{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "zmIrvWXUe6yQ"
   },
   "source": [
    "## Preprocessing and Metrics\n",
    "\n",
    "The package only contains methods to simplify your code, so it doesn't cover all steps of machine learning process, but only those that save you lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "kYcJFGAIGMUz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (3.9.1)\n",
      "Requirement already satisfied: click in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from nltk) (8.2.1)\n",
      "Requirement already satisfied: joblib in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from nltk) (1.5.2)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from nltk) (2025.7.34)\n",
      "Requirement already satisfied: tqdm in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from nltk) (4.67.1)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: wordcloud in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (1.9.4)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from wordcloud) (2.3.2)\n",
      "Requirement already satisfied: pillow in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from wordcloud) (11.3.0)\n",
      "Requirement already satisfied: matplotlib in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from wordcloud) (3.10.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (4.59.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (1.4.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (25.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib->wordcloud) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.17.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: mlxtend in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (0.23.4)\n",
      "Requirement already satisfied: scipy>=1.2.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (1.16.1)\n",
      "Requirement already satisfied: numpy>=1.16.2 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (2.3.2)\n",
      "Requirement already satisfied: pandas>=0.24.2 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (2.3.2)\n",
      "Requirement already satisfied: scikit-learn>=1.3.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (1.7.1)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (3.10.5)\n",
      "Requirement already satisfied: joblib>=0.13.2 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from mlxtend) (1.5.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (4.59.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.9)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from matplotlib>=3.0.0->mlxtend) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from pandas>=0.24.2->mlxtend) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from pandas>=0.24.2->mlxtend) (2025.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from scikit-learn>=1.3.1->mlxtend) (3.6.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.17.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "/home/bilardi/github/bilardi/smltk/docs/source/usage\n",
      "['/usr/lib64/python313.zip', '/home/bilardi/github/bilardi/smltk/docs/source/usage/../../../', '/usr/lib64/python3.13', '/usr/lib64/python3.13/lib-dynload', '', '/home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages', '/home/bilardi/github/bilardi/smltk/.env/lib/python3.13/site-packages']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt_tab to\n",
      "[nltk_data]     /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger_eng to\n",
      "[nltk_data]     /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger_eng is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /home/bilardi/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#!pip install smltk==2.2.2\n",
    "\n",
    "!pip install nltk\n",
    "!pip install wordcloud\n",
    "%pip install mlxtend --upgrade\n",
    "\n",
    "import os\n",
    "import sys\n",
    "sys.path.insert(1, os.getcwd() + '/../../../')\n",
    "print(os.getcwd())\n",
    "print(sys.path)\n",
    "\n",
    "import numpy as np\n",
    "from smltk.data_processing import Ntk\n",
    "nlp = Ntk()\n",
    "from smltk.modeling import Modeling\n",
    "mdl = Modeling()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hBlIDZZ0fHIF",
    "tags": []
   },
   "source": [
    "### Example with nltk library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o_GP3h5dAMU3",
    "outputId": "e541580f-23da-44a0-d141-31f02b677e22"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     /home/bilardi/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/movie_reviews.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('movie_reviews')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "5qULqa89J7vV"
   },
   "outputs": [],
   "source": [
    "docs_pos = [nltk.corpus.movie_reviews.raw(review) for review in nltk.corpus.movie_reviews.fileids(categories=[\"pos\"])]\n",
    "docs_neg = [nltk.corpus.movie_reviews.raw(review) for review in nltk.corpus.movie_reviews.fileids(categories=[\"neg\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T7f2sJT69Qjd"
   },
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Ep2PAmUIyKPw"
   },
   "outputs": [],
   "source": [
    "# data reduction\n",
    "vocab_pos = nlp.create_vocab_from_docs(docs_pos)\n",
    "#vocab_pos = nlp.create_vocab_from_docs(nltk.Text(nltk.corpus.movie_reviews.words(categories=[\"pos\"])))\n",
    "vocab_neg = nlp.create_vocab_from_docs(docs_neg)\n",
    "#vocab_neg = nlp.create_vocab_from_docs(nltk.Text(nltk.corpus.movie_reviews.words(categories=[\"neg\"])))\n",
    "vocabs = {'pos': vocab_pos, 'neg': vocab_neg}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "-vNeQ3jNLqbl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pos\n",
      "(15790, 27557)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neg\n",
      "(14667, 25746)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution\n",
    "for target in vocabs.keys():\n",
    "  print(target)\n",
    "  print(nlp.get_stats_vocab(vocabs[target], 2))\n",
    "  nlp.create_words_cloud(nlp.create_words_map(vocabs[target]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "rMdfY_42MUTA"
   },
   "outputs": [],
   "source": [
    "# data cleaning\n",
    "vocabs_cleaned = nlp.get_vocabs_cleaned(vocabs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "AE5uybJLJ-rV"
   },
   "outputs": [],
   "source": [
    "# data wrangling\n",
    "top_100_pos = nlp.get_words_top(vocabs_cleaned['pos'], 100)\n",
    "top_100_neg = nlp.get_words_top(vocabs_cleaned['neg'], 100)\n",
    "\n",
    "features = nlp.create_features_from_docs(docs_pos, 'pos', words_top = top_100_pos)\n",
    "features.extend(nlp.create_features_from_docs(docs_neg, 'neg', words_top = top_100_neg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "fMqKXGyr9B2D"
   },
   "outputs": [],
   "source": [
    "# splitting\n",
    "from nltk.sentiment.util import split_train_test\n",
    "X_train, X_test = split_train_test(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1C3D0bMkLG3R"
   },
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "PmNZ2gpK8ytr"
   },
   "outputs": [],
   "source": [
    "# fitting\n",
    "classifier = nltk.NaiveBayesClassifier.train(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "6toeFxcQ8-VO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'pos', 'neg', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'neg', 'pos', 'neg', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'pos', 'neg', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'pos', 'neg', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'pos', 'pos', 'neg', 'neg', 'neg', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'pos', 'neg', 'neg', 'pos', 'neg', 'neg', 'pos', 'pos', 'neg', 'pos', 'pos', 'pos', 'pos', 'neg', 'pos', 'pos', 'neg', 'pos', 'neg', 'pos', 'neg', 'neg', 'neg', 'pos', 'neg', 'pos']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[106,  76],\n",
       "       [ 86, 132]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_test, y_pred = mdl.prediction(classifier, 'classify', X_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "3rX5Vpr6RfGg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.18730944485936252,\n",
       " 'ROC_AUC': 0.5939610847867728,\n",
       " 'Accuracy': 0.595,\n",
       " 'Precision': array([0.55208333, 0.63461538]),\n",
       " 'Recall': array([0.58241758, 0.60550459]),\n",
       " 'Fscore': array([0.56684492, 0.61971831]),\n",
       " 'Support': array([182, 218])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(nlp.get_features(docs_pos[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neg'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(nlp.get_features(docs_neg[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GeXePQ87NPHa",
    "tags": []
   },
   "source": [
    "### Example with sklearn library - wine dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Xl64mLhpNPHa"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "data = load_wine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KKoOYjrILNbZ"
   },
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "B5LXftodNPHa"
   },
   "outputs": [],
   "source": [
    "# splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FcyCQEc2LQXf"
   },
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "XhKUjsAiNPHb",
    "outputId": "632237de-a58a-45f6-af0f-7fa759f39567"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\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",
       "  display: none;\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",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\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: 0.5em;\n",
       "  text-align: center;\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",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">0.15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;optimal&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eta0',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">eta0&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
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       "        <tr class=\"default\">\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('average',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">average&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "</script></body>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "uC1ttkhvNPHc",
    "outputId": "6cca15b0-a6ba-47b8-d815-ef589dd0f8f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 2 2 0 0 1 0 0 0 2 1 2 0 1 0 1 0 0 0 1 1 1 1 0 2 2 0 1 1 0 0 2 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  1],\n",
       "       [ 2,  4,  5],\n",
       "       [ 3,  1,  4]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_pred = model.predict(X_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "2arn7BdbNPHc",
    "outputId": "7c08ec49-ee14-4541-bd63-1c7d29ff0334"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': np.float64(0.7443055555555557),\n",
       " 'Bias': np.float64(0.3698465277777778),\n",
       " 'Variance': np.float64(0.3744590277777778),\n",
       " 'MCC': 0.4802259242337604,\n",
       " 'ROC_AUC': 0,\n",
       " 'Accuracy': 0.6666666666666666,\n",
       " 'Precision': array([0.76190476, 0.8       , 0.4       ]),\n",
       " 'Recall': array([0.94117647, 0.36363636, 0.5       ]),\n",
       " 'Fscore': array([0.84210526, 0.5       , 0.44444444]),\n",
       " 'Support': array([17, 11,  8])}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"model\": model,\n",
    "    \"X_train\": np.array(X_train),\n",
    "    \"y_train\": np.array(y_train),\n",
    "    \"X_test\": np.array(X_test),\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6111111111111112"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.save_model(model, 'model.pkl')\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6111111111111112"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_resumed = mdl.resume_model('model.pkl')\n",
    "model_resumed.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z_0Czn4cbTox",
    "tags": []
   },
   "source": [
    "### Example with sklearn library - text dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kzArJcn1pCu-",
    "outputId": "041e646e-b1a4-4985-dee1-2ee25348753f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 208\n",
      "-rw-r--r--. 1 bilardi bilardi 58226 30 nov  2024 amazon_cells_labelled.txt\n",
      "-rw-r--r--. 1 bilardi bilardi  1070 30 nov  2024 readme.txt\n",
      "-rw-r--r--. 1 bilardi bilardi 61320 30 nov  2024 yelp_labelled.txt\n",
      "-rw-r--r--. 1 bilardi bilardi 85285 30 nov  2024 imdb_labelled.txt\n"
     ]
    }
   ],
   "source": [
    "!if [ ! -f 'sentiment.zip' ]; then curl https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip --output sentiment.zip; fi\n",
    "!if [ ! -d 'sentiment labelled sentences' ]; then unzip 'sentiment.zip'; fi\n",
    "!ls -ltr 'sentiment labelled sentences'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8uLwQmHfo2uB",
    "outputId": "019fb647-19fe-47ca-da40-68085c58e26a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So there is no way for me to plug it in here in the US unless I go by a converter.\t0\n",
      "Good case, Excellent value.\t1\n",
      "Great for the jawbone.\t1\n",
      "Tied to charger for conversations lasting more than 45 minutes.MAJOR PROBLEMS!!\t0\n",
      "The mic is great.\t1\n"
     ]
    }
   ],
   "source": [
    "!cat 'sentiment labelled sentences/amazon_cells_labelled.txt' | head -n 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "P2lbXXWaeMau"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\n",
    "  'sentiment labelled sentences/amazon_cells_labelled.txt',\n",
    "  sep='\\t',\n",
    "  names=['text', 'sentiment']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eSOWjyQgvkfx"
   },
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "wrRDIH7WwJld",
    "outputId": "c5cb7546-20e8-4774-e8ab-a179771e9e9f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(609, 1464)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x7ff72537ccd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution\n",
    "vocabulary = nlp.create_vocab_from_docs(data['text'])\n",
    "print(nlp.get_stats_vocab(vocabulary, 2))\n",
    "nlp.create_words_cloud(nlp.create_words_map(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "jvKhUisVz-I-"
   },
   "outputs": [],
   "source": [
    "# data cleaning\n",
    "data['text_cleaned'] = data['text'].apply(nlp.get_doc_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "rpjkInbF0BFy",
    "outputId": "f612f586-c29d-4807-df2a-de0e2cbf5914"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(602, 1451)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x7ff72537cb90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution\n",
    "vocabulary = nlp.create_vocab_from_docs(data['text_cleaned'])\n",
    "print(nlp.get_stats_vocab(vocabulary, 2))\n",
    "nlp.create_words_cloud(nlp.create_words_map(vocabulary), is_test = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Modeling by vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "n-qby41DuVdF"
   },
   "outputs": [],
   "source": [
    "# splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data['text_cleaned'], data['sentiment'], test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "FK1ysukbusHH"
   },
   "outputs": [],
   "source": [
    "# vectorization\n",
    "X_vect_train = nlp.vectorize_docs(X_train, is_lemma = True)\n",
    "X_vect_test = nlp.vectorize_docs(X_test, is_lemma = True, is_test = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qLeIZpUcuitP",
    "outputId": "7562bdbc-1253-4f1d-8836-3ea55daaeee3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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: 0.5em;\n",
       "  text-align: center;\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-2 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-2 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-2 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-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">0.15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
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       "</script></body>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_vect_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PVmu4Gmq6xHr",
    "outputId": "7d8c6049-93a7-4709-8222-970cca8d12b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642    0\n",
      "762    1\n",
      "909    1\n",
      "199    0\n",
      "586    1\n",
      "      ..\n",
      "164    0\n",
      "782    0\n",
      "575    0\n",
      "992    0\n",
      "713    1\n",
      "Name: sentiment, Length: 200, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[85, 20],\n",
       "       [29, 66]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_pred = model.predict(X_vect_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred, is_test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "1NPZ0Jf34CMu",
    "outputId": "de09a6a7-8823-4c17-fb1c-39af8d0ebda8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.5086392633425091,\n",
       " 'ROC_AUC': 0.7521303258145364,\n",
       " 'Accuracy': 0.755,\n",
       " 'Precision': array([0.74561404, 0.76744186]),\n",
       " 'Recall': array([0.80952381, 0.69473684]),\n",
       " 'Fscore': array([0.77625571, 0.72928177]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Modeling by vectors and tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bilardi/github/bilardi/smltk/.env/lib64/python3.13/site-packages/sklearn/feature_extraction/text.py:517: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# vectorization\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer(analyzer = 'word', tokenizer = nlp) #, ngram_range = (1, 2)\n",
    "X_vect_train = vectorizer.fit_transform(X_train)\n",
    "X_vect_test = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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: 0.5em;\n",
       "  text-align: center;\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-3 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-3 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-3 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-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">0.15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;optimal&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eta0',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">eta0&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('average',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">average&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_vect_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642    0\n",
      "762    1\n",
      "909    1\n",
      "199    0\n",
      "586    1\n",
      "      ..\n",
      "164    0\n",
      "782    0\n",
      "575    0\n",
      "992    0\n",
      "713    1\n",
      "Name: sentiment, Length: 200, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[85, 20],\n",
       "       [29, 66]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAGdCAYAAADJ366iAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAEytJREFUeJzt3HuY3/Od9/HXTA4TZZOKRCKqqtzopeuUEIcGqWgcqtSW1dY6b6lbkLgpraXcSLdonau1tejdA+2WbdeucqeU22aFEKeijm2IJNJIIonMJDNz/0FjRwnj8P5p5vG4rt8f8/l+5ne955q55vn7/r7fmabOzs7OAECB5kYPAEDPIToAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKNO70QP8ydI5TzZ6BHhPrTJsVKNHgPfMsrZn39I+ZzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0VlJtbe356LvXZ2xnzs4w0fvlV33PSSX/fOP0tnZuXzP1848Lx/ffrcujyMmnNLQueGt+sqJR2fyf96QF/74aGY8c1/+5Wffz4Ybrt9lT0tLSy684KzMeu7BzJv7u1x7zfey5pqDGjYzSe9GD8B74/v/56e55vobctYpx2eD9dbNQ4/8Lqec9e2sttqqOWDfvZbv+8Q2I3LmV8cv/7hPnz4Nmhi6Z4dR2+Q737kqd0+dlt69e+fMM07Kf9zwo/z1Zjtl8eKXkiTnnfv17L7bztn/80dk/vwFufCCs/Kza/8pO+y0d6PH77FEZyU17cGHM3rUNtlxu62TJGuvNST/fvNv8sBvH+2yr2+fPhm0xsAGTQlv3x57HtDl40MPPy4zZzyQ4Vtumtv/353p3/+vcugh++eAA4/OLbfekSQ57O/H56EHbsvIrbfMnVPuadDkPZu311ZSm3/8Y7nz7ml5+g/PJEkeeezJ3HP/Qxm1zYgu++669/7ssMf++fT+h+eMcy7KvPkLGjQxvDMDBvRPksx9YV6SZPiWm6Zv376ZNOn25XseffSJ/P73z2SbbYY3bM6erttnOnPmzMkVV1yRyZMnZ+bMmUmSoUOHZrvttsvBBx+cwYMHvxdz0k2H/91+WbR4cfb8wpfSq7k57R0dOeZLB+XTYz+5fM/22wzPmB23z9rDhmT6s8/lgu9emSOP/4f88LvfSq9evRo6P3RHU1NTvnXu6bnjjil56KGXz+aHDB2c1tbWzH/NC6nZs5/P0KF+TzVKt6Jz1113ZezYsfnABz6QMWPGZMMNN0ySzJo1KxdeeGG+8Y1v5Fe/+lVGjBixwudpbW1Na2trl7Xm1ta0tLS8na+B13Hjr2/Lv910S/7x6ydmg/XWzSOPPZl/vOC7WXPQwOy1+y5Jkt3H7LR8/4brr5cN118vu+13aO669/5sM2KLBk4P3XPRhWdnk002yo6jP9voUXgT3YrOuHHjsu++++ayyy5LU1NTl2OdnZ058sgjM27cuEyePHmFzzNx4sScfvrpXdZOOeGYnHrisd0ZhxU475Lv5/AD9lselg3XXy/PzZydf/rBtcuj81rrrL1WVv9g//zhmedEh78YF5x/ZvbYfUxG77xPnn32ueXrs2Y+n5aWlgwY0L/L2c6aaw7OzJnPN2haunVN57777sv48eP/LDh55fR2/PjxmTZt2ps+z8knn5z58+d3eXzl2CO7NzkrtGRJa5qau36fmpub0/Hfbpl+rZmzn8+8+S9msBsL+AtxwflnZu+9ds0uY/fL009P73Js6j33p62tLZ/85CeWr2244fpZd90P5b/+a2oDpiXdPdMZOnRopkyZko033vh1j0+ZMiVDhgx50+dpaWn5s7fSlrbN6c4ovImdth+Zy6/6SdYasmY2WG/dPPy7x3P1NT/PZ/f4VJJk8eKXcukVP8wuO22fQWsMzPRnZ+Rbl16RD39oWLYfuWWjx4c3ddGFZ+fz+++dff7m0Lz44sIMGfLydZr581/MkiVLsmDBi7nin3+Sc795Wl6YOy8LFryYC84/M5Mn3+3OtQZq6uxcwUvf17jkkkty/PHH54gjjsjOO++8PDCzZs3KpEmTcvnll+fcc8/NUUcd1e1Bls55stufwxtbtGhxLrr86ky6bXLmvjAvgwcNzO677JQvH/KF9OnTJ0taW3PMSWfkkd89kQULF2XNQQOz3dZb5ui/PzCDBq7e6PFXSqsMG9XoEVYqy9qefd31Qw8bn6t/cG3yygvcc755avb/273S0tKSm26+NUeP+2pmzfL22rvtjb4fr9Wt6CTJNddck29/+9uZOnVq2tvbkyS9evXK8OHDM2HChOy3335va2DRYWUnOqzM3rPo/MnSpUszZ87Lb4kNGjToHf8lu+iwshMdVmZvNTpv+z8S9OnTJ2uttdbb/XQAeiD/kQCAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACjTu9ED/Ml2mx7c6BHgPTXv2K0bPQI0nDMdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUKZ3owfgvXHw0V/M6N13yLobrJvWJa25/+4Hc/FZl+X3T0xfvmftdYfl2FOPyuZbb5o+fftk8i135txTLsjcOS80dHZ4q5r6D0zfPQ5M7422TPr2TcecmWm99qJ0PPPEq3vW/FBadv+79ProJkmvXumYNT1Lrv5mOufNaejsPZXorKS23Hbz/PTK6/LbaY+kV+9eOeqkL+WiH5+X/XY8MEteWpJ+q/TLxT8+L4/99ol8ed/jkiRHnnhYvnXVN3LIp49MZ2dno78EWLFVVs0q/3Ni2p94IC99/3+nc+H8NA9eK50vLVq+pWmNofnAUWdn6V3/N203/SSdrS+lecg6ydKlDR29JxOdldQxXzyhy8enH3d2bn7wl/nYphvl3jvvy2Zb/3XWWmdoDvjUYVm0cHGS5OvHnp1fP3xDtvrElply+9QGTQ5vTd+d9knnvDlpvfbi5WvtL8zuumfXL2TZI1PTdsPVr+7548zSOelKdHqI1fqvliRZMG9BkqRv3z7p7OxMW9urr/jaWtvS0dGRzbbeVHR43+u9yVZZ9ui96XfACWlef5N0zv9jlv7njVk25eaXNzQ1pffGI9L2m+vS7/BT07z2R9M5d1bafv0vaX9oSqPH77HcSNADNDU1ZcLp4zJtyv154tGnkiQPTH0oSxYvybivHZmWVVrSb5V+OfbUo9K7d+8MWnONRo8Mb6pp4JD02XbXdMyZkSWXn56lk29My96Hpffw0S8fX21Amvqtkr6j90n7o/dmyeVfz7IH70y/A7+S5o9u0ujxe6x3PTrTp0/PoYceusI9ra2tWbBgQZdHR2fHuz0Krzjx7PFZf+P18rUvn758bd7c+TnpiNMyapftcttjv8otj/57/qr/ann4/kfT0eF7wV+ApqZ0PPtk2m78YTpmPJVld96cpXfenD7bjl1+PEmWPTQlS2//ZTpmPJ2lt/w87Q/fnT7bjG3s7D3Yux6duXPn5qqrrlrhnokTJ2bAgAFdHs8tnL7Cz+HtOeGs4zJql+3y5c8dl9nPPd/l2J2/uSuf3e7z+dSme2WXj38mpx1zVtYcOijP/mFGw+aFt6rzxRfSMavr742O2c+k6YODXj6+6MV0ti973T3Nqw8qnZVXdfuazi9+8YsVHn/yySff9DlOPvnkTJgwocva6I127+4ovIkTzjouO+06Kkd+7tjMmP7cG+6bP3d+kmTE9ltm9UGr5/ab7iicEt6e9qcfSfPgtbusNQ8als4XXnlx1b4sHdMf//M9g4el44WuL8Co0+3o7L333mlqalrhLbVNr5zWvpGWlpa0tLR0WWtucnnp3fSVs8dn7GfH5H8d8tUsXrg4awwemCRZ+OLCtC5pS5Ls+be75anHfp8X/jgvmw7fJBPOOCY//t5Pu/wtD7xfLb3tl1nl6Inp88m/ybL77kivdf5H+mzzqbT+7DvL97T95vr0++LxaX/yt2l/4oH03miL9PrYVnnpsn9o6Ow9WVNnN/8gY+21186ll16avfba63WPT5s2LcOHD097e3u3Btlq2A7d2s+K3TXjttddP/24s/Nv196YJDn6q0fk0/vtmv4f7J8Z02fm5z/41/zoe9cWT9pz3PJFN2i823p9bET67nZAmgetlc65s9N22y9evXvtFb232jl9R++Tpg+ukY7nZ6Ttpp+4e+09sNo5172lfd2Ozmc+85lsvvnmOeOMM173+H333Zctttii2xejRYeVneiwMnur0en222snnHBCFi1a9IbHN9hgg9xyyy3dfVoAeoBuR2fUqFErPL7qqqtmxx13fCczAbCScvUegDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJQRHQDKiA4AZUQHgDKiA0AZ0QGgjOgAUEZ0ACgjOgCUER0AyogOAGVEB4AyogNAGdEBoIzoAFBGdAAo09TZ2dnZ6CGo1dramokTJ+bkk09OS0tLo8eBd52f8fcv0emBFixYkAEDBmT+/Pnp379/o8eBd52f8fcvb68BUEZ0ACgjOgCUEZ0eqKWlJaeddpoLrKy0/Iy/f7mRAIAyznQAKCM6AJQRHQDKiA4AZUSnB7rkkkvykY98JP369cvIkSMzZcqURo8E74rbbrste+65Z4YNG5ampqZcf/31jR6J1xCdHuaaa67JhAkTctppp+Wee+7JZpttlrFjx2b27NmNHg3esUWLFmWzzTbLJZdc0uhReANume5hRo4cma222ioXX3xxkqSjoyPrrLNOxo0bl5NOOqnR48G7pqmpKdddd1323nvvRo/Cf+NMpwdpa2vL1KlTM2bMmOVrzc3NGTNmTCZPntzQ2YCeQXR6kDlz5qS9vT1Dhgzpsj5kyJDMnDmzYXMBPYfoAFBGdHqQQYMGpVevXpk1a1aX9VmzZmXo0KENmwvoOUSnB+nbt2+GDx+eSZMmLV/r6OjIpEmTsu222zZ0NqBn6N3oAag1YcKEHHTQQRkxYkS23nrrnH/++Vm0aFEOOeSQRo8G79jChQvz+OOPL//4qaeeyrRp0zJw4MB8+MMfbuhsvMwt0z3QxRdfnHPOOSczZ87M5ptvngsvvDAjR45s9Fjwjt16660ZPXr0n60fdNBBufLKKxsyE12JDgBlXNMBoIzoAFBGdAAoIzoAlBEdAMqIDgBlRAeAMqIDQBnRAaCM6ABQRnQAKCM6AJT5/yFmYROFzwiEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_pred = model.predict(X_vect_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred, is_test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.5086392633425091,\n",
       " 'ROC_AUC': 0.7521303258145364,\n",
       " 'Accuracy': 0.755,\n",
       " 'Precision': array([0.74561404, 0.76744186]),\n",
       " 'Recall': array([0.80952381, 0.69473684]),\n",
       " 'Fscore': array([0.77625571, 0.72928177]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Modeling by features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data wrangling\n",
    "top_100 = nlp.get_words_top(vocabulary, 100)\n",
    "\n",
    "features = nlp.get_features_from_docs(data.text, words_top = top_100)\n",
    "features_df = pd.DataFrame.from_dict(features, orient='columns')\n",
    "data = pd.concat([data, features_df], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[['words_top', 'neg', 'neu', 'pos', 'compound']], data['sentiment'], test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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: 0.5em;\n",
       "  text-align: center;\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-4 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-4 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-4 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-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
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       "        <tr class=\"default\">\n",
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       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">alpha&nbsp;</td>\n",
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       "    \n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">0.15</td>\n",
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       "    \n",
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       "        <tr class=\"default\">\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1000</td>\n",
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       "        <tr class=\"default\">\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">tol&nbsp;</td>\n",
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       "    \n",
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       "            <td class=\"param\">epsilon&nbsp;</td>\n",
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       "    \n",
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       "            <td class=\"param\">random_state&nbsp;</td>\n",
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       "    \n",
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       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
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       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "            <td class=\"param\">eta0&nbsp;</td>\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "        </tr>\n",
       "    \n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">average&nbsp;</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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       "</script></body>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642    0\n",
      "762    1\n",
      "909    1\n",
      "199    0\n",
      "586    1\n",
      "      ..\n",
      "164    0\n",
      "782    0\n",
      "575    0\n",
      "992    0\n",
      "713    1\n",
      "Name: sentiment, Length: 200, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[98,  7],\n",
       "       [34, 61]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_pred = model.predict(X_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred, is_test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': np.float64(0.23532499999999998),\n",
       " 'Bias': np.float64(0.153603875),\n",
       " 'Variance': np.float64(0.081721125),\n",
       " 'MCC': 0.6066162196580951,\n",
       " 'ROC_AUC': 0.787719298245614,\n",
       " 'Accuracy': 0.795,\n",
       " 'Precision': array([0.74242424, 0.89705882]),\n",
       " 'Recall': array([0.93333333, 0.64210526]),\n",
       " 'Fscore': array([0.82700422, 0.74846626]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"model\": model,\n",
    "    \"X_train\": np.array(X_train),\n",
    "    \"y_train\": np.array(y_train),\n",
    "    \"X_test\": np.array(X_test),\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Modeling by vectors and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data reduction\n",
    "features_mix = data[['text_cleaned', 'words_top', 'neg', 'neu', 'pos', 'compound']].to_dict(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_mix, data['sentiment'], test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vectorization\n",
    "X_vect_train = nlp.vectorize_docs(X_train)\n",
    "X_vect_test = nlp.vectorize_docs(X_test, is_test = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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: 0.5em;\n",
       "  text-align: center;\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-5 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-5 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-5 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-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(random_state=3)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SGDClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">0.15</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;optimal&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('eta0',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">eta0&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('average',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">average&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "SGDClassifier(random_state=3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_vect_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642    0\n",
      "762    1\n",
      "909    1\n",
      "199    0\n",
      "586    1\n",
      "      ..\n",
      "164    0\n",
      "782    0\n",
      "575    0\n",
      "992    0\n",
      "713    1\n",
      "Name: sentiment, Length: 200, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[95, 10],\n",
       "       [31, 64]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing\n",
    "y_pred = model.predict(X_vect_test)\n",
    "mdl.create_confusion_matrix(y_test, y_pred, is_test = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.5982989333106916,\n",
       " 'ROC_AUC': 0.7892230576441103,\n",
       " 'Accuracy': 0.795,\n",
       " 'Precision': array([0.75396825, 0.86486486]),\n",
       " 'Recall': array([0.9047619 , 0.67368421]),\n",
       " 'Fscore': array([0.82251082, 0.75739645]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.get_classification_metrics({\n",
    "    \"y_test\": np.array(y_test),\n",
    "    \"y_pred\": y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example with sklearn library - pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data['text'], data['sentiment'], test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.5887959168743058,\n",
       " 'ROC_AUC': 0.7942355889724311,\n",
       " 'Accuracy': 0.795,\n",
       " 'Precision': array([0.80188679, 0.78723404]),\n",
       " 'Recall': array([0.80952381, 0.77894737]),\n",
       " 'Fscore': array([0.8056872 , 0.78306878]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data preparation\n",
    "vectorizer = CountVectorizer()\n",
    "X_vect_train = vectorizer.fit_transform(X_train) \n",
    "X_vect_test = vectorizer.transform(X_test)\n",
    "# modeling\n",
    "model = SGDClassifier(random_state=3)\n",
    "model.fit(X_vect_train, y_train) # training\n",
    "mdl.scoring(model, X_vect_test, y_test) # testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Loss': 0,\n",
       " 'Bias': 0,\n",
       " 'Variance': 0,\n",
       " 'MCC': 0.5887959168743058,\n",
       " 'ROC_AUC': 0.7942355889724311,\n",
       " 'Accuracy': 0.795,\n",
       " 'Precision': array([0.80188679, 0.78723404]),\n",
       " 'Recall': array([0.80952381, 0.77894737]),\n",
       " 'Fscore': array([0.8056872 , 0.78306878]),\n",
       " 'Support': array([105,  95])}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "# data preparation\n",
    "pipeline = Pipeline([\n",
    "    ('count', CountVectorizer()),\n",
    "    ('class', SGDClassifier(random_state=3))\n",
    "])\n",
    "# modeling\n",
    "mdl.modeling(pipeline, X_train, y_train, X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "usage.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "smltk",
   "language": "python",
   "name": "smltk"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}