06_pokemon_lime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler,Normalizer,MinMaxScaler, MaxAbsScaler,  LabelEncoder\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz, plot_tree, export_text\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_poke_xy(dummies = True, label_encode = False, scaler = None, test_size = 0.3, random_state = 777, n_folds = 5):\n",
    "    df = pd.read_csv(\"pokemon.zip\", index_col = \"Name\")\n",
    "\n",
    "    cat_cols = [\"Type 1\", \"Type 2\", \"Generation\"]\n",
    "    num_cols = ['Total', 'HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
    "    target_col = \"Legendary\"\n",
    "    drop_cols = [\"#\"]\n",
    "    y_all = df[target_col].astype(\"int\")\n",
    "    \n",
    "    X_all = df.drop(target_col , axis = 1)\n",
    "    X_all.drop(drop_cols,axis=1, inplace = True)\n",
    "    \n",
    "    if label_encode:\n",
    "        for c in cat_cols:\n",
    "            try:\n",
    "                X_all[c] = LabelEncoder().fit_transform(X_all[c].fillna(\"NaN\"))\n",
    "            except TypeError:\n",
    "                print(\"could not encode: \", c)\n",
    "    if dummies:\n",
    "        X_all = X_all.drop(cat_cols, axis=1).join(pd.get_dummies(X_all[cat_cols].astype(\"category\")))\n",
    "    \n",
    "    \n",
    "    if scaler:\n",
    "        sc = scaler()\n",
    "        X_all[num_cols]  = sc.fit_transform(X_all[num_cols])\n",
    "    \n",
    "    kf = StratifiedKFold(n_splits = n_folds )\n",
    "    folds = list(kf.split(X_all, y_all))\n",
    "    \n",
    "    return X_all, y_all, folds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(X, y, folds, model = LogisticRegression, \n",
    "                model_params = {\"penalty\" :\"l1\", \"solver\" :\"liblinear\"}, \n",
    "                fit_params = None):\n",
    "    \n",
    "    colors = [\"r\",\"g\", \"b\", \"y\", \"c\"]\n",
    "    oof_preds = np.zeros(X.shape[0])\n",
    "    scores = []\n",
    "    models = []\n",
    "\n",
    "    plt.subplots(figsize = (10, 10))\n",
    "    #print(tgt, \":\")\n",
    "    plt.subplot(1, 1, 1)\n",
    "    for i, (i_tr, i_te) in enumerate(folds):\n",
    "        \n",
    "        Xtr, Xte = X.iloc[i_tr], X.iloc[i_te]\n",
    "        ytr, yte = y.iloc[i_tr], y.iloc[i_te]\n",
    "        \n",
    "        if model_params:\n",
    "            mdl = model(**model_params)\n",
    "        else: mdl = model()\n",
    "\n",
    "        if fit_params:\n",
    "            mdl.fit(Xtr, ytr, **fit_params)\n",
    "        else:\n",
    "            mdl.fit(Xtr, ytr)\n",
    "    \n",
    "        models.append(mdl)\n",
    "        preds =  mdl.predict_proba(Xte)[:, 1]\n",
    "        oof_preds[i_te] = preds\n",
    "        score = roc_auc_score(yte, preds)\n",
    "        scores.append(score)\n",
    "        fpr, tpr, threshold = roc_curve(yte, preds)\n",
    "        plt.plot(fpr, tpr, color = colors[i] , label = f\"fold:{i}, AUC:{score:.3f}\")\n",
    "        \n",
    "    plt.title(f\"{model.__name__} AUC:{np.mean(scores):.3f}\")\n",
    "    plt.legend(loc=4)\n",
    "    plt.show()\n",
    "    return oof_preds, scores, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_all, y_all, folds  = get_poke_xy(scaler = StandardScaler)\n",
    "svm_preds, svm_scores, svm_models = train_model(X_all, y_all, folds, model = SVC, \n",
    "                                         model_params = {\"kernel\":\"linear\" ,\n",
    "                                                         \"probability\" : True, \"gamma\":\"auto\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lime import lime_tabular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all_lab, y_all_lab, folds  = get_poke_xy(dummies = False, label_encode=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb_preds, lgb_scores, lgb_models = train_model(X_all_lab, y_all_lab, folds, model = lgb.LGBMClassifier, \n",
    "                                         model_params = {\"n_estimators\" : 500,\n",
    "                                                         \"learning_rate\" : 0.01})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer =  lime_tabular.LimeTabularExplainer(training_data = X_all_lab.values, \n",
    "                                               mode=\"classification\",\n",
    "                                               training_labels=y_all_lab,\n",
    "                                               feature_names = X_all_lab.columns, \n",
    "                                               categorical_features = [0, 1, 9],\n",
    "                                               class_names = [\"Common\", \"Legendary\"]\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9022493d48ac454aa3af7d888188ab37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntText(value=0, description='fold'), IntText(value=0, description='i'), Output()), _dom…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.show_lgb_explain(fold, i)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def show_lgb_explain(fold, i):\n",
    "    exp = explainer.explain_instance(X_all_lab.iloc[folds[fold][1],:].iloc[i,:].values, \n",
    "                                     lgb_models[fold].predict_proba, num_features = 10)\n",
    "    return exp.show_in_notebook(show_all=True)\n",
    "\n",
    "interact(show_lgb_explain, \n",
    "         fold = widgets.IntText(min=0, max = 4), \n",
    "         i = widgets.IntText(min=0, max =len(folds[0][1] ) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}