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sentiment-analysis

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Tymec commited on May 16

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88f3204

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1 Parent(s): 9534cfa

Hyperparameter tuning in notebook done

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notebook.ipynb +142 -507

notebook.ipynb CHANGED Viewed

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,16 +20,18 @@
     "if TYPE_CHECKING:\n",
     "    from sklearn.base import BaseEstimator\n",
     "\n",
     "import re\n",
     "import warnings\n",
     "from functools import cache\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import nltk\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from joblib import Memory\n",
     "from nltk.corpus import stopwords\n",
     "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
     "from sklearn.linear_model import LogisticRegression\n",
@@ -38,7 +40,7 @@
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.svm import SVC\n",
     "\n",
-    "from app.constants import CACHE_DIR, SENTIMENT140_PATH\n",
     "from app.model import TextCleaner, TextLemmatizer"
    ]
   },
@@ -64,7 +66,7 @@
       "[nltk_data] Downloading package wordnet to /home/tymec/nltk_data...\n",
       "[nltk_data]   Package wordnet is already up-to-date!\n",
       "[nltk_data] Downloading package stopwords to /home/tymec/nltk_data...\n",
-      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
      ]
     },
     {
@@ -99,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +143,7 @@
        "      <td>NO_QUERY</td>\n",
        "      <td>_TheSpecialOne_</td>\n",
        "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
-       "      <td>negative</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -151,7 +153,7 @@
        "      <td>NO_QUERY</td>\n",
        "      <td>scotthamilton</td>\n",
        "      <td>is upset that he can't update his Facebook by ...</td>\n",
-       "      <td>negative</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -161,7 +163,7 @@
        "      <td>NO_QUERY</td>\n",
        "      <td>mattycus</td>\n",
        "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
-       "      <td>negative</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -171,7 +173,7 @@
        "      <td>NO_QUERY</td>\n",
        "      <td>ElleCTF</td>\n",
        "      <td>my whole body feels itchy and like its on fire</td>\n",
-       "      <td>negative</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -181,7 +183,7 @@
        "      <td>NO_QUERY</td>\n",
        "      <td>Karoli</td>\n",
        "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
-       "      <td>negative</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -202,15 +204,15 @@
        "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
        "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
        "\n",
-       "  sentiment  \n",
-       "0  negative  \n",
-       "1  negative  \n",
-       "2  negative  \n",
-       "3  negative  \n",
-       "4  negative  "
       ]
      },
-     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -234,7 +236,7 @@
     "data = data[data[\"target\"] != 2]\n",
     "\n",
     "# Map the sentiment values\n",
-    "data[\"sentiment\"] = data[\"target\"].map({0: \"negative\", 4: \"positive\"})\n",
     "\n",
     "# Show the first few rows\n",
     "data.head()"
@@ -249,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -291,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +371,7 @@
        "4   my  314024"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -384,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -430,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,12 +454,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Preprocess and tokenize the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,497 +469,26 @@
     "        # Text preprocessing\n",
     "        (\"clean\", TextCleaner()),\n",
     "        (\"lemma\", TextLemmatizer()),\n",
-    "        # Preprocess (NOTE: Can be replaced with TfidfVectorizer, but left for clarity)\n",
     "        (\"vectorize\", CountVectorizer(stop_words=stopwords_en, ngram_range=(1, 2), max_features=MAX_FEATURES)),\n",
     "        (\"tfidf\", TfidfTransformer()),\n",
     "    ],\n",
-    "    memory=Memory(CACHE_DIR, verbose=0),\n",
     "    verbose=True,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[Pipeline] ......... (step 3 of 4) Processing vectorize, total=  41.9s\n",
       "[Pipeline] ............. (step 4 of 4) Processing tfidf, total=   0.0s\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/tymec/university/projekt-psi/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning: Persisting input arguments took 0.55s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n",
-      "  return self._cached_call(args, kwargs, shelving=False)[0]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=Memory(location=.cache),\n",
-       "         steps=[(&#x27;clean&#x27;, TextCleaner()), (&#x27;lemma&#x27;, TextLemmatizer()),\n",
-       "                (&#x27;vectorize&#x27;,\n",
-       "                 CountVectorizer(max_features=20000, ngram_range=(1, 2),\n",
-       "                                 stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;,\n",
-       "                                             &#x27;our&#x27;, &#x27;ours&#x27;, &#x27;ourselves&#x27;, &#x27;you&#x27;,\n",
-       "                                             &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
-       "                                             &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;,\n",
-       "                                             &#x27;yourself&#x27;, &#x27;yourselves&#x27;, &#x27;he&#x27;,\n",
-       "                                             &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;,\n",
-       "                                             &quot;she&#x27;s&quot;, &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;,\n",
-       "                                             &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;, &#x27;itself&#x27;, ...])),\n",
-       "                (&#x27;tfidf&#x27;, TfidfTransformer())],\n",
-       "         verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(memory=Memory(location=.cache),\n",
-       "         steps=[(&#x27;clean&#x27;, TextCleaner()), (&#x27;lemma&#x27;, TextLemmatizer()),\n",
-       "                (&#x27;vectorize&#x27;,\n",
-       "                 CountVectorizer(max_features=20000, ngram_range=(1, 2),\n",
-       "                                 stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;,\n",
-       "                                             &#x27;our&#x27;, &#x27;ours&#x27;, &#x27;ourselves&#x27;, &#x27;you&#x27;,\n",
-       "                                             &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
-       "                                             &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;,\n",
-       "                                             &#x27;yourself&#x27;, &#x27;yourselves&#x27;, &#x27;he&#x27;,\n",
-       "                                             &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;,\n",
-       "                                             &quot;she&#x27;s&quot;, &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;,\n",
-       "                                             &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;, &#x27;itself&#x27;, ...])),\n",
-       "                (&#x27;tfidf&#x27;, TfidfTransformer())],\n",
-       "         verbose=True)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">TextCleaner</label><div class=\"sk-toggleable__content fitted\"><pre>TextCleaner()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">TextLemmatizer</label><div class=\"sk-toggleable__content fitted\"><pre>TextLemmatizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;CountVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\">?<span>Documentation for CountVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>CountVectorizer(max_features=20000, ngram_range=(1, 2),\n",
-       "                stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;, &#x27;our&#x27;, &#x27;ours&#x27;,\n",
-       "                            &#x27;ourselves&#x27;, &#x27;you&#x27;, &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
-       "                            &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;, &#x27;yourself&#x27;, &#x27;yourselves&#x27;,\n",
-       "                            &#x27;he&#x27;, &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;, &quot;she&#x27;s&quot;,\n",
-       "                            &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;, &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;,\n",
-       "                            &#x27;itself&#x27;, ...])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html\">?<span>Documentation for TfidfTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfTransformer()</pre></div> </div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "Pipeline(memory=Memory(location=.cache),\n",
-       "         steps=[('clean', TextCleaner()), ('lemma', TextLemmatizer()),\n",
-       "                ('vectorize',\n",
-       "                 CountVectorizer(max_features=20000, ngram_range=(1, 2),\n",
-       "                                 stop_words=['i', 'me', 'my', 'myself', 'we',\n",
-       "                                             'our', 'ours', 'ourselves', 'you',\n",
-       "                                             \"you're\", \"you've\", \"you'll\",\n",
-       "                                             \"you'd\", 'your', 'yours',\n",
-       "                                             'yourself', 'yourselves', 'he',\n",
-       "                                             'him', 'his', 'himself', 'she',\n",
-       "                                             \"she's\", 'her', 'hers', 'herself',\n",
-       "                                             'it', \"it's\", 'its', 'itself', ...])),\n",
-       "                ('tfidf', TfidfTransformer())],\n",
-       "         verbose=True)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -969,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -978,11 +509,33 @@
     "X_test_preprocessed = preprocess_pipeline.transform(X_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Pick the classifier"
    ]
   },
   {
@@ -990,6 +543,24 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def evaluate_model(clf: BaseEstimator) -> None:\n",
     "    # Calculate the accuracy\n",
@@ -1027,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1055,12 +626,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1078,7 +657,7 @@
     "    lr_clf,\n",
     "    {\n",
     "        \"C\": np.logspace(-4, 4, 20),\n",
-    "        \"solver\": [\"liblinear\", \"saga\"],\n",
     "        \"penalty\": [\"l1\", \"l2\"],\n",
     "    },\n",
     ")\n",
@@ -1109,6 +688,62 @@
     "# Evaluate the model\n",
     "evaluate_model(best_svm_clf)"
    ]
   }
  ],
  "metadata": {

   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "if TYPE_CHECKING:\n",
     "    from sklearn.base import BaseEstimator\n",
     "\n",
+    "import json\n",
     "import re\n",
     "import warnings\n",
     "from functools import cache\n",
+    "from pathlib import Path\n",
     "\n",
+    "import joblib\n",
     "import matplotlib.pyplot as plt\n",
     "import nltk\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
     "from nltk.corpus import stopwords\n",
     "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.svm import SVC\n",
     "\n",
+    "from app.constants import CACHE_DIR, MODELS_DIR, SENTIMENT140_PATH\n",
     "from app.model import TextCleaner, TextLemmatizer"
    ]
   },
       "[nltk_data] Downloading package wordnet to /home/tymec/nltk_data...\n",
       "[nltk_data]   Package wordnet is already up-to-date!\n",
       "[nltk_data] Downloading package stopwords to /home/tymec/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
      ]
     },
     {
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
        "      <td>NO_QUERY</td>\n",
        "      <td>_TheSpecialOne_</td>\n",
        "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NO_QUERY</td>\n",
        "      <td>scotthamilton</td>\n",
        "      <td>is upset that he can't update his Facebook by ...</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NO_QUERY</td>\n",
        "      <td>mattycus</td>\n",
        "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>NO_QUERY</td>\n",
        "      <td>ElleCTF</td>\n",
        "      <td>my whole body feels itchy and like its on fire</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>NO_QUERY</td>\n",
        "      <td>Karoli</td>\n",
        "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
        "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
        "\n",
+       "   sentiment  \n",
+       "0          0  \n",
+       "1          0  \n",
+       "2          0  \n",
+       "3          0  \n",
+       "4          0  "
       ]
      },
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
     "data = data[data[\"target\"] != 2]\n",
     "\n",
     "# Map the sentiment values\n",
+    "data[\"sentiment\"] = data[\"target\"].map({0: 0, 4: 1})\n",
     "\n",
     "# Show the first few rows\n",
     "data.head()"
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
   },
   {
    "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
        "4   my  314024"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
   },
   {
    "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Create a tokenizer and transform the data"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "        # Text preprocessing\n",
     "        (\"clean\", TextCleaner()),\n",
     "        (\"lemma\", TextLemmatizer()),\n",
+    "        # Tokenize (NOTE: Can be replaced with TfidfVectorizer, but left for clarity)\n",
     "        (\"vectorize\", CountVectorizer(stop_words=stopwords_en, ngram_range=(1, 2), max_features=MAX_FEATURES)),\n",
     "        (\"tfidf\", TfidfTransformer()),\n",
     "    ],\n",
+    "    memory=joblib.Memory(CACHE_DIR, verbose=0),\n",
     "    verbose=True,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[Pipeline] ............. (step 4 of 4) Processing tfidf, total=   0.0s\n"
      ]
     }
    ],
    "source": [
   },
   {
    "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_test_preprocessed = preprocess_pipeline.transform(X_test)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['.cache/X_test_preprocessed.pkl']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cache the preprocessed data\n",
+    "joblib.dump(X_train_preprocessed, CACHE_DIR / \"X_train_preprocessed.pkl\")\n",
+    "joblib.dump(X_test_preprocessed, CACHE_DIR / \"X_test_preprocessed.pkl\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Or load cached data"
    ]
   },
   {
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Load the transformed data\n",
+    "X_train_preprocessed = joblib.load(CACHE_DIR / \"X_train_preprocessed.pkl\")\n",
+    "X_test_preprocessed = joblib.load(CACHE_DIR / \"X_test_preprocessed.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pick the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def evaluate_model(clf: BaseEstimator) -> None:\n",
     "    # Calculate the accuracy\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
+      "Best parameters: {'solver': 'liblinear', 'penalty': 'l2', 'C': 1438.44988828766}\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
     "    lr_clf,\n",
     "    {\n",
     "        \"C\": np.logspace(-4, 4, 20),\n",
+    "        \"solver\": [\"liblinear\", \"saga\"],  # lbfgs takes too long\n",
     "        \"penalty\": [\"l1\", \"l2\"],\n",
     "    },\n",
     ")\n",
     "# Evaluate the model\n",
     "evaluate_model(best_svm_clf)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export the final model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_clf = best_lr_clf  # TODO: Pick the best classifier\n",
+    "best_params = lr_params  # TODO: Pick the best parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the tokenizer and the best classifier\n",
+    "model = Pipeline(\n",
+    "    [\n",
+    "        (\"preprocess\", preprocess_pipeline),\n",
+    "        (\"clf\", best_clf),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export the model and the parameters\n",
+    "joblib.dump(model, MODELS_DIR / \"best_model.pkl\")\n",
+    "with Path.open(MODELS_DIR / \"best_params.json\", \"w\") as f:\n",
+    "    json.dump(best_params, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import and test the model\n",
+    "model = joblib.load(MODELS_DIR / \"best_model.pkl\")\n",
+    "assert model.predict([\"I love this!\"])[0] == 1  # noqa: S101\n",
+    "assert model.predict([\"I hate this!\"])[0] == 0  # noqa: S101"
+   ]
   }
  ],
  "metadata": {