Repository: ARM-software/mango (language: python)

Issue:
Scaling input parameters
Hey there. Today I tried to plot the optimization progress of a mango run using a contour plot but then I ran into an issue. In my data, there's two continuous input parameters with very different scales (see code example below). After fitting the GaussianProcessRegressor the results were pretty much unusable, as can be seen by the plot (provided below). Only after scaling both features to the [0,1] interval, the results were what I expected them to be. 

Correct me if I'm wrong, but after looking through the codebase, I couldn't find any scaling applied to continuous variables. I guess my question is, are we supposed to scale our parameters before passing them to Tuner as param_dict? If so, this should probably be mentioned in the docs, as this behavior is not very intuitive. I would also like to make a feature request, to make the scaling part of mango. The limits used for scaling are already known from the distributions defined in param_dict.

Here's some code to test the behavior:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

# Generate data
data = [[0.519712, 1438.106604, 0.9890267207690076],
 [0.588307, 1220.578838, 0.6265765135227674],
 [0.55075, 906.374136, 0.9308240637759998],
 [0.355543, 1403.904598, 1.001475488132783],
 [0.430634, 840.591423, 1.0267490857885169],
 [0.477513, 1062.385949, 0.4883283241359153],
 [0.571532, 392.193584, 0.8084230446619398],
 [0.310248, 1012.246997, 0.9610352617817592],
 [0.464245, 248.483425, 0.701506256396162],
 [0.526382, 591.378226, 0.8894154056980089],
 [0.434667, 1302.143909, 0.9839202744250197],
 [0.391176, 488.536072, 0.9349843519807586],
 [0.474439, 1071.135636, 0.5342269053336064],
 [0.548409, 231.415683, 0.9947894009673353],
 [0.47567, 718.013638, 0.5847223173442994],
 [0.435327, 1207.326743, 1.0041979428572427]]

df = pd.DataFrame(data,columns=("varA","varB","result"))

# Specify parameter limits
xmin = np.array([0.3,200])
xmax = np.array([0.6,1500])

# Define gp
kernel = Matern(nu=2.5)
gp = GaussianProcessRegressor(
    kernel=kernel,
    n_restarts_optimizer=100,
    random_state=1,
    normalize_y=True,
)

def minmax_scale(X, xmin, xmax):
    return (X - xmin) / (xmax - xmin)

X_train = df.iloc[:,:-1].values
X_train = minmax_scale(X=X_train,xmin = xmin, xmax = xmax) # comment out for comparison
y_train = df.iloc[:,-1].values

gp.fit(X=X_train, y=y_train)


def generate_contour_plot(X_train, gp_model, resolution=100):
    x_min, x_max = X_train[:, 0].min(), X_train[:, 0].max()
    y_min, y_max = X_train[:, 1].min(), X_train[:, 1].max()
    x_range = np.linspace(x_min, x_max, resolution)
    y_range = np.linspace(y_min, y_max, resolution)
    X, Y = np.meshgrid(x_range, y_range)
    xy = np.column_stack([X.ravel(), Y.ravel()])

    Z_mean = gp_model.predict(xy)
    Z_mean = Z_mean.reshape(X.shape)

    fig, ax = plt.subplots(figsize=(10, 6))
    contour = ax.contourf(X, Y, Z_mean, cmap='viridis', alpha=0.8, levels=50)
    fig.colorbar(contour, ax=ax, label='result')
    ax.scatter(X_train[:, 0], X_train[:, 1], c='red', marker='x', label='Training Data')
    ax.set_xlabel("varA")
    ax.set_ylabel("varB")
    ax.set_title('Gaussian Process Contour Plot')
    ax.legend()
    plt.show()

generate_contour_plot(X_train=X_train, gp_model=gp)
```
The gp response with scaled data:
![image](https://github.com/ARM-software/mango/assets/46023583/0842155d-9a80-44b8-b92c-07450cb4dc8d)

The gp response with raw data:
![image](https://github.com/ARM-software/mango/assets/46023583/b8ed954a-1871-41a9-958c-7829c7977d71)



Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 49f1d8c..6088187 100644
--- a/README.md
+++ b/README.md
@@ -127,7 +127,12 @@ Mango search space is compatible with scikit-learn's parameter space definitions
 [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)
  or [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).
 The search space is defined as a dictionary with keys being the parameter names (string) and values being
-list of discreet choices, range of integers or the distributions. Example of some common search spaces are:
+list of discreet choices, range of integers or the distributions. 
+
+> [!NOTE]  
+> Mango does not scale or normalize the search space parameters by default. Users should use their judgement on whether input space needs to be normalized.
+
+Example of some common search spaces are:
 
 ### Integer
 Following space defines `x` as an integer parameters with values in `range(-10, 11)` (11 is not included):
@@ -151,7 +156,7 @@ param_space = dict(max_features=['auto', 0.2, 0.3])
 Lists are uniformly sampled and are assumed to be unordered. They are one-hot encoded internally.
 
 ### Distributions
-All the distributions supported by [`scipy.stats`](https://docs.scipy.org/doc/scipy/reference/stats.html) are supported.
+All the distributions, including multivariate, supported by [`scipy.stats`](https://docs.scipy.org/doc/scipy/reference/stats.html) are supported.
 In general, distributions must provide a `rvs` method for sampling.
 
 #### Uniform distribution
@@ -383,10 +388,18 @@ It can be either:
   This allows the user to customize the initial evaluation points and therefore guide the optimization process.
 It also enables starting the optimizer from the results of a previous tuner run (see [this](<examples/warmup.ipynb>) notebook for a working example).
 Note that if `initial_custom` option is given then `initial_random` is ignored.  
+- *scale_params*: `True` or `False` (default: `False`). Scales the search space parameter space using `MinMaxScaler`. Can be useful when the range of parameters is not comparable like below:
+```python
+{
+    'x': uniform(-1, 2), # -1 to 1
+    'y': uniform(-1000, 2000) # -1000 to 1000
+}
+```
+However, use this option with caution as it could have unintended consequences. 
 
 
-The default configuration parameters can be modified, as shown below. Only the parameters whose values need to adjusted can be passed as the dictionary.
 
+The configuration options can be modified, as shown below:
 ```python
 conf_dict = dict(num_iteration=40, domain_size=10000, initial_random=3)
 
diff --git a/dev_notes.md b/dev_notes.md
new file mode 100644
index 0000000..f951845
--- /dev/null
+++ b/dev_notes.md
@@ -0,0 +1,9 @@
+# 2024 Aug 16: Scaling parameters in GP space
+Added an option `scale_params` to scale the parameters in GP space using min max scaler. Using 10 repeated `test_six_hump` test with param `y` scaled by a factor of 1000 we get the following results:
+
+Base case (y param not scaled in inputs or GP space): 0 out of 10 tests failed
+Y param not scaled, GP space scaled:                  1 out of 10 tests failed
+Without scaling GP space:                             0 out of 10 tests failed 
+With scaling GP space:                                2 out of 10 tests failed
+
+Scaling is not clearly useful so not exposing it as an option. 
\ No newline at end of file
diff --git a/examples/Failure_Handling.ipynb b/examples/Failure_Handling.ipynb
index 2e285e8..6e063b3 100644
--- a/examples/Failure_Handling.ipynb
+++ b/examples/Failure_Handling.ipynb
@@ -19,15 +19,22 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation failed\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1c23a4d25654b0c98935b4b48dad76d",
+       "model_id": "8824fb1e3ebe4fcb9f6941c6a2713e06",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=20.0), HTML(value='')))"
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -41,9 +48,9 @@
       "Evaluation failed\n",
       "Evaluation failed\n",
       "Evaluation failed\n",
-      "\n",
-      "best parameters: {'x': -0.016781443194481938, 'y': -0.022917881560944764}\n",
-      "best objective: -0.0008068461309311162\n"
+      "Evaluation failed\n",
+      "best parameters: {'x': np.float64(0.0276446020826544), 'y': np.float64(0.28614807644106754)}\n",
+      "best objective: -0.08264494567523134\n"
      ]
     }
    ],
@@ -94,6 +101,100 @@
     "print('best objective:',results['best_objective'])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = results[\"params_tried\"]\n",
+    "y = results[\"objective_values\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(y)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-20.74736909502119 -20.74736909502119\n",
+      "-42.31845686613488 -42.31845686613488\n",
+      "-28.24377819521724 -28.24377819521724\n",
+      "-0.10681853281376912 -0.10681853281376912\n",
+      "-4.671679570731408 -4.671679570731408\n",
+      "-20.018842110693964 -20.018842110693964\n",
+      "-13.151638672697791 -13.151638672697791\n",
+      "-14.253843472516818 -14.253843472516818\n",
+      "-24.466383649475585 -24.466383649475585\n",
+      "-1.9945915709452409 -1.9945915709452409\n",
+      "-16.554145481793704 -16.554145481793704\n",
+      "-1.3652187718750919 -1.3652187718750919\n",
+      "-0.5475161352600229 -0.5475161352600229\n",
+      "-0.1409257338336861 -0.1409257338336861\n",
+      "-0.9355543392875246 -0.9355543392875246\n",
+      "-0.08264494567523134 -0.08264494567523134\n",
+      "-2.3269622867369497 -2.3269622867369497\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p, o in zip(x, y):\n",
+    "    xx = p['x']\n",
+    "    yy = p['y']\n",
+    "    oo = -(xx**2 + yy**2)\n",
+    "    print(o, oo)\n",
+    "          \n",
+    "    assert oo == o "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -104,7 +205,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -118,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/warmup.ipynb b/examples/warmup.ipynb
index 9d31878..53753e2 100644
--- a/examples/warmup.ipynb
+++ b/examples/warmup.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "64520ba9",
    "metadata": {},
    "outputs": [],
@@ -28,14 +28,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "8906f595",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7842b7e7cf8d455b99ed74d4fc0d7c4b",
+       "model_id": "886f322be5a44eac9b42079e3c8c4f44",
        "version_major": 2,
        "version_minor": 0
       },
@@ -70,38 +70,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "b74f0d1e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[({'b': 99, 'a': 71}, 170),\n",
-       " ({'b': 92, 'a': 35}, 127),\n",
-       " ({'a': 40, 'b': 14}, 54),\n",
-       " ({'a': 69, 'b': 97}, 166),\n",
-       " ({'a': 66, 'b': 98}, 164),\n",
-       " ({'a': 52, 'b': 21}, 73),\n",
-       " ({'a': 38, 'b': 77}, 115),\n",
-       " ({'a': 67, 'b': 4}, 71),\n",
-       " ({'a': 99, 'b': 99}, 198),\n",
-       " ({'a': 27, 'b': 7}, 34),\n",
-       " ({'a': 99, 'b': 98}, 197),\n",
-       " ({'a': 98, 'b': 99}, 197),\n",
-       " ({'a': 15, 'b': 3}, 18),\n",
-       " ({'a': 54, 'b': 24}, 78),\n",
-       " ({'a': 51, 'b': 91}, 142),\n",
-       " ({'a': 99, 'b': 97}, 196),\n",
-       " ({'a': 97, 'b': 99}, 196),\n",
-       " ({'a': 47, 'b': 87}, 134),\n",
-       " ({'a': 85, 'b': 18}, 103),\n",
-       " ({'a': 76, 'b': 17}, 93),\n",
-       " ({'a': 98, 'b': 98}, 196),\n",
-       " ({'a': 99, 'b': 96}, 195)]"
+       "[({'b': 8, 'a': 85}, np.int64(93)),\n",
+       " ({'b': 15, 'a': 93}, np.int64(108)),\n",
+       " ({'a': 71, 'b': 19}, np.int64(90)),\n",
+       " ({'a': 41, 'b': 95}, np.int64(136)),\n",
+       " ({'a': 80, 'b': 22}, np.int64(102)),\n",
+       " ({'a': 61, 'b': 51}, np.int64(112)),\n",
+       " ({'a': 41, 'b': 65}, np.int64(106)),\n",
+       " ({'a': 72, 'b': 20}, np.int64(92)),\n",
+       " ({'a': 55, 'b': 99}, np.int64(154)),\n",
+       " ({'a': 11, 'b': 29}, np.int64(40)),\n",
+       " ({'a': 98, 'b': 66}, np.int64(164)),\n",
+       " ({'a': 56, 'b': 32}, np.int64(88)),\n",
+       " ({'a': 40, 'b': 3}, np.int64(43)),\n",
+       " ({'a': 99, 'b': 99}, np.int64(198)),\n",
+       " ({'a': 53, 'b': 14}, np.int64(67)),\n",
+       " ({'a': 98, 'b': 99}, np.int64(197)),\n",
+       " ({'a': 99, 'b': 98}, np.int64(197)),\n",
+       " ({'a': 97, 'b': 99}, np.int64(196)),\n",
+       " ({'a': 99, 'b': 97}, np.int64(196)),\n",
+       " ({'a': 91, 'b': 16}, np.int64(107)),\n",
+       " ({'a': 89, 'b': 15}, np.int64(104)),\n",
+       " ({'a': 98, 'b': 98}, np.int64(196))]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,14 +123,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "65029dad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28039081a31f44ddbab7a84499bf5875",
+       "model_id": "5698243cdab745359c6bd57559ceb018",
        "version_major": 2,
        "version_minor": 0
       },
@@ -159,31 +159,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "8115b7a3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([{'b': 99, 'a': 71}, {'b': 92, 'a': 35}, {'a': 40, 'b': 14},\n",
-       "       {'a': 69, 'b': 97}, {'a': 66, 'b': 98}, {'a': 52, 'b': 21},\n",
-       "       {'a': 38, 'b': 77}, {'a': 67, 'b': 4}, {'a': 99, 'b': 99},\n",
-       "       {'a': 27, 'b': 7}, {'a': 99, 'b': 98}, {'a': 98, 'b': 99},\n",
-       "       {'a': 15, 'b': 3}, {'a': 54, 'b': 24}, {'a': 51, 'b': 91},\n",
-       "       {'a': 99, 'b': 97}, {'a': 97, 'b': 99}, {'a': 47, 'b': 87},\n",
-       "       {'a': 85, 'b': 18}, {'a': 76, 'b': 17}, {'a': 98, 'b': 98},\n",
-       "       {'a': 99, 'b': 96}, {'a': 46, 'b': 44}, {'a': 79, 'b': 68},\n",
-       "       {'a': 96, 'b': 99}, {'a': 75, 'b': 30}, {'a': 80, 'b': 54},\n",
-       "       {'a': 98, 'b': 97}, {'a': 10, 'b': 27}, {'a': 97, 'b': 98},\n",
-       "       {'a': 99, 'b': 95}, {'a': 6, 'b': 91}, {'a': 9, 'b': 99},\n",
-       "       {'a': 95, 'b': 99}, {'a': 97, 'b': 97}, {'a': 91, 'b': 34},\n",
-       "       {'a': 19, 'b': 55}, {'a': 98, 'b': 96}, {'a': 96, 'b': 98},\n",
-       "       {'a': 38, 'b': 97}, {'a': 22, 'b': 59}, {'a': 99, 'b': 94}],\n",
+       "array([{'b': 8, 'a': 85}, {'b': 15, 'a': 93}, {'a': 71, 'b': 19},\n",
+       "       {'a': 41, 'b': 95}, {'a': 80, 'b': 22}, {'a': 61, 'b': 51},\n",
+       "       {'a': 41, 'b': 65}, {'a': 72, 'b': 20}, {'a': 55, 'b': 99},\n",
+       "       {'a': 11, 'b': 29}, {'a': 98, 'b': 66}, {'a': 56, 'b': 32},\n",
+       "       {'a': 40, 'b': 3}, {'a': 99, 'b': 99}, {'a': 53, 'b': 14},\n",
+       "       {'a': 98, 'b': 99}, {'a': 99, 'b': 98}, {'a': 97, 'b': 99},\n",
+       "       {'a': 99, 'b': 97}, {'a': 91, 'b': 16}, {'a': 89, 'b': 15},\n",
+       "       {'a': 98, 'b': 98}, {'a': 50, 'b': 76}, {'a': 80, 'b': 88},\n",
+       "       {'a': 96, 'b': 99}, {'a': 17, 'b': 12}, {'a': 99, 'b': 96},\n",
+       "       {'a': 39, 'b': 48}, {'a': 98, 'b': 97}, {'a': 97, 'b': 98},\n",
+       "       {'a': 38, 'b': 1}, {'a': 95, 'b': 99}, {'a': 45, 'b': 77},\n",
+       "       {'a': 99, 'b': 95}, {'a': 85, 'b': 9}, {'a': 22, 'b': 93},\n",
+       "       {'a': 97, 'b': 97}, {'a': 96, 'b': 98}, {'a': 76, 'b': 57},\n",
+       "       {'a': 98, 'b': 96}, {'a': 98, 'b': 13}, {'a': 94, 'b': 99}],\n",
        "      dtype=object)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,7 +217,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/mango/domain/batch_parameter_sampler.py b/mango/domain/batch_parameter_sampler.py
deleted file mode 100644
index ba7e4a7..0000000
--- a/mango/domain/batch_parameter_sampler.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from collections.abc import Mapping, Iterable
-import warnings
-
-import numpy as np
-from sklearn.utils import check_random_state
-from sklearn.utils.random import sample_without_replacement
-from sklearn.model_selection import ParameterGrid
-
-
-class BatchParameterSampler:
-
-    def __init__(self, param_distribution, n_iter, *, random_state=None):
-        if not isinstance(param_distribution, (Mapping)):
-            raise TypeError(
-                "Parameter distribution is not a dict or "
-                "({!r})".format(param_distribution)
-            )
-
-        dist = param_distribution
-        for key in dist:
-            if not isinstance(dist[key], Iterable) and not hasattr(dist[key], "rvs"):
-                raise TypeError(
-                    "Parameter value is not iterable "
-                    "or distribution (key={!r}, value={!r})".format(key, dist[key])
-                )
-        self.n_iter = n_iter
-        self.random_state = random_state
-        self.dist = param_distribution
-
-    def _is_all_lists(self):
-        return all(not hasattr(v, "rvs") for v in self.dist.values())
-
-    def __iter__(self):
-        rng = check_random_state(self.random_state)
-
-        # if all distributions are given as lists, we want to sample without
-        # replacement
-        if self._is_all_lists():
-            # look up sampled parameter settings in parameter grid
-            param_grid = ParameterGrid(self.dist)
-            grid_size = len(param_grid)
-            n_iter = self.n_iter
-
-            if grid_size < n_iter:
-                warnings.warn(
-                    "The total space of parameters %d is smaller "
-                    "than n_iter=%d. Running %d iterations. For exhaustive "
-                    "searches, use GridSearchCV." % (grid_size, self.n_iter, grid_size),
-                    UserWarning,
-                )
-                n_iter = grid_size
-            for i in sample_without_replacement(grid_size, n_iter, random_state=rng):
-                yield param_grid[i]
-
-        else:
-            # Always sort the keys of a dictionary, for reproducibility
-            items = sorted(self.dist.items())
-            samples = []
-            keys = []
-            for k, v in items:
-                keys.append(k)
-                if hasattr(v, "rvs"):
-                    samples.append(v.rvs(random_state=rng, size=self.n_iter))
-                else:
-                    # forcing object dtype to avoid conversion of mixed datatypes
-                    v = np.array(v, dtype=object)
-                    samples.append(rng.choice(v, size=self.n_iter))
-
-            for sample in zip(*samples):
-                yield dict(zip(keys, sample))
-
-    def __len__(self):
-        """Number of points that will be sampled."""
-        if self._is_all_lists():
-            grid_size = len(ParameterGrid(self.dist))
-            return min(self.n_iter, grid_size)
-        else:
-            return self.n_iter
diff --git a/mango/domain/domain_space.py b/mango/domain/domain_space.py
index 5459345..d092e29 100644
--- a/mango/domain/domain_space.py
+++ b/mango/domain/domain_space.py
@@ -1,41 +1,77 @@
-"""
-Define the domain space abstractions for the Optimizer
-"""
-
+from collections.abc import Mapping, Iterable
 import math
-from scipy.stats._distn_infrastructure import rv_frozen
 import numpy as np
-from collections.abc import Iterable, Callable
+from collections.abc import Callable
 import warnings
 from itertools import compress
+from functools import cached_property
+import warnings
 
-from .batch_parameter_sampler import BatchParameterSampler
+from scipy.stats._distn_infrastructure import rv_frozen
+from scipy.stats._multivariate import multi_rv_frozen
+from sklearn.preprocessing import MinMaxScaler
 
+from mango.domain.parameter_sampler import parameter_sampler
 
-class domain_space:
-    """
-    initializer
-    1) We expect a parameter dictionary as an input from which we will creating a mapping.
-    2) The mapping is storing the categorical variables or discrete variables along with the discrete possible values
-    3) The categorical variables are handeled as one hot encoding values
-    """
 
+class DomainSpace:
     def __init__(
         self,
-        param_dict,
-        domain_size,
+        param_dist: dict,
+        *,
+        param_sampler: Callable = parameter_sampler,
         constraint: Callable = None,
         constraint_max_retries: int = 10,
+        scale_params: bool = False,
     ):
-        self.param_dict = param_dict
+        self.dist = param_dist
+        if not isinstance(self.dist, (Mapping)):
+            raise TypeError(
+                "Parameter distribution is not a dict or " "({!r})".format(self.dist)
+            )
+
+        for key in self.dist:
+            if not isinstance(self.dist[key], Iterable) and not hasattr(
+                self.dist[key], "rvs"
+            ):
+                raise TypeError(
+                    "Parameter value is not iterable "
+                    "or distribution (key={!r}, value={!r})".format(key, self.dist[key])
+                )
 
-        # the domain size to explore using the parameter sampler
-        self.domain_size = domain_size
+        self.param_sampler = param_sampler
         self.constraint = constraint
         self.constraint_max_tries = constraint_max_retries
-
-        # creating a mapping of categorical variables
-        self.create_mappings()
+        self.scale_params = scale_params
+        (
+            self.categorical_params,
+            self.integer_params,
+            self.multivariate_params,
+            self.univariate_params,
+        ) = self.classify_parameters(self.dist)
+        self.domain_size = self.calc_domain_size()
+
+    @cached_property
+    def categorical_index_lookup(self):
+        res = {}
+        for param in self.categorical_params:
+            res[param] = {ele: pos for pos, ele in enumerate(self.dist[param])}
+        return res
+
+    @cached_property
+    def multivariate_param_dimensions(self):
+        res = {}
+        for param in self.multivariate_params:
+            res[param] = len(self.dist[param].mean())
+        return res
+
+    @cached_property
+    def gp_scaler(self):
+        data = self.get_domain()
+        x_gp = self._convert_GP_space(data)
+        scaler = MinMaxScaler()
+        scaler.fit(x_gp)
+        return scaler
 
     def get_domain(self):
         return self.get_random_sample(self.domain_size)
@@ -67,146 +103,186 @@ def get_random_sample(self, size):
         return samples[:size]
 
     def _get_random_sample(self, size):
-        domain_list = list(BatchParameterSampler(self.param_dict, n_iter=size))
-        return domain_list
-
-    """
-    Categorical values/discrete values are considered from the list of each value being str
-
-    We will finally do one-hot-encoding of the list values.
-    Here we will keep some book-keeping information, like number of different values
-    and the mapping of each
-
-    Integer values are considered from list of each value as int or from a range
-    """
-
-    def create_mappings(self):
-        mapping_categorical = dict()
-        mapping_int = dict()
-
-        param_dict = self.param_dict
-        for par in param_dict:
-            if isinstance(param_dict[par], rv_frozen):
-                # FIXME: what if the distribution generators ints , GP would convert it to float
-                pass  # we are not doing anything at present, and will directly use its value for GP.
-
-            elif isinstance(param_dict[par], range):
-                mapping_int[par] = list(param_dict[par])
-
-            elif isinstance(param_dict[par], Iterable):
-
-                # for list with all int we are considering it as non-categorical
-                try:
-                    # this check takes care of numpy ints as well
-                    all_int = all(
-                        x == int(x) and type(x) != bool for x in param_dict[par]
-                    )
-                except (ValueError, TypeError):
-                    all_int = False
-
-                if all_int:
-                    mapping_int[par] = list(param_dict[par])
-
-                # For lists with mixed type, floats or strings we consider them categorical or discrete
-                else:
-                    mapping_categorical[par] = list(param_dict[par])
+        return self.param_sampler(self.dist, size)
 
-        self.mapping_categorical = mapping_categorical
-        self.mapping_int = mapping_int
-
-    """
-    convert the hyperparameters from the param_dict space to the GP space, by converting the
-    categorical variables to one hotencoded, and return a numpy array which can be used to train the GP
-
-    input is the domain_list generated using the Parameter Sampler.
-    """
-
-    def convert_GP_space(self, domain_list):
-        mapping_categorical = self.mapping_categorical
+    def _convert_GP_space(self, domain_list):
+        categorical_params = self.categorical_params
 
         X = []
         for domain in domain_list:
             curr_x = []
-            # for x in domain:
             for x in sorted(domain.keys()):
-                # this value can be directly used, for int too, we will consider it as a float for GP
-                if x not in mapping_categorical:
-                    curr_x.append(domain[x])
-
                 # this is a categorical variable which require special handling
-                elif x in mapping_categorical:
-
-                    size = len(mapping_categorical[x])  # total number of categories.
-
-                    # we need to see the index where: domain[x] appears in mapping[x]
-                    index = mapping_categorical[x].index(domain[x])
-
+                if x in categorical_params:
+                    size = len(self.dist[x])  # total number of categories.
+                    # we need to see the index where domain[x] appears in param_dict[x]
+                    index = self.categorical_index_lookup[x][domain[x]]
                     listofzeros = [0.0] * size
-
                     # We will set the value to one for one hot encoding
                     listofzeros[index] = 1.0
-
                     # expanding current list
                     curr_x = curr_x + listofzeros
+                elif x in self.multivariate_params:
+                    curr_x = curr_x + list(domain[x])
+
+                # this value can be directly used, for int too, we will consider it as a float for GP
+                else:
+                    curr_x.append(domain[x])
 
             X.append(curr_x)
 
         X = np.array(X)
-
         return X
 
-    """
-    Convert from the X_gp space which is a numpy array that can be given input to the gaussian process
-    to the parameter sampler space which is a list of the dict
-
-    We have to reverse the one-hotencoded transformation of the categories to the category name
-    """
+    def convert_GP_space(self, domain_list):
+        """
+        convert the hyperparameters from the param_dict space to the GP space, by converting the
+        categorical variables to one hotencoded, and return a numpy array which can be used to train the GP
+
+        input is the domain_list generated using the Parameter Sampler.
+        """
+        X = self._convert_GP_space(domain_list)
+        if self.scale_params:
+            X = self.gp_scaler.transform(X)
+        return X
 
     def convert_PS_space(self, X_gp):
+        """
+        Convert from the X_gp space which is a numpy array that can be given input to the gaussian process
+        to the parameter sampler space which is a list of the dict
+
+        We have to reverse the one-hotencoded transformation of the categories to the category name
+        """
         X_ps = []
 
-        mapping_categorical = self.mapping_categorical
-        mapping_int = self.mapping_int
-        param_dict = self.param_dict
+        categorical_params = self.categorical_params
+        int_params = self.integer_params
+        mv_params = self.multivariate_params
+        param_dict = self.dist
+
+        if self.scale_params:
+            X_gp = self.gp_scaler.inverse_transform(X_gp)
 
         for i in range(X_gp.shape[0]):
 
             curr_x_gp = X_gp[i]
             # we will create a list of dict, same as parameter samplers
             curr_x_ps = dict()
-
             index = 0
 
             # every sample is from the param_dict
-
             for par in sorted(param_dict.keys()):
-                # for par in param_dict:
-                # print('par is:',par)
-
                 # this has to have integer values
-                if par in mapping_int:
+                if par in int_params:
                     curr_x_ps[par] = int(round(curr_x_gp[index]))
                     index = index + 1
 
                 # this par is a categorical variable and we need to handle it carefully
-                elif par in mapping_categorical:
-                    size = len(mapping_categorical[par])  # total number of categories.
-
+                elif par in categorical_params:
+                    size = len(param_dict[par])  # total number of categories.
                     one_hot_encoded = curr_x_gp[index : index + size]
-                    category_type = np.argmax(one_hot_encoded)
-
-                    category_type = mapping_categorical[par][category_type]
-
-                    curr_x_ps[par] = category_type
-
+                    category_idx = np.argmax(one_hot_encoded)
+                    category_val = param_dict[par][category_idx]
+                    curr_x_ps[par] = category_val
                     # we have processed the entire one-hotencoded
                     index = index + size
 
+                elif par in mv_params:
+                    dim = self.multivariate_param_dimensions[par]
+                    curr_x_ps[par] = curr_x_gp[index : index + dim].tolist()
+                    index += dim
+
                 # this is a float value
                 else:
                     curr_x_ps[par] = curr_x_gp[index]
                     index = index + 1
 
             X_ps.append(curr_x_ps)
-
         return X_ps
+
+    def calc_domain_size(self) -> int:
+        """
+        Return an estimate of number of points in the search space.
+        """
+        # Minimum and maximum domain size
+        domain_min = 50000
+        domain_max = 500000
+
+        ans = 1
+
+        for par, dist in self.dist.items():
+            if par in self.univariate_params:
+                loc, scale = dist.args
+                min_scale = 1
+                scale = int(scale)
+                if scale < min_scale:
+                    scale = min_scale
+
+                ans = ans * scale * 50
+
+            elif par in self.multivariate_params:
+                # FIXME:  assuming scale 10 for multivariate as don't see a way to get it from the dist
+                ans = ans * 10
+
+            elif par in self.integer_params:
+                ans = ans * len(dist)
+
+            elif par in self.categorical_params:
+                ans = ans * len(dist)
+
+        if ans < domain_min:
+            ans = domain_min
+
+        if ans > domain_max:
+            ans = domain_max
+
+        return ans
+
+    @staticmethod
+    def classify_parameters(param_dict: dict) -> (set, set, set, set):
+        """
+        Identify parameters that are categorical or integer types.
+        Categorical values/discrete values are considered from the list of each value being str
+        Integer values are considered from list of each value as int or from a range
+        Multivariate values are random variables defined using scipy stats multivariate distribution
+        Univariate values are random variable defined using scipy stats distribution
+
+        :param param_dict: dictionary of parameter distributions
+        :return: a tuple of four sets where first element is the set of parameter that are categorical,
+        second element is the set of parameters that are integer, third element is the set of params that are
+        multivariate, and fourth element is the set of params that are univariate
+        """
+        categorical_params = set()
+        int_params = set()
+        multivariate_params = set()
+        univariate_params = set()
+
+        for par in param_dict:
+            if isinstance(param_dict[par], rv_frozen):
+                # FIXME: what if the distribution generators ints , GP would convert it to float
+                univariate_params.add(par)
+            elif isinstance(param_dict[par], multi_rv_frozen):
+                multivariate_params.add(par)
+
+            elif isinstance(param_dict[par], range):
+                int_params.add(par)
+
+            elif isinstance(param_dict[par], Iterable):
+                # for list with all int we are considering it as non-categorical
+                try:
+                    # this check takes care of numpy ints as well
+                    all_int = all(
+                        x == int(x) and type(x) != bool for x in param_dict[par]
+                    )
+                except (ValueError, TypeError):
+                    all_int = False
+
+                if all_int:
+                    int_params.add(par)
+                # For lists with mixed type, floats or strings we consider them categorical or discrete
+                else:
+                    categorical_params.add(par)
+            else:
+                warnings.warn(f"Parameter {par} could not be classified")
+
+        return (categorical_params, int_params, multivariate_params, univariate_params)
diff --git a/mango/domain/parameter_sampler.py b/mango/domain/parameter_sampler.py
new file mode 100644
index 0000000..b5bed6b
--- /dev/null
+++ b/mango/domain/parameter_sampler.py
@@ -0,0 +1,57 @@
+from abc import ABCMeta, abstractmethod
+
+import warnings
+
+import numpy as np
+from sklearn.utils import check_random_state
+from sklearn.utils.random import sample_without_replacement
+from sklearn.model_selection import ParameterGrid
+
+
+def parameter_sampler(
+    param_dist: dict, n_size: int, *, random_state=None
+) -> list[dict]:
+    """
+    :param param_dist: dict of parameter distributions
+    :param n_size: number of samples to return
+    :param random_state: specify to reproduce results
+    :return: list of samples mapping each parameter to one of its
+        allowed values.
+    """
+    rng = check_random_state(random_state)
+
+    # if all distributions are given as lists, we want to sample without
+    # replacement
+    if all(not hasattr(v, "rvs") for v in param_dist.values()):
+        # look up sampled parameter settings in parameter grid
+        param_grid = ParameterGrid(param_dist)
+        grid_size = len(param_grid)
+
+        if grid_size < n_size:
+            warnings.warn(
+                "The total space of parameters %d is smaller "
+                "than n_iter=%d. Running %d iterations. For exhaustive "
+                "searches, use GridSearchCV." % (grid_size, n_size, grid_size),
+                UserWarning,
+            )
+            n_size = grid_size
+        return [
+            param_grid[i]
+            for i in sample_without_replacement(grid_size, n_size, random_state=rng)
+        ]
+
+    else:
+        # Always sort the keys of a dictionary, for reproducibility
+        items = sorted(param_dist.items())
+        samples = []
+        keys = []
+        for k, v in items:
+            keys.append(k)
+            if hasattr(v, "rvs"):
+                samples.append(v.rvs(random_state=rng, size=n_size).tolist())
+            else:
+                # forcing object dtype to avoid conversion of mixed datatypes
+                v = np.array(v, dtype=object)
+                samples.append(rng.choice(v, size=n_size))
+
+        return [dict(zip(keys, sample)) for sample in zip(*samples)]
diff --git a/mango/metatuner.py b/mango/metatuner.py
index 62e7da5..78eb037 100644
--- a/mango/metatuner.py
+++ b/mango/metatuner.py
@@ -6,9 +6,8 @@
 """
 
 from dataclasses import dataclass
-from mango.domain.domain_space import domain_space
+from mango.domain.domain_space import DomainSpace
 from mango.optimizer.bayesian_learning import BayesianLearning
-from scipy.stats._distn_infrastructure import rv_frozen
 import numpy as np
 from tqdm.auto import tqdm
 import random
@@ -77,43 +76,6 @@ def run(self):
         self.results = self.runExponentialTuner()
         return self.results
 
-    @staticmethod
-    def calculateDomainSize(param_dict):
-        """
-        Calculating the domain size to be explored for finding
-        optimum of bayesian optimizer
-        """
-        # Minimum and maximum domain size
-        domain_min = 5000
-        domain_max = 30000
-
-        domain_size = 1
-
-        for par in param_dict:
-            if isinstance(param_dict[par], rv_frozen):
-                distrib = param_dict[par]
-                loc, scale = distrib.args
-                min_scale = 1
-                scale = int(scale)
-                if scale < min_scale:
-                    scale = min_scale
-
-                domain_size = domain_size * scale * 50
-
-            elif isinstance(param_dict[par], range):
-                domain_size = domain_size * len(param_dict[par])
-
-            elif isinstance(param_dict[par], list):
-                domain_size = domain_size * len(param_dict[par])
-
-        if domain_size < domain_min:
-            domain_size = domain_min
-
-        if domain_size > domain_max:
-            domain_size = domain_max
-
-        return domain_size
-
     # return the max value of Y from current evaluations
     def get_max_y_value(self, Y_dict_array):
         values = Y_dict_array[0]
@@ -155,8 +117,7 @@ def runExponentialTuner(self):
 
         ds = []
         for i in self.param_dict_list:
-            domain_size = self.calculateDomainSize(i)
-            ds.append(domain_space(i, domain_size))
+            ds.append(DomainSpace(i))
 
         # dict of list for each obj function
         X_dict_list = {}
diff --git a/mango/tuner.py b/mango/tuner.py
index 09219e9..23b6b51 100644
--- a/mango/tuner.py
+++ b/mango/tuner.py
@@ -1,147 +1,114 @@
 """
 Main Tuner Class which uses other abstractions.
-General usage is to find the optimal hyper-parameters of the classifier
+General usage is to find the optimal hyperparameters of the classifier
 """
 
 import copy
-from collections.abc import Mapping, Iterable
+from collections.abc import Mapping
 from dataclasses import dataclass
 import logging
 import random
 from typing import Callable
-
-from mango.domain.domain_space import domain_space
-from mango.optimizer.bayesian_learning import BayesianLearning
-from scipy.stats._distn_infrastructure import rv_frozen
+import warnings
 
 from tqdm.auto import tqdm
 import numpy as np
 
-# setting warnings to ignore for now
-import warnings
+from mango.domain.domain_space import DomainSpace
+from mango.optimizer.bayesian_learning import BayesianLearning
+from mango.domain.parameter_sampler import parameter_sampler
 
+# setting warnings to ignore for now
 warnings.filterwarnings("ignore")
 
 _logger = logging.getLogger(__name__)
 
 
-class Tuner:
-    @dataclass
-    class Config:
-        domain_size: int = None
-        initial_random: int = 2
-        initial_custom: list = None
-        num_iteration: int = 20
-        batch_size: int = 1
-        optimizer: str = "Bayesian"
-        parallel_strategy: str = "clustering"
-        surrogate: object = None  # used to test different kernel functions
-        valid_optimizers = ["Bayesian", "Random"]
-        valid_parallel_strategies = ["penalty", "clustering"]
-        alpha: float = 2.0
-        exploration: float = 1.0
-        exploration_decay: float = 0.9
-        exploration_min: float = 0.1
-        fixed_domain: bool = False
-        early_stopping: Callable = None
-        constraint: Callable = None
-
-        def __post_init__(self):
-            if self.optimizer not in self.valid_optimizers:
-                raise ValueError(
-                    f"optimizer: {self.optimizer} is not valid, should be one of {self.valid_optimizers}"
-                )
-            if self.parallel_strategy not in self.valid_parallel_strategies:
-                raise ValueError(
-                    f"parallel strategy: {self.parallel_strategy} is not valid, should be one of {self.valid_parallel_strategies}"
-                )
+@dataclass
+class TunerConfig:
+    domain_size: int = None
+    initial_random: int = 2
+    initial_custom: list = None
+    num_iteration: int = 20
+    batch_size: int = 1
+    optimizer: str = "Bayesian"
+    parallel_strategy: str = "clustering"
+    surrogate: object = None  # used to test different kernel functions
+    valid_optimizers = ["Bayesian", "Random"]
+    valid_parallel_strategies = ["penalty", "clustering"]
+    alpha: float = 2.0
+    exploration: float = 1.0
+    exploration_decay: float = 0.9
+    exploration_min: float = 0.1
+    fixed_domain: bool = False
+    early_stopping: Callable = None
+    constraint: Callable = None
+    param_sampler: Callable = parameter_sampler
+    scale_params: bool = False
+
+    def __post_init__(self):
+        if self.optimizer not in self.valid_optimizers:
+            raise ValueError(
+                f"optimizer: {self.optimizer} is not valid, should be one of {self.valid_optimizers}"
+            )
+        if self.parallel_strategy not in self.valid_parallel_strategies:
+            raise ValueError(
+                f"parallel strategy: {self.parallel_strategy} is not valid,"
+                f" should be one of {self.valid_parallel_strategies}"
+            )
+
+    @property
+    def is_bayesian(self):
+        return self.optimizer == "Bayesian"
 
-        @property
-        def is_bayesian(self):
-            return self.optimizer == "Bayesian"
+    @property
+    def is_random(self):
+        return self.optimizer == "Random"
 
-        @property
-        def is_random(self):
-            return self.optimizer == "Random"
+    @property
+    def strategy_is_penalty(self):
+        return self.parallel_strategy == "penalty"
 
-        @property
-        def strategy_is_penalty(self):
-            return self.parallel_strategy == "penalty"
+    @property
+    def strategy_is_clustering(self):
+        return self.parallel_strategy == "clustering"
 
-        @property
-        def strategy_is_clustering(self):
-            return self.parallel_strategy == "clustering"
+    def early_stop(self, results):
+        if self.early_stopping is None:
+            return False
 
-        def early_stop(self, results):
-            if self.early_stopping is None:
-                return False
+        results = copy.deepcopy(results)
+        return self.early_stopping(results)
 
-            results = copy.deepcopy(results)
-            return self.early_stopping(results)
+
+class Tuner:
 
     def __init__(self, param_dict, objective, conf_dict=None):
 
-        self.param_dict = param_dict
         self.objective_function = objective
         self.maximize_objective = True
 
         if conf_dict is None:
             conf_dict = {}
 
-        self.config = Tuner.Config(**conf_dict)
+        self.config = TunerConfig(**conf_dict)
+        self.ds = DomainSpace(
+            param_dict,
+            param_sampler=self.config.param_sampler,
+            constraint=self.config.constraint,
+            scale_params=self.config.scale_params,
+        )
 
-        if self.config.domain_size is None:
-            self.config.domain_size = self.calculateDomainSize(self.param_dict)
+        if self.config.domain_size is not None:
+            self.ds.domain_size = self.config.domain_size
 
         # overwrite batch size if given as a property of objective function
         if hasattr(objective, "batch_size"):
             self.config.batch_size = objective.batch_size
 
-        # save domain size
-        self.ds = domain_space(
-            self.param_dict, self.config.domain_size, constraint=self.config.constraint
-        )
-
         # stores the results of using the tuner
         self.results = dict()
 
-    @staticmethod
-    def calculateDomainSize(param_dict):
-        """
-        Calculating the domain size to be explored for finding
-        optimum of bayesian optimizer
-        """
-        # Minimum and maximum domain size
-        domain_min = 50000
-        domain_max = 500000
-
-        domain_size = 1
-
-        for par in param_dict:
-            if isinstance(param_dict[par], rv_frozen):
-                distrib = param_dict[par]
-                loc, scale = distrib.args
-                min_scale = 1
-                scale = int(scale)
-                if scale < min_scale:
-                    scale = min_scale
-
-                domain_size = domain_size * scale * 50
-
-            elif isinstance(param_dict[par], range):
-                domain_size = domain_size * len(param_dict[par])
-
-            elif isinstance(param_dict[par], list):
-                domain_size = domain_size * len(param_dict[par])
-
-        if domain_size < domain_min:
-            domain_size = domain_min
-
-        if domain_size > domain_max:
-            domain_size = domain_max
-
-        return domain_size
-
     def run(self):
         if self.config.is_bayesian:
             self.results = self.runBayesianOptimizer()
@@ -163,15 +130,15 @@ def process_initial_custom(self):
         init_values = self.config.initial_custom
 
         if all(isinstance(v, Mapping) for v in init_values):
-            X_tried = copy.deepcopy(init_values)
-            X_list, Y_list = self.runUserObjective(X_tried)
-            return X_list, Y_list, X_tried
+            X_init = copy.deepcopy(init_values)
+            X_list, Y_list = self.runUserObjective(X_init)
+            return X_list, Y_list
         elif all(isinstance(v, tuple) and len(v) == 2 for v in init_values):
             X_list = copy.deepcopy([v[0] for v in init_values])
             Y_list = np.array([v[1] for v in init_values])
             if self.maximize_objective is False:
                 Y_list = -1 * Y_list
-            return X_list, Y_list, X_list
+            return X_list, Y_list
         else:
             raise TypeError(
                 f"Elements of initial_custom param should be either a dict of params or tuple (params, y),"
@@ -183,17 +150,16 @@ def run_initial(self):
             return self.process_initial_custom()
         else:
             # getting first few random values
-            X_tried = self.ds.get_random_sample(self.config.initial_random)
-            X_list, Y_list = self.runUserObjective(X_tried)
+            X_init = self.ds.get_random_sample(self.config.initial_random)
+            X_list, Y_list = self.runUserObjective(X_init)
 
             # in case initial random results are invalid try different samples
             n_tries = 1
             while len(Y_list) < self.config.initial_random and n_tries < 3:
-                X_tried2 = self.ds.get_random_sample(
+                X_init = self.ds.get_random_sample(
                     self.config.initial_random - len(Y_list)
                 )
-                X_list2, Y_list2 = self.runUserObjective(X_tried2)
-                X_tried2.extend(X_tried2)
+                X_list2, Y_list2 = self.runUserObjective(X_init)
                 X_list = np.append(X_list, X_list2)
                 Y_list = np.append(Y_list, Y_list2)
                 n_tries += 1
@@ -202,18 +168,18 @@ def run_initial(self):
                 raise ValueError(
                     "No valid configuration found to initiate the Bayesian Optimizer"
                 )
-        return X_list, Y_list, X_tried
+        return X_list, Y_list
 
     def runBayesianOptimizer(self):
         results = dict()
 
-        X_list, Y_list, X_tried = self.run_initial()
+        X_list, Y_list = self.run_initial()
 
-        # evaluated hyper parameters are used
+        # evaluated hyperparameters are used
         X_init = self.ds.convert_GP_space(X_list)
         Y_init = Y_list.reshape(len(Y_list), 1)
 
-        # setting the initial random hyper parameters tried
+        # setting the initial random hyperparameters tried
         results["random_params"] = X_list
         results["random_params_objective"] = Y_list
 
@@ -226,7 +192,7 @@ def runBayesianOptimizer(self):
         X_sample = X_init
         Y_sample = Y_init
 
-        hyper_parameters_tried = X_tried
+        hyper_parameters_tried = X_list
         objective_function_values = Y_list
         surrogate_values = Y_list
 
@@ -234,11 +200,10 @@ def runBayesianOptimizer(self):
 
         domain_list = self.ds.get_domain()
         X_domain_np = self.ds.convert_GP_space(domain_list)
-        context = None
 
         # running the iterations
         pbar = tqdm(range(self.config.num_iteration))
-        for i in pbar:
+        for _ in pbar:
 
             # adding a Minimum exploration to explore independent of UCB
             if random.random() < self.config.exploration:
@@ -301,7 +266,7 @@ def runBayesianOptimizer(self):
             X_sample = np.vstack((X_sample, X_next_batch))
             Y_sample = np.vstack((Y_sample, Y_next_batch))
 
-            # referesh domain if not fixed
+            # refresh domain if not fixed
             if not self.config.fixed_domain:
                 domain_list = self.ds.get_domain()
                 X_domain_np = self.ds.convert_GP_space(domain_list)
diff --git a/poetry.lock b/poetry.lock
index d048deb..7d5257e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,14 +2,14 @@
 
 [[package]]
 name = "anyio"
-version = "4.3.0"
+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
-    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
 ]
 
 [package.dependencies]
@@ -165,54 +165,39 @@ six = "*"
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.14.0"
+version = "2.16.0"
 description = "Internationalization utilities"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
-    {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
-    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
-    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
-]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -237,34 +222,34 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "24.2.0"
+version = "24.8.0"
 description = "The uncompromising code formatter."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "black-24.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d533d5e3259720fdbc1b37444491b024003e012c5173f7d06825a77508085430"},
-    {file = "black-24.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:61a0391772490ddfb8a693c067df1ef5227257e72b0e4108482b8d41b5aee13f"},
-    {file = "black-24.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:992e451b04667116680cb88f63449267c13e1ad134f30087dec8527242e9862a"},
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+    {file = "black-24.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:73bbf84ed136e45d451a260c6b73ed674652f90a2b3211d6a35e78054563a9bb"},
+    {file = "black-24.8.0-py3-none-any.whl", hash = "sha256:972085c618ee94f402da1af548a4f218c754ea7e5dc70acb168bfaca4c2542ed"},
+    {file = "black-24.8.0.tar.gz", hash = "sha256:2500945420b6784c38b9ee885af039f5e7471ef284ab03fa35ecdde4688cd83f"},
 ]
 
 [package.dependencies]
@@ -303,76 +288,91 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
-    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
 name = "cffi"
-version = "1.16.0"
+version = "1.17.0"
 description = "Foreign Function Interface for Python calling C code."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
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 [[package]]
@@ -581,14 +581,14 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
@@ -611,14 +611,14 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastjsonschema"
-version = "2.19.1"
+version = "2.20.0"
 description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -650,14 +650,14 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.4"
+version = "1.0.5"
 description = "A minimal low-level HTTP client."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -668,7 +668,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (>=1.0.0,<2.0.0)"]
-trio = ["trio (>=0.22.0,<0.25.0)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
 
 [[package]]
 name = "httpx"
@@ -697,55 +697,16 @@ socks = ["socksio (>=1.0.0,<2.0.0)"]
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.8"
 description = "Internationalized Domain Names in Applications (IDNA)"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
-files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
-]
-
-[[package]]
-name = "importlib-metadata"
-version = "7.0.1"
-description = "Read metadata from Python packages"
-category = "dev"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_metadata-7.0.1-py3-none-any.whl", hash = "sha256:4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e"},
-    {file = "importlib_metadata-7.0.1.tar.gz", hash = "sha256:f238736bb06590ae52ac1fab06a3a9ef1d8dce2b7a35b5ab329371d6c8f5d2cc"},
-]
-
-[package.dependencies]
-zipp = ">=0.5"
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.1.2"
-description = "Read resources from Python packages"
-category = "dev"
-optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 files = [
-    {file = "importlib_resources-6.1.2-py3-none-any.whl", hash = "sha256:9a0a862501dc38b68adebc82970140c9e4209fc99601782925178f8386339938"},
-    {file = "importlib_resources-6.1.2.tar.gz", hash = "sha256:308abf8474e2dba5f867d279237cd4076482c3de7104a40b41426370e891549b"},
+    {file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
+    {file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
 ]
 
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -760,14 +721,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.3"
+version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.3-py3-none-any.whl", hash = "sha256:5aa086a4175b0229d4eca211e181fb473ea78ffd9869af36ba7694c947302a21"},
-    {file = "ipykernel-6.29.3.tar.gz", hash = "sha256:e14c250d1f9ea3989490225cc1a542781b095a18a19447fcf2b5eaf7d0ac5bd2"},
+    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
+    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
 ]
 
 [package.dependencies]
@@ -794,62 +755,61 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.26.0"
 description = "IPython: Productive Interactive Computing"
 category = "dev"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
-    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
+    {file = "ipython-8.26.0-py3-none-any.whl", hash = "sha256:e6b347c27bdf9c32ee9d31ae85defc525755a1869f14057e900675b9e8d6e6ff"},
+    {file = "ipython-8.26.0.tar.gz", hash = "sha256:1cec0fbba8404af13facebe83d04436a7434c7400e59f47acf467c64abd0956c"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
-traitlets = ">=5"
-typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+traitlets = ">=5.13.0"
+typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "ipywidgets"
-version = "8.1.2"
+version = "8.1.5"
 description = "Jupyter interactive widgets"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
-    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
+    {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"},
+    {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"},
 ]
 
 [package.dependencies]
 comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.10,<3.1.0"
+jupyterlab-widgets = ">=3.0.12,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.10,<4.1.0"
+widgetsnbextension = ">=4.0.12,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -891,14 +851,14 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
 ]
 
 [package.dependencies]
@@ -909,74 +869,69 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.3.2"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
-    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
 [[package]]
 name = "json5"
-version = "0.9.22"
+version = "0.9.25"
 description = "A Python implementation of the JSON5 data format."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.22-py3-none-any.whl", hash = "sha256:6621007c70897652f8b5d03885f732771c48d1925591ad989aa80c7e0e5ad32f"},
-    {file = "json5-0.9.22.tar.gz", hash = "sha256:b729bde7650b2196a35903a597d2b704b8fdf8648bfb67368cfb79f1174a17bd"},
+    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
 ]
 
-[package.extras]
-dev = ["hypothesis"]
-
 [[package]]
 name = "jsonpointer"
-version = "2.4"
+version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+python-versions = ">=3.7"
 files = [
-    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.21.1"
+version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
-    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
 ]
 
 [package.dependencies]
 attrs = ">=22.2.0"
 fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
 uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
 
 [[package]]
 name = "jsonschema-specifications"
@@ -991,7 +946,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -1017,18 +971,17 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.6.0"
+version = "8.6.2"
 description = "Jupyter protocol implementation and client libraries"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
-    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
+    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
+    {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
@@ -1037,7 +990,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-console"
@@ -1066,14 +1019,14 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.1"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
-    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
@@ -1083,18 +1036,18 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.9.0"
+version = "0.10.0"
 description = "Jupyter Event System library"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
-    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
 ]
 
 [package.dependencies]
@@ -1113,67 +1066,66 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.4"
+version = "2.2.5"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.4.tar.gz", hash = "sha256:5e50033149344065348e688608f3c6d654ef06d9856b67655bd7b6bac9ee2d59"},
-    {file = "jupyter_lsp-2.2.4-py3-none-any.whl", hash = "sha256:da61cb63a16b6dff5eac55c2699cc36eac975645adee02c41bdfc03bf4802e77"},
+    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
+    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.13.0"
+version = "2.14.2"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.13.0-py3-none-any.whl", hash = "sha256:77b2b49c3831fbbfbdb5048cef4350d12946191f833a24e5f83e5f8f4803e97b"},
-    {file = "jupyter_server-2.13.0.tar.gz", hash = "sha256:c80bfb049ea20053c3d9641c2add4848b38073bf79f1729cea1faed32fc1c78e"},
+    {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"},
+    {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"},
 ]
 
 [package.dependencies]
 anyio = ">=3.1.0"
-argon2-cffi = "*"
-jinja2 = "*"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 jupyter-events = ">=0.9.0"
-jupyter-server-terminals = "*"
+jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
-overrides = "*"
-packaging = "*"
-prometheus-client = "*"
-pywinpty = {version = "*", markers = "os_name == \"nt\""}
+overrides = ">=5.0"
+packaging = ">=22.0"
+prometheus-client = ">=0.9"
+pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
 pyzmq = ">=24"
 send2trash = ">=1.8.2"
 terminado = ">=0.8.3"
 tornado = ">=6.2.0"
 traitlets = ">=5.6.0"
-websocket-client = "*"
+websocket-client = ">=1.7"
 
 [package.extras]
-docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
+docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.2"
+version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.5.2-py3-none-any.whl", hash = "sha256:1b80c12765da979513c42c90215481bbc39bd8ae7c0350b4f85bc3eb58d0fa80"},
-    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
 ]
 
 [package.dependencies]
@@ -1186,38 +1138,38 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.1.3"
+version = "4.2.4"
 description = "JupyterLab computational environment"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.3-py3-none-any.whl", hash = "sha256:67dbec7057c6ad46f08a3667a80bdb890df9453822c93b5ddfd5e8313a718ef9"},
-    {file = "jupyterlab-4.1.3.tar.gz", hash = "sha256:b85bd8766f995d23461e1f68a0cbc688d23e0af2b6f42a7768fc7b1826b2ec39"},
+    {file = "jupyterlab-4.2.4-py3-none-any.whl", hash = "sha256:807a7ec73637744f879e112060d4b9d9ebe028033b7a429b2d1f4fc523d00245"},
+    {file = "jupyterlab-4.2.4.tar.gz", hash = "sha256:343a979fb9582fd08c8511823e320703281cd072a0049bcdafdc7afeda7f2537"},
 ]
 
 [package.dependencies]
 async-lru = ">=1.0.0"
 httpx = ">=0.25.0"
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
-ipykernel = "*"
+ipykernel = ">=6.5.0"
 jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.19.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
-tomli = {version = "*", markers = "python_version < \"3.11\""}
+setuptools = ">=40.1.0"
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.2.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.1)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post6)", "matplotlib (==3.8.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
+docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
 
 [[package]]
 name = "jupyterlab-pygments"
@@ -1233,19 +1185,18 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.3"
+version = "2.27.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
-    {file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
+    {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
+    {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
 ]
 
 [package.dependencies]
 babel = ">=2.10"
-importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0.3"
 json5 = ">=0.9.0"
 jsonschema = ">=4.18.0"
@@ -1256,18 +1207,18 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.10"
+version = "3.0.13"
 description = "Jupyter interactive widgets for JupyterLab"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_widgets-3.0.10-py3-none-any.whl", hash = "sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64"},
-    {file = "jupyterlab_widgets-3.0.10.tar.gz", hash = "sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0"},
+    {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"},
+    {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
 ]
 
 [[package]]
@@ -1342,14 +1293,14 @@ files = [
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.6"
+version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-inline-0.1.6.tar.gz", hash = "sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304"},
-    {file = "matplotlib_inline-0.1.6-py3-none-any.whl", hash = "sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311"},
+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
 ]
 
 [package.dependencies]
@@ -1381,14 +1332,14 @@ files = [
 
 [[package]]
 name = "nbclient"
-version = "0.9.0"
+version = "0.10.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "dev"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "nbclient-0.9.0-py3-none-any.whl", hash = "sha256:a3a1ddfb34d4a9d17fc744d655962714a866639acd30130e9be84191cd97cd15"},
-    {file = "nbclient-0.9.0.tar.gz", hash = "sha256:4b28c207877cf33ef3a9838cdc7a54c5ceff981194a82eac59d558f05487295e"},
+    {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
+    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
 ]
 
 [package.dependencies]
@@ -1400,25 +1351,24 @@ traitlets = ">=5.4"
 [package.extras]
 dev = ["pre-commit"]
 docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
-test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.16.2"
+version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.16.2-py3-none-any.whl", hash = "sha256:0c01c23981a8de0220255706822c40b751438e32467d6a686e26be08ba784382"},
-    {file = "nbconvert-7.16.2.tar.gz", hash = "sha256:8310edd41e1c43947e4ecf16614c61469ebc024898eb808cce0999860fc9fb16"},
+    {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
+    {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
 bleach = "!=5.0.0"
 defusedxml = "*"
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
@@ -1433,30 +1383,30 @@ tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
 docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
 webpdf = ["playwright"]
 
 [[package]]
 name = "nbformat"
-version = "5.9.2"
+version = "5.10.4"
 description = "The Jupyter Notebook format"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbformat-5.9.2-py3-none-any.whl", hash = "sha256:1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9"},
-    {file = "nbformat-5.9.2.tar.gz", hash = "sha256:5f98b5ba1997dff175e77e0c17d5c10a96eaed2cbd1de3533d1fc35d5e111192"},
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
 ]
 
 [package.dependencies]
-fastjsonschema = "*"
+fastjsonschema = ">=2.15"
 jsonschema = ">=2.6"
-jupyter-core = "*"
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -1477,27 +1427,27 @@ files = [
 
 [[package]]
 name = "notebook"
-version = "7.1.1"
+version = "7.2.1"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "notebook-7.1.1-py3-none-any.whl", hash = "sha256:197d8e0595acabf4005851c8716e952a81b405f7aefb648067a761fbde267ce7"},
-    {file = "notebook-7.1.1.tar.gz", hash = "sha256:818e7420fa21f402e726afb9f02df7f3c10f294c02e383ed19852866c316108b"},
+    {file = "notebook-7.2.1-py3-none-any.whl", hash = "sha256:f45489a3995746f2195a137e0773e2130960b51c9ac3ce257dbc2705aab3a6ca"},
+    {file = "notebook-7.2.1.tar.gz", hash = "sha256:4287b6da59740b32173d01d641f763d292f49c30e7a51b89c46ba8473126341e"},
 ]
 
 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.1.1,<4.2"
-jupyterlab-server = ">=2.22.1,<3"
+jupyterlab = ">=4.2.0,<4.3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
 
 [package.extras]
 dev = ["hatch", "pre-commit"]
 docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
 name = "notebook-shim"
@@ -1519,40 +1469,64 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numpy"
-version = "1.24.4"
+version = "2.1.0"
 description = "Fundamental package for array computing in Python"
 category = "main"
 optional = false
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-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.2.2"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1707,14 +1658,14 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.43"
+version = "3.0.47"
 description = "Library for building powerful interactive command lines in Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 [package.dependencies]
@@ -1722,28 +1673,29 @@ wcwidth = "*"
 
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+version = "6.0.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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 ]
 
 [package.extras]
@@ -1763,14 +1715,14 @@ files = [
 
 [[package]]
 name = "pure-eval"
-version = "0.2.2"
+version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -1778,30 +1730,29 @@ tests = ["pytest"]
 
 [[package]]
 name = "pycparser"
-version = "2.21"
+version = "2.22"
 description = "C parser in Python"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "pygments"
-version = "2.17.2"
+version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
@@ -1854,18 +1805,6 @@ files = [
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 ]
 
-[[package]]
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-description = "World timezone definitions, modern and historical"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
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-
 [[package]]
 name = "pywin32"
 version = "306"
@@ -1908,166 +1847,184 @@ files = [
 
 [[package]]
 name = "pyyaml"
-version = "6.0.1"
+version = "6.0.2"
 description = "YAML parser and emitter for Python"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 name = "send2trash"
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 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 category = "dev"
 optional = false
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 files = [
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 [package.extras]
@@ -2395,6 +2366,23 @@ nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
 objc = ["pyobjc-framework-Cocoa"]
 win32 = ["pywin32"]
 
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+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (>=1.11.0,<1.12.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -2421,14 +2409,14 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.5"
+version = "2.6"
 description = "A modern CSS selector implementation for Beautiful Soup."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
-    {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
+    {file = "soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9"},
+    {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"},
 ]
 
 [[package]]
@@ -2453,14 +2441,14 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "terminado"
-version = "0.18.0"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "terminado-0.18.0-py3-none-any.whl", hash = "sha256:87b0d96642d0fe5f5abd7783857b9cab167f221a39ff98e3b9619a788a3c0f2e"},
-    {file = "terminado-0.18.0.tar.gz", hash = "sha256:1ea08a89b835dd1b8c0c900d92848147cef2537243361b2e3f4dc15df9b6fded"},
+    {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"},
+    {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
 ]
 
 [package.dependencies]
@@ -2475,26 +2463,26 @@ typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.3.0"
+version = "3.5.0"
 description = "threadpoolctl"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "threadpoolctl-3.3.0-py3-none-any.whl", hash = "sha256:6155be1f4a39f31a18ea70f94a77e0ccd57dced08122ea61109e7da89883781e"},
-    {file = "threadpoolctl-3.3.0.tar.gz", hash = "sha256:5dac632b4fa2d43f42130267929af3ba01399ef4bd1882918e92dbc30365d30c"},
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
 ]
 
 [[package]]
 name = "tinycss2"
-version = "1.2.1"
+version = "1.3.0"
 description = "A tiny CSS parser"
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"},
-    {file = "tinycss2-1.2.1.tar.gz", hash = "sha256:8cff3a8f066c2ec677c06dbc7b45619804a6938478d9d73c284b29d14ecb0627"},
+    {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
+    {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
 ]
 
 [package.dependencies]
@@ -2502,7 +2490,7 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "tomli"
@@ -2518,35 +2506,35 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.4"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "dev"
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
 files = [
-    {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
-    {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
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-    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
-    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
-    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
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-    {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
-    {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
+    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
+    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"},
+    {file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"},
+    {file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"},
+    {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
 ]
 
 [[package]]
 name = "tqdm"
-version = "4.66.2"
+version = "4.66.5"
 description = "Fast, Extensible Progress Meter"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
-    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
+    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
+    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
 ]
 
 [package.dependencies]
@@ -2560,42 +2548,42 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.1"
+version = "5.14.3"
 description = "Traitlets Python configuration system"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
-    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.20240106"
+version = "2.9.0.20240821"
 description = "Typing stubs for python-dateutil"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
-    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
+    {file = "types-python-dateutil-2.9.0.20240821.tar.gz", hash = "sha256:9649d1dcb6fef1046fb18bebe9ea2aa0028b160918518c34589a46045f6ebd98"},
+    {file = "types_python_dateutil-2.9.0.20240821-py3-none-any.whl", hash = "sha256:f5889fcb4e63ed4aaa379b44f93c32593d50b9a94c9a60a0c854d8cc3511cd57"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
 [[package]]
@@ -2615,14 +2603,14 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
-    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]
 
 [package.extras]
@@ -2645,19 +2633,19 @@ files = [
 
 [[package]]
 name = "webcolors"
-version = "1.13"
+version = "24.8.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"},
-    {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"},
+    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
+    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
 ]
 
 [package.extras]
 docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["pytest", "pytest-cov"]
+tests = ["coverage[toml]"]
 
 [[package]]
 name = "webencodings"
@@ -2673,50 +2661,34 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.7.0"
+version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
-    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.10"
+version = "4.0.13"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
-    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
+    {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
+    {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
 ]
 
-[[package]]
-name = "zipp"
-version = "3.17.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "dev"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8"
-content-hash = "e63a6fd8bd6c0776e77ce5e7b1e7d2cc50c7b51cee32ec7b58f1d24d77258537"
+python-versions = ">=3.10"
+content-hash = "e768b5265631ec75d305b67870dcfa4b53d57bf3a4a2709e5e5991fd8e171a35"
diff --git a/pyproject.toml b/pyproject.toml
index 5f8ad85..420c2f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "arm-mango"
-version = "1.4.3"
+version = "1.5.0"
 description = "parallel bayesian optimization over complex search spaces"
 authors = ["Sandeep Singh Sandha <sandha.iitr@gmail.com>", "Mohit Aggarwal <mohitagg@gmail.com>"]
 license = "Apache-2.0"
@@ -15,7 +15,7 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.8"
+python = ">=3.10"
 numpy = ">=1.17.0"
 scipy = ">=1.4.1"
 scikit_learn = ">=0.21.3"


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..6114dfb
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,19 @@
+def pytest_addoption(parser):
+    parser.addoption(
+        "--repeat", action="store", help="Number of times to repeat each test"
+    )
+
+
+def pytest_generate_tests(metafunc):
+    if metafunc.config.option.repeat is not None:
+        count = int(metafunc.config.option.repeat)
+
+        # We're going to duplicate these tests by parametrizing them,
+        # which requires that each test has a fixture to accept the parameter.
+        # We can add a new fixture like so:
+        metafunc.fixturenames.append("tmp_ct")
+
+        # Now we parametrize. This is what happens when we do e.g.,
+        # @pytest.mark.parametrize('tmp_ct', range(count))
+        # def test_foo(): pass
+        metafunc.parametrize("tmp_ct", range(count))
diff --git a/tests/test_config.py b/tests/test_config.py
index 4c5b927..66a4792 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -1,4 +1,5 @@
 import pytest
+import random
 
 from mango import Tuner
 
@@ -40,3 +41,31 @@ def objfun(params):
         config = {"initial_custom": xy_init}
         tuner = Tuner(param_space, objfun, conf_dict=config)
         tuner.run()
+
+
+def test_custom_sampler():
+
+    def objfun(params):
+        return [param["a"] + param["b"] for param in params]
+
+    param_space = {
+        "a": range(1, 100),
+        "b": range(1, 100),
+    }
+
+    def custom_sampler(param_dist: dict, size: int):
+        # Always sort the keys of a dictionary, for reproducibility
+        items = sorted(param_dist.items())
+        samples = []
+        keys = []
+        for key, value in items:
+            keys.append(key)
+            samples.append(random.choices(value, k=size))
+        return [dict(zip(keys, sample)) for sample in zip(*samples)]
+
+    config = {"param_sampler": custom_sampler}
+    tuner = Tuner(param_space, objfun, conf_dict=config)
+    results = tuner.run()
+
+    assert results["best_params"]["a"] == pytest.approx(100, abs=2)
+    assert results["best_params"]["b"] == pytest.approx(100, abs=2)
diff --git a/tests/test_domain_space.py b/tests/test_domain_space.py
index 4c7e852..6246593 100644
--- a/tests/test_domain_space.py
+++ b/tests/test_domain_space.py
@@ -1,7 +1,7 @@
 import numpy as np
-from scipy.stats import uniform, loguniform
+from scipy.stats import uniform, loguniform, dirichlet
 
-from mango.domain.domain_space import domain_space
+from mango.domain.domain_space import DomainSpace
 from mango.domain.distribution import loguniform as mango_loguniform
 
 
@@ -14,9 +14,10 @@ def test_domain():
         "f": ["1", "-1"],
         "h": [True, False],
     }
-    ds = domain_space(params, domain_size=1000)
-    assert all(k in ds.mapping_int for k in ["a", "b"])
-    assert all(k in ds.mapping_categorical for k in ["d", "e", "f", "h"])
+    ds = DomainSpace(params)
+    ds.domain_size = 1000
+    assert all(k in ds.integer_params for k in ["a", "b"])
+    assert all(k in ds.categorical_params for k in ["d", "e", "f", "h"])
     samples = ds.get_domain()
 
     for sample in samples:
@@ -26,8 +27,9 @@ def test_domain():
     params = {
         "a": [1],
     }
-    ds = domain_space(params, domain_size=1000)
-    assert all(k in ds.mapping_int for k in ["a"])
+    ds = DomainSpace(params)
+    ds.domain_size = 1000
+    assert all(k in ds.integer_params for k in ["a"])
     samples = ds.get_domain()
 
     for sample in samples:
@@ -36,14 +38,15 @@ def test_domain():
 
 
 def test_mango_loguniform():
-    space = {"a": mango_loguniform(-3, 6)}
-    ds = domain_space(space, domain_size=1000)
+    params = {"a": mango_loguniform(-3, 6)}
+    ds = DomainSpace(params)
+    ds.domain_size = 1000
     samples = ds.get_domain()
     assert all(1e-3 < sample["a"] < 1e3 for sample in samples)
 
 
 def test_gp_samples_to_params():
-    space = {
+    params = {
         "a": range(10),
         "b": uniform(-10, 20),
         "c": ["cat1", 1, "cat2"],
@@ -52,6 +55,10 @@ def test_gp_samples_to_params():
         "g": loguniform(0.001, 100),
         "h": [10],
     }
+
+    ds = DomainSpace(params)
+    ds.domain_size = 1000
+
     X = np.array(
         [
             # 4, -8, 'cat2', 1, 'const', 1 , 10
@@ -62,19 +69,15 @@ def test_gp_samples_to_params():
             [9, 10.0, 0, 1, 0, 2, 1, 100.0, 10],
         ]
     )
-
+    calculated = ds.convert_PS_space(X)
     expected = [
         dict(a=4, b=-8.0, c="cat2", e=1, f="const", g=1, h=10),
         dict(a=0, b=-10.0, c="cat1", e=3, f="const", g=0.001, h=10),
         dict(a=9, b=10.0, c=1, e=2, f="const", g=100, h=10),
     ]
 
-    ds = domain_space(space, domain_size=1000)
-
-    params = ds.convert_PS_space(X)
-
-    for act, exp in zip(params, expected):
-        for k, v in act.items():
+    for calc, exp in zip(calculated, expected):
+        for k, v in calc.items():
             if k == "g":
                 assert np.isclose(v, exp[k])
             else:
@@ -82,7 +85,7 @@ def test_gp_samples_to_params():
 
 
 def test_gp_space():
-    space = {
+    params = {
         "f": range(10),
         "h": uniform(-10, 20),
         "e": ["cat1", 1, "cat2"],
@@ -94,7 +97,8 @@ def test_gp_space():
         "i": [True, False],
     }
 
-    ds = domain_space(space, domain_size=10000)
+    ds = DomainSpace(params)
+    ds.domain_size = 1000
     domain_list = ds.get_domain()
     X = ds.convert_GP_space(domain_list)
 
@@ -104,20 +108,20 @@ def test_gp_space():
     assert np.isin(X[:, 4:7], np.eye(3)).all()  # e
     assert X.shape == (ds.domain_size, 12)
 
-    params = ds.convert_PS_space(X)
-
-    for param in params:
-        assert param["a"] == "const"
-        assert param["b"] == 10
-        assert param["c"] in space["c"]
-        assert 0.0 <= param["d"] <= 1.0
-        assert param["e"] in space["e"]
-        assert param["f"] in space["f"]
-        assert 0.001 <= param["g"] <= 100
-        assert -10 <= param["h"] <= 10
-        assert param["i"] in space["i"]
+    samples = ds.convert_PS_space(X)
 
-    X2 = ds.convert_GP_space(params)
+    for sample in samples:
+        assert sample["a"] == "const"
+        assert sample["b"] == 10
+        assert sample["c"] in params["c"]
+        assert 0.0 <= sample["d"] <= 1.0
+        assert sample["e"] in params["e"]
+        assert sample["f"] in params["f"]
+        assert 0.001 <= sample["g"] <= 100
+        assert -10 <= sample["h"] <= 10
+        assert sample["i"] in params["i"]
+
+    X2 = ds.convert_GP_space(samples)
     assert np.isclose(X2, X).all()
 
 
@@ -132,21 +136,44 @@ def constraint(samples):
         p2 = np.array([s["p2"] for s in samples])
         return ((p1 == "a") & (p2 < 0.5)) | ((p1 != "a") & (p2 >= 0.5))
 
-    ds = domain_space(param_dict, domain_size=100, constraint=constraint)
+    ds = DomainSpace(param_dict, constraint=constraint)
+    ds.domain_size = 100
+
     samples = ds.get_domain()
     assert len(samples) == 100
     assert all(constraint(samples))
 
 
 def test_np_space():
-    space = {
+    param_dict = {
         "x": np.array(["a", "b", "c"]),
         "y": np.arange(100),
     }
+    ds = DomainSpace(param_dict)
+    ds.domain_size = 10
 
-    ds = domain_space(space, domain_size=10)
     params = ds.get_domain()
     assert len(params) == 10
 
     gp_params = ds.convert_GP_space(params)
     assert gp_params.shape == (10, 4)
+
+
+def test_multivar_space():
+    param_dict = {
+        "x": np.array(["a", "b", "c"]),
+        "y": uniform(-1, 1),
+        "z": dirichlet([0.4, 0.1, 0.8]),
+    }
+    ds = DomainSpace(param_dict)
+    x_ps = ds.get_random_sample(5)
+    x_gp = ds.convert_GP_space(x_ps)
+    x_ps2 = ds.convert_PS_space(x_gp)
+    for x in x_gp:
+        assert len(x) == 7
+    for x, y in zip(x_ps, x_ps2):
+        for k, v in x.items():
+            if k == "z":
+                assert v == y[k]
+            else:
+                assert v == y[k]
diff --git a/tests/test_tuner.py b/tests/test_tuner.py
index 67d1dab..9ec9c21 100644
--- a/tests/test_tuner.py
+++ b/tests/test_tuner.py
@@ -1,48 +1,40 @@
-"""
-Testing the capabilities of Mango
-- Test the domain space transformations
-- Test the sampling capabilities
-- Test the bayesian learning optimizer iterations
-- Test the results of tuner for simple objective function
-$  python -m pytest tests/ --disable-warnings
-"""
 import math
 import time
-from pytest import approx
+import pytest
 import numpy as np
+import random
 
-from mango.domain.domain_space import domain_space
+from mango.domain.domain_space import DomainSpace
 from mango import Tuner, scheduler
-from scipy.stats import uniform
+from scipy.stats import uniform, dirichlet
 
 # Simple param_dict
-param_dict = {"a": uniform(0, 1),  # uniform distribution
-              "b": range(1, 5),  # Integer variable
-              "c": [1, 2],  # Integer variable
-              "d": ["-1", "1"]  # Categorical variable
-              }
+param_dict = {
+    "a": uniform(0, 1),  # uniform distribution
+    "b": range(1, 5),  # Integer variable
+    "c": [1, 2],  # Integer variable
+    "d": ["-1", "1"],  # Categorical variable
+}
 
 
 # Simple objective function
 def objectiveFunction(args_list):
     results = []
     for hyper_par in args_list:
-        a = hyper_par['a']
-        b = hyper_par['b']
-        c = hyper_par['c']
-        d = hyper_par['d']
-        result = (a + b + c + int(d))
+        a = hyper_par["a"]
+        b = hyper_par["b"]
+        c = hyper_par["c"]
+        d = hyper_par["d"]
+        result = a + b + c + int(d)
         results.append(result)
     return results
 
 
-domain_size = 10
-
-
 # test the functionality of domain space transformations
 def test_domain():
-    ds = domain_space(param_dict, domain_size)
-
+    domain_size = 10
+    ds = DomainSpace(param_dict)
+    ds.domain_size = domain_size
     # getting the samples from the domain
     domain_list = ds.get_domain()
 
@@ -74,8 +66,9 @@ def test_domain():
         for key in l1.keys():
             assert key in param_dict.keys()
 
-    ps = dict(x=range(1, 100), y=['a', 'b'], z=uniform(-10, 20))
-    ds = domain_space(ps, 100)
+    ps = dict(x=range(1, 100), y=["a", "b"], z=uniform(-10, 20))
+    ds = DomainSpace(ps)
+    ds.domain_size = 100
 
     x = ds.get_domain()
     x_gp = ds.convert_GP_space(x)
@@ -85,7 +78,7 @@ def test_domain():
             v1 = x1[k]
             v2 = x2[k]
             if isinstance(v1, np.float64):
-                assert v1 == approx(v2, abs=1e-5)
+                assert v1 == pytest.approx(v2, abs=1e-5)
             else:
                 if not v1 == v2:
                     print(k)
@@ -100,44 +93,46 @@ def test_tuner():
     tuner_user = Tuner(param_dict, objectiveFunction)
     results = tuner_user.run()
     # max objective is 8, and minimum is 1
-    assert results['best_objective'] > 1
+    assert results["best_objective"] > 1
 
 
 # test on Rosenbrock's Valley
 # Rosenbrock's valley (a.k.k the banana function) has a global optimimum lying inside a long, narrow parabolic valley with a flat floor
 def test_rosenbrock():
     param_dict = {
-        'x': range(-10, 10),
-        'y': range(-10, 10),
+        "x": range(-10, 10),
+        "y": range(-10, 10),
     }
     a = 1
     b = 100
     x_opt = a
-    y_opt = a ** 2
+    y_opt = a**2
 
     def objfunc(args_list):
         results = []
         for hyper_par in args_list:
-            x = hyper_par['x']
-            y = hyper_par['y']
-            result = -(b * ((y - x ** 2) ** 2) + ((a - x) ** 2))
+            x = hyper_par["x"]
+            y = hyper_par["y"]
+            result = -(b * ((y - x**2) ** 2) + ((a - x) ** 2))
             results.append(result)
         return results
 
-    tuner = Tuner(param_dict, objfunc, conf_dict=dict(domain_size=100000, num_iteration=40))
+    tuner = Tuner(
+        param_dict, objfunc, conf_dict=dict(domain_size=100000, num_iteration=40)
+    )
     results = tuner.run()
 
-    print('best hyper parameters:', results['best_params'])
-    print('best Accuracy:', results['best_objective'])
+    print("best hyper parameters:", results["best_params"])
+    print("best Accuracy:", results["best_objective"])
 
-    assert abs(results['best_params']['x'] - x_opt) <= 2
-    assert abs(results['best_params']['x'] - y_opt) <= 2
+    assert abs(results["best_params"]["x"] - x_opt) <= 2
+    assert abs(results["best_params"]["x"] - y_opt) <= 2
 
 
 def test_config():
     param_dict = {
-        'x': range(-10, 10),
-        'y': range(-10, 10),
+        "x": range(-10, 10),
+        "y": range(-10, 10),
     }
 
     x_opt = 0
@@ -146,25 +141,25 @@ def test_config():
     def objfunc(args_list):
         results = []
         for hyper_par in args_list:
-            x = hyper_par['x']
-            y = hyper_par['y']
-            result = (x ** 2 + y ** 2) / 1e4
+            x = hyper_par["x"]
+            y = hyper_par["y"]
+            result = (x**2 + y**2) / 1e4
             results.append(result)
         return results
 
     def check(results, error_msg):
-        assert abs(results['best_params']['x'] - x_opt) <= 3, error_msg
-        assert abs(results['best_params']['y'] - y_opt) <= 3, error_msg
+        assert abs(results["best_params"]["x"] - x_opt) <= 3, error_msg
+        assert abs(results["best_params"]["y"] - y_opt) <= 3, error_msg
 
-    tuner = Tuner(param_dict, objfunc, conf_dict=dict(optimizer='Random'))
+    tuner = Tuner(param_dict, objfunc, conf_dict=dict(optimizer="Random"))
     results = tuner.minimize()
-    check(results, 'error while minimizing random')
+    check(results, "error while minimizing random")
 
 
 def test_convex():
     param_dict = {
-        'x': range(-100, 10),
-        'y': range(-10, 20),
+        "x": range(-100, 10),
+        "y": range(-10, 20),
     }
 
     x_opt = 0
@@ -173,26 +168,26 @@ def test_convex():
     def objfunc(args_list):
         results = []
         for hyper_par in args_list:
-            x = hyper_par['x']
-            y = hyper_par['y']
-            result = (x ** 2 + y ** 2) / 1e4
+            x = hyper_par["x"]
+            y = hyper_par["y"]
+            result = (x**2 + y**2) / 1e4
             results.append(result)
         return results
 
     tuner = Tuner(param_dict, objfunc)
     results = tuner.minimize()
 
-    print('best hyper parameters:', results['best_params'])
-    print('best Accuracy:', results['best_objective'])
+    print("best hyper parameters:", results["best_params"])
+    print("best Accuracy:", results["best_objective"])
 
-    assert abs(results['best_params']['x'] - x_opt) <= 3
-    assert abs(results['best_params']['y'] - y_opt) <= 3
+    assert abs(results["best_params"]["x"] - x_opt) <= 3
+    assert abs(results["best_params"]["y"] - y_opt) <= 3
 
 
 def test_initial_custom():
     param_dict = {
-        'x': range(-100, 10),
-        'y': range(-10, 20),
+        "x": range(-100, 10),
+        "y": range(-10, 20),
     }
 
     x_opt = 0
@@ -201,31 +196,28 @@ def test_initial_custom():
     def objfunc(args_list):
         results = []
         for hyper_par in args_list:
-            x = hyper_par['x']
-            y = hyper_par['y']
-            result = (x ** 2 + y ** 2) / 1e4
+            x = hyper_par["x"]
+            y = hyper_par["y"]
+            result = (x**2 + y**2) / 1e4
             results.append(result)
         return results
 
-    config = dict(initial_custom=[dict(x=-100, y=20),
-                                 dict(x=10, y=20)]
-                 )
+    config = dict(initial_custom=[dict(x=-100, y=20), dict(x=10, y=20)])
 
     tuner = Tuner(param_dict, objfunc, conf_dict=config)
     results = tuner.minimize()
 
-    print('best hyper parameters:', results['best_params'])
-    print('best Accuracy:', results['best_objective'])
+    print("best hyper parameters:", results["best_params"])
+    print("best Accuracy:", results["best_objective"])
 
-    assert abs(results['best_params']['x'] - x_opt) <= 3
-    assert abs(results['best_params']['y'] - y_opt) <= 3
-    assert results['random_params'][0] == config['initial_custom'][0]
-    assert results['random_params'][1] == config['initial_custom'][1]
+    assert abs(results["best_params"]["x"] - x_opt) <= 3
+    assert abs(results["best_params"]["y"] - y_opt) <= 3
+    assert results["random_params"][0] == config["initial_custom"][0]
+    assert results["random_params"][1] == config["initial_custom"][1]
 
 
 def test_local_scheduler():
-    param_space = dict(x=range(-10, 10),
-                       y=range(-10, 10))
+    param_space = dict(x=range(-10, 10), y=range(-10, 10))
 
     @scheduler.serial
     def obj(x, y):
@@ -233,8 +225,8 @@ def obj(x, y):
 
     results = Tuner(param_space, obj).maximize()
 
-    assert abs(results['best_params']['x'] - 10) <= 3
-    assert abs(results['best_params']['y'] + 10) <= 3
+    assert abs(results["best_params"]["x"] - 10) <= 3
+    assert abs(results["best_params"]["y"] + 10) <= 3
 
     @scheduler.parallel(n_jobs=-1)
     def obj(x, y):
@@ -242,8 +234,8 @@ def obj(x, y):
 
     results = Tuner(param_space, obj).maximize()
 
-    assert abs(results['best_params']['x'] - 10) <= 3
-    assert abs(results['best_params']['y'] + 10) <= 3
+    assert abs(results["best_params"]["x"] - 10) <= 3
+    assert abs(results["best_params"]["y"] + 10) <= 3
 
     @scheduler.parallel(n_jobs=2)
     def obj(x, y):
@@ -251,42 +243,47 @@ def obj(x, y):
 
     results = Tuner(param_space, obj).maximize()
 
-    assert abs(results['best_params']['x'] - 10) <= 3
-    assert abs(results['best_params']['y'] + 10) <= 3
+    assert abs(results["best_params"]["x"] - 10) <= 3
+    assert abs(results["best_params"]["y"] + 10) <= 3
 
 
-def test_six_hump():
+@pytest.mark.parametrize(
+    "y_scale, scale_params", [(1, False), (1, True), (1000.0, False), (1000.0, True)]
+)
+def test_six_hump(y_scale, scale_params):
+
     def camel(x, y):
+        y = y / y_scale
         x2 = math.pow(x, 2)
         x4 = math.pow(x, 4)
         y2 = math.pow(y, 2)
         return (4.0 - 2.1 * x2 + (x4 / 3.0)) * x2 + x * y + (-4.0 + 4.0 * y2) * y2
 
     param_dict = {
-        'x': uniform(-3, 3),
-        'y': uniform(-2, 2),
+        "x": uniform(-3, 3),
+        "y": uniform(-2 * y_scale, 2 * y_scale),
     }
 
-    x_opt = 0.0898  # or -0;0898
-    y_opt = -0.7126  # or 0.7126
+    x_opt = 0.0898  # or -0.0898
+    y_opt = -0.7126 * y_scale  # or 0.7126
 
     def objfunc(args_list):
         results = []
         for hyper_par in args_list:
-            x = hyper_par['x']
-            y = hyper_par['y']
-            result = - camel(x, y)
+            x = hyper_par["x"]
+            y = hyper_par["y"]
+            result = -camel(x, y)
             results.append(result)
         return results
 
-    tuner = Tuner(param_dict, objfunc)
+    tuner = Tuner(param_dict, objfunc, dict(scale_params=scale_params))
     results = tuner.run()
 
-    print('best hyper parameters:', results['best_params'])
-    print('best objective:', results['best_objective'])
+    print("best hyper parameters:", results["best_params"])
+    print("best objective:", results["best_objective"])
 
-    assert abs(results['best_params']['x']) - abs(x_opt) <= 0.1
-    assert abs(results['best_params']['y']) - abs(y_opt) <= 0.2
+    assert abs(results["best_params"]["x"]) - abs(x_opt) <= 0.1
+    assert abs(results["best_params"]["y"]) - abs(y_opt) <= 0.2 * y_scale
 
 
 def test_celery_scheduler():
@@ -318,7 +315,7 @@ def objective(x):
 
     results = tuner.minimize()
 
-    assert abs(results['best_params']['x']) <= 0.1
+    assert abs(results["best_params"]["x"]) <= 0.1
 
     class MockTask:
 
@@ -340,7 +337,7 @@ def objective(x):
     tuner = Tuner(param_space, objective)
     results = tuner.minimize()
 
-    assert abs(results['best_params']['x'] - 5) <= 0.1
+    assert abs(results["best_params"]["x"] - 5) <= 0.1
 
 
 def test_custom_scheduler():
@@ -350,37 +347,44 @@ def test_custom_scheduler():
     @scheduler.custom(n_jobs=2)
     def objective(params):
         assert len(params) == 2
-        return [p['x'] * p['x'] for p in params]
+        return [p["x"] * p["x"] for p in params]
 
     tuner = Tuner(param_space, objective, dict(initial_random=2))
     results = tuner.minimize()
 
-    assert abs(results['best_params']['x'] - 0) <= 0.1
+    assert abs(results["best_params"]["x"] - 0) <= 0.1
 
 
 def test_early_stopping_simple():
     param_dict = dict(x=range(-10, 10))
+
     def objfunc(p_list):
-        return [p['x'] ** 2 for p in p_list]
+        return [p["x"] ** 2 for p in p_list]
 
     def early_stop(results):
-        if len(results['params_tried']) >= 5:
+        if len(results["params_tried"]) >= 5:
             return True
 
     config = dict(num_iteration=20, initial_random=1, early_stopping=early_stop)
 
     tuner = Tuner(param_dict, objfunc, conf_dict=config)
     results = tuner.minimize()
-    assert(len(results['params_tried']) == 5)
+    assert len(results["params_tried"]) == 5
 
-    config = dict(num_iteration=20, initial_random=1, early_stopping=early_stop, optimizer='Random')
+    config = dict(
+        num_iteration=20,
+        initial_random=1,
+        early_stopping=early_stop,
+        optimizer="Random",
+    )
 
     tuner = Tuner(param_dict, objfunc, conf_dict=config)
     results = tuner.minimize()
-    assert (len(results['params_tried']) == 5)
+    assert len(results["params_tried"]) == 5
+
 
 def test_early_stopping_complex():
-    '''testing early stop by time since last improvement'''
+    """testing early stop by time since last improvement"""
     param_dict = dict(x=range(-10, 10))
 
     def objfunc(p_list):
@@ -397,16 +401,20 @@ class Context:
     def early_stop(results):
         context = Context
 
-        current_best = results['best_objective']
+        current_best = results["best_objective"]
         current_time = time.time()
         _stop = False
 
         if context.previous_best is None:
             context.previous_best = current_best
             context.previous_best_time = current_time
-        elif (current_best <= context.previous_best + context.objective_variation) and \
-                (current_time - context.previous_best_time > context.min_improvement_secs):
-            print("no improvement in %d seconds: stopping early." % context.min_improvement_secs)
+        elif (current_best <= context.previous_best + context.objective_variation) and (
+            current_time - context.previous_best_time > context.min_improvement_secs
+        ):
+            print(
+                "no improvement in %d seconds: stopping early."
+                % context.min_improvement_secs
+            )
             _stop = True
         else:
             context.previous_best = current_best
@@ -418,23 +426,24 @@ def early_stop(results):
 
     tuner = Tuner(param_dict, objfunc, conf_dict=config)
     results = tuner.minimize()
-    assert (len(results['params_tried']) == 3)
+    assert len(results["params_tried"]) == 3
 
 
 def test_constrainted_opt():
-    param_dict = {"a": uniform(0, 1),  # uniform distribution
-                  "b": range(1, 5),  # Integer variable
-                  "c": [1, 2, 3],  # Integer variable
-                  "d": ["-1", "1"]  # Categorical variable
-                  }
+    param_dict = {
+        "a": uniform(0, 1),  # uniform distribution
+        "b": range(1, 5),  # Integer variable
+        "c": [1, 2, 3],  # Integer variable
+        "d": ["-1", "1"],  # Categorical variable
+    }
 
     def constraint(samples):
         is_valid = []
         for sample in samples:
-            if sample['a'] < 0.5:
-                v = sample['b'] >= 3
+            if sample["a"] < 0.5:
+                v = sample["b"] >= 3
             else:
-                v = sample['b'] < 3
+                v = sample["b"] < 3
             is_valid.append(v)
 
         return is_valid
@@ -442,22 +451,118 @@ def constraint(samples):
     def objectiveFunction(args_list):
         results = []
         for hyper_par in args_list:
-            a = hyper_par['a']
-            b = hyper_par['b']
-            c = hyper_par['c']
-            d = hyper_par['d']
-            result = (a + b + c + int(d))
+            a = hyper_par["a"]
+            b = hyper_par["b"]
+            c = hyper_par["c"]
+            d = hyper_par["d"]
+            result = a + b + c + int(d)
             results.append(result)
         return results
 
-    config = dict(
-        num_iteration=20,
-        constraint=constraint
-    )
+    config = dict(num_iteration=20, constraint=constraint)
     tuner = Tuner(param_dict, objectiveFunction, config)
 
     results = tuner.maximize()
 
-    assert results['best_objective'] > 8.4
-    assert results['best_params']['b'] == 4
-    assert constraint([results['best_params']])
\ No newline at end of file
+    assert results["best_objective"] > 8.4
+    assert results["best_params"]["b"] == 4
+    assert constraint([results["best_params"]])
+
+
+def test_multivar():
+    def objfun(params):
+        res = []
+        for param in params:
+            res.append(sum([v**2 for v in param["multi"]]) - param["min"])
+        return res
+
+    param_space = {
+        "multi": dirichlet([1, 1, 1]),
+        "min": range(10, 100),
+    }
+
+    tuner = Tuner(param_space, objfun, conf_dict={"batch_size": 2})
+    results = tuner.run()
+
+    # for dirichlet distribution the maximum  sum of squares is at an edge
+    assert results["best_params"]["min"] == pytest.approx(10)
+    assert max(results["best_params"]["multi"]) == pytest.approx(1, abs=0.5)
+    assert min(results["best_params"]["multi"]) == pytest.approx(0, abs=0.5)
+
+
+def test_multivar_with_fails():
+    @scheduler.custom(n_jobs=3)
+    def objfun(batch):
+        loss = []
+        suc = []
+        for params in batch:
+            if random.choice([True, False]):
+                l = params["a"] + params["b"] + sum(params["c"])
+                loss.append(l)
+                suc.append(params)
+        return suc, loss
+
+    param_space = {
+        "a": range(1, 100),
+        "b": range(1, 100),
+        "c": dirichlet([1.0] * 3),
+    }
+
+    tuner = Tuner(param_space, objfun, conf_dict={"initial_random": 4})
+    results = tuner.run()
+    assert results["best_params"]["a"] == pytest.approx(99)
+    assert results["best_params"]["b"] == pytest.approx(99)
+
+
+def test_failure_handling():
+    param_dict = {
+        "x": uniform(-5, 10),
+        "y": uniform(-5, 10),
+    }
+
+    # Randomly fail the evaluatioon
+    def objfunc(args_list):
+        hyper_evaluated = []
+        objective_evaluated = []
+        for hyper_par in args_list:
+            if random.random() > 0.3:
+                x = hyper_par["x"]
+                y = hyper_par["y"]
+                objective = -(x**2 + y**2)
+                objective_evaluated.append(objective)
+                hyper_evaluated.append(hyper_par)
+            else:
+                continue
+
+        return hyper_evaluated, objective_evaluated
+
+    tuner = Tuner(param_dict, objfunc)
+    results = tuner.maximize()
+    x = results["params_tried"]
+    y = results["objective_values"]
+    for params, obj in zip(x, y):
+        assert obj == -(params["x"] ** 2 + params["y"] ** 2)
+
+
+def test_warmup():
+    def objfun(params):
+        return [param["a"] + param["b"] for param in params]
+
+    param_space = {
+        "a": range(1, 100),
+        "b": range(1, 100),
+    }
+
+    tuner = Tuner(param_space, objfun)
+    results = tuner.run()
+
+    x = results["params_tried"]
+    y = results["objective_values"]
+    xy = list(zip(x, y))
+
+    config = {"initial_custom": xy}
+
+    tuner = Tuner(param_space, objfun, conf_dict=config)
+    results = tuner.run()
+
+    assert all([i in results["params_tried"] for i in x])