Random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook gives \n",
    "## X_train, y_train\n",
    "## X_val, y_val\n",
    "## X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/meenakshijhalani/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import preprocessing \n",
    "from scipy.optimize import minimize\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import metrics\n",
    "from datetime import datetime\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('yelp-data/train.csv')\n",
    "val = pd.read_csv('yelp-data/dev.csv')\n",
    "test = pd.read_csv('test_no_label.csv').drop('label', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:    (250874, 7)\n",
      "Validation data:  (35918, 7)\n",
      "Test data:        (72165, 6)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data:   \", train.shape)\n",
    "print(\"Validation data: \", val.shape)\n",
    "print(\"Test data:       \", test.shape)  # no label\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ex_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>prod_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>label</th>\n",
       "      <th>date</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-12-08</td>\n",
       "      <td>The food at snack is a selection of popular Gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>924</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-16</td>\n",
       "      <td>This little place in Soho is wonderful. I had ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>925</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>ordered lunch for 15 from Snack last Friday.  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>926</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-07-28</td>\n",
       "      <td>This is a beautiful quaint little restaurant o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>927</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>Snack is great place for a  casual sit down lu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>928</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-09-02</td>\n",
       "      <td>A solid 4 stars for this greek food spot.  If ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>930</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-20</td>\n",
       "      <td>Love this place!  Try the Chicken sandwich or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>931</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2005-12-27</td>\n",
       "      <td>My friend and I were intrigued by the nightly ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>933</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>pretty cool place...good food...good people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>935</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>Fabulous Authentic Greek Food!!! This little s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ex_id  user_id  prod_id  rating  label        date  \\\n",
       "0      0      923        0     3.0      1  2014-12-08   \n",
       "1      1      924        0     3.0      1  2013-05-16   \n",
       "2      2      925        0     4.0      1  2013-07-01   \n",
       "3      3      926        0     4.0      1  2011-07-28   \n",
       "4      4      927        0     4.0      1  2010-11-01   \n",
       "5      5      928        0     4.0      1  2009-09-02   \n",
       "6      7      930        0     4.0      1  2007-05-20   \n",
       "7      8      931        0     4.0      1  2005-12-27   \n",
       "8     10      933        0     5.0      1  2014-01-21   \n",
       "9     12      935        0     5.0      1  2011-01-31   \n",
       "\n",
       "                                              review  \n",
       "0  The food at snack is a selection of popular Gr...  \n",
       "1  This little place in Soho is wonderful. I had ...  \n",
       "2  ordered lunch for 15 from Snack last Friday.  ...  \n",
       "3  This is a beautiful quaint little restaurant o...  \n",
       "4  Snack is great place for a  casual sit down lu...  \n",
       "5  A solid 4 stars for this greek food spot.  If ...  \n",
       "6  Love this place!  Try the Chicken sandwich or ...  \n",
       "7  My friend and I were intrigued by the nightly ...  \n",
       "8        pretty cool place...good food...good people  \n",
       "9  Fabulous Authentic Greek Food!!! This little s...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corpus = train['review']\n",
    "val_corpus = val['review']\n",
    "test_corpus=test['review']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf = TfidfVectorizer(binary = False, ngram_range = (1,2), stop_words = 'english', max_features=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(binary = False, ngram_range = (1,2), stop_words = 'english',min_df=0.01,max_df=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tfidf = tf.fit_transform(train_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(250874, 923)\n"
     ]
    }
   ],
   "source": [
    "print(train_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'scipy.sparse.csr.csr_matrix'>\n"
     ]
    }
   ],
   "source": [
    "print(type(train_tfidf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features=train_tfidf.toarray()   #convert to numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_tfidf=tf.transform(val_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_features=val_tfidf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35918, 923)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_tfidf=tf.transform(test_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features=test_tfidf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72165, 923)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Add the column for rating and divide by 5 to bring the value between [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.c_[train_features,train['rating'].values/5] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val=np.c_[val_features,val['rating'].values/5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=np.c_[test_features,test['rating'].values/5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_val[:,-1][:10]*5 #check top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test[:,-1][:10]*5 #check top 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['label'].values\n",
    "y_val = val['label'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processed Data for Training, Validation and Testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is  (250874, 924) and labels are  (250874,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data is \", X_train.shape, \"and labels are \", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation data is  (35918, 924) and labels are  (35918,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation data is \", X_val.shape, \"and labels are \", y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data is  (72165, 924) and labels will be predicted by model \n"
     ]
    }
   ],
   "source": [
    "print(\"Test data is \", X_test.shape, \"and labels will be predicted by model \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savetxt('X_train.csv', X_train, delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100building tree 2 of 100\n",
      "building tree 3 of 100building tree 4 of 100\n",
      "\n",
      "\n",
      "building tree 5 of 100\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  4.5min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 100\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 14.7min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    2.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.69683529169317\n",
      "0.20665728036905942\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, class_weight = 'balanced', \n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100\n",
      "building tree 2 of 100\n",
      "building tree 3 of 100building tree 4 of 100\n",
      "\n",
      "building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100\n",
      "building tree 8 of 100\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
      "building tree 19 of 100\n",
      "building tree 20 of 100\n",
      "building tree 21 of 100\n",
      "building tree 22 of 100\n",
      "building tree 23 of 100\n",
      "building tree 24 of 100\n",
      "building tree 25 of 100\n",
      "building tree 26 of 100\n",
      "building tree 27 of 100\n",
      "building tree 28 of 100\n",
      "building tree 29 of 100\n",
      "building tree 30 of 100\n",
      "building tree 31 of 100\n",
      "building tree 32 of 100\n",
      "building tree 33 of 100\n",
      "building tree 34 of 100\n",
      "building tree 35 of 100\n",
      "building tree 36 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  3.5min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 100\n",
      "building tree 38 of 100\n",
      "building tree 39 of 100\n",
      "building tree 40 of 100\n",
      "building tree 41 of 100\n",
      "building tree 42 of 100\n",
      "building tree 43 of 100\n",
      "building tree 44 of 100\n",
      "building tree 45 of 100\n",
      "building tree 46 of 100\n",
      "building tree 47 of 100\n",
      "building tree 48 of 100\n",
      "building tree 49 of 100\n",
      "building tree 50 of 100\n",
      "building tree 51 of 100\n",
      "building tree 52 of 100\n",
      "building tree 53 of 100\n",
      "building tree 54 of 100\n",
      "building tree 55 of 100\n",
      "building tree 56 of 100\n",
      "building tree 57 of 100\n",
      "building tree 58 of 100\n",
      "building tree 59 of 100\n",
      "building tree 60 of 100\n",
      "building tree 61 of 100\n",
      "building tree 62 of 100\n",
      "building tree 63 of 100\n",
      "building tree 64 of 100\n",
      "building tree 65 of 100\n",
      "building tree 66 of 100\n",
      "building tree 67 of 100\n",
      "building tree 68 of 100\n",
      "building tree 69 of 100\n",
      "building tree 70 of 100\n",
      "building tree 71 of 100\n",
      "building tree 72 of 100\n",
      "building tree 73 of 100\n",
      "building tree 74 of 100\n",
      "building tree 75 of 100\n",
      "building tree 76 of 100\n",
      "building tree 77 of 100\n",
      "building tree 78 of 100\n",
      "building tree 79 of 100\n",
      "building tree 80 of 100\n",
      "building tree 81 of 100\n",
      "building tree 82 of 100\n",
      "building tree 83 of 100\n",
      "building tree 84 of 100\n",
      "building tree 85 of 100\n",
      "building tree 86 of 100\n",
      "building tree 87 of 100\n",
      "building tree 88 of 100\n",
      "building tree 89 of 100\n",
      "building tree 90 of 100\n",
      "building tree 91 of 100\n",
      "building tree 92 of 100\n",
      "building tree 93 of 100\n",
      "building tree 94 of 100\n",
      "building tree 95 of 100\n",
      "building tree 96 of 100\n",
      "building tree 97 of 100\n",
      "building tree 98 of 100\n",
      "building tree 99 of 100\n",
      "building tree 100 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  9.6min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    1.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6909068359619221\n",
      "0.19621074164584418\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, class_weight = 'balanced', \n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparamter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 200building tree 2 of 200\n",
      "\n",
      "building tree 3 of 200\n",
      "building tree 4 of 200\n",
      "building tree 5 of 200\n",
      "building tree 6 of 200\n",
      "building tree 7 of 200\n",
      "building tree 8 of 200\n",
      "building tree 9 of 200\n",
      "building tree 10 of 200\n",
      "building tree 11 of 200\n",
      "building tree 12 of 200\n",
      "building tree 13 of 200\n",
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      "building tree 35 of 200\n",
      "building tree 36 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    8.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 200\n",
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      "building tree 150 of 200\n",
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      "building tree 153 of 200\n",
      "building tree 154 of 200\n",
      "building tree 155 of 200\n",
      "building tree 156 of 200\n",
      "building tree 157 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   38.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 158 of 200\n",
      "building tree 159 of 200\n",
      "building tree 160 of 200\n",
      "building tree 161 of 200\n",
      "building tree 162 of 200\n",
      "building tree 163 of 200\n",
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      "building tree 171 of 200\n",
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      "building tree 185 of 200\n",
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      "building tree 190 of 200\n",
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      "building tree 194 of 200\n",
      "building tree 195 of 200\n",
      "building tree 196 of 200\n",
      "building tree 197 of 200\n",
      "building tree 198 of 200\n",
      "building tree 199 of 200\n",
      "building tree 200 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   49.2s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6687077645306324\n",
      "0.17661394369984243\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=200, n_jobs=-1, class_weight = 'balanced', max_depth = 2,\n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 200building tree 2 of 200\n",
      "building tree 3 of 200\n",
      "building tree 4 of 200\n",
      "\n",
      "building tree 5 of 200\n",
      "building tree 6 of 200\n",
      "building tree 7 of 200\n",
      "building tree 8 of 200\n",
      "building tree 9 of 200\n",
      "building tree 10 of 200\n",
      "building tree 11 of 200\n",
      "building tree 12 of 200\n",
      "building tree 13 of 200\n",
      "building tree 14 of 200\n",
      "building tree 15 of 200\n",
      "building tree 16 of 200\n",
      "building tree 17 of 200\n",
      "building tree 18 of 200\n",
      "building tree 19 of 200\n",
      "building tree 20 of 200\n",
      "building tree 21 of 200\n",
      "building tree 22 of 200\n",
      "building tree 23 of 200\n",
      "building tree 24 of 200\n",
      "building tree 25 of 200\n",
      "building tree 26 of 200\n",
      "building tree 27 of 200\n",
      "building tree 28 of 200\n",
      "building tree 29 of 200\n",
      "building tree 30 of 200\n",
      "building tree 31 of 200\n",
      "building tree 32 of 200\n",
      "building tree 33 of 200\n",
      "building tree 34 of 200\n",
      "building tree 35 of 200\n",
      "building tree 36 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   35.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 200\n",
      "building tree 38 of 200\n",
      "building tree 39 of 200\n",
      "building tree 40 of 200\n",
      "building tree 41 of 200\n",
      "building tree 42 of 200\n",
      "building tree 43 of 200\n",
      "building tree 44 of 200\n",
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      "building tree 155 of 200\n",
      "building tree 156 of 200\n",
      "building tree 157 of 200\n",
      "building tree 158 of 200building tree 159 of 200\n",
      "\n",
      "building tree 160 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  2.6min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 161 of 200\n",
      "building tree 162 of 200\n",
      "building tree 163 of 200\n",
      "building tree 164 of 200\n",
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      "building tree 193 of 200\n",
      "building tree 194 of 200\n",
      "building tree 195 of 200\n",
      "building tree 196 of 200\n",
      "building tree 197 of 200\n",
      "building tree 198 of 200\n",
      "building tree 199 of 200\n",
      "building tree 200 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:  3.4min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed:    0.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6825662609275357\n",
      "0.18668806890985604\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=200, n_jobs=-1, class_weight = 'balanced', max_depth = 10,\n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 200building tree 2 of 200\n",
      "\n",
      "building tree 3 of 200\n",
      "building tree 4 of 200\n",
      "building tree 5 of 200\n",
      "building tree 6 of 200\n",
      "building tree 7 of 200\n",
      "building tree 8 of 200\n",
      "building tree 9 of 200\n",
      "building tree 10 of 200\n",
      "building tree 11 of 200\n",
      "building tree 12 of 200\n",
      "building tree 13 of 200\n",
      "building tree 14 of 200\n",
      "building tree 15 of 200\n",
      "building tree 16 of 200\n",
      "building tree 17 of 200\n",
      "building tree 18 of 200\n",
      "building tree 19 of 200\n",
      "building tree 20 of 200\n",
      "building tree 21 of 200\n",
      "building tree 22 of 200\n",
      "building tree 23 of 200\n",
      "building tree 24 of 200\n",
      "building tree 25 of 200\n",
      "building tree 26 of 200\n",
      "building tree 27 of 200\n",
      "building tree 28 of 200\n",
      "building tree 29 of 200\n",
      "building tree 30 of 200\n",
      "building tree 31 of 200\n",
      "building tree 32 of 200\n",
      "building tree 33 of 200\n",
      "building tree 34 of 200\n",
      "building tree 35 of 200\n",
      "building tree 36 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 200\n",
      "building tree 38 of 200\n",
      "building tree 39 of 200\n",
      "building tree 40 of 200\n",
      "building tree 41 of 200\n",
      "building tree 42 of 200\n",
      "building tree 43 of 200\n",
      "building tree 44 of 200\n",
      "building tree 45 of 200\n",
      "building tree 46 of 200\n",
      "building tree 47 of 200\n",
      "building tree 48 of 200\n",
      "building tree 49 of 200\n",
      "building tree 50 of 200\n",
      "building tree 51 of 200\n",
      "building tree 52 of 200\n",
      "building tree 53 of 200\n",
      "building tree 54 of 200\n",
      "building tree 55 of 200\n",
      "building tree 56 of 200\n",
      "building tree 57 of 200\n",
      "building tree 58 of 200\n",
      "building tree 59 of 200\n",
      "building tree 60 of 200\n",
      "building tree 61 of 200\n",
      "building tree 62 of 200\n",
      "building tree 63 of 200\n",
      "building tree 64 of 200\n",
      "building tree 65 of 200\n",
      "building tree 66 of 200\n",
      "building tree 67 of 200\n",
      "building tree 68 of 200\n",
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      "building tree 156 of 200\n",
      "building tree 157 of 200\n",
      "building tree 158 of 200\n",
      "building tree 159 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  4.6min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 160 of 200\n",
      "building tree 161 of 200\n",
      "building tree 162 of 200\n",
      "building tree 163 of 200\n",
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      "building tree 194 of 200\n",
      "building tree 195 of 200\n",
      "building tree 196 of 200\n",
      "building tree 197 of 200\n",
      "building tree 198 of 200\n",
      "building tree 199 of 200\n",
      "building tree 200 of 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:  5.9min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed:    1.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6860950590277212\n",
      "0.19168772731585632\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=200, n_jobs=-1, class_weight = 'balanced', max_depth = 20,\n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 1000building tree 2 of 1000\n",
      "building tree 3 of 1000\n",
      "\n",
      "building tree 4 of 1000\n",
      "building tree 5 of 1000\n",
      "building tree 6 of 1000\n",
      "building tree 7 of 1000\n",
      "building tree 8 of 1000\n",
      "building tree 9 of 1000\n",
      "building tree 10 of 1000\n",
      "building tree 11 of 1000\n",
      "building tree 12 of 1000\n",
      "building tree 13 of 1000\n",
      "building tree 14 of 1000\n",
      "building tree 15 of 1000\n",
      "building tree 16 of 1000\n",
      "building tree 17 of 1000\n",
      "building tree 18 of 1000\n",
      "building tree 19 of 1000\n",
      "building tree 20 of 1000\n",
      "building tree 21 of 1000\n",
      "building tree 22 of 1000\n",
      "building tree 23 of 1000\n",
      "building tree 24 of 1000\n",
      "building tree 25 of 1000\n",
      "building tree 26 of 1000\n",
      "building tree 27 of 1000\n",
      "building tree 28 of 1000\n",
      "building tree 29 of 1000\n",
      "building tree 30 of 1000\n",
      "building tree 31 of 1000\n",
      "building tree 32 of 1000\n",
      "building tree 33 of 1000\n",
      "building tree 34 of 1000\n",
      "building tree 35 of 1000\n",
      "building tree 36 of 1000\n",
      "building tree 37 of 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   39.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 38 of 1000\n",
      "building tree 39 of 1000\n",
      "building tree 40 of 1000\n",
      "building tree 41 of 1000\n",
      "building tree 42 of 1000\n",
      "building tree 43 of 1000\n",
      "building tree 44 of 1000\n",
      "building tree 45 of 1000\n",
      "building tree 46 of 1000\n",
      "building tree 47 of 1000\n",
      "building tree 48 of 1000\n",
      "building tree 49 of 1000\n",
      "building tree 50 of 1000\n",
      "building tree 51 of 1000\n",
      "building tree 52 of 1000\n",
      "building tree 53 of 1000\n",
      "building tree 54 of 1000\n",
      "building tree 55 of 1000\n",
      "building tree 56 of 1000\n",
      "building tree 57 of 1000\n",
      "building tree 58 of 1000\n",
      "building tree 59 of 1000\n",
      "building tree 60 of 1000\n",
      "building tree 61 of 1000\n",
      "building tree 62 of 1000\n",
      "building tree 63 of 1000\n",
      "building tree 64 of 1000\n",
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      "building tree 711 of 1000\n",
      "building tree 712 of 1000\n",
      "building tree 713 of 1000building tree 714 of 1000\n",
      "\n",
      "building tree 715 of 1000\n",
      "building tree 716 of 1000\n",
      "building tree 717 of 1000\n",
      "building tree 718 of 1000\n",
      "building tree 719 of 1000\n",
      "building tree 720 of 1000\n",
      "building tree 721 of 1000\n",
      "building tree 722 of 1000\n",
      "building tree 723 of 1000\n",
      "building tree 724 of 1000\n",
      "building tree 725 of 1000\n",
      "building tree 726 of 1000\n",
      "building tree 727 of 1000\n",
      "building tree 728 of 1000\n",
      "building tree 729 of 1000\n",
      "building tree 730 of 1000\n",
      "building tree 731 of 1000\n",
      "building tree 732 of 1000\n",
      "building tree 733 of 1000\n",
      "building tree 734 of 1000\n",
      "building tree 735 of 1000\n",
      "building tree 736 of 1000\n",
      "building tree 737 of 1000\n",
      "building tree 738 of 1000\n",
      "building tree 739 of 1000\n",
      "building tree 740 of 1000\n",
      "building tree 741 of 1000\n",
      "building tree 742 of 1000\n",
      "building tree 743 of 1000\n",
      "building tree 744 of 1000\n",
      "building tree 745 of 1000\n",
      "building tree 746 of 1000\n",
      "building tree 747 of 1000\n",
      "building tree 748 of 1000\n",
      "building tree 749 of 1000\n",
      "building tree 750 of 1000\n",
      "building tree 751 of 1000\n",
      "building tree 752 of 1000\n",
      "building tree 753 of 1000\n",
      "building tree 754 of 1000\n",
      "building tree 755 of 1000\n",
      "building tree 756 of 1000\n",
      "building tree 757 of 1000\n",
      "building tree 758 of 1000\n",
      "building tree 759 of 1000\n",
      "building tree 760 of 1000\n",
      "building tree 761 of 1000\n",
      "building tree 762 of 1000\n",
      "building tree 763 of 1000\n",
      "building tree 764 of 1000\n",
      "building tree 765 of 1000\n",
      "building tree 766 of 1000\n",
      "building tree 767 of 1000\n",
      "building tree 768 of 1000\n",
      "building tree 769 of 1000\n",
      "building tree 770 of 1000\n",
      "building tree 771 of 1000\n",
      "building tree 772 of 1000\n",
      "building tree 773 of 1000\n",
      "building tree 774 of 1000\n",
      "building tree 775 of 1000\n",
      "building tree 776 of 1000\n",
      "building tree 777 of 1000\n",
      "building tree 778 of 1000\n",
      "building tree 779 of 1000\n",
      "building tree 780 of 1000\n",
      "building tree 781 of 1000\n",
      "building tree 782 of 1000\n",
      "building tree 783 of 1000\n",
      "building tree 784 of 1000\n",
      "building tree 785 of 1000\n",
      "building tree 786 of 1000\n",
      "building tree 787 of 1000\n",
      "building tree 788 of 1000\n",
      "building tree 789 of 1000\n",
      "building tree 790 of 1000\n",
      "building tree 791 of 1000\n",
      "building tree 792 of 1000\n",
      "building tree 793 of 1000\n",
      "building tree 794 of 1000\n",
      "building tree 795 of 1000\n",
      "building tree 796 of 1000\n",
      "building tree 797 of 1000\n",
      "building tree 798 of 1000\n",
      "building tree 799 of 1000\n",
      "building tree 800 of 1000\n",
      "building tree 801 of 1000\n",
      "building tree 802 of 1000\n",
      "building tree 803 of 1000\n",
      "building tree 804 of 1000\n",
      "building tree 805 of 1000\n",
      "building tree 806 of 1000\n",
      "building tree 807 of 1000\n",
      "building tree 808 of 1000\n",
      "building tree 809 of 1000\n",
      "building tree 810 of 1000\n",
      "building tree 811 of 1000\n",
      "building tree 812 of 1000\n",
      "building tree 813 of 1000\n",
      "building tree 814 of 1000\n",
      "building tree 815 of 1000\n",
      "building tree 816 of 1000\n",
      "building tree 817 of 1000\n",
      "building tree 818 of 1000\n",
      "building tree 819 of 1000\n",
      "building tree 820 of 1000\n",
      "building tree 821 of 1000\n",
      "building tree 822 of 1000\n",
      "building tree 823 of 1000\n",
      "building tree 824 of 1000\n",
      "building tree 825 of 1000\n",
      "building tree 826 of 1000\n",
      "building tree 827 of 1000\n",
      "building tree 828 of 1000\n",
      "building tree 829 of 1000\n",
      "building tree 830 of 1000\n",
      "building tree 831 of 1000\n",
      "building tree 832 of 1000\n",
      "building tree 833 of 1000\n",
      "building tree 834 of 1000\n",
      "building tree 835 of 1000\n",
      "building tree 836 of 1000\n",
      "building tree 837 of 1000\n",
      "building tree 838 of 1000\n",
      "building tree 839 of 1000\n",
      "building tree 840 of 1000\n",
      "building tree 841 of 1000\n",
      "building tree 842 of 1000\n",
      "building tree 843 of 1000\n",
      "building tree 844 of 1000\n",
      "building tree 845 of 1000\n",
      "building tree 846 of 1000\n",
      "building tree 847 of 1000\n",
      "building tree 848 of 1000\n",
      "building tree 849 of 1000\n",
      "building tree 850 of 1000\n",
      "building tree 851 of 1000\n",
      "building tree 852 of 1000\n",
      "building tree 853 of 1000\n",
      "building tree 854 of 1000\n",
      "building tree 855 of 1000\n",
      "building tree 856 of 1000\n",
      "building tree 857 of 1000\n",
      "building tree 858 of 1000\n",
      "building tree 859 of 1000\n",
      "building tree 860 of 1000\n",
      "building tree 861 of 1000\n",
      "building tree 862 of 1000\n",
      "building tree 863 of 1000\n",
      "building tree 864 of 1000\n",
      "building tree 865 of 1000\n",
      "building tree 866 of 1000\n",
      "building tree 867 of 1000\n",
      "building tree 868 of 1000\n",
      "building tree 869 of 1000\n",
      "building tree 870 of 1000\n",
      "building tree 871 of 1000\n",
      "building tree 872 of 1000\n",
      "building tree 873 of 1000\n",
      "building tree 874 of 1000\n",
      "building tree 875 of 1000\n",
      "building tree 876 of 1000\n",
      "building tree 877 of 1000\n",
      "building tree 878 of 1000\n",
      "building tree 879 of 1000\n",
      "building tree 880 of 1000\n",
      "building tree 881 of 1000\n",
      "building tree 882 of 1000\n",
      "building tree 883 of 1000\n",
      "building tree 884 of 1000\n",
      "building tree 885 of 1000\n",
      "building tree 886 of 1000\n",
      "building tree 887 of 1000\n",
      "building tree 888 of 1000\n",
      "building tree 889 of 1000\n",
      "building tree 890 of 1000\n",
      "building tree 891 of 1000\n",
      "building tree 892 of 1000\n",
      "building tree 893 of 1000\n",
      "building tree 894 of 1000\n",
      "building tree 895 of 1000\n",
      "building tree 896 of 1000\n",
      "building tree 897 of 1000\n",
      "building tree 898 of 1000\n",
      "building tree 899 of 1000\n",
      "building tree 900 of 1000\n",
      "building tree 901 of 1000\n",
      "building tree 902 of 1000\n",
      "building tree 903 of 1000\n",
      "building tree 904 of 1000\n",
      "building tree 905 of 1000\n",
      "building tree 906 of 1000\n",
      "building tree 907 of 1000\n",
      "building tree 908 of 1000\n",
      "building tree 909 of 1000\n",
      "building tree 910 of 1000\n",
      "building tree 911 of 1000\n",
      "building tree 912 of 1000\n",
      "building tree 913 of 1000\n",
      "building tree 914 of 1000\n",
      "building tree 915 of 1000\n",
      "building tree 916 of 1000\n",
      "building tree 917 of 1000\n",
      "building tree 918 of 1000\n",
      "building tree 919 of 1000\n",
      "building tree 920 of 1000\n",
      "building tree 921 of 1000\n",
      "building tree 922 of 1000\n",
      "building tree 923 of 1000\n",
      "building tree 924 of 1000\n",
      "building tree 925 of 1000\n",
      "building tree 926 of 1000\n",
      "building tree 927 of 1000\n",
      "building tree 928 of 1000\n",
      "building tree 929 of 1000\n",
      "building tree 930 of 1000\n",
      "building tree 931 of 1000\n",
      "building tree 932 of 1000\n",
      "building tree 933 of 1000\n",
      "building tree 934 of 1000\n",
      "building tree 935 of 1000\n",
      "building tree 936 of 1000\n",
      "building tree 937 of 1000\n",
      "building tree 938 of 1000\n",
      "building tree 939 of 1000\n",
      "building tree 940 of 1000\n",
      "building tree 941 of 1000\n",
      "building tree 942 of 1000\n",
      "building tree 943 of 1000\n",
      "building tree 944 of 1000\n",
      "building tree 945 of 1000\n",
      "building tree 946 of 1000\n",
      "building tree 947 of 1000\n",
      "building tree 948 of 1000\n",
      "building tree 949 of 1000\n",
      "building tree 950 of 1000\n",
      "building tree 951 of 1000\n",
      "building tree 952 of 1000\n",
      "building tree 953 of 1000\n",
      "building tree 954 of 1000\n",
      "building tree 955 of 1000\n",
      "building tree 956 of 1000\n",
      "building tree 957 of 1000\n",
      "building tree 958 of 1000\n",
      "building tree 959 of 1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 960 of 1000\n",
      "building tree 961 of 1000\n",
      "building tree 962 of 1000\n",
      "building tree 963 of 1000\n",
      "building tree 964 of 1000\n",
      "building tree 965 of 1000\n",
      "building tree 966 of 1000\n",
      "building tree 967 of 1000\n",
      "building tree 968 of 1000\n",
      "building tree 969 of 1000\n",
      "building tree 970 of 1000\n",
      "building tree 971 of 1000\n",
      "building tree 972 of 1000\n",
      "building tree 973 of 1000\n",
      "building tree 974 of 1000\n",
      "building tree 975 of 1000\n",
      "building tree 976 of 1000\n",
      "building tree 977 of 1000\n",
      "building tree 978 of 1000\n",
      "building tree 979 of 1000\n",
      "building tree 980 of 1000\n",
      "building tree 981 of 1000\n",
      "building tree 982 of 1000\n",
      "building tree 983 of 1000\n",
      "building tree 984 of 1000\n",
      "building tree 985 of 1000\n",
      "building tree 986 of 1000\n",
      "building tree 987 of 1000\n",
      "building tree 988 of 1000\n",
      "building tree 989 of 1000\n",
      "building tree 990 of 1000\n",
      "building tree 991 of 1000\n",
      "building tree 992 of 1000\n",
      "building tree 993 of 1000\n",
      "building tree 994 of 1000\n",
      "building tree 995 of 1000\n",
      "building tree 996 of 1000\n",
      "building tree 997 of 1000\n",
      "building tree 998 of 1000\n",
      "building tree 999 of 1000\n",
      "building tree 1000 of 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 16.0min finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=4)]: Done 357 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=4)]: Done 640 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=4)]: Done 1000 out of 1000 | elapsed:    4.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.682485591350937\n",
      "0.1870487645120605\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=1000, n_jobs=-1, class_weight = 'balanced', max_depth = 10,\n",
    "                                    random_state=42, verbose = 2)\n",
    "rf.fit(X_train, y_train)\n",
    "rf_probs = rf.predict_proba(X_val)[:, 1]    \n",
    "fpr,tpr,threshold = metrics.roc_curve(y_val, rf_probs)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)\n",
    "plt.figure(figsize=(12 ,10))\n",
    "plt.plot(fpr, tpr, color='red', label='( ROC, AUC=%0.3f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], color='black', linestyle='--')\n",
    "plt.xlabel('False Positive Rate(FPR)')\n",
    "plt.ylabel('True Positive Rate(TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=4) #4:lower right legend\n",
    "average_precision = average_precision_score(y_val, rf_probs)\n",
    "print(average_precision)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}