tlniIqKqtay9Xb3ExERqacff/wR//nPf+Dt7V3pE3KJVCGEgFwux//+9z/s37+/WssyqSEioucik8ng6+vb0GEQ8fQTERERqQcmNURERKQWmNQQERGRWmBSQ0RERGqBSQ0RERGpBSY1REREpBaY1BAREZFaYFJDREREaoFJDREREakFJjVERESkFpjUEBERkVpgUkNERERqgUkNERERqQUmNURERKQWmNQQERGRWmBSQ0RERGqBSQ0RERGpBSY1REREpBaY1BAREZFaYFJDREREaoFJDREREakFJjVERESkFpjUEBERkVpgUkNERERqgUkNERERqQUmNURERKQWmNQQERGRWmBSQ0RERGqBSQ0RERGpBSY1REREpBaY1BAREZFaYFJDREREaoFJDREREamFaiU1mpqa8PDwwODBgwEA+vr60NfXr5PAiIiIiKpDW9WG7dq1w759+/Dw4UO0aNECxsbG6NWrF8aOHYthw4bVZYxEREREVVL5SM26deswd+5ctGvXDsXFxQCAM2fOoFu3bnUWHBEREZGqVE5qXF1dsXfvXgCAEAIAUFhYCD09vbqJjIiIiKgaVE5qbt68iUGDBimVDRgwAFevXq31oIiIiIiqS+VraqZNm4aQkBBcvXoVjRo1wu7du9GpU6cyiQ4RERFRQ1D5SM2NGzfg5uaGbdu2YdGiRdi3bx/atWuHqKgolZa3srJCaGgo8vPzERkZic6dO5fbztnZGRcuXEB+fj7Cw8Ph7OysVD9p0iTcvn0bCoUC165dg7m5uapDICIiIjWmclJjZmYGIQR27dqFFStWIDg4GKWlpTA1NVVp+YCAACQkJMDCwgJ+fn7Yu3cvdHV1y7TbtWsXDh06hMaNG+Pw4cPYuXOnVPfOO+9g6tSp6N+/P4yNjfHRRx+hsLBQ1SEQERGRGlM5qQkNDYWLi4tSmaurK0JCQqpc1sjICIMGDcKSJUtQWFiIoKAgKBQK9OzZU6mdm5sbnJyc4Ofnh0ePHuH777+Hi4sLXF1dAQBffPEFZs6cibi4OABATEwMCgoKVB0CERERqTGVk5pWrVrh+vXrSmXXrl1D69atq1zWxcUFOTk5SE9Pl8oiIyPh7u6u1M7d3R2xsbEoKSkBABQXFyM2Nhbu7u7Q0tLCa6+9hvbt2yM5ORlxcXH47LPPKlznpEmTEBERgYiICFhaWqo6TCIiInpJqZzUpKWlSUdMnnJ1dUVmZmaVyxoaGkIulyuVyeVyGBkZqdzO2toa2tra6N27N9zd3TFw4EDMmzcPb775ZrnrDAwMhIeHBzw8PFSKkYiIiF5uKic1q1evxsGDBzF69Gh0794do0aNwv79+/HTTz9VuWx+fj6MjY2VykxMTJCXl6dyu6enmfz8/KBQKHDr1i1s374db7/9tqpDICIiIjWm8i3dq1atQlZWFsaMGQNbW1skJydj6dKlShfyViQuLg7m5uawtraWTkG1adMGGzZsUGoXHR0NNzc3aGlpobS0FDo6OnB1dUV0dDRycnKQmppazeERERHRv0W1Xmi5Y8cOvPXWW2jdujX69++vUkIDAHl5eTh8+DAWLVoEPT09jBw5EiYmJjhz5oxSu9jYWCQkJGD27NnQ1dXF7NmzER8fj1u3bgEAgoKCMGvWLDRq1AgtW7bE8OHDERoaWp0hEBERkZpS+UgNAPTp0wft2rWDoaGhUvlXX31V5bJTp05FUFAQsrOzkZiYCG9vbxQVFWH+/Pmwt7fH1KlTAQA+Pj4ICgqCr68vbty4gY8++kjqY/HixQgICMD9+/chk8nw3Xff4fTp09UZAhEREakplZOagIAADBw4EKdOncLDhw+rvaL09HT079+/TPny5cuV5uPi4uDl5VVuH48ePcK4ceMwbty4aq+fiIiI1JvKSc3QoUPh5ubGO4mIiIjohaTyNTV37tyBtna1zlYRERER1RuVs5T9+/fj6NGjCAwMVHqIHgDs3r271gMjIiIiqg6Vk5pevXohOzsb3t7eSuVCCCY1RERE1OBUTmoqenIvERER0YugWhfJaGhowNHRERYWFtDQ0JDKIyIiaj0wIiIioupQOanp0qULgoODYWhoCFNTU8jlchgbGyM5ORlOTk51GSMRERFRlar1moRvv/0Wa9euRXZ2NiwsLDBnzhzo6urWZXxEREREKlH5lm5nZ2esXbsWwJOLgwHghx9+wLRp0+omMiIiIqJqUDmpefDgAZo2bQoASExMRK9eveDu7g4tLa06C46IiIhIVSqffvr+++/Rvn173L9/H1999RUOHz4MHR0d/Pe//63L+IiIiIhUonJSs3HjRunfBw4cgJmZGfT09JCXl1cngRERERFVh8qnn/7880+l+eLiYuTl5SE8PLzWgyIiIiKqLpWTmldeeaVMmYaGBlxdXWs1ICIiIqKaqPL0U0hICIQQ0NPTw5EjR5TqbG1t+eA9IiIieiFUmdTs3LkTwJN3P+3atUsqF0IgPT0dYWFhdRcdERERkYqqTGqCgoKgqakJOzs77Ny5E0VFRfURFxEREVG1qHRNzePHjzF9+nQUFxfXdTxERERENaLyhcLr1q3DnDlz6jIWIiIiohpT+Tk13t7ecHZ2xvz583H//n3pVQkA0Lp16zoJjoiIiEhVKic1U6ZMqcs4iIiIiJ6LyknNH3/8If3b0tISmZmZdRIQERERUU2ofE2Nqakptm3bhoKCAty/fx8FBQXYtm0bzM3N6zI+IiIiIpWonNQEBASgtLQULi4u0NHRgYuLC4qKihAQEFCX8RERERGpROXTT71794atra30nJp79+5h6tSpuHfvXp0FR0RERKQqlY/UZGdno02bNkpl7u7uyM7OrvWgiIiIiKpL5SM1ixYtwokTJ7Bnzx4kJyfD3t4eQ4YMwSeffFKX8RERERGpROWkZvfu3bh58ya8vb3RrFkz3L17F927d0dMTExdxkdERESkEpWTGgCIiYnB119/zVu6iYiI6IWj8jU1FhYWCA4ORmFhIVJTU1FYWIgdO3bA0tKyLuMjIiIiUonKSc22bduQn5+Pli1bQldXF05OTsjPz8fWrVvrMj4iIiIilah8+snLywtWVlbSm7pTUlLw6aefIiMjo86CIyIiIlKVykdqzp07h+7duyuVde3aVen1CUREREQNReUjNTKZDIcPH8b//vc/3Lt3D7a2tnjjjTewd+9erFmzRmr36aef1kmgRERERJVROamJi4vDt99+K80nJycjPDy8ToIiIiIiqi6Vk5qlS5fWZRxEREREz6Vaz6kZNGgQ2rVrB0NDQ6XyhQsX1mpQRERERNWlclKzdetWdOrUCSdPnkRBQUFdxkRERERUbSonNQMHDoS9vT3y8vLqMh4iIiKiGlH5lu7Lly/D1ta2LmMhIiIiqjGVj9SMHDkShw8fxqVLl5Cenq5U99VXX9V6YERERETVoXJS8/3338PKygpGRkbQ0tKqy5iIiIiIqk3lpGbIkCGwt7dHTk5OHYZDREREVDMqX1Nz9epVvpGbiIiIXlgqH6n566+/cPr0aezatavMNTV+fn61HhgRERFRdaic1JiamuLkyZOwsLCAhYWFVC6EqJPAiIiIiKqjyqTGw8MDALB27drnWpGVlRWCgoLwxhtvICEhARMnTsSlS5fKtHN2dkZQUBDat2+PGzduYNSoUYiPj1dq4+DggJiYGGzZsgVTp059rriIiIhIPVSZ1OzatavSeiEEnJycqlxRQEAAEhIS8P777+PDDz/E3r170bJlSxQVFZVZ3549e9CrVy/Mnj0bO3fuRKdOnZTa+Pv746+//qpynURERPTvUWVS07Jly+deiZGREQYNGgQ7OzsUFhYiKCgI8+fPR8+ePXH8+HGpnZubG5ycnODn54eSkhJ8//33+O9//wtXV1fcunULANCvXz9oaGjgxIkTsLGxee7YiIiISD2ofPfT83BxcUFOTo7SBcaRkZFwd3dXaufu7o7Y2FiUlJQAAIqLixEbGyu109HRgZ+fH2bNmlUfYRMREdFLpFpv6a4pQ0NDyOVypTK5XA4jI6NqtZs5cyZCQkKQkJBQ5TonTZqEyZMnAwBvRSciIvoXqJekJj8/H8bGxkplJiYmZV6OWVm7Zs2aYfz48XjttddUWmdgYCACAwMBABEREc8RPREREb0M6iWpiYuLg7m5OaytraVTUG3atMGGDRuU2kVHR8PNzQ1aWlooLS2Fjo4OXF1dER0dDQ8PD9jZ2Ul3QhkZGUFTUxN2dnYYNGhQfQyDiIiIXmD1ck1NXl4eDh8+jEWLFkFPTw8jR46EiYkJzpw5o9QuNjYWCQkJmD17NnR1dTF79mzEx8fj1q1bCA0NRYsWLdChQwd06NABAQEB2L17N0aNGlUfQyAiIqIXXL0kNQAwdepUuLi4IDs7G/PmzYO3tzeKioowf/58pWfg+Pj44L333kNOTg4GDx6Mjz76CABQVFSEtLQ0acrLy0NBQQFkMll9DYGIiIheYPVy+gkA0tPT0b9//zLly5cvV5qPi4uDl5dXlf0tWbKk1mIjIiKil1+9HakhIiIiqktMaoiIiEgtMKkhIiIitcCkhoiIiNQCkxoiIiJSC0xqiIiISC0wqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXApIaIiIjUApMaIiIiUgtMaoiIiEgtMKkhIiIitcCkhoiIiNQCkxoiIiJSC0xqiIiISC0wqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXApIaIiIjUApMaIiIiUgtMaoiIiEgtMKkhIiIitcCkhoiIiNQCkxoiIiJSC0xqiIiISC0wqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXApIaIiIjUApMaIiIiUgtMaoiIiEgtMKkhIiIitcCkhoiIiNQCkxoiIiJSC0xqiIiISC0wqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrVQb0mNlZUVQkNDkZ+fj8jISHTu3Lncds7Ozrhw4QLy8/MRHh4OZ2dnqW7lypW4ffs25HI5/vzzT3Tt2rW+wiciIqIXXL0lNQEBAUhISICFhQX8/Pywd+9e6Orqlmm3a9cuHDp0CI0bN8bhw4exc+dOqS43Nxf9+vWDqakpvv/+exw4cABGRkb1NQQiIiJ6gdVLUmNkZIRBgwZhyZIlKCwsRFBQEBQKBXr27KnUzs3NDU5OTvDz88OjR4/w/fffw8XFBa6urgCApUuX4vbt2xBCYM+ePSgsLJTqiIiI6N9Nuz5W4uLigpycHKSnp0tlkZGRcHd3x/Hjx6Uyd3d3xMbGoqSkBABQXFyM2NhYuLu749atW0p9Ojo6onHjxoiPjy93nZMmTcLkyZMBAJaWlrU9JCJSI0VLZtd4WV3fFbUYCRE9j3o5UmNoaAi5XK5UJpfLy5w6UrWdjo4OgoKC4OfnV6b9U4GBgfDw8ICHhwcyMzNrYRRERET0IquXpCY/Px/GxsZKZSYmJsjLy6tRuy1btuDBgwdYvHhxncRLREREL596SWri4uJgbm4Oa2trqaxNmzaIjo5WahcdHQ03NzdoaWkBeHJExtXVVandqlWrYGNjg5EjR0IIUR/hExER0UugXpKavLw8HD58GIsWLYKenh5GjhwJExMTnDlzRqldbGwsEhISMHv2bOjq6mL27NmIj4+Xrqfx9fVF165dMXjwYBQVFdVH6ERERPSSqLdbuqdOnQoXFxdkZ2dj3rx58Pb2RlFREebPn4+1a9dK7Xx8fPDee+8hJycHgwcPxkcffSTVLV68GK1atUJqaioUCgUUCgWGDx9eX0MgIiKiF1i93P0EAOnp6ejfv3+Z8uXLlyvNx8XFwcvLq9w+NDQ06iQ2IiIievnxNQlERESkFpjUEBERkVpgUkNERERqgUkNERERqQUmNURERKQW6u3uJyJ1FHYitsbL9urrVouREBERj9QQERGRWmBSQ0RERGqBSQ0RERGpBSY1REREpBaY1BAREZFaYFJDREREaoFJDREREakFJjVERESkFvjwvQbyWByq8bKaGu/UYiTUUIqWzK7xsrq+K2oxEiIi9cAjNURERKQWmNQQERGRWmBSQ0RERGqBSQ0RERGpBSY1REREpBZ49xOpjZWR4TVedlZbr1qMhIiIGgKP1BAREZFa4JGafxk+G4WIiNQVkxoi1PxhiP87WcuBEBFRjfH0ExEREakFJjVERESkFpjUEBERkVpgUkNERERqgRcKExERUbletjtmeaSGiIiI1AKTGiIiIlILTGqIiIhILfCaGiIiohdcTR8QCgCaGu/UYiQvNh6pISIiIrXApIaIiIjUAk8/ERER1YOVkeHPsXRmrcWhznikhoiIiNQCkxoiIiJSC0xqiIiISC0wqSEiIiK1wAuFiYjohVLT9w01xLuG6MXCpIaIiEiNhZ2IrfnCFwJrL5B6wKSGiF4oz/Pk1P+drMVAiOilw6TmJfRvyrrp5cTncaiH50swXWu+Yn5PUQ0xqXlONf/y5hc3ERFRbeLdT0RERKQW6i2psbKyQmhoKPLz8xEZGYnOnTuX287Z2RkXLlxAfn4+wsPD4ezsLNUZGBhgx44dUCgUuH37NgYOHFhf4RMREdELrt6SmoCAACQkJMDCwgJ+fn7Yu3cvdHV1y7TbtWsXDh06hMaNG+Pw4cPYuXOnVPf111+jUaNGsLGxwccff4xt27bB2tq6voZAREREL7B6SWqMjIwwaNAgLFmyBIWFhQgKCoJCoUDPnj2V2rm5ucHJyQl+fn549OgRvv/+e7i4uMDV9ckFZ8OHD8eyZcuQn5+PkydP4uLFi3jvvffqYwhERET0gquXpMbFxQU5OTlIT0+XyiIjI+Hu7q7Uzt3dHbGxsSgpKQEAFBcXIzY2Fu7u7jA3N4eNjQ1u3rxZaR9ERET071Qvdz8ZGhpCLpcrlcnlchgZGancztDQECUlJSgoKFCqc3R0LHedkyZNwuTJkwE8OQIUERFRCyMpR2HNFrty2abGqzQyk1dab2lpiczM8u+uuj5gWI3XG/Ecy9aLGu4LoOb7g/uiAg2wL4DK9wf3RfU1xL4Aar4/uC/K1xD7Aqi7/eHg4FBhXb0kNfn5+TA2NlYqMzExQV5ensrt8vPzoa2tDX19fRQWFlbYx1OBgYEIDPx3PusgIiICHh4eDR0GgfviRcJ98eLgvnhxqNu+qJfTT3FxcTA3N1e6qLdNmzaIjo5WahcdHQ03NzdoaWkBAHR0dODq6oro6GjIZDI8ePAAbdq0qbQPIiIi+neql6QmLy8Phw8fxqJFi6Cnp4eRI0fCxMQEZ86cUWoXGxuLhIQEzJ49G7q6upg9ezbi4+Nx69YtAMCOHTuwcOFCGBoaonfv3vDy8sL+/fvrYwhERET0EhD1MVlbW4ujR4+K/Px8cfPmTdG5c2cBQMyfP1+sXbtWaufi4iLCw8PFw4cPxcWLF4WLi4tU16hRIxEcHCzy8vJEQkKCGDRoUL3E/rJNkyZNavAYOHFfvGgT98WLM3FfvDiTuu0Ljf/7BxEREdFLja9JICIiIrXApIbU3s2bN9GpU6eGDoNeQJs2bcLcuXMbOgyicjk4OCg9xqQ+jBkzBqGhofW6ztrEpKaWJCYmlrnwuabCwsIwbJjq9/cLIdCkSZMarWvTpk0oLCyEQqFAZmYm9u/fj6ZNm6q87Ivwg5CYmIj8/HxpDDt27FB6NECbNm1w+fLlBoywesLCwlBQUACFQoG0tDQEBQWVeaZTZctW52+nLlW1XyrzIvxtvez74UXd/s/7oymEQF5eHhQKBZKSkrBw4cJqLVvT78ra8s/98nQaPnw4gBfj774yvr6+KCoqgkKhgEwmw6lTp9CqVSuVl127dm0dR8ikplZ069YN5ubm6NKlC2xtbRs6nGrz9fWFsbExWrZsCQMDA6xYsaKhQ6q2N998E8bGxnB0dISpqSnmzZvX0CE9l7Fjx8LY2Bjt2rVDu3btMH/+/IYOqUZe9v3ysu+Hhtj+Tx/JUZecnJxgbGyMIUOGYP78+ejbt2+dr7M2Pd0vT6cdO3Y0dEgq27hxI4yNjWFjY4O7d+9iw4YNDR2SEiY1tWDEiBHYvXs3zp49i48++kip7sMPP0RkZCTkcjlu3LghvcfKwcEBISEhyMrKws2bN/Hmm28CAObPn4/u3btj8+bNUCgUmDFjBjQ0NLBv3z6kpaUhKysLO3bsgImJCQAgJCQEAHD79m0oFArpNMu0adMQFxeHjIwMbNiwAfr6+lWOQy6XY//+/ejQoYNUNnHiRNy6dQtyuRxXr15F165dATx5D9eIESOwZMkSKBQK+Pv7AwDatWuHP/74A9nZ2bh06RLat2//HFu2+p4+PuCf/3tITEyU3gpvbm6OXbt2ITMzE7GxsRg6dKjULiwsDIsXL8bVq1ehUCjw7bffwsnJCX/++SdkMplSsufp6YmIiAjk5uYiMTFReno1AHh5eeHatWuQy+VISkrC6NGjKy2vTFpaGo4dO6a0T7744gskJSUhNzcXZ8+elcZa3t8OAPTs2RNXrlyBTCbDyZMnK30aZ10pb788+7/mmJgY9OjRo8K/rTVr1iA1NRUymQwhISFo1qyZUl9TpkxBUlIS0tLSlPZHZftcVS/7fnje7b9+/XqlIyKampq4f/8+3N3dMWbMGBw/fhyBgYHIzc2Fj49PhZ8POzs7BAQEoE+fPlAoFNIRVAsLCwQHByM9PR3x8fHw8fFRaVxXrlxBVFSUtF/q67uyLlT0dw8AU6ZMQVpaGpKTk/HOO+9I5RV9PwNPvs++/PJL/PXXX5DJZFi/fr1Up6mpia+//hr37t3DvXv38PXXX0NTs3rpwKNHj/Dbb78pfSbeffdd6ffu77//xuDBgwE8+Y//ggULMGHCBCgUCuzbtw9Axb+Dz6vBb8F6mSdtbW2RmZkp+vTpIz7++GNx7do1qa5Lly4iLS1NeHp6CgDCzc1N2NjYCE1NTREZGSk++eQToaWlJbp16ybS09OFubm5ACDCwsLEsGHD/v8tahoaYsSIEcLAwECYmZmJU6dOieXLl0v1QgjRpEkTaf7DDz8U165dE3Z2dsLAwEDs3r1bfPXVV+XGv2nTJjF37lwBQJibm4sTJ06IgwcPSvUDBgwQtra2QlNTU3z66afi7t27QktLq8yyAISRkZFISUkRgwcPFpqammLo0KHi9u3bQltbu073QWJiovSIABMTExEaGqoU1z/rd+zYITZt2iT09PRE586dRU5OjnjllVek7X7jxg3RtGlT4eDgIBQKhThx4oSwtbUVzZo1E5mZmaJdu3YCgOjYsaN49dVXhYaGhujcubOQy+WiVatWAoAIDw8XPj4+AoCwsLAQ7u7ulZY/O/1z/zdr1kzcuHFD/Pzzz1L9kCFDhKWlpdDW1hbffvutCA8PL3dZAMLe3l6kp6eLLl26CE1NTTFr1ixx9uzZevlsVLVfnv27jYmJET169Cj3bwuA+Oijj4SxsbHQ19cX27ZtE8HBwUp9bdu2TRgYGIhu3bqJ/Px8YWpqWuU+L2896rIfanP79+jRQ0RGRkrzffr0kb7rxowZI4qLi8WIESOEhoaG0NPTq/TzMWbMGBEaGqoUa0hIiFi+fLnQ0dER7u7u4v79+8LV1bXccf0z7tdff13k5+eLIUOGCKBuvyvrYr88Oz273R0cHERpaan4/vvvhY6Ojhg5cqRISUmR6iv7fn76fda8eXNhbW0t7t27J/r16ycAiMmTJ4urV6+KJk2aCBsbG3H9+nXp1u7y9s/TydfXV3oES6NGjURQUJC4ceOG0t+Jq6ur0NDQEO+++67Izc0VjRs3LrMsgCp/B59jqvsvN3We3nnnHZGWliY0NTWFpaWlKC4uln6s1q9fLxYvXlxmGU9PTxEVFaVUdujQIelL8NkvxGend999V+kL8dkP6tGjR8XQoUOl+Y4dO4qYmJhy+9q0aZN4+PChyMnJEUIIERUVJRwdHStcd1ZWlnBycpKW/ecH0MfHRxw5ckSpfWRkZIUf4NqaEhMThVwuFzKZTJSUlIioqChhaWmpVN+5c2ehqakpioqKRPPmzaW6jRs3ikWLFknbfdq0aVLdmTNnxJw5c6T5w4cPi5EjR5Ybw759+8SECRMEAPHHH3+IhQsXSj+qT6eKyp+dwsLCRF5ensjNzRVCCHHu3Dnpi+HZycTERJSUlAgdHZ1y/3bmzZsn1qxZo7RMbm6u0t9LQ+2X6iY1/5zatWsnkpOTlfpq27atNJ+UlCQ6depU5T6vKql5mfdDbW5/DQ0NcffuXdG6dWsBQGzYsEGqHzNmjLh582alsfzz8/Hsj6aNjY3Izc0VmpqaUtmqVasq3C9CCJGTkyPy8/OFEEKsWbNGaGholNu2Nr8r62K/PJ3at29f7nZ3cHAQQghhbGwsAAgtLS1RUlKitB//Of3z+/nZ77MtW7aI2bNnCwDi1KlTYsSIEVLdqFGjxKlTp8rdP/+cfH19RWFhoZDJZKK0tFTcu3dPvPrqqxWO9a+//hK9e/eWlv1nUlPV72BNJ55+ek4jRozA3r178fjxY2RmZuLMmTMYMWIEAMDW1haJiYlllrGzs4OLiwtkMpk09erVq8ILdLW0tODv7487d+4gNzcX27dvh4WFRYUx2dnZYePGjVLfp06dgpWVVYXtlyxZAjMzM7zyyito1KgR7O3tpbrBgwdLh8xlMhnMzMwqXLednR169+6tNK6WLVsqnSaoK3379oW5uTkaNWqEgwcPlnshopWVFTQ1NZGSkiKVJSUlKW33f75JvqCgoMy8oaEhgCdvlD9+/DjS09ORk5ODgQMHSttl0qRJePXVV5GYmIhTp05Jr/aoqLw8EyZMgKmpKbp06QJHR0dYWlpKdRMnTkRUVBRycnKQlJQELS0tmJubl9uPnZ0dxo0bp7RPdHR00Lx580q3Z21RZb+oauHChYiLi5NO9zz7d/jPffXw4UMYGhqqtM8r87Lvh9ra/kIIBAcH46OPPoKOjg7ef/997Ny5U6q/d++eUvvKPh/PsrOzg6GhIbKysqRtM3bsWNjYVPwCRzc3NxgZGeHTTz9Ft27doKurC6Duvytry9P98nS6fv16hW2f3sgBAKWlpSguLpa+h6r6fi7vMwEAzZo1Q3JyslRXnc/Epk2bYG5uDjs7O2RmZiqd0uzatSvOnz8v7cu2bdtWut+r8zuoKiY1z8HIyAjvvPMOhg8fjvv37+P+/fvw8vKSrqtJTk4u9y3iKSkpiIyMVPqjNjIywo8//gjgyRfIP40YMQLdu3eHl5cXTE1NMWLECGhoaFQYV0pKCkaMGCH1bWZmpvRlXJHY2FgsWrRIikNXVxfBwcGYO3cuLCwsYG5ujtzcXGndz8aZkpKCI0eOKI3L0NAQv//+e5Xrri1FRUXYunUrOnXqVObDlJGRgcePHyv9kNjb2+P+/fvVXs/q1atx7tw52Nvbw8zMDEeOHJG2S2xsLD744ANYW1vjjz/+QEBAQKXllQkPD0dAQAC+++47AE/OQf/www8YOXIkzMzMpOsyKtsna9euVdonjRo1wl9//VXtMT+P8vZLfn4+DAwMpDb//DF5dhxvvPEGJk+ejP79+8PU1BTdu3ev9DPwVG3t85d9Pzzv9geA7du3Y9iwYejfvz/+/vtvJCUlVdi+ss9HedsmJydHadsYGxtL1yJVRAiBX375BRkZGZgyZQqA+vuurCvlbfeKVPX9XJnU1FTY2dlJ8zX5TKSmpuKzzz7DihUroKenBwDYunUrNmzYgCZNmsDc3ByRkZGV7vfKfgdriknNcxgyZAjS0tLg5uaGDh06oEOHDmjTpg2srKzQpUsXbNmyBVOnTsXrr78OAHB1dYWNjQ0uXboEHR0dTJw4Edra2tDV1cUbb7whZajp6elKyZCxsTEKCwshk8lgZWWFmTNnKsWRlpam1P7XX3/FwoULpbKmTZuqfHdAcHAwLC0t8fbbb0NPTw+6urrIyMiAhoYG5syZI110V16chw8fRqdOnTBo0CBoamrCwMAAAwYMQKNGjaqxVZ+PtrY2hg8fLl0o+E+PHz/Gvn37sHTpUujp6cHDwwPe3t7Ys2dPtddjbGwMmUyGwsJC9OzZE/369ZPqPvroI5iZmaGkpAR5eXl4/PhxpeVVWbNmDfr06QN3d3cYGRnh8ePHyMjIgK6uLhYvXqzU9tl9smPHDgwbNgxdunSR4n7//ferPd7nVd5+iYyMhI+PDzQ1NTF9+nSYmppK7cv7DBQXFyMrKwtGRkZYsGCBSuutzX3+Mu+H593+AHDjxg0UFhbim2++QXBwcKXrq+zzkZ6eDltbW+nC1NTUVERERGDx4sXQ19eHlpYWOnbsKN1UURU/Pz/Mnj0bOjo69fpdWRfK2+4Vqer7uTK7d+/GrFmzYG1tjSZNmmD27Nn47bffqh3vuXPnkJSUhPHjxwN4st+zsrJQWloKHx8fpaPR6enpShfHV/U7WFNMap7DiBEjsGHDBqSlpUnTnTt3EBwcjBEjRuDChQuYMWMGNm/eDLlcjt27d8PExASlpaUYNGgQBg4ciPv37+PevXuYO3eu9CFfs2YNJk+eDJlMhunTpyMoKAgKhQIZGRk4c+ZMmUPIy5Ytw4EDByCTydCxY0fs3LkTmzdvRmhoKHJzc3H69GmVvyBKS0vx888/Y/78+VAoFJgzZw5OnTqF+/fvQ19fX+mQ5ebNm9GtWzdkZ2dj5cqVkMvlGDRoEP7zn/8gIyMDCQkJGDduXO1t8EqcPn1aeh5Hr1698N5775Xb7tNPP4WJiQlSU1Oxfft2TJ06FTExMdVe39y5czF9+nTI5XJ88sknOHLkiFQ3cOBA6TSJt7c3Pvvss0rLqyKTybBlyxbMmzcPUVFR2LBhA27evImEhIQyb6l/9m/nzp07GD58OFauXAmZTIbo6GgMGDCg2uOtqcr2y4wZMzB+/HhkZmbC3Nwc8fHxUt2zf1tHjx7FxYsXcffuXVy/fh3nzp1TOYba2ucv436ore3/1Pbt2/HKK69U+QNY2ecjLCwMqampyMzMxKVLlwA8+S51dHREYmIi0tPTlf73X5Vjx44hJycHo0aNqtfvyufxdL88nebMmQOg4u1enqq+nyuzfv16HD16FFevXsVff/2FkJAQBAYG1mgsK1euxJw5c6ClpYVp06Zh7dq1yM7OxhtvvIHw8HCp3b59+9C4cWNkZ2dj9+7dVf4O1hTf/URERCoZPnw4xowZg7feequhQyEqF4/UEBFRlfT09DBlyhT8+uuvDR0KUYWY1BARUaU6duwo3dGye/fuhg6HqEI8/URERERqgUdqiIiISC0wqSEiIiK1wKSGiIiI1AKTGiIiIlILTGqIiIhILTCpISIiIrXw/wAqAHmM70q3KgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(gases_feature_importance_df)\n",
+    "\n",
+    "# This helps with formatting \n",
+    "x = np.arange(5)  # the label locations\n",
+    "width = 0.15  # the width of the bars\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "for counter, (index, row) in enumerate(gases_feature_importance_df.transpose().iterrows()):\n",
+    "    output_feature_importances = list(row)\n",
+    "    if index != 'flow rate':\n",
+    "        index = index + ' Concentration' \n",
+    "    else:\n",
+    "        index = 'Flow Rate'\n",
+    "    rects = ax.bar(x - 0.3 + 0.15*counter, output_feature_importances , width, label=f'{index}')\n",
+    "\n",
+    "# # Add some text for labels, title and custom x-axis tick labels, etc.\n",
+    "ax.set_ylabel(f'Importance')\n",
+    "\n",
+    "ax.set_title(f'Feature Importance of Gases')\n",
+    "ax.set_xticks(x)\n",
+    "labels = ['Acetate Rate', 'Biomass Rate', 'Butanol Rate', 'Butyrate Rate', 'Ethanol Rate']\n",
+    "ax.set_xticklabels(labels)\n",
+    "ax.legend()\n",
+    "# ax.set_yscale('log')\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.savefig(f'../figures/feature_importance_gases.png', dpi=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot feature importance of each gas for each output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acetate</th>\n",
+       "      <td>0.002198</td>\n",
+       "      <td>0.012518</td>\n",
+       "      <td>0.002577</td>\n",
+       "      <td>0.009944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>biomass</th>\n",
+       "      <td>0.010512</td>\n",
+       "      <td>0.009644</td>\n",
+       "      <td>0.015882</td>\n",
+       "      <td>0.012827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butanol</th>\n",
+       "      <td>0.002354</td>\n",
+       "      <td>0.002810</td>\n",
+       "      <td>0.100623</td>\n",
+       "      <td>0.020277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>butyrate</th>\n",
+       "      <td>0.091786</td>\n",
+       "      <td>0.002317</td>\n",
+       "      <td>0.002280</td>\n",
+       "      <td>0.003468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ethanol</th>\n",
+       "      <td>0.005121</td>\n",
+       "      <td>0.006199</td>\n",
+       "      <td>0.003100</td>\n",
+       "      <td>0.007709</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                CO       CO2        H2  flow rate\n",
+       "acetate   0.002198  0.012518  0.002577   0.009944\n",
+       "biomass   0.010512  0.009644  0.015882   0.012827\n",
+       "butanol   0.002354  0.002810  0.100623   0.020277\n",
+       "butyrate  0.091786  0.002317  0.002280   0.003468\n",
+       "ethanol   0.005121  0.006199  0.003100   0.007709"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(gases_feature_importance_df)\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 11.5})\n",
+    "fig, ax = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "fig.suptitle(f'Feature importance of each gas for each output')\n",
+    "\n",
+    "labels = [label.capitalize() for label in gases_feature_importance_df.index]\n",
+    "\n",
+    "ax[0,0].title.set_text(f'Importance of CO on Metabolite Production Rate')\n",
+    "ax[0,0].set_ylabel(f'Importance')\n",
+    "ax[0,0].axis(ymin=0.0,ymax=0.07)\n",
+    "rects = ax[0,0].bar(labels, gases_feature_importance_df['CO'])\n",
+    "\n",
+    "ax[0,1].title.set_text(f'Importance of CO2 on Metabolite Production Rate')\n",
+    "ax[0,1].set_ylabel(f'Importance')\n",
+    "ax[0,1].axis(ymin=0.0,ymax=0.07)\n",
+    "rects = ax[0,1].bar(labels, gases_feature_importance_df['CO2'])\n",
+    "\n",
+    "ax[1,0].title.set_text(f'Importance of H2 on Metabolite Production Rate')\n",
+    "ax[1,0].set_ylabel(f'Importance')\n",
+    "ax[1,0].axis(ymin=0.0,ymax=0.07)\n",
+    "rects = ax[1,0].bar(labels, gases_feature_importance_df['H2'])\n",
+    "\n",
+    "ax[1,1].title.set_text(f'Importance of Flow Rate on Metabolite Production Rate')\n",
+    "ax[1,1].set_ylabel(f'Importance')\n",
+    "ax[1,1].axis(ymin=0.0,ymax=0.07)\n",
+    "rects = ax[1,1].bar(labels, gases_feature_importance_df['flow rate'])\n",
+    "\n",
+    "plt.savefig(f'../figures/feature_importance_per_gas.png', dpi=100)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save gases_feature_importance as csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gases_feature_importance_df.to_csv('../data/gases_importance.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### -- Stopping here --"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.9 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b3f28f6adfd1eea15c9e00a250e9367c9752811d529c8274081719d51bde14e5"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/notebooks/D_time_course.ipynb b/data/notebooks/D_time_course.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..be2c1d807de0db727049572ce783ce2e027e40d9
--- /dev/null
+++ b/data/notebooks/D_time_course.ipynb
@@ -0,0 +1,2290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notebook D: Plotting time course curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from itertools import *\n",
+    "import sklearn\n",
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load trained models to a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dictionary to hold all trained models\n",
+    "models = {}\n",
+    "\n",
+    "for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "    models[algorithm] = {}\n",
+    "    for output in ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']:\n",
+    "        filename = f'../trained_models/{output}/{algorithm}.pkl'\n",
+    "        models[algorithm][output] = pickle.load(open(filename,'rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load smoothed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the rates data: 836 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "rates_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the rates data: {rates_df.shape[0]} rows by {rates_df.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit Min Max Scaler to match scaler that was applied to training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MinMaxScaler()"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = rates_df[rates_df.composition.isin([1,2,3,4,5,6,7])]\n",
+    "X = train_data [[\n",
+    "    'biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', \n",
+    "    'butyrate (mM)', 'N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)'\n",
+    "]]\n",
+    "\n",
+    "Scaler = sklearn.preprocessing.MinMaxScaler()\n",
+    "Scaler.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get list of unique trials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = []\n",
+    "\n",
+    "for index, row in rates_df.iterrows():\n",
+    "\n",
+    "    # add unique trials to list as a tuple\n",
+    "    trial = (row['composition'], row['trial'])\n",
+    "    \n",
+    "    if (trial not in trials):\n",
+    "        trials.append(trial)\n",
+    "\n",
+    "len(trials)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate simulated data for each trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_fine\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.432014</td>\n",
+       "      <td>15.479542</td>\n",
+       "      <td>16.906172</td>\n",
+       "      <td>0.038598</td>\n",
+       "      <td>0.056240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434586</td>\n",
+       "      <td>15.085316</td>\n",
+       "      <td>16.943084</td>\n",
+       "      <td>0.049784</td>\n",
+       "      <td>0.042031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437249</td>\n",
+       "      <td>14.688834</td>\n",
+       "      <td>17.014004</td>\n",
+       "      <td>0.063562</td>\n",
+       "      <td>0.027509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440008</td>\n",
+       "      <td>14.290437</td>\n",
+       "      <td>17.132495</td>\n",
+       "      <td>0.080086</td>\n",
+       "      <td>0.012624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.426869</td>\n",
+       "      <td>85.963025</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.168106</td>\n",
+       "      <td>0.414633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.422695</td>\n",
+       "      <td>87.125709</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.263269</td>\n",
+       "      <td>0.360459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.418362</td>\n",
+       "      <td>88.245367</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.350965</td>\n",
+       "      <td>0.304709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.413871</td>\n",
+       "      <td>89.325630</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.430966</td>\n",
+       "      <td>0.247447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.409225</td>\n",
+       "      <td>90.370557</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.503077</td>\n",
+       "      <td>0.188738</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432014     15.479542     16.906172   \n",
+       "2            1.0    1.0   0.2       0.434586     15.085316     16.943084   \n",
+       "3            1.0    1.0   0.3       0.437249     14.688834     17.014004   \n",
+       "4            1.0    1.0   0.4       0.440008     14.290437     17.132495   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.426869     85.963025      0.000000   \n",
+       "832         10.0    2.0   3.6       0.422695     87.125709      0.000000   \n",
+       "833         10.0    2.0   3.7       0.418362     88.245367      0.000000   \n",
+       "834         10.0    2.0   3.8       0.413871     89.325630      0.000000   \n",
+       "835         10.0    2.0   3.9       0.409225     90.370557      0.000000   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.038598       0.056240  \n",
+       "2        0.049784       0.042031  \n",
+       "3        0.063562       0.027509  \n",
+       "4        0.080086       0.012624  \n",
+       "..            ...            ...  \n",
+       "831      5.168106       0.414633  \n",
+       "832      5.263269       0.360459  \n",
+       "833      5.350965       0.304709  \n",
+       "834      5.430966       0.247447  \n",
+       "835      5.503077       0.188738  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_coarse\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.431311</td>\n",
+       "      <td>15.569167</td>\n",
+       "      <td>17.028067</td>\n",
+       "      <td>0.032532</td>\n",
+       "      <td>0.082760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.433155</td>\n",
+       "      <td>15.268922</td>\n",
+       "      <td>17.166065</td>\n",
+       "      <td>0.037896</td>\n",
+       "      <td>0.097939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.435060</td>\n",
+       "      <td>14.970760</td>\n",
+       "      <td>17.307119</td>\n",
+       "      <td>0.046235</td>\n",
+       "      <td>0.115877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.437029</td>\n",
+       "      <td>14.675046</td>\n",
+       "      <td>17.450478</td>\n",
+       "      <td>0.057844</td>\n",
+       "      <td>0.136749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.466077</td>\n",
+       "      <td>67.614329</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.159369</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.464660</td>\n",
+       "      <td>68.306439</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.369557</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.463139</td>\n",
+       "      <td>68.933360</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.580341</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.461517</td>\n",
+       "      <td>69.507298</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.791943</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.459796</td>\n",
+       "      <td>70.039844</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11.004542</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.431311     15.569167     17.028067   \n",
+       "2            1.0    1.0   0.2       0.433155     15.268922     17.166065   \n",
+       "3            1.0    1.0   0.3       0.435060     14.970760     17.307119   \n",
+       "4            1.0    1.0   0.4       0.437029     14.675046     17.450478   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.466077     67.614329      0.000000   \n",
+       "832         10.0    2.0   3.6       0.464660     68.306439      0.000000   \n",
+       "833         10.0    2.0   3.7       0.463139     68.933360      0.000000   \n",
+       "834         10.0    2.0   3.8       0.461517     69.507298      0.000000   \n",
+       "835         10.0    2.0   3.9       0.459796     70.039844      0.000000   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.032532       0.082760  \n",
+       "2        0.037896       0.097939  \n",
+       "3        0.046235       0.115877  \n",
+       "4        0.057844       0.136749  \n",
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+       "835     11.004542       0.000000  \n",
+       "\n",
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+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
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+       "4            1.0    1.0   0.4       0.424173     15.830296     22.804078   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
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+       "835         10.0    2.0   3.9       1.145986     67.863833     27.382335   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
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+       "834     17.830097       0.000000  \n",
+       "835     18.667287       0.000000  \n",
+       "\n",
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+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
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+       "835         10.0    2.0   3.9       0.680120     59.687172     24.192422   \n",
+       "\n",
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+       "834     12.277473       3.330053  \n",
+       "835     12.767437       3.194768  \n",
+       "\n",
+       "[836 rows x 8 columns]"
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.614279</td>\n",
+       "      <td>50.754610</td>\n",
+       "      <td>39.717259</td>\n",
+       "      <td>7.176111</td>\n",
+       "      <td>2.763627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.620437</td>\n",
+       "      <td>52.506765</td>\n",
+       "      <td>39.330646</td>\n",
+       "      <td>7.604363</td>\n",
+       "      <td>2.747658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.626539</td>\n",
+       "      <td>54.296126</td>\n",
+       "      <td>38.891499</td>\n",
+       "      <td>8.048661</td>\n",
+       "      <td>2.720214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.632580</td>\n",
+       "      <td>56.122346</td>\n",
+       "      <td>38.400341</td>\n",
+       "      <td>8.509246</td>\n",
+       "      <td>2.680408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.638557</td>\n",
+       "      <td>57.985069</td>\n",
+       "      <td>37.857799</td>\n",
+       "      <td>8.986358</td>\n",
+       "      <td>2.627331</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432037     16.013903     18.260049   \n",
+       "2            1.0    1.0   0.2       0.434644     16.182674     19.591642   \n",
+       "3            1.0    1.0   0.3       0.437344     16.377525     20.886915   \n",
+       "4            1.0    1.0   0.4       0.440132     16.598493     22.144498   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.614279     50.754610     39.717259   \n",
+       "832         10.0    2.0   3.6       0.620437     52.506765     39.330646   \n",
+       "833         10.0    2.0   3.7       0.626539     54.296126     38.891499   \n",
+       "834         10.0    2.0   3.8       0.632580     56.122346     38.400341   \n",
+       "835         10.0    2.0   3.9       0.638557     57.985069     37.857799   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.107784       0.234883  \n",
+       "2        0.193432       0.405254  \n",
+       "3        0.287082       0.581064  \n",
+       "4        0.389010       0.762065  \n",
+       "..            ...            ...  \n",
+       "831      7.176111       2.763627  \n",
+       "832      7.604363       2.747658  \n",
+       "833      8.048661       2.720214  \n",
+       "834      8.509246       2.680408  \n",
+       "835      8.986358       2.627331  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lasso\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
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+       "      <th>butyrate (mM)</th>\n",
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+       "      <th>0</th>\n",
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+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.1</td>\n",
+       "      <td>0.432036</td>\n",
+       "      <td>16.016089</td>\n",
+       "      <td>18.596465</td>\n",
+       "      <td>0.107784</td>\n",
+       "      <td>0.235610</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434674</td>\n",
+       "      <td>16.192511</td>\n",
+       "      <td>20.240359</td>\n",
+       "      <td>0.194157</td>\n",
+       "      <td>0.407948</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437434</td>\n",
+       "      <td>16.400294</td>\n",
+       "      <td>21.822592</td>\n",
+       "      <td>0.289261</td>\n",
+       "      <td>0.586894</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440308</td>\n",
+       "      <td>16.639262</td>\n",
+       "      <td>23.340638</td>\n",
+       "      <td>0.393371</td>\n",
+       "      <td>0.772123</td>\n",
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+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.612423</td>\n",
+       "      <td>50.776583</td>\n",
+       "      <td>33.701417</td>\n",
+       "      <td>7.248235</td>\n",
+       "      <td>2.741759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.617981</td>\n",
+       "      <td>52.438738</td>\n",
+       "      <td>32.695054</td>\n",
+       "      <td>7.664781</td>\n",
+       "      <td>2.701489</td>\n",
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+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.623429</td>\n",
+       "      <td>54.126142</td>\n",
+       "      <td>31.621313</td>\n",
+       "      <td>8.095096</td>\n",
+       "      <td>2.647123</td>\n",
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+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.628766</td>\n",
+       "      <td>55.837890</td>\n",
+       "      <td>30.482708</td>\n",
+       "      <td>8.539261</td>\n",
+       "      <td>2.577708</td>\n",
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+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
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+       "      <td>3.9</td>\n",
+       "      <td>0.633986</td>\n",
+       "      <td>57.573084</td>\n",
+       "      <td>29.281987</td>\n",
+       "      <td>8.997349</td>\n",
+       "      <td>2.492283</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
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+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432036     16.016089     18.596465   \n",
+       "2            1.0    1.0   0.2       0.434674     16.192511     20.240359   \n",
+       "3            1.0    1.0   0.3       0.437434     16.400294     21.822592   \n",
+       "4            1.0    1.0   0.4       0.440308     16.639262     23.340638   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.612423     50.776583     33.701417   \n",
+       "832         10.0    2.0   3.6       0.617981     52.438738     32.695054   \n",
+       "833         10.0    2.0   3.7       0.623429     54.126142     31.621313   \n",
+       "834         10.0    2.0   3.8       0.628766     55.837890     30.482708   \n",
+       "835         10.0    2.0   3.9       0.633986     57.573084     29.281987   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.107784       0.235610  \n",
+       "2        0.194157       0.407948  \n",
+       "3        0.289261       0.586894  \n",
+       "4        0.393371       0.772123  \n",
+       "..            ...            ...  \n",
+       "831      7.248235       2.741759  \n",
+       "832      7.664781       2.701489  \n",
+       "833      8.095096       2.647123  \n",
+       "834      8.539261       2.577708  \n",
+       "835      8.997349       2.492283  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
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+     "text": [
+      "knn\n"
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+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
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+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
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+       "      <td>16.893540</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.427040</td>\n",
+       "      <td>15.740754</td>\n",
+       "      <td>17.044157</td>\n",
+       "      <td>0.028288</td>\n",
+       "      <td>0.057515</td>\n",
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+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.422349</td>\n",
+       "      <td>15.524351</td>\n",
+       "      <td>17.650487</td>\n",
+       "      <td>0.026712</td>\n",
+       "      <td>0.045338</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.414849</td>\n",
+       "      <td>15.232301</td>\n",
+       "      <td>18.707065</td>\n",
+       "      <td>0.025136</td>\n",
+       "      <td>0.032566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.406761</td>\n",
+       "      <td>14.861683</td>\n",
+       "      <td>20.092511</td>\n",
+       "      <td>0.024390</td>\n",
+       "      <td>0.021919</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>63.136891</td>\n",
+       "      <td>22.700234</td>\n",
+       "      <td>6.889430</td>\n",
+       "      <td>2.307406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>63.788829</td>\n",
+       "      <td>21.920976</td>\n",
+       "      <td>7.064213</td>\n",
+       "      <td>2.377510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>64.440693</td>\n",
+       "      <td>21.141714</td>\n",
+       "      <td>7.238995</td>\n",
+       "      <td>2.447619</td>\n",
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+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>65.092487</td>\n",
+       "      <td>20.362448</td>\n",
+       "      <td>7.413776</td>\n",
+       "      <td>2.517733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>65.744211</td>\n",
+       "      <td>19.583178</td>\n",
+       "      <td>7.588555</td>\n",
+       "      <td>2.587853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.427040     15.740754     17.044157   \n",
+       "2            1.0    1.0   0.2       0.422349     15.524351     17.650487   \n",
+       "3            1.0    1.0   0.3       0.414849     15.232301     18.707065   \n",
+       "4            1.0    1.0   0.4       0.406761     14.861683     20.092511   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.525739     63.136891     22.700234   \n",
+       "832         10.0    2.0   3.6       0.525739     63.788829     21.920976   \n",
+       "833         10.0    2.0   3.7       0.525739     64.440693     21.141714   \n",
+       "834         10.0    2.0   3.8       0.525739     65.092487     20.362448   \n",
+       "835         10.0    2.0   3.9       0.525739     65.744211     19.583178   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.028288       0.057515  \n",
+       "2        0.026712       0.045338  \n",
+       "3        0.025136       0.032566  \n",
+       "4        0.024390       0.021919  \n",
+       "..            ...            ...  \n",
+       "831      6.889430       2.307406  \n",
+       "832      7.064213       2.377510  \n",
+       "833      7.238995       2.447619  \n",
+       "834      7.413776       2.517733  \n",
+       "835      7.588555       2.587853  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bayesian\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.432038</td>\n",
+       "      <td>16.016624</td>\n",
+       "      <td>18.511826</td>\n",
+       "      <td>0.110446</td>\n",
+       "      <td>0.232064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434673</td>\n",
+       "      <td>16.192371</td>\n",
+       "      <td>20.062051</td>\n",
+       "      <td>0.199193</td>\n",
+       "      <td>0.400361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437422</td>\n",
+       "      <td>16.398086</td>\n",
+       "      <td>21.541751</td>\n",
+       "      <td>0.296357</td>\n",
+       "      <td>0.574726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440278</td>\n",
+       "      <td>16.633409</td>\n",
+       "      <td>22.948622</td>\n",
+       "      <td>0.402182</td>\n",
+       "      <td>0.754789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.600663</td>\n",
+       "      <td>47.392041</td>\n",
+       "      <td>25.945417</td>\n",
+       "      <td>6.488046</td>\n",
+       "      <td>1.974172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.605871</td>\n",
+       "      <td>48.858047</td>\n",
+       "      <td>25.009363</td>\n",
+       "      <td>6.849406</td>\n",
+       "      <td>1.906514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.610998</td>\n",
+       "      <td>50.346140</td>\n",
+       "      <td>24.033811</td>\n",
+       "      <td>7.222358</td>\n",
+       "      <td>1.826244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.616041</td>\n",
+       "      <td>51.855843</td>\n",
+       "      <td>23.022166</td>\n",
+       "      <td>7.606991</td>\n",
+       "      <td>1.732648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.621002</td>\n",
+       "      <td>53.386715</td>\n",
+       "      <td>21.978031</td>\n",
+       "      <td>8.003397</td>\n",
+       "      <td>1.625010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432038     16.016624     18.511826   \n",
+       "2            1.0    1.0   0.2       0.434673     16.192371     20.062051   \n",
+       "3            1.0    1.0   0.3       0.437422     16.398086     21.541751   \n",
+       "4            1.0    1.0   0.4       0.440278     16.633409     22.948622   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.600663     47.392041     25.945417   \n",
+       "832         10.0    2.0   3.6       0.605871     48.858047     25.009363   \n",
+       "833         10.0    2.0   3.7       0.610998     50.346140     24.033811   \n",
+       "834         10.0    2.0   3.8       0.616041     51.855843     23.022166   \n",
+       "835         10.0    2.0   3.9       0.621002     53.386715     21.978031   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.110446       0.232064  \n",
+       "2        0.199193       0.400361  \n",
+       "3        0.296357       0.574726  \n",
+       "4        0.402182       0.754789  \n",
+       "..            ...            ...  \n",
+       "831      6.488046       1.974172  \n",
+       "832      6.849406       1.906514  \n",
+       "833      7.222358       1.826244  \n",
+       "834      7.606991       1.732648  \n",
+       "835      8.003397       1.625010  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# algorithm = 'rf'\n",
+    "\n",
+    "time_step = 0.1\n",
+    "\n",
+    "outputs = ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']\n",
+    "concentration_labels = ['biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', 'butyrate (mM)']\n",
+    "gas_labels = ['N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)']\n",
+    "\n",
+    "for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "    print(algorithm)\n",
+    "    # save output data as a list of dictionaries. Will be converted to data frame below\n",
+    "    output_data = []\n",
+    "\n",
+    "    for trial in trials:\n",
+    "        # print(trial)\n",
+    "\n",
+    "        comp_num = trial[0]\n",
+    "        trial_num = trial[1]\n",
+    "\n",
+    "        # get trial data\n",
+    "        trial_data = rates_df[(rates_df['composition'] == comp_num)  & (rates_df['trial'] == trial_num)]\n",
+    "\n",
+    "        # get max time\n",
+    "        max_time = round(max(trial_data['time']), 1)\n",
+    "\n",
+    "        # get initial concentrations and gas composition for trial\n",
+    "        initial_values = trial_data[trial_data['time'] == 0]\n",
+    "\n",
+    "        # get the row number of the initial values\n",
+    "        index = initial_values.index[0]\n",
+    "\n",
+    "        # make list of gas values\n",
+    "        gas_values = [initial_values[label][index]  for label in gas_labels]\n",
+    "\n",
+    "        # make list of inital concentration values\n",
+    "        concentration_values = [initial_values[label][index]  for label in concentration_labels]\n",
+    "\n",
+    "        # loop over all time points of each trial\n",
+    "        time = 0\n",
+    "        while (round(time, 1) <= max_time):\n",
+    "            # add current data to output_data list\n",
+    "            output_data.append({\n",
+    "              'composition': comp_num,\n",
+    "              'trial': trial_num,\n",
+    "              'time': time,\n",
+    "              'biomass (g/L)': concentration_values[0],\n",
+    "              'ethanol (mM)': concentration_values[1],\n",
+    "              'acetate (mM)': concentration_values[2],\n",
+    "              'butanol (mM)': concentration_values[3],\n",
+    "              'butyrate (mM)': concentration_values[4],\n",
+    "            })\n",
+    "\n",
+    "            # make array for ml input\n",
+    "            input_values = np.array(list(chain(concentration_values,gas_values))).reshape(1, -1)\n",
+    "\n",
+    "            # Scale values to match how the model was trained\n",
+    "            input_values = Scaler.transform(input_values)\n",
+    "\n",
+    "            # get rates for each output\n",
+    "            rates = [models[algorithm][output].predict(input_values) for output in outputs]\n",
+    "\n",
+    "            # update concentration values -- not allowing for negative concentrations\n",
+    "            concentration_values = [max(concentration + time_step * rate[0], 0) for concentration, rate in zip(concentration_values, rates)]\n",
+    "\n",
+    "            # increment time\n",
+    "            time += time_step\n",
+    "\n",
+    "\n",
+    "    prediction_df = pd.DataFrame(output_data)\n",
+    "\n",
+    "    # save data as csv\n",
+    "    filename = f'../data/rate_time_course_data/{algorithm}.csv'\n",
+    "    prediction_df.to_csv(filename, index=False)\n",
+    "\n",
+    "    display(prediction_df)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function to help calculate r squared and rmse of predicted curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to take a dataframe with two trials and get average values\n",
+    "def average_of_trials(two_trial_df):\n",
+    "\n",
+    "  # get list of unique times\n",
+    "  times = []\n",
+    "  for _, row in two_trial_df.iterrows():\n",
+    "    if row.time not in times:\n",
+    "      times.append(row.time)\n",
+    "\n",
+    "  # for each time, get the average values of the rows\n",
+    "  average_values = []\n",
+    "  for time in times:\n",
+    "    rows = two_trial_df[two_trial_df['time'] == time]\n",
+    "    average_values.append(rows.mean())\n",
+    "\n",
+    "  return pd.DataFrame(average_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get table of r squared values for each composition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r squared data\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nn_fine</th>\n",
+       "      <th>nn_coarse</th>\n",
+       "      <th>svm_rbf</th>\n",
+       "      <th>rf</th>\n",
+       "      <th>en</th>\n",
+       "      <th>lasso</th>\n",
+       "      <th>knn</th>\n",
+       "      <th>bayesian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.851</td>\n",
+       "      <td>0.910</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>0.668</td>\n",
+       "      <td>0.653</td>\n",
+       "      <td>0.839</td>\n",
+       "      <td>0.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.753</td>\n",
+       "      <td>0.909</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>0.632</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.941</td>\n",
+       "      <td>0.929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.617</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>0.969</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.971</td>\n",
+       "      <td>0.946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.946</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.980</td>\n",
+       "      <td>0.977</td>\n",
+       "      <td>0.956</td>\n",
+       "      <td>0.974</td>\n",
+       "      <td>0.997</td>\n",
+       "      <td>0.981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.945</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>0.378</td>\n",
+       "      <td>0.944</td>\n",
+       "      <td>0.343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.864</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.811</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.786</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.789</td>\n",
+       "      <td>0.838</td>\n",
+       "      <td>0.923</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.912</td>\n",
+       "      <td>0.449</td>\n",
+       "      <td>0.904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>0.638</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.716</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.684</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.958</td>\n",
+       "      <td>0.927</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    nn_fine  nn_coarse  svm_rbf     rf     en  lasso    knn  bayesian\n",
+       "1     0.851      0.910    0.820  0.942  0.668  0.653  0.839     0.642\n",
+       "2     0.753      0.909    0.922  0.632  0.873  0.925  0.941     0.929\n",
+       "3     0.896      0.617    0.942  0.969  0.847  0.925  0.971     0.946\n",
+       "4     0.935      0.950    0.867  0.453  0.841  0.819  0.897     0.835\n",
+       "5     0.946      0.992    0.980  0.977  0.956  0.974  0.997     0.981\n",
+       "6     0.945      0.875    0.868  0.818  0.587  0.378  0.944     0.343\n",
+       "7     0.864      0.820    0.811  0.894  0.763  0.786  0.897     0.809\n",
+       "8     0.861      0.789    0.838  0.923  0.892  0.912  0.449     0.904\n",
+       "9     0.208      0.085    0.638  0.891  0.716  0.469  0.016     0.134\n",
+       "10    0.684      0.556    0.873  0.809  0.869  0.896  0.958     0.927"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rmse data\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nn_fine</th>\n",
+       "      <th>nn_coarse</th>\n",
+       "      <th>svm_rbf</th>\n",
+       "      <th>rf</th>\n",
+       "      <th>en</th>\n",
+       "      <th>lasso</th>\n",
+       "      <th>knn</th>\n",
+       "      <th>bayesian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.75</td>\n",
+       "      <td>9.26</td>\n",
+       "      <td>6.31</td>\n",
+       "      <td>4.83</td>\n",
+       "      <td>9.08</td>\n",
+       "      <td>8.87</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>8.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15.19</td>\n",
+       "      <td>8.44</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>10.72</td>\n",
+       "      <td>7.66</td>\n",
+       "      <td>6.82</td>\n",
+       "      <td>7.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.99</td>\n",
+       "      <td>20.87</td>\n",
+       "      <td>7.94</td>\n",
+       "      <td>6.17</td>\n",
+       "      <td>13.62</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>5.22</td>\n",
+       "      <td>7.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.88</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>17.16</td>\n",
+       "      <td>7.22</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>7.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>23.23</td>\n",
+       "      <td>11.36</td>\n",
+       "      <td>2.61</td>\n",
+       "      <td>6.57</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>12.72</td>\n",
+       "      <td>1.23</td>\n",
+       "      <td>14.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.98</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>7.39</td>\n",
+       "      <td>20.54</td>\n",
+       "      <td>13.81</td>\n",
+       "      <td>17.05</td>\n",
+       "      <td>7.28</td>\n",
+       "      <td>17.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.26</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>8.91</td>\n",
+       "      <td>6.20</td>\n",
+       "      <td>9.70</td>\n",
+       "      <td>9.03</td>\n",
+       "      <td>6.14</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32.65</td>\n",
+       "      <td>14.72</td>\n",
+       "      <td>8.57</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>10.82</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>12.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>26.94</td>\n",
+       "      <td>25.58</td>\n",
+       "      <td>16.15</td>\n",
+       "      <td>11.18</td>\n",
+       "      <td>15.56</td>\n",
+       "      <td>19.78</td>\n",
+       "      <td>24.59</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14.17</td>\n",
+       "      <td>14.47</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>8.17</td>\n",
+       "      <td>6.74</td>\n",
+       "      <td>6.02</td>\n",
+       "      <td>4.42</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    nn_fine  nn_coarse  svm_rbf     rf     en  lasso    knn  bayesian\n",
+       "1      6.75       9.26     6.31   4.83   9.08   8.87   8.32      8.76\n",
+       "2     15.19       8.44     8.11  16.67  10.72   7.66   6.82      7.04\n",
+       "3     10.99      20.87     7.94   6.17  13.62   8.33   5.22      7.08\n",
+       "4      4.88       4.30     6.49  17.16   7.22   7.45   5.66      7.09\n",
+       "5     23.23      11.36     2.61   6.57   7.38  12.72   1.23     14.55\n",
+       "6      4.98      14.96     7.39  20.54  13.81  17.05   7.28     17.56\n",
+       "7      7.26       9.30     8.91   6.20   9.70   9.03   6.14      8.49\n",
+       "8     32.65      14.72     8.57   7.00   7.31  10.82  18.75     12.77\n",
+       "9     26.94      25.58    16.15  11.18  15.56  19.78  24.59     23.50\n",
+       "10    14.17      14.47     7.00   8.17   6.74   6.02   4.42      6.07"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_squared_data = {}\n",
+    "rmse_data = {}\n",
+    "for comp_num in range(1,11):\n",
+    "  r_squared_comp_data = {}\n",
+    "  rmse_comp_data = {}\n",
+    "  for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "\n",
+    "    # get the average of experimental concentrations for compositions with multiple trials\n",
+    "    experimental_df = rates_df[rates_df['composition'] == comp_num]\n",
+    "    experimental_df =  average_of_trials(experimental_df)\n",
+    "\n",
+    "    # get the average of predictions for compositions with multiple trials\n",
+    "    filename = f'../data/rate_time_course_data/{algorithm}.csv'\n",
+    "    prediction_df = pd.read_csv(filename)\n",
+    "    prediction_df = prediction_df[prediction_df['composition'] == comp_num]\n",
+    "    prediction_df =  average_of_trials(prediction_df)\n",
+    "\n",
+    "    experimental_concentrations = []\n",
+    "    predicted_concentrations = []\n",
+    "\n",
+    "    for output in ['ethanol (mM)',\t'acetate (mM)',\t'butanol (mM)',\t'butyrate (mM)']:\n",
+    "      experimental_concentrations.extend(list(experimental_df[output]))\n",
+    "      predicted_concentrations.extend(list(prediction_df[output]))\n",
+    "\n",
+    "    _, _, r_value, _, _ = scipy.stats.linregress(experimental_concentrations, predicted_concentrations)\n",
+    "    r_squared_comp_data[algorithm] = round(r_value**2, 3)\n",
+    "\n",
+    "    rmse_value = sklearn.metrics.mean_squared_error(experimental_concentrations, predicted_concentrations, squared=False)\n",
+    "    rmse_comp_data[algorithm] = round(rmse_value, 2)\n",
+    "  \n",
+    "  r_squared_data[comp_num] = r_squared_comp_data\n",
+    "  rmse_data[comp_num] = rmse_comp_data\n",
+    "\n",
+    "r_squared_df = pd.DataFrame(r_squared_data).transpose()\n",
+    "rmse_df = pd.DataFrame(rmse_data).transpose()\n",
+    "\n",
+    "print('r squared data')\n",
+    "display(r_squared_df)\n",
+    "\n",
+    "print('rmse data')\n",
+    "display(rmse_df)\n",
+    "\n",
+    "    # print(f'composition {comp_num} {algorithm} r-squared = {r_value**2}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "b3f28f6adfd1eea15c9e00a250e9367c9752811d529c8274081719d51bde14e5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.9 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/notebooks/E_time_as_a_variable.ipynb b/data/notebooks/E_time_as_a_variable.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a6417f320e3139adb48de05f0d9bd050a0b1190b
--- /dev/null
+++ b/data/notebooks/E_time_as_a_variable.ipynb
@@ -0,0 +1,1070 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Syngas prediction with consideration of time\n",
+    "\n",
+    "Copyright 2021-2022 Forrest Sheng Bao at ISU, and Garrett Troll and Yinjie Tang at WUSTL\n",
+    "\n",
+    "In this approach, we consider building a function \n",
+    "$f(N_2, CO, H_2, CO_2, \\text{flowrate}, \\text{time}) = (\\text{ biomass, acetate, butanol, butyrate, ethanol})$\n",
+    "where the outputs are concentration. \n",
+    "\n",
+    "The dataset has multiple trials (done by different labs under different conditions), \n",
+    "some of which for training while the remaining of which for test. \n",
+    "\n",
+    "Because the raw data is very sparse, we also tried data augmentation (curving) using polynomial fitting, \n",
+    "resulting multiple versions of the models (a model always predicts y given X, i.e., y=f(X)): \n",
+    "\n",
+    "| X (input)   | y (output) | X source | y source | note | \n",
+    "| ----------- | ---------- | -------- | ---------| ---- |\n",
+    "| concentration of gas, flow rate, time | concentration | raw | raw |  |\n",
+    "| concentration of gas, flow rate, time | concentration | curved | curved |  |\n",
+    "| concentration of gas, flow rate, time | concentration | curved | raw |   |\n",
+    "| concentration | rate | raw | raw | impossible  |\n",
+    "| concentration | rate | curved | curved | not needed  | \n",
+    "| concentration | rate | curved | raw |  impossible | \n",
+    "\n",
+    "In the concentration-to-rate cases, the function $f$ becomes: \n",
+    "$f(CO, H_2, CO_2, \\text{flowrate}, \\text{time}) = (\\Delta biomass, \\Delta acetate, \\Delta butanol, \\Delta butyrate, \\Delta ethanol)$\n",
+    "where $\\Delta = {\\partial \\text{ concentration} \\over \\partial \\text{ time}}$. \n",
+    "\n",
+    "\n",
+    "## Organization of code (key functions and variables):\n",
+    "* `complete_loop()`: the main function, training and test trial split is hard-coded (`training_test_composition_splits`) in it. \n",
+    "  * `prepare_data_one_y()`: Return the X and y as in common ML tasks from one Pandas dataframe, given the training and test trial ID (integers). The trial ID is in the dataframe. \n",
+    "  * `cv_fixed_trial_split()`: Run cross-validation to find the best hyperparameters for several types of ML regressors (NNs, SVMs, DTs, RFs, etc.). \n",
+    "    * `gen_NN_uni()`: Enumerate fully-connected layers/MLPs given maximal number of layers and maximal number of neurons at each layer. \n",
+    "      * `gen_NN_fixed_n_layers()`: Given the number of layers and maximal number of neurons per layer, produce various MLPs.\n",
+    "  * `beautiful_plot()`: Visualize the results.\n",
+    "  * `full_cfgs` and `test_cfgs`: two dicts storing hyperparameter grids for various types of ML regressors. \n",
+    "\n",
+    "The call of `complete_loop` is at the end of this notebook where the three versions are presented with different input CSV files and arguments. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 0. Load modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import sys\n",
+    "import sklearn.model_selection\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "import sklearn, sklearn.preprocessing, sklearn.pipeline, sklearn.model_selection\n",
+    "import sklearn.svm, sklearn.tree, sklearn.ensemble, sklearn.neural_network\n",
+    "import sklearn.linear_model, sklearn.gaussian_process, sklearn.neighbors\n",
+    "import sklearn.multioutput\n",
+    "import numpy\n",
+    "import functools, operator, itertools\n",
+    "import time\n",
+    "import json\n",
+    "import scipy.stats, scipy\n",
+    "# import scikitlearn_plus.neural_network\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import typing \n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Defining functions needed for experiments\n",
+    "## 1.1 The function to extraction training and test data for one output/regressor from the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the training data: 836 rows by 18 columns\n",
+      "Shape of the test data: 176 rows by 13 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "def prepare_data_one_y(train_df, test_df, training_trial_indexes:typing.List[int], test_trial_indexes:typing.List[int], X_columns:typing.List[str], y_column:str, scaler_type:str, columns_to_scale:typing.List[str]):\n",
+    "    \"\"\"Prepare the X and y for one yield prediction, given the yield name, and training and test trial indexes \n",
+    "\n",
+    "    train_df, test_df: DataFrames storing X and y. We allow training data and test data to be of different sources, \n",
+    "                e.g., one curved and the other raw.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # scale for X and y \n",
+    "    if scaler_type == \"minmax\":    \n",
+    "        Scaler = sklearn.preprocessing.MinMaxScaler() \n",
+    "    elif scaler_type == \"standard\":\n",
+    "        Scaler = sklearn.preprocessing.StandardScaler() \n",
+    "    else: \n",
+    "        print (\"wrong scaler type \")\n",
+    "        exit(0) \n",
+    "\n",
+    "\n",
+    "    Scaler.fit(train_df[columns_to_scale])\n",
+    "    train_df[columns_to_scale] = Scaler.transform(train_df[columns_to_scale])\n",
+    "    test_df[columns_to_scale] = Scaler.transform(test_df[columns_to_scale])\n",
+    "\n",
+    "    # Separate training and testing \n",
+    "    training_data = train_df[train_df['composition'].isin(training_trial_indexes)]\n",
+    "    test_data = test_df[test_df['composition'].isin(test_trial_indexes)]\n",
+    "\n",
+    "    training_X, training_y = training_data[X_columns], training_data[y_column]\n",
+    "    test_X, test_y = test_data[X_columns], test_data[y_column]\n",
+    "\n",
+    "    return training_X, training_y, test_X, test_y, Scaler\n",
+    "\n",
+    "\n",
+    "# Test \n",
+    "\n",
+    "train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the training data: {train_df.shape[0]} rows by {train_df.shape[1]} columns')\n",
+    "\n",
+    "test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "print(f'Shape of the test data: {test_df.shape[0]} rows by {test_df.shape[1]} columns')\n",
+    "\n",
+    "training_trial_indexes = [1,2,3,4,5,6,7]\n",
+    "test_trial_indexes = [8,9,10]\n",
+    "\n",
+    "y_column = 'biomass (g/L)' # 'biomass', 'ethanol', 'acetate', 'butanol', or 'butyrate'\n",
+    "X_columns = ['flow rate (mL/min)', 'H2', 'CO', 'CO2'] \n",
+    "columns_to_scale = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)', 'flow rate (mL/min)', 'H2', 'CO', 'CO2']\n",
+    "training_X, training_y, test_X, test_y, Scaler = prepare_data_one_y(train_df,  test_df, training_trial_indexes, test_trial_indexes, X_columns, y_column, \"minmax\", columns_to_scale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "flow rate (mL/min)    0.0\n",
+       "H2                    0.2\n",
+       "CO                    0.0\n",
+       "CO2                   0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_X.min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Two functions to enumerate network structures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_NN_fixed_n_layers(n_layers, n_neurons, neuron_step):\n",
+    "    \"\"\"Generate NN hidden_layer_sizes of n_layers and up to n_neurons per layer \n",
+    "    \"\"\"\n",
+    "    # print (n_layers)\n",
+    "    if n_layers == 1: \n",
+    "        return [[i] for i in range(neuron_step, n_neurons+1, neuron_step)]\n",
+    "    else:\n",
+    "        pairs =  [  (i,  tail) for tail in gen_NN_fixed_n_layers(n_layers-1, n_neurons+1, neuron_step) for i in range(neuron_step, n_neurons+1, neuron_step) ]\n",
+    "        return [[i]+ t for (i, t) in pairs]\n",
+    "\n",
+    "# print (gen_NN_fixed_n_layers(4, 10, 5))\n",
+    "\n",
+    "def gen_NN_uni(n_layers, n_neurons, layer_step, neuron_step):\n",
+    "    \"\"\"Generate hidden layers of various number of layers and number of neurons \n",
+    "    \"\"\" \n",
+    "    various_NNs = [ gen_NN_fixed_n_layers(i , n_neurons, neuron_step) for i in range(2, n_layers+1, layer_step)]\n",
+    "    return  functools.reduce(operator.add, various_NNs)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 The function and hyperparameter grid for grid-searching hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cv_fixed_trial_split(training_X, training_y, test_X, test_y, model_cfgs, refit:str):\n",
+    "    \"\"\"Given training and test sets fixed on trials, do Grid search CV to tune hyperparameter and then test on test sets. \n",
+    "    \"\"\"\n",
+    "    estimator, hyperparam_grid = model_cfgs['estimator'], model_cfgs['param_grid']\n",
+    "    \n",
+    "    search = sklearn.model_selection.GridSearchCV(\n",
+    "            estimator = estimator, \n",
+    "            param_grid = hyperparam_grid, \n",
+    "            # scoring = ['neg_root_mean_squared_error'],\n",
+    "            # refit = 'neg_root_mean_squared_error',\n",
+    "            scoring = ['r2',  'neg_mean_absolute_percentage_error'],\n",
+    "            refit = refit,\n",
+    "            cv = sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.2, random_state=0), \n",
+    "            n_jobs=15,\n",
+    "            verbose=0\n",
+    "        )\n",
+    "\n",
+    "    # run grid search\n",
+    "    search.fit(training_X, training_y)\n",
+    "\n",
+    "    # test on test set \n",
+    "    test_score = search.score(test_X, test_y)\n",
+    "    # print (\"test score %0.3f:\" % test.score)\n",
+    "\n",
+    "    test_y_hat = search.predict(test_X)\n",
+    "    \n",
+    "#     cc = scipy.stats.pearsonr(test_y_hat, test_y)[0]\n",
+    "    try:\n",
+    "        _, _, cc, _, _ = scipy.stats.linregress(test_y_hat, test_y)\n",
+    "    except ValueError: # when test_y_hat are all the same \n",
+    "        cc = 0 # no correlation at all \n",
+    "\n",
+    "    return test_score, search.best_score_, search.best_params_, cc, search.cv_results_, search.best_estimator_\n",
+    "\n",
+    "\n",
+    "test_cfgs = {\n",
+    "#     \"nn\":{\n",
+    "#         'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n",
+    "# #            Test grid\n",
+    "#         'param_grid':   {\n",
+    "#             # hidden_layer_sizes made the search space many order of magnitudes larger\n",
+    "#             'activation':         ['tanh', 'logistic', 'relu'], \n",
+    "#             'max_iter':           [400*i for i in range(1, 2)], \n",
+    "#             'learning_rate':      ['adaptive']\n",
+    "#         }\n",
+    "#     },\n",
+    "#     \"svm_rbf\":{\n",
+    "#             'estimator': sklearn.svm.SVR(kernel='rbf'),\n",
+    "# #               Test grid\n",
+    "#             'param_grid':   {\n",
+    "#                 'C':       [10**i for i in range(-1, 1)], \n",
+    "#                 'epsilon': [10**i for i in range(-1, 1)],\n",
+    "#             }\n",
+    "#     },\n",
+    "#     \"rf\":{\n",
+    "#         'estimator': sklearn.ensemble.RandomForestRegressor(),\n",
+    "# #            Test grid\n",
+    "#         'param_grid':   {\n",
+    "#             'n_estimators': [10*i for i in range(1, 2)],\n",
+    "#             'max_depth':     [2*i for i in range(1, 1+1)],\n",
+    "#         }\n",
+    "#     }, \n",
+    "#     \"gauss\":{\n",
+    "#         'estimator': sklearn.gaussian_process.GaussianProcessRegressor(),\n",
+    "# #             Full grid \n",
+    "#         'param_grid':   {\n",
+    "#             'alpha': [10**i for i in range(0, 5)], \n",
+    "#             'normalize_y': [True, False], \n",
+    "#             # 'n_restarts_optimizer': [2**i for i in range(0,5)]\n",
+    "#         }\n",
+    "#     },\n",
+    "#     \"knn\":{\n",
+    "#         'estimator': sklearn.neighbors.KNeighborsRegressor(),\n",
+    "# #             Full grid \n",
+    "#         'param_grid':   {\n",
+    "#             'n_neighbors': [i for i in range(1, 30+1,5)], \n",
+    "#             'weights': ['distance'], \n",
+    "#             'algorithm': ['ball_tree'],    \n",
+    "#         }\n",
+    "#     },\n",
+    "    \"bayesian\":{\n",
+    "        'estimator': sklearn.linear_model.BayesianRidge(),\n",
+    "        'param_grid':   {\n",
+    "            'n_iter':  [300, 500], \n",
+    "            'alpha_1': [10**i for i in range(-1, 1)], \n",
+    "            'alpha_2': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_1': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_2': [10**i for i in range(-1, 1)], \n",
+    "        }\n",
+    "    },\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "full_cfgs = {\n",
+    "    \"nn\":{\n",
+    "        'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n",
+    "#             Full grid\n",
+    "        'param_grid':   {\n",
+    "            'hidden_layer_sizes': gen_NN_uni(4, 100, 1, 20),  \n",
+    "            'activation':         ['tanh',  'relu'], \n",
+    "            'max_iter':           [5000], \n",
+    "            'learning_rate':      ['adaptive']\n",
+    "        }                \n",
+    "    },\n",
+    "    \"svm_rbf\":{\n",
+    "            'estimator': sklearn.svm.SVR(kernel='rbf'),\n",
+    "#       Full grid\n",
+    "            'param_grid':   {\n",
+    "                'C':       [10**i for i in range(-5, 5)], \n",
+    "                'epsilon': [10**i for i in range(-5, 5)],\n",
+    "                'gamma':   [10**i for i in range(-5, 5)] # gamma gave me an error\n",
+    "            }\n",
+    "    },\n",
+    "    \"rf\":{\n",
+    "        'estimator': sklearn.ensemble.RandomForestRegressor(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'n_estimators': [10*i for i in range(1, 20)],\n",
+    "            'max_depth':     [2*i for i in range(20)], \n",
+    "            'max_samples': [0.05*i for i in range(1, 10+1)] # max samples gave me an error\n",
+    "        }\n",
+    "    }, \n",
+    "    \"en\":{\n",
+    "        'estimator': sklearn.linear_model.ElasticNet(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)],\n",
+    "            'l1_ratio': [0.1*i for i in range(1, 10+1)] \n",
+    "        }\n",
+    "    },\n",
+    "    \"lasso\":{\n",
+    "        'estimator': sklearn.linear_model.Lasso(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)]\n",
+    "        }\n",
+    "    }, \n",
+    "    \"gauss\":{\n",
+    "        'estimator': sklearn.gaussian_process.GaussianProcessRegressor(),\n",
+    "#   Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)], \n",
+    "            'normalize_y': [True, False], \n",
+    "        }\n",
+    "    },\n",
+    "    \"knn\":{\n",
+    "        'estimator': sklearn.neighbors.KNeighborsRegressor(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'n_neighbors': [i for i in range(1, 30+1)], \n",
+    "            'weights': ['distance'], \n",
+    "            'algorithm': ['ball_tree', 'kd_tree'], \n",
+    "            'leaf_size': [5*i for i in range(1, 10+1)]                \n",
+    "        }\n",
+    "    },\n",
+    "    \"bayesian\":{\n",
+    "        'estimator': sklearn.linear_model.BayesianRidge(),\n",
+    "        'param_grid':   {\n",
+    "            'n_iter':  [300, 500], \n",
+    "            'alpha_1': [10**i for i in range(-1, 1)], \n",
+    "            'alpha_2': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_1': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_2': [10**i for i in range(-1, 1)], \n",
+    "        }\n",
+    "    },\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "# Test \n",
+    "# for name, cfg in test_cfgs.items():\n",
+    "#     print (name)\n",
+    "#     _ = \\\n",
+    "#     ml_fixed_trial_split(training_X, training_y, test_X, test_y, cfg, 'neg_mean_absolute_percentage_error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.4 The exhaustive CV loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def complete_loop(train_df, test_df, model_cfgs, X_columns, y_columns, result_pickle_file, model_pickle_file, refit:str):\n",
+    "    \"\"\"Complete multi-layer loops for entire ML task \n",
+    "\n",
+    "    train_df, test_df: pandas dataframes loaded from CSV files\n",
+    "\n",
+    "    model_cfgs: dict, keys as str (model type) and values as sklearn grid CV parameter dict \n",
+    "\n",
+    "    y_columns: list of str, \n",
+    "        To predict absolute value, use: ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']\n",
+    "        To predict rate, use: ['biomass_Δ', 'ethanol_Δ', 'acetate_Δ', 'butanol_Δ', 'butyrate_Δ']\n",
+    "\n",
+    "    X_columns: list of str, \n",
+    "        e.g.,  ['flow rate', 'H2', 'CO', 'CO2'] \n",
+    "\n",
+    "    pickle_file: str\n",
+    "        where to save the results using various regressors\n",
+    "\n",
+    "    refit: str\n",
+    "        the sklearn.metrics string \n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    loo = sklearn.model_selection.LeaveOneOut()\n",
+    "    compositions = np.array(range(1, 10+1))\n",
+    "    training_test_composition_splits = [\n",
+    "        ( (1,2,3,4,5,6,7), (8,9,10) ),  # training in compositions 1 to 7 and test in compositions 8 to 10\n",
+    "    ] \\\n",
+    "    # + \\\n",
+    "    # [(compositions[train_indexes], compositions[test_indexes]) \n",
+    "    #   for train_indexes, test_indexes in \n",
+    "    #       loo.split(range(7))\n",
+    "    # ] # leave one composition as test \n",
+    "\n",
+    "    # print (training_test_composition_splits)\n",
+    "\n",
+    "    columns_to_scale = X_columns + y_columns\n",
+    "\n",
+    "    full_results = {}\n",
+    "\n",
+    "    best_models, best_score_for_ys = {}, {}\n",
+    "\n",
+    "    for y_column in y_columns:\n",
+    "        # print (y_column)\n",
+    "        for (training_compositions, test_compositions) in training_test_composition_splits: \n",
+    "            # for scaler_type in ['minmax', 'standard']:\n",
+    "            for scaler_type in ['minmax']:\n",
+    "                training_X, training_y, test_X, test_y, Scaler = \\\n",
+    "                    prepare_data_one_y(train_df, test_df, training_compositions, test_compositions, X_columns, y_column, scaler_type, columns_to_scale)\n",
+    "                for estimator_type, cfg in model_cfgs.items():\n",
+    "                # print (estimator_type)\n",
+    "                    test_score, train_score, best_params, cc, cv_results, best_estimator = \\\n",
+    "                        cv_fixed_trial_split(training_X, training_y, test_X, test_y, cfg, refit)\n",
+    "\n",
+    "                    # save result\n",
+    "                    if type(test_compositions) == numpy.ndarray: \n",
+    "                        test_compositions = tuple(test_compositions.tolist())\n",
+    "\n",
+    "                    r2_per_sk = np.mean(cv_results['mean_test_r2'])\n",
+    "\n",
+    "                    cv_results.update({ \"my_cc\":cc, \"r2_per_sk\":r2_per_sk, \"my_test_score\": test_score, \"my_train_score\": train_score})\n",
+    "                    exp_identifier = (scaler_type, y_column, test_compositions, estimator_type)\n",
+    "                    full_results[ exp_identifier] = cv_results\n",
+    "\n",
+    "                    # now print the result \n",
+    "                    line = [scaler_type, y_column, test_compositions, estimator_type, \"{0:.3f}\".format(cc), \"{0:.3f}\".format(r2_per_sk), best_params]\n",
+    "                    line = map(str, line)\n",
+    "                    print (\"\\t\".join(line))\n",
+    "\n",
+    "                    # Store/update the best estimator for each Y\n",
+    "                    if exp_identifier in best_score_for_ys: \n",
+    "                        best_score_so_far =  best_score_for_ys.get(exp_identifier, 0)\n",
+    "                        if best_score_so_far < test_score: \n",
+    "                            best_score_for_ys[exp_identifier[:-1]] = best_score_so_far\n",
+    "                            best_models[exp_identifier[:-1]] =  (best_estimator, exp_identifier[-1], Scaler)\n",
+    "                    else:  # first time \n",
+    "                        best_score_for_ys[exp_identifier] = test_score\n",
+    "                        best_score_for_ys[exp_identifier[:-1]] = test_score\n",
+    "                        best_models[exp_identifier[:-1]] =  (best_estimator, exp_identifier[-1], Scaler)\n",
+    "    \n",
+    "    # json.dump(full_results, open(\"results.json\", \"w\"), indent=2, )\n",
+    "    pickle.dump(full_results, open(result_pickle_file, \"bw\"))\n",
+    "    pickle.dump(best_models, open(model_pickle_file, \"bw\"))\n",
+    "\n",
+    "    return full_results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  1.5 The plot function to visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def beautiful_plot(full_results, Y_names, metric, main_title, savefig_path):\n",
+    "    \"\"\"\n",
+    "\n",
+    "    metric: str, \"my_cc\" or \"r2_per_sk'\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    model_name_order   = [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]\n",
+    "    product_name_order = [\"biomass (g/L)\", \"acetate (mM)\", \"ethanol (mM)\", \"butyrate (mM)\", \"butanol (mM)\"]\n",
+    "    table_column_width = 10 \n",
+    "    \n",
+    "    scaler = 'minmax'\n",
+    "    test_trial = (8,9,10)\n",
+    "    bar_width = 0.3\n",
+    "\n",
+    "    CC = {condition: CV_numbers[metric] for condition, CV_numbers in full_results.items() }\n",
+    "\n",
+    "#     from CC to R2\n",
+    "    if metric == \"my_cc\":\n",
+    "        CC = {key:value**2 for key,value in CC.items()}\n",
+    "\n",
+    "    model_names = [condition[3] for condition in full_results.keys()]\n",
+    "    model_names = list(set(model_names))\n",
+    "#     model_names.sort() \n",
+    "    model_names = [name for name in model_name_order if name in model_names]\n",
+    "\n",
+    "    table = [ \n",
+    "                [ \n",
+    "                    CC[(scaler, y, test_trial, model_name,)]\n",
+    "                    for model_name in model_names\n",
+    "                ]\n",
+    "                  for y in Y_names\n",
+    "            ]\n",
+    "\n",
+    "    Y_max = np.nanmax(np.nanmax(table))\n",
+    "\n",
+    "    \n",
+    "    figure = plt.figure(figsize=(25,4))\n",
+    "    index = np.arange(0, len(model_names))*bar_width + bar_width/2\n",
+    "    for row_number , row in enumerate(table):\n",
+    "        # print (100 + len(Y_names)*10 + row_number + 1, index, row)\n",
+    "        plt.subplot(100 + len(Y_names)*10 + row_number + 1)\n",
+    "        plt.ylim(0, Y_max + 0.1)\n",
+    "        plt.xticks(rotation=45)\n",
+    "        plt.bar(index, row, \n",
+    "            width = bar_width, tick_label=model_names, \n",
+    "            color = plt.cm.gist_ncar(np.linspace(0, 0.5, len(model_names))))\n",
+    "        plt.title(Y_names[row_number])\n",
+    "               \n",
+    "        # break \n",
+    "\n",
+    "    figure.suptitle(main_title )\n",
+    "    plt.savefig(savefig_path)\n",
+    "    \n",
+    "    # print the numbers\n",
+    "    header = [name.split()[0] for name in product_name_order ]\n",
+    "    header += [\"mean +- sd\"]\n",
+    "    header = [i.ljust(table_column_width) for i in header]\n",
+    "    header = [\" \"*table_column_width] + header\n",
+    "    print (\"\".join(header))\n",
+    "    for model_name in model_names:\n",
+    "        print (model_name.ljust(table_column_width), end=\": \")\n",
+    "        R_list = []\n",
+    "        for product in product_name_order: \n",
+    "            print (f'{CC.get((scaler, product, test_trial, model_name,), 0):<{table_column_width}.3f}', end=\"\")\n",
+    "            R_list.append(CC.get((scaler, product, test_trial, model_name,), 0))\n",
+    "        print (f'{numpy.mean(R_list):.3f}+-{numpy.std(R_list):.3f}')\n",
+    "#     numbers = {\n",
+    "#         {\n",
+    "#             product: CC[(scaler, product, test_trial, model_name,)]\n",
+    "#             for product in product_names\n",
+    "#         }\n",
+    "#         for model_name in [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]\n",
+    "#     } # keys as models and value as dict: {product: R2}\n",
+    "\n",
+    "    \n",
+    "# Test \n",
+    "# full_results = pickle.load(open(\"test.pickle\", \"br\"))\n",
+    "# beautiful_plot(full_results, \n",
+    "#     ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate'],\n",
+    "#     \"my_cc\",\n",
+    "#     \"1. Concentration to concentration\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.6 Add a function to visulize the predicted and groundtruth time courses on test data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_time_course(\n",
+    "    test_df, # already scaled Pandas data frame\n",
+    "    X_columns: typing.List[str], # input features\n",
+    "    y_columns: typing.List[str], # output targets, need to control the order \n",
+    "    test_compositions: typing.Tuple[int], # \n",
+    "    best_models, \n",
+    "    metric_short: str, \n",
+    "    source: str,  # source data modality\n",
+    "    target: str,  # target data modality \n",
+    "    plot_format: str\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    best_models: dict, \n",
+    "            keys as (scaler_type:str, y_column:str, test_compositions: list[int]), \n",
+    "            values as (scikit-learn.estimator, esitmator_type: str, Scaler: scikit-learn.preprocessing.MinMaxScaler-like)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    columns_to_scale = X_columns + y_columns\n",
+    "\n",
+    "    # for key, value in best_models.items():\n",
+    "    #     (scaler_type, y_column, trials) = key\n",
+    "    #     (estimator, estimator_type) = value\n",
+    "\n",
+    "    figure = plt.figure(figsize=(16,4*3*2))\n",
+    "    subplot_counter = 1\n",
+    "\n",
+    "    for column_number, y_column in enumerate(y_columns):\n",
+    "        key = (scaler_type, y_column, test_compositions)\n",
+    "        (estimator, estimator_type, Scaler) = best_models[key]\n",
+    "\n",
+    "        if scaler_type == \"minmax\":    \n",
+    "            Scaler = sklearn.preprocessing.MinMaxScaler() \n",
+    "        elif scaler_type == \"standard\":\n",
+    "            Scaler = sklearn.preprocessing.StandardScaler() \n",
+    "        else: \n",
+    "            print (\"wrong scaler type \")\n",
+    "            exit(0) \n",
+    "\n",
+    "        test_df[columns_to_scale] = Scaler.fit_transform(test_df[columns_to_scale])\n",
+    "        # FIXME: why the Scaler loaded from Pickle cannot scale data? \n",
+    "\n",
+    "        for row_number, test_composition in enumerate(test_compositions): \n",
+    "            test_data_segment = test_df[test_df['composition'] == test_composition]\n",
+    "\n",
+    "            test_data_segment = test_data_segment[test_data_segment['trial'] == 1]\n",
+    "\n",
+    "            test_X = test_data_segment[X_columns]\n",
+    "            test_y = test_data_segment[y_column]\n",
+    "            test_y_hat = estimator.predict(test_X)\n",
+    "\n",
+    "            test_time = test_data_segment['time']\n",
+    "            \n",
+    "            plt.subplot(len(Y_columns), len(test_compositions), subplot_counter)\n",
+    "            plt.plot(test_time, test_y, \"k-\")\n",
+    "            plt.plot(test_time, test_y_hat, \"r-\")\n",
+    "\n",
+    "            short_y_column = y_column.split()[0] # drop unit. \n",
+    "            plt.title(F\"{short_y_column}, {estimator_type}, composition: {test_composition}\")\n",
+    "\n",
+    "            subplot_counter += 1\n",
+    "\n",
+    "    plot_name = F\"time_course_{source}_{target}_{metric_short}\"\n",
+    "    figure.suptitle(plot_name)\n",
+    "    plt.savefig(plot_name + F\".{plot_format}\")\n",
+    "\n",
+    "\n",
+    "# test "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Experiments \n",
+    "## 2.1 When Refit goal is MAPE\n",
+    "### 2.1.1. Running the experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Why Jupyter is bad here. \n",
+    "# i want variables train_df and test_df to be different for each experiment \n",
+    "# rather than being globa l\n",
+    "\n",
+    "metric = 'neg_mean_absolute_percentage_error'\n",
+    "metric_short = \"MAPE\"\n",
+    "X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)']\n",
+    "\n",
+    "for i in range(3):\n",
+    "\n",
+    "    # 1. raw concentration => raw concentration\n",
+    "    if i == 0:\n",
+    "        train_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "    elif i==1:\n",
+    "        # 2. curved concentration => curved concentration \n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "\n",
+    "    elif i==2: \n",
+    "        # 3. curved concentration => raw concentration\n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "\n",
+    "    # elif i==3:\n",
+    "    #     # 5. curved concentration => curved rate\n",
+    "    #     train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "    #     test_df = train_df\n",
+    "    #     source, target = \"curved_concentration\", \"curved_rate\"\n",
+    "    #     metric = 'neg_mean_absolute_percentage_error'\n",
+    "    #     metric_short = \"MAPE\"\n",
+    "    #     X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "    #     y_columns = ['biomass rate','ethanol rate','acetate rate','butanol rate','butyrate rate']\n",
+    "\n",
+    "    results_raw_concentration = complete_loop(\n",
+    "        train_df, test_df, \n",
+    "#         test_cfgs,  \n",
+    "        full_cfgs,\n",
+    "        X_columns , y_columns , \n",
+    "        F\"result_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        F\"model_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        metric)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.2 Visualize MAPE-based results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          biomass   acetate   ethanol   butyrate  butanol   mean +- sd\n",
+      "nn        : 0.340     0.074     0.247     0.101     0.110     0.174+-0.102\n",
+      "svm_rbf   : 0.276     0.379     0.224     0.104     0.108     0.218+-0.104\n",
+      "rf        : 0.422     0.004     0.053     0.257     0.108     0.169+-0.153\n",
+      "en        : 0.443     0.005     0.280     0.109     0.000     0.167+-0.171\n",
+      "lasso     : 0.443     0.005     0.280     0.109     0.000     0.167+-0.171\n",
+      "knn       : 0.044     0.494     0.001     0.756     0.040     0.267+-0.304\n",
+      "bayesian  : 0.443     0.004     0.278     0.110     0.107     0.188+-0.155\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_format = \"pdf\"\n",
+    "visual_metric = \"my_cc\"     # this on test set, using square of CC \n",
+    "# visual_metric = \"r2_per_sk\" # this is on validation set, R2 per Scikit-learn\n",
+    "# visual_metric = \"my_test_score\" # this is on test set, MAPE per scikit-learn\n",
+    "# visual_metric = \"my_train_score\" # this is on training set, MAPE per scikit-learn\n",
+    "\n",
+    "refit_metric = \"MAPE\"\n",
+    "Y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)', 'butyrate (mM)']\n",
+    "for i in range(1,2):\n",
+    "    if i == 0 : \n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"1. Raw concentration to raw concentration, refit w/ MAPE\"\n",
+    "    elif i == 1: \n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "        plot_title = \"2. Curved concentration to curved concentration, refit w/ MAPE\"\n",
+    "    elif i == 2:\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"3. Curved concentration to raw concentration, refit w/ MAPE\"\n",
+    "    \n",
+    "\n",
+    "    result_pickle = F\"result_{source}_to_{target}_{refit_metric}.pickle\"\n",
+    "    plot_save = F\"bar_{visual_metric}.{source}.{target}.{refit_metric}.{plot_format}\"\n",
+    "\n",
+    "    full_results = pickle.load(open(result_pickle, \"br\"))\n",
+    "    beautiful_plot(full_results, Y_columns, visual_metric, plot_title, plot_save)\n",
+    "    \n",
+    "    # print order \n",
+    "#     for model_name in [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]:\n",
+    "#     for product in [\"acetate (mM)\", \"ethanol (mM)\", \"butyrate (mM)\", \"butanol (mM)\"]: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Plot time course"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# The plot function might be wrong. To fix. \n",
+    "\n",
+    "\n",
+    "# plot_format = \"pdf\"\n",
+    "# metric_short = \"MAPE\"\n",
+    "# source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "# model_file = F\"model_{source}_to_{target}_{metric_short}.pickle\"\n",
+    "# test_compositions = (8,9,10)\n",
+    "# scaler_type = \"minmax\"\n",
+    "\n",
+    "# best_models = pickle.load(open(model_file, \"br\"))\n",
+    "\n",
+    "# plot_time_course(\n",
+    "#     test_df, \n",
+    "#     X_columns, y_columns, test_compositions, \n",
+    "#     best_models, \n",
+    "#     metric_short, source, target, \n",
+    "#     plot_format\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 When Refit goal is R2\n",
+    "\n",
+    "### 2.2.1 Running the experiments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "metric = metric_short = \"r2\"\n",
+    "X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)']\n",
+    "\n",
+    "for i in range(1,3):\n",
+    "\n",
+    "    # 1. raw concentration => raw concentration\n",
+    "    if i == 0:\n",
+    "        train_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "    elif i==1:\n",
+    "        # 2. curved concentration => curved concentration \n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "\n",
+    "    elif i==2: \n",
+    "        # 3. curved concentration => raw concentration\n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "\n",
+    "    # elif i==3:\n",
+    "    #     # 5. curved concentration => curved rate\n",
+    "    #     train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "    #     test_df = train_df\n",
+    "    #     source, target = \"curved_concentration\", \"curved_rate\"\n",
+    "    #     metric = 'neg_mean_absolute_percentage_error'\n",
+    "    #     metric_short = \"MAPE\"\n",
+    "    #     X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "    #     y_columns = ['biomass rate','ethanol rate','acetate rate','butanol rate','butyrate rate']\n",
+    "\n",
+    "    results_raw_concentration = complete_loop(\n",
+    "        train_df, test_df, \n",
+    "#         test_cfgs, \n",
+    "        full_cfgs, \n",
+    "        X_columns , y_columns , \n",
+    "        F\"result_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        F\"model_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        metric)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Visualize R2-based results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          biomass   acetate   ethanol   butyrate  butanol   mean +- sd\n",
+      "nn        : 0.135     0.396     0.297     0.693     0.111     0.327+-0.211\n",
+      "svm_rbf   : 0.387     0.295     0.305     0.398     0.110     0.299+-0.103\n",
+      "rf        : 0.423     0.021     0.118     0.253     0.111     0.185+-0.140\n",
+      "en        : 0.443     0.004     0.280     0.109     0.092     0.186+-0.157\n",
+      "lasso     : 0.443     0.003     0.280     0.109     0.103     0.188+-0.156\n",
+      "knn       : 0.313     0.493     0.008     0.632     0.005     0.290+-0.253\n",
+      "bayesian  : 0.443     0.002     0.278     0.110     0.109     0.188+-0.155\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_format = \"pdf\"\n",
+    "visual_metric = \"my_cc\"     # this on test set, using square of CC \n",
+    "# visual_metric = \"r2_per_sk\" # this is on validation set, R2 per Scikit-learn\n",
+    "# visual_metric = \"my_test_score\" # this is on test set, R2 per scikit-learn\n",
+    "# visual_metric = \"my_train_score\" # this is on training set, R2 per scikit-learn\n",
+    "Y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)', 'butyrate (mM)']\n",
+    "\n",
+    "refit_metric = \"r2\"\n",
+    "\n",
+    "for i in range(1,2):\n",
+    "    if i == 0 : \n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"1. Raw concentration to raw concentration, refit w/ R2\"\n",
+    "    elif i == 1: \n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "        plot_title = \"2. Curved concentration to curved concentration, refit w/ R2\"\n",
+    "    elif i == 2:\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"3. Curved concentration to raw concentration, refit w/ R2\"\n",
+    "    \n",
+    "\n",
+    "    result_pickle = F\"result_{source}_to_{target}_{refit_metric}.pickle\"\n",
+    "    plot_save = F\"bar_{visual_metric}.{source}.{target}.{refit_metric}.{plot_format}\"\n",
+    "\n",
+    "    full_results = pickle.load(open(result_pickle, \"br\"))\n",
+    "    beautiful_plot(full_results, Y_columns, visual_metric, plot_title, plot_save)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.3 Plot time course comparison between our prediction and groundtruth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The plot function might be wrong. To fix. \n",
+    "\n",
+    "# plot_format = \"pdf\"\n",
+    "# metric_short = \"r2\"\n",
+    "# source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "# model_file = F\"model_{source}_to_{target}_{metric_short}.pickle\"\n",
+    "# test_compositions = (8,9,10)\n",
+    "# scaler_type = \"minmax\"\n",
+    "\n",
+    "# best_models = pickle.load(open(model_file, \"br\"))\n",
+    "\n",
+    "# plot_time_course(\n",
+    "#     test_df, \n",
+    "#     X_columns, y_columns, test_compositions, \n",
+    "#     best_models, \n",
+    "#     metric_short, source, target, \n",
+    "#     plot_format\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys([('minmax', 'biomass (g/L)', (8, 9, 10)), ('minmax', 'ethanol (mM)', (8, 9, 10)), ('minmax', 'acetate (mM)', (8, 9, 10)), ('minmax', 'butanol (mM)', (8, 9, 10)), ('minmax', 'butyrate (mM)', (8, 9, 10))])"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_models.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/requirements.txt b/data/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..427f6f5228eda3c60999b32e4040c3e4791da997
--- /dev/null
+++ b/data/requirements.txt
@@ -0,0 +1,15 @@
+cycler==0.10.0
+joblib==1.0.1
+kiwisolver==1.3.2
+matplotlib==3.4.3
+numpy==1.21.2
+pandas==1.3.2
+Pillow==8.3.1
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+scikit-learn==0.24.2
+scipy==1.7.1
+six==1.16.0
+sklearn==0.0
+threadpoolctl==2.2.0
diff --git a/data/trained_models/acetate/bayesian.pkl b/data/trained_models/acetate/bayesian.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..bd30629c6200cef1153cbb44a59385e28b7a8ace
--- /dev/null
+++ b/data/trained_models/acetate/bayesian.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8138bcbe04331dc3e45a97b6bab3cd32fed6e6daa254631e48f1dd47a747fd04
+size 1889
diff --git a/data/trained_models/acetate/en.pkl b/data/trained_models/acetate/en.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..dd98e30570034eb36529043fe80b23e9233a90b1
--- /dev/null
+++ b/data/trained_models/acetate/en.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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