{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "i-B5sPHELBBj"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import (\n",
    "    AdaBoostClassifier,\n",
    "    GradientBoostingClassifier,\n",
    "    RandomForestClassifier,\n",
    ")\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "uxhFjfeHLBBr"
   },
   "outputs": [],
   "source": [
    "# Read the pokedex we scraped in web_scrape.ipynb into a DataFrame\n",
    "pkmn = pd.read_csv(\"pokemon.csv\")\n",
    "pkmn.rename(columns={\"Unnamed: 0\": \"wiki_index\"}, inplace=True)\n",
    "pkmn = pkmn[pkmn.primary_type != \"Bird\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 908
    },
    "id": "1HfxKWKEPpNU",
    "outputId": "b3d9ab9d-7cd2-4010-f060-bb7ac2f843f1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_table = pkmn[[\"primary_type\", \"secondary_type\"]].value_counts().unstack().fillna(0)\n",
    "long_form = pkmn[[\"primary_type\", \"secondary_type\"]].value_counts()\n",
    "\n",
    "ax = sns.heatmap(pivot_table)\n",
    "ax.figure.set_size_inches(15, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RTUdYUv3LBBt",
    "outputId": "1098fdc6-500c-4c9a-ade1-57b380a0ef84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(935, 6229) (935,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '01',\n",
       " '02',\n",
       " '03',\n",
       " '04',\n",
       " '05',\n",
       " '10',\n",
       " '100',\n",
       " '1000',\n",
       " '100x',\n",
       " '101',\n",
       " '108',\n",
       " '11',\n",
       " '12',\n",
       " '120',\n",
       " '13',\n",
       " '14',\n",
       " '148',\n",
       " '15',\n",
       " '150',\n",
       " '16',\n",
       " '17',\n",
       " '18',\n",
       " '180',\n",
       " '19',\n",
       " '1950s',\n",
       " '1970s',\n",
       " '1997',\n",
       " '1px',\n",
       " '20',\n",
       " '200',\n",
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       " 'carp',\n",
       " 'carracosta',\n",
       " 'carried',\n",
       " 'carriers',\n",
       " 'carries',\n",
       " 'carrot',\n",
       " 'carrots',\n",
       " 'carry',\n",
       " 'carrying',\n",
       " 'cars',\n",
       " 'cartoonist',\n",
       " 'carvanha',\n",
       " 'cascoon',\n",
       " 'case',\n",
       " 'cast',\n",
       " 'caste',\n",
       " 'castform',\n",
       " 'castle',\n",
       " 'cat',\n",
       " 'catch',\n",
       " 'catcher',\n",
       " 'catches',\n",
       " 'category',\n",
       " 'catfish',\n",
       " 'caught',\n",
       " 'cause',\n",
       " 'caused',\n",
       " 'causes',\n",
       " 'causing',\n",
       " 'cautionary',\n",
       " 'cave',\n",
       " 'cavernous',\n",
       " 'caves',\n",
       " 'ceilings',\n",
       " 'celebi',\n",
       " 'celebration',\n",
       " 'celesteela',\n",
       " 'cell',\n",
       " 'cells',\n",
       " 'celsius',\n",
       " 'cemetery',\n",
       " 'center',\n",
       " 'centers',\n",
       " 'centipede',\n",
       " 'centiskorch',\n",
       " 'central',\n",
       " 'centres',\n",
       " 'centrifugal',\n",
       " 'ceo',\n",
       " 'ceremonies',\n",
       " 'certain',\n",
       " 'chains',\n",
       " 'challenge',\n",
       " 'chameleon',\n",
       " 'champion',\n",
       " 'championship',\n",
       " 'championships',\n",
       " 'chan',\n",
       " 'chance',\n",
       " 'chandelure',\n",
       " 'change',\n",
       " 'changed',\n",
       " 'changes',\n",
       " 'changing',\n",
       " 'chansey',\n",
       " 'chaos',\n",
       " 'chaplin',\n",
       " 'char',\n",
       " 'character',\n",
       " 'characterized',\n",
       " 'characters',\n",
       " 'charge',\n",
       " 'charged',\n",
       " 'charges',\n",
       " 'charging',\n",
       " 'charizard',\n",
       " 'charjabug',\n",
       " 'charlie',\n",
       " 'charm',\n",
       " 'charmander',\n",
       " 'charmeleon',\n",
       " 'chase',\n",
       " 'chases',\n",
       " 'chatter',\n",
       " 'check',\n",
       " 'cheek',\n",
       " 'cheeks',\n",
       " 'cheer',\n",
       " 'cheerful',\n",
       " 'chefs',\n",
       " 'chemical',\n",
       " 'chemically',\n",
       " 'cherish',\n",
       " 'chesnaught',\n",
       " 'chespin',\n",
       " 'chess',\n",
       " 'chest',\n",
       " 'chestnut',\n",
       " 'chewable',\n",
       " 'chewing',\n",
       " 'chewtle',\n",
       " 'chick',\n",
       " 'chickadee',\n",
       " 'chicken',\n",
       " 'chicks',\n",
       " 'chief',\n",
       " 'chikorita',\n",
       " 'child',\n",
       " 'children',\n",
       " 'chilling',\n",
       " 'chills',\n",
       " 'chimchar',\n",
       " 'chime',\n",
       " 'chimera',\n",
       " 'chimes',\n",
       " 'chinchou',\n",
       " 'chinese',\n",
       " 'chipped',\n",
       " 'chipping',\n",
       " 'choice',\n",
       " 'chomps',\n",
       " 'choose',\n",
       " 'chooses',\n",
       " 'chop',\n",
       " 'chose',\n",
       " 'chosen',\n",
       " 'church',\n",
       " 'chōchin',\n",
       " 'cicada',\n",
       " 'cinccino',\n",
       " 'cinderace',\n",
       " 'cinders',\n",
       " 'circle',\n",
       " 'circles',\n",
       " 'circulates',\n",
       " 'circulating',\n",
       " 'circumstances',\n",
       " 'citation',\n",
       " 'cited',\n",
       " 'cites',\n",
       " 'cities',\n",
       " 'city',\n",
       " 'civilization',\n",
       " 'civilizations',\n",
       " 'claim',\n",
       " 'claimed',\n",
       " 'claiming',\n",
       " 'claims',\n",
       " ...]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a TF-IDF vectorizer to transform the text into a matrix of features\n",
    "vectorizer = TfidfVectorizer(stop_words=\"english\")\n",
    "X = vectorizer.fit_transform(pkmn[\"Notes\"]).toarray()\n",
    "y = pkmn[\"primary_type\"]\n",
    "print(X.shape, y.shape)\n",
    "list(vectorizer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "ICefCkn6LBBv"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GpuzXYA0LBBw",
    "outputId": "2216c89a-d8f8-46e8-c1ee-442a6961aa29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dummy :  0.12455516014234876\n",
      "Nearest Neighbors :  0.3665480427046263\n",
      "Linear SVM :  0.12455516014234876\n",
      "RBF SVM :  0.17437722419928825\n",
      "LinearSVC :  0.47330960854092524\n",
      "Decision Tree :  0.16370106761565836\n",
      "Random Forest :  0.13167259786476868\n",
      "Neural Net :  0.298932384341637\n",
      "Gradient Boosting :  0.33451957295373663\n",
      "AdaBoost :  0.1494661921708185\n",
      "Naive Bayes :  0.3701067615658363\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/discriminant_analysis.py:878: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QDA :  0.05693950177935943\n",
      "Best classifier:  LinearSVC  with score:  0.47330960854092524\n"
     ]
    }
   ],
   "source": [
    "names = [\n",
    "    \"Dummy\",\n",
    "    \"Nearest Neighbors\",\n",
    "    \"Linear SVM\",\n",
    "    \"RBF SVM\",\n",
    "    \"LinearSVC\",\n",
    "    # \"Gaussian Process\",\n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\",\n",
    "    \"Neural Net\",\n",
    "    \"Gradient Boosting\",\n",
    "    \"AdaBoost\",\n",
    "    \"Naive Bayes\",\n",
    "    \"QDA\",\n",
    "]\n",
    "\n",
    "classifiers = [\n",
    "    DummyClassifier(),\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    LinearSVC(),\n",
    "    # GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    GradientBoostingClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "]\n",
    "\n",
    "\n",
    "max_so_far = 0\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(name, \": \", clf.score(X_test, y_test))\n",
    "\n",
    "    if clf.score(X_test, y_test) > max_so_far:\n",
    "        max_so_far = clf.score(X_test, y_test)\n",
    "        best_clf = clf\n",
    "        best_clf_name = name\n",
    "\n",
    "clf = best_clf\n",
    "print(\"Best classifier: \", best_clf_name, \" with score: \", max_so_far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w6v8QJ3xWLfw",
    "outputId": "0aae6cf5-f26b-4e7c-b93e-cd5215225d93"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
      "20 fits failed out of a total of 80.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "20 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/svm/_classes.py\", line 272, in fit\n",
      "    sample_weight=sample_weight,\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py\", line 1185, in _fit_liblinear\n",
      "    solver_type = _get_liblinear_solver_type(multi_class, penalty, loss, dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py\", line 1026, in _get_liblinear_solver_type\n",
      "    % (error_string, penalty, loss, dual)\n",
      "ValueError: Unsupported set of arguments: The combination of penalty='l1' and loss='squared_hinge' are not supported when dual=True, Parameters: penalty='l1', loss='squared_hinge', dual=True\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [       nan 0.40510863 0.41276571 0.41276571        nan 0.40971227\n",
      " 0.41276571 0.41276571        nan 0.40971227 0.41276571 0.41276571\n",
      "        nan 0.41122725 0.41276571 0.41276571]\n",
      "  category=UserWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.41276570757486786"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {\n",
    "    \"penalty\": [\"l1\", \"l2\"],\n",
    "    \"C\": [1, 10, 100, 1000],\n",
    "    \"multi_class\": [\"ovr\", \"crammer_singer\"],\n",
    "}\n",
    "\n",
    "gs_clf = GridSearchCV(clf, parameters, cv=5, n_jobs=-1)\n",
    "gs_clf.fit(X_train, y_train)\n",
    "gs_clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ct1IDpeghQan",
    "outputId": "1159ea06-a44d-4528-eb9e-4a158d71f05e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'multi_class': 'crammer_singer', 'penalty': 'l1'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "AUubKCsvhuC9",
    "outputId": "36862862-bf28-4d05-f8f3-a57b21684c89"
   },
   "outputs": [
    {
     "data": {
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_multi_class</th>\n",
       "      <th>param_penalty</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.022543</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 1, 'multi_class': 'ovr', 'penalty': 'l1'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.083652</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.005357</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>1</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1, 'multi_class': 'ovr', 'penalty': 'l2'}</td>\n",
       "      <td>0.465649</td>\n",
       "      <td>0.404580</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.405109</td>\n",
       "      <td>0.038100</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.116112</td>\n",
       "      <td>0.014720</td>\n",
       "      <td>0.008172</td>\n",
       "      <td>0.003883</td>\n",
       "      <td>1</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 1, 'multi_class': 'crammer_singer', 'pen...</td>\n",
       "      <td>0.473282</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.419847</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.036297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.120234</td>\n",
       "      <td>0.012157</td>\n",
       "      <td>0.007247</td>\n",
       "      <td>0.003175</td>\n",
       "      <td>1</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1, 'multi_class': 'crammer_singer', 'pen...</td>\n",
       "      <td>0.473282</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.419847</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.036297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015091</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 10, 'multi_class': 'ovr', 'penalty': 'l1'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.246430</td>\n",
       "      <td>0.018034</td>\n",
       "      <td>0.005265</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>10</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 10, 'multi_class': 'ovr', 'penalty': 'l2'}</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.404580</td>\n",
       "      <td>0.389313</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.409712</td>\n",
       "      <td>0.039597</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.132618</td>\n",
       "      <td>0.005460</td>\n",
       "      <td>0.005094</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>10</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 10, 'multi_class': 'crammer_singer', 'pe...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.146367</td>\n",
       "      <td>0.014093</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>10</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 10, 'multi_class': 'crammer_singer', 'pe...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.014114</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 100, 'multi_class': 'ovr', 'penalty': 'l1'}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.834156</td>\n",
       "      <td>0.030855</td>\n",
       "      <td>0.005086</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>100</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 100, 'multi_class': 'ovr', 'penalty': 'l2'}</td>\n",
       "      <td>0.473282</td>\n",
       "      <td>0.404580</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.409712</td>\n",
       "      <td>0.036214</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.519592</td>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>100</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 100, 'multi_class': 'crammer_singer', 'p...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.513260</td>\n",
       "      <td>0.013659</td>\n",
       "      <td>0.004989</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>100</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 100, 'multi_class': 'crammer_singer', 'p...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.013891</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 1000, 'multi_class': 'ovr', 'penalty': '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.626005</td>\n",
       "      <td>0.106514</td>\n",
       "      <td>0.005055</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>1000</td>\n",
       "      <td>ovr</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1000, 'multi_class': 'ovr', 'penalty': '...</td>\n",
       "      <td>0.473282</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.427481</td>\n",
       "      <td>0.404580</td>\n",
       "      <td>0.353846</td>\n",
       "      <td>0.411227</td>\n",
       "      <td>0.039130</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.000573</td>\n",
       "      <td>0.146304</td>\n",
       "      <td>0.005013</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>1000</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l1</td>\n",
       "      <td>{'C': 1000, 'multi_class': 'crammer_singer', '...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.954307</td>\n",
       "      <td>0.184881</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>1000</td>\n",
       "      <td>crammer_singer</td>\n",
       "      <td>l2</td>\n",
       "      <td>{'C': 1000, 'multi_class': 'crammer_singer', '...</td>\n",
       "      <td>0.480916</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.412214</td>\n",
       "      <td>0.396947</td>\n",
       "      <td>0.361538</td>\n",
       "      <td>0.412766</td>\n",
       "      <td>0.038780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "  "
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0        0.022543      0.004529         0.000000        0.000000       1   \n",
       "1        0.083652      0.001697         0.005357        0.000484       1   \n",
       "2        0.116112      0.014720         0.008172        0.003883       1   \n",
       "3        0.120234      0.012157         0.007247        0.003175       1   \n",
       "4        0.015091      0.002333         0.000000        0.000000      10   \n",
       "5        0.246430      0.018034         0.005265        0.000301      10   \n",
       "6        0.132618      0.005460         0.005094        0.000080      10   \n",
       "7        0.146367      0.014093         0.005040        0.000053      10   \n",
       "8        0.014114      0.001050         0.000000        0.000000     100   \n",
       "9        0.834156      0.030855         0.005086        0.000078     100   \n",
       "10       0.519592      0.021908         0.005176        0.000095     100   \n",
       "11       0.513260      0.013659         0.004989        0.000057     100   \n",
       "12       0.013891      0.000794         0.000000        0.000000    1000   \n",
       "13       0.626005      0.106514         0.005055        0.000083    1000   \n",
       "14       4.000573      0.146304         0.005013        0.000032    1000   \n",
       "15       3.954307      0.184881         0.004761        0.000606    1000   \n",
       "\n",
       "   param_multi_class param_penalty  \\\n",
       "0                ovr            l1   \n",
       "1                ovr            l2   \n",
       "2     crammer_singer            l1   \n",
       "3     crammer_singer            l2   \n",
       "4                ovr            l1   \n",
       "5                ovr            l2   \n",
       "6     crammer_singer            l1   \n",
       "7     crammer_singer            l2   \n",
       "8                ovr            l1   \n",
       "9                ovr            l2   \n",
       "10    crammer_singer            l1   \n",
       "11    crammer_singer            l2   \n",
       "12               ovr            l1   \n",
       "13               ovr            l2   \n",
       "14    crammer_singer            l1   \n",
       "15    crammer_singer            l2   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0     {'C': 1, 'multi_class': 'ovr', 'penalty': 'l1'}                NaN   \n",
       "1     {'C': 1, 'multi_class': 'ovr', 'penalty': 'l2'}           0.465649   \n",
       "2   {'C': 1, 'multi_class': 'crammer_singer', 'pen...           0.473282   \n",
       "3   {'C': 1, 'multi_class': 'crammer_singer', 'pen...           0.473282   \n",
       "4    {'C': 10, 'multi_class': 'ovr', 'penalty': 'l1'}                NaN   \n",
       "5    {'C': 10, 'multi_class': 'ovr', 'penalty': 'l2'}           0.480916   \n",
       "6   {'C': 10, 'multi_class': 'crammer_singer', 'pe...           0.480916   \n",
       "7   {'C': 10, 'multi_class': 'crammer_singer', 'pe...           0.480916   \n",
       "8   {'C': 100, 'multi_class': 'ovr', 'penalty': 'l1'}                NaN   \n",
       "9   {'C': 100, 'multi_class': 'ovr', 'penalty': 'l2'}           0.473282   \n",
       "10  {'C': 100, 'multi_class': 'crammer_singer', 'p...           0.480916   \n",
       "11  {'C': 100, 'multi_class': 'crammer_singer', 'p...           0.480916   \n",
       "12  {'C': 1000, 'multi_class': 'ovr', 'penalty': '...                NaN   \n",
       "13  {'C': 1000, 'multi_class': 'ovr', 'penalty': '...           0.473282   \n",
       "14  {'C': 1000, 'multi_class': 'crammer_singer', '...           0.480916   \n",
       "15  {'C': 1000, 'multi_class': 'crammer_singer', '...           0.480916   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0                 NaN                NaN                NaN   \n",
       "1            0.404580           0.412214           0.396947   \n",
       "2            0.396947           0.419847           0.412214   \n",
       "3            0.396947           0.419847           0.412214   \n",
       "4                 NaN                NaN                NaN   \n",
       "5            0.412214           0.404580           0.389313   \n",
       "6            0.412214           0.412214           0.396947   \n",
       "7            0.412214           0.412214           0.396947   \n",
       "8                 NaN                NaN                NaN   \n",
       "9            0.404580           0.412214           0.396947   \n",
       "10           0.412214           0.412214           0.396947   \n",
       "11           0.412214           0.412214           0.396947   \n",
       "12                NaN                NaN                NaN   \n",
       "13           0.396947           0.427481           0.404580   \n",
       "14           0.412214           0.412214           0.396947   \n",
       "15           0.412214           0.412214           0.396947   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0                 NaN              NaN             NaN               13  \n",
       "1            0.346154         0.405109        0.038100               12  \n",
       "2            0.361538         0.412766        0.036297                1  \n",
       "3            0.361538         0.412766        0.036297                1  \n",
       "4                 NaN              NaN             NaN               14  \n",
       "5            0.361538         0.409712        0.039597               10  \n",
       "6            0.361538         0.412766        0.038780                1  \n",
       "7            0.361538         0.412766        0.038780                1  \n",
       "8                 NaN              NaN             NaN               15  \n",
       "9            0.361538         0.409712        0.036214               10  \n",
       "10           0.361538         0.412766        0.038780                1  \n",
       "11           0.361538         0.412766        0.038780                1  \n",
       "12                NaN              NaN             NaN               16  \n",
       "13           0.353846         0.411227        0.039130                9  \n",
       "14           0.361538         0.412766        0.038780                1  \n",
       "15           0.361538         0.412766        0.038780                1  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(gs_clf.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KKBzq6-gLBBy",
    "outputId": "45199ff5-8d74-4610-f782-ff61d30f05d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'In the green springtime, this creature photosynthesizes under the sun.' => Grass\n",
      "'This create is native to caves in the Arctic. They make friends with polar bears, and sometimes drink hot cocoa.' => Ice\n"
     ]
    }
   ],
   "source": [
    "docs_new = [\n",
    "    \"In the green springtime, this creature photosynthesizes under the sun.\",\n",
    "    \"This create is native to caves in the Arctic. They make friends with polar bears, and sometimes drink hot cocoa.\",\n",
    "]\n",
    "\n",
    "X_new_counts = vectorizer.transform(docs_new)\n",
    "predicted = clf.predict(X_new_counts.toarray())\n",
    "\n",
    "for doc, cat in zip(docs_new, predicted):\n",
    "    print(f\"{doc!r} => {cat}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "aF8K0Ku5LBB1",
    "outputId": "b2492636-9610-4606-f976-aff09b460b6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07e6ce1f90>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N/paUX4/JQG+SdLvtj6pzm/Nv9K/CvHrs73ttX6HO6cqTkj7WtwK76C4LFIILdEAhCDtQCMIOFIKwA4Ug7EAhCDtQCMIOFOL/ADLR+Kg8vVraAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(confusion_matrix(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lUVDLT01LBB2",
    "outputId": "d4fd1914-ceb1-4c39-d6c6-a8acc794fddf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Accuracy:  0.47330960854092524\n",
      "\n",
      " Classification Report\n",
      "==================================\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         Bug       0.57      0.64      0.60        25\n",
      "        Dark       1.00      0.27      0.42        15\n",
      "      Dragon       1.00      0.17      0.29         6\n",
      "    Electric       0.55      0.55      0.55        11\n",
      "       Fairy       1.00      0.14      0.25         7\n",
      "    Fighting       0.33      0.30      0.32        10\n",
      "        Fire       0.58      0.37      0.45        19\n",
      "      Flying       0.00      0.00      0.00         1\n",
      "       Ghost       0.38      0.30      0.33        10\n",
      "       Grass       0.49      0.53      0.51        32\n",
      "      Ground       0.57      0.31      0.40        13\n",
      "         Ice       0.80      0.40      0.53        10\n",
      "      Normal       0.23      0.46      0.31        28\n",
      "      Poison       0.80      0.50      0.62         8\n",
      "     Psychic       0.63      0.52      0.57        23\n",
      "        Rock       0.54      0.35      0.42        20\n",
      "       Steel       0.40      0.25      0.31         8\n",
      "       Water       0.47      0.83      0.60        35\n",
      "\n",
      "    accuracy                           0.47       281\n",
      "   macro avg       0.57      0.38      0.42       281\n",
      "weighted avg       0.55      0.47      0.47       281\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Accuracy: \", accuracy_score(y_test, clf.predict(X_test)))\n",
    "print(\"\\n Classification Report\")\n",
    "print(\"==================================\")\n",
    "print(\"\\n\", classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XamB14MKPJs-",
    "outputId": "f758f37c-7945-4139-e067-b19ef8656510"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Accuracy:  0.49466192170818507\n",
      "\n",
      " Classification Report\n",
      "==================================\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         Bug       0.57      0.64      0.60        25\n",
      "        Dark       1.00      0.40      0.57        15\n",
      "      Dragon       0.50      0.17      0.25         6\n",
      "    Electric       0.50      0.55      0.52        11\n",
      "       Fairy       0.50      0.14      0.22         7\n",
      "    Fighting       0.50      0.30      0.37        10\n",
      "        Fire       0.41      0.37      0.39        19\n",
      "      Flying       0.00      0.00      0.00         1\n",
      "       Ghost       0.42      0.50      0.45        10\n",
      "       Grass       0.50      0.50      0.50        32\n",
      "      Ground       0.67      0.31      0.42        13\n",
      "         Ice       0.67      0.40      0.50        10\n",
      "      Normal       0.30      0.50      0.38        28\n",
      "      Poison       0.80      0.50      0.62         8\n",
      "     Psychic       0.67      0.61      0.64        23\n",
      "        Rock       0.47      0.35      0.40        20\n",
      "       Steel       0.33      0.25      0.29         8\n",
      "       Water       0.49      0.83      0.62        35\n",
      "\n",
      "    accuracy                           0.49       281\n",
      "   macro avg       0.52      0.41      0.43       281\n",
      "weighted avg       0.53      0.49      0.49       281\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Accuracy: \", accuracy_score(y_test, gs_clf.predict(X_test)))\n",
    "print(\"\\n Classification Report\")\n",
    "print(\"==================================\")\n",
    "print(\"\\n\", classification_report(y_test, gs_clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, we can see the grid search classifier is better than the best classifier, however, an accuracy around 0.49 still can be improved.\n",
    "We're going to pick this analysis up in the next notebook where we'll look at using HuggingFace Transformers to improve the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "nlp.ipynb",
   "provenance": []
  },
  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.10.0 ('projects-vBrzsZbN-py3.10')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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