ex4_RandomForrest_diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder , StandardScaler , PolynomialFeatures , MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection  import train_test_split , cross_val_score , KFold\n",
    "from sklearn.metrics import mean_squared_error , r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense , Dropout\n",
    "from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, plot_confusion_matrix\n",
    "\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras import regularizers\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils import to_categorical\n",
    "import joblib\n",
    "\n",
    "from scipy.spatial.distance import euclidean\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = 'diabetes' # diabetes , german_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>insu</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>5.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.499</td>\n",
       "      <td>30.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>3.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.970</td>\n",
       "      <td>31.0</td>\n",
       "      <td>b'tested_positive'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>5.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>0.261</td>\n",
       "      <td>41.0</td>\n",
       "      <td>b'tested_positive'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.646</td>\n",
       "      <td>24.0</td>\n",
       "      <td>b'tested_positive'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>1.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.607</td>\n",
       "      <td>28.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>9.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.302</td>\n",
       "      <td>49.0</td>\n",
       "      <td>b'tested_positive'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.564</td>\n",
       "      <td>29.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>5.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.744</td>\n",
       "      <td>57.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.966</td>\n",
       "      <td>33.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.368</td>\n",
       "      <td>21.0</td>\n",
       "      <td>b'tested_negative'</td>\n",
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       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     preg   plas  pres  skin   insu  mass   pedi   age               class\n",
       "365   5.0   99.0  54.0  28.0   83.0  34.0  0.499  30.0  b'tested_negative'\n",
       "716   3.0  173.0  78.0  39.0  185.0  33.8  0.970  31.0  b'tested_positive'\n",
       "219   5.0  112.0  66.0   0.0    0.0  37.8  0.261  41.0  b'tested_positive'\n",
       "732   2.0  174.0  88.0  37.0  120.0  44.5  0.646  24.0  b'tested_positive'\n",
       "470   1.0  144.0  82.0  40.0    0.0  41.3  0.607  28.0  b'tested_negative'\n",
       "..    ...    ...   ...   ...    ...   ...    ...   ...                 ...\n",
       "355   9.0  165.0  88.0   0.0    0.0  30.4  0.302  49.0  b'tested_positive'\n",
       "46    1.0  146.0  56.0   0.0    0.0  29.7  0.564  29.0  b'tested_negative'\n",
       "278   5.0  114.0  74.0   0.0    0.0  24.9  0.744  57.0  b'tested_negative'\n",
       "35    4.0  103.0  60.0  33.0  192.0  24.0  0.966  33.0  b'tested_negative'\n",
       "163   2.0  100.0  64.0  23.0    0.0  29.7  0.368  21.0  b'tested_negative'\n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if data_set == 'diabetes':\n",
    "    data = arff.loadarff('/home/odedrot/DL_ex_4/Assignment 4 files/diabetes.arff')\n",
    "if data_set == 'german_credit':\n",
    "    data = arff.loadarff('/home/odedrot/DL_ex_4/Assignment 4 files/german_credit.arff')\n",
    "\n",
    "df = pd.DataFrame(data[0])\n",
    "np.random.seed()\n",
    "df = df.reindex(np.random.permutation(df.index))\n",
    "Ndata_points=len(df) # len(df)  for max\n",
    "df=df[0:Ndata_points] \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame()\n",
    "y = pd.DataFrame()\n",
    "X = df.drop(['class'], axis=1)\n",
    "y = df['class']  \n",
    "X = X.to_numpy()\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "y = labelencoder.fit_transform(y)\n",
    "y = np.hstack(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268\n",
      "500\n"
     ]
    }
   ],
   "source": [
    "print( len(y[y==1]) )\n",
    "print( len(y[y==0]) ) # more 0's than 1's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split\n",
    "X, y = shuffle(X,y)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "358\n",
      "358\n",
      "179\n",
      "179\n"
     ]
    }
   ],
   "source": [
    "# balance training data\n",
    "sampling='nothing'\n",
    "\n",
    "# cut label 0 samples to be equal to label 1\n",
    "if sampling == 'nothing':\n",
    "    X_0_train = X_train[y_train==0]\n",
    "    y_0_train = y_train[y_train==0]\n",
    "    X_1_train = X_train[y_train==1]\n",
    "    y_1_train = y_train[y_train==1]\n",
    "    \n",
    "if sampling == 'undersample':\n",
    "    X_0_train = X_train[y_train==0][:len(y_train[y_train==1])]\n",
    "    y_0_train = y_train[y_train==0][:len(y_train[y_train==1])]\n",
    "    X_1_train = X_train[y_train==1]\n",
    "    y_1_train = y_train[y_train==1]\n",
    "if sampling=='oversample':\n",
    "    X_0_train = X_train[y_train==0]\n",
    "    y_0_train = y_train[y_train==0]\n",
    "    delta = np.abs(len(y_train[y_train==0]) - len(y_train[y_train==1]))\n",
    "    X_1_train = np.concatenate((X_train[y_train==1], X_train[y_train==1][:delta]))\n",
    "    y_1_train = np.concatenate((y_train[y_train==1], y_train[y_train==1][:delta]))\n",
    "    \n",
    "    print(len(X_0_train)) \n",
    "    print(len(y_0_train)) \n",
    "    print(len(X_1_train)) \n",
    "    print(len(X_1_train)) \n",
    "    \n",
    "    X_0_train = np.concatenate((X_0_train, X_0_train, X_0_train, X_0_train))\n",
    "    y_0_train = np.concatenate((y_0_train, y_0_train, y_0_train, y_0_train))\n",
    "    X_1_train = np.concatenate((X_1_train, X_1_train, X_1_train, X_1_train))\n",
    "    y_1_train = np.concatenate((y_1_train, y_1_train, y_1_train, y_1_train))\n",
    "                              \n",
    "print(len(X_0_train))\n",
    "print(len(y_0_train))\n",
    "print(len(X_1_train))\n",
    "print(len(X_1_train))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179\n",
      "358\n",
      "89\n",
      "142\n"
     ]
    }
   ],
   "source": [
    "print( len(y_train[y_train==1]) )\n",
    "print( len(y_train[y_train==0]) )\n",
    "print( len(y_test[y_test==1]) )\n",
    "print( len(y_test[y_test==0]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # normalize feature space \n",
    "sc = StandardScaler() # MinMaxScaler()  StandardScaler()\n",
    "X_train = sc.fit_transform(X_train) # use training data to fit and transform\n",
    "X_test = sc.transform(X_test) # only transormthe test data\n",
    "\n",
    "# scaler = StandardScaler().fit(X)\n",
    "# X_scaled = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_train.flatten(), 100)\n",
    "plt.hist(X_test.flatten(), 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestClassifier(n_estimators = 100, random_state =42,  n_jobs=-1, verbose = 0)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 1.0\n",
      "Test Accuracy: 0.7662337662337663\n"
     ]
    }
   ],
   "source": [
    "train_prob = rf_model.predict(X_train)\n",
    "test_prob = rf_model.predict(X_test)\n",
    "\n",
    "print(\"Train Accuracy:\",metrics.accuracy_score(y_train, train_prob))\n",
    "print(\"Test Accuracy:\",metrics.accuracy_score(y_test, test_prob))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(rf_model, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7445883441258094\n"
     ]
    }
   ],
   "source": [
    "print(scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'rf train set')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(rf_model, X_train, y_train, display_labels=['No', 'Yes'], cmap=plt.cm.Blues,normalize=None)\n",
    "disp.ax_.set_title('rf train set')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'rf test set')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAEWCAYAAAATsp59AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdPklEQVR4nO3de5xd873/8dd7JhchCSLixC3uQUI0jRLUL4RSHJy2NHX5BdE05yCqnIpqD40THD163NuGIq4Vlx6XtkJTqbqL0EhC3RKEECFIIo3M5HP+WGvYppOZNTt7z5496/30WI+913ddvp+Zefjk+11rfb9LEYGZWZ7VVDoAM7NKcyI0s9xzIjSz3HMiNLPccyI0s9xzIjSz3HMiNAAk9Zf0rKQlksZWOh6ztuREaA1+CEyLiB4RcVnjjZKmSTpxTSuRNEzS/DU9z2rOPU/SfuU4t3VsToQ5J6lT+rUfMLuSsZhVihNhDqUtpzMlzQSWSfoTsA9whaSlkrZrtP8E4KsF269Iy7eX9KCkDyT9TdKRBcccJGlO2tV+S9IZktYB/gBsnJ5nqaSNm4jvH44t2HaIpOckfSjpMUk7p+U3ApsD96bn/WHJf3HWcUWEl5wtwDzgOWAzoFtaNg04sZljvrAdWAd4Ezge6AQMBhYBA9LtC4Cvpt/XBwan34cB81uIb3XHDgYWArsBtcDI9GfpWvBz7Vfp36+X6lvcIsyvyyLizYhYXuTxhwDzIuK6iKiLiBnAncC30u0rgR0l9YyIxen2rFZ37HeBX0XEkxFRHxGTgBXA7kX+DGaAu8Z59uYaHt8P2C3ton4o6UPgaOCf0u3fBA4CXpf0Z0lDW3Hu1R3bDzi9UZ2bAf/QvTZrjU4t72IdVGunHWq8/5vAnyNi/yZ3jngaOExSZ+BkYDJJ0mqx3maOfROYEBETMsZololbhJbVu8BWBev3AdtJOlZS53TZVdIOkrpIOlrSuhGxEvgYqC84zwaS1m2qkhaOvRoYI2k3JdaRdLCkHquJ0SwTJ0LL6lLgW5IWS7osIpYAXwNGAG8D7wD/BXRN9z8WmCfpY2AMcAxARLwI3Aq8lnZvm+rWru7Y6STXCa8AFgOvAMcVHHcB8OP0vGdglpEi3Jsws3xzi9DMcs+J0Mxyz4nQzHLPidDMcq/qniNUp26hLj1a3tHajS/tsHmlQ7BWmjHjmUURsWGxx9f27BdRl23QUix/b0pEHFhsXaVQfYmwSw+69j+y5R2t3Xj0ySsqHYK1UrfOen1Njo+65Zn/P/37c1f2XpO6SqHqEqGZVQOBqufKmxOhmZWegJraSkeRmROhmZWHVOkIMnMiNLMycNfYzMwtQjPLOeEWoZnlnaqqRVg9KdvMqktNbbalBZKulbRQ0qyCsp9JelHSTEm/lbRewbazJL2SvlDsgEyhFvPzmZk1L71ZkmVp2fVA45EnDwIDI2Jn4CXgLABJO5LMkTkgPeYqSS1mWydCMys9kXSNsywtiIiHgQ8alT0QEXXp6hPApun3w4DfRMSKiJhLMnnvV1qqw4nQzMoje4uwt6TpBcvoVtZ0Asn7sgE24YsvJpufljXLN0vMrAxa9RzhoogYUlQt0tlAHXDz5xX/gxan4XciNLPSE1Bb3iF2kkaSvF97eHz+zpH5JG88bLApyTt1muWusZmVR4muETZ9ah0InAkcGhGfFGy6BxghqaukLYFtgadaOp9bhGZWBqUbYifpVmAYybXE+cA5JHeJuwIPKkmmT0TEmIiYLWkyMIeky3xSRNQ3febPORGaWXmU6IHqiPhOE8W/bmb/CcCE1tThRGhm5eEhdmaWa2tw/a8SnAjNrDw8MauZ5ZvnIzQzc9fYzHLO8xGamblrbGbmmyVmZr5GaGb5JneNzczcIjQzkxOhmeVZMlO/E6GZ5ZmEapwIzSzn3CI0s9xzIjSz3HMiNLN8E02/T66dciI0s5ITcovQzKymxiNLzCzn3CI0s3zzNUIzM7cIzSznfLPEzAw8xM7Mck7uGpuZORGamTkRmlmu+WaJmRn4OUIzyzlV1xC76onUzKqKpExLhvNcK2mhpFkFZb0kPSjp5fRz/YJtZ0l6RdLfJB2QJVYnQjMrD2VcWnY9cGCjsnHA1IjYFpiariNpR2AEMCA95ipJLb5p3l3jNnL5T47mgL0GsmjxEvYYcT4A48cezgFfHcjKlfXMnb+Ik8bfxMdLlwMwYJuN+flZ36FH97WIVcG+Iy9ixad1lfwRcu3k8Tcx5ZFZ9F6/B4/fdvZn5RNvm8bVkx+mU20N++81kPFjD69ckO1MqW6WRMTDkrZoVHwYMCz9PgmYBpyZlv8mIlYAcyW9AnwFeLy5OtqkRSgpJF1csH6GpHPbou724tb7nuBbY6/8QtlDT77IHiPOZ6+jLuDVNxbyg+O+BkBtbQ2/Gj+S0y/8DXt8ewKHjLmUlXX1lQjbUt85ZHfuuOykL5T9ZfpL/P7Pz/PIrWfx+OQfc8oxwysUXfuTtVucJsvekqYXLKMzVLFRRCwASD/7pOWbAG8W7Dc/LWtWW3WNVwDfkNS7jeprdx579lUWf/zJF8oeevJF6utXAfD0rLlsvNF6AOy72/bMfuUtZr38FgCLP1rGqlXRpvHaF+05eBvW77n2F8quvfMvfH/k/nTt0hmADXv1qERo7VYrEuGiiBhSsExck2qbKGvxf562SoR1wETgtMYbJPWTNFXSzPRz8zaKqV055tCh/PGxOQBs3a8PEXDHZScx7cYzGXvsfhWOzpryyusLefy5V9nvuJ9x8OhLmDH79UqH1K6oRpmWIr0rqS9A+rkwLZ8PbFaw36bA2y2drC1vllwJHC1p3UblVwA3RMTOwM3AZY0PlDS6odkcdcvbINS2dfrxB1BXt4rJf3gagE61tew+aCtG/+R6vn7izzl42CD23nW7CkdpjdXVr+LDJZ/w4HVnMP7Uwzn+R9cS4ZZ7g1LdNV6Ne4CR6feRwN0F5SMkdZW0JbAt8FRLJ2uzRBgRHwM3AGMbbRoK3JJ+vxHYq4ljJzY0m9WpW3kDbWMjDt6Nr+01kNE/uf6zsrff/ZBHn32FDz5axvIVK3nwsdkM6r/Z6k9iFbFJn/X4530GIYkvD9iCGon3P1xa6bDaB5X08ZlbSW529Jc0X9Io4EJgf0kvA/un60TEbGAyMAe4HzgpIlq8wN7Wj89cAowC1mlmn9z8kzp86A6c+v/346jTf8XyFSs/K5/6xBwGbLMJ3bp2pra2hj0Hb8Pf5r5TwUitKQcN25mHn34JgFdef5dPV9axwXrdKxxV+yBAyra0JCK+ExF9I6JzRGwaEb+OiPcjYnhEbJt+flCw/4SI2Doi+kfEH7LE26aPz0TEB5ImkyTDa9Pix0ie+7kROBp4pC1jaivX/Odx7Pnlbdlgve7Muu88Lpz4e0477mt07dKJ3155MgDTn5/HDy78DR8tWc5Vt/yJqTf8ECJ48NHZPPDo7Ar/BPk26uzrePSZl3n/w6UMOPjHjBt9EMccOpSTx9/M0G9PoEvnWn5x7rFVNb62vKprrLHa4pqGpKUR0T39vhEwF7goIs5Nnw+6FugNvAccHxFvrO5cNWv3ia79jyx7zFY6i5++otIhWCt166xnImJIscev9U/bRb+Rl2fa96WLDlyjukqhTVqEDUkw/f4usHbB+jxg37aIw8zaSMZub3vhkSVmVnICajxVv5nlnVuEZpZ71XSzxInQzErP1wjNLO+EqmpiVidCMysLtwjNLPd8jdDM8s3XCM0s75KxxtWTCZ0IzawsqigPOhGaWXl4ZImZ5ZvcNTaznGuYj7BaOBGaWRlU13yEToRmVhZVlAedCM2sDOSbJWaWc36O0MwMJ0IzM18jNDNzi9DM8s2TLphZ3iUTs1ZPJnQiNLOyqKmiJqEToZmVRRXlQSdCMys9edIFMzOookuEq0+Eki4HYnXbI2JsWSIysw6ho9wsmd5mUZhZhyKSO8clO590GnAiSePseeB4YG3gNmALYB5wZEQsLub8q02EETGpUSDrRMSyYioxs/wpVYNQ0ibAWGDHiFguaTIwAtgRmBoRF0oaB4wDziwq1gxBDJU0B3ghXR8k6apiKjOznFAyH2GWJaNOQDdJnUhagm8DhwENDbZJwOHFhpvlVfSXAAcA7wNExF+BvYut0MzyQcq2AL0lTS9YRheeJyLeAv4beANYAHwUEQ8AG0XEgnSfBUCfYmPNdNc4It5slLnri63QzDo+0aoHqhdFxJDVnktan6T1tyXwIXC7pGPWNMZCWRLhm5L2AEJSF5K++gulDMLMOp4S3jXeD5gbEe8BSLoL2AN4V1LfiFggqS+wsNgKsnSNxwAnAZsAbwG7pOtmZk3K2i3O2Gh8A9hd0tpKuqbDSRpj9wAj031GAncXG2+LLcKIWAQcXWwFZpZPpRprHBFPSroDmAHUAc8CE4HuwGRJo0iS5RHF1tFiIpS0FXApsDvJMzyPA6dFxGvFVmpmHV8pH6eOiHOAcxoVryBpHa6xLF3jW4DJQF9gY+B24NZSVG5mHVeJH58pqyyJUBFxY0TUpctNNDP0zswsuWucbWkPmhtr3Cv9+lD61PZvSBLgt4HftUFsZlat1HEmZn2GJPE1/DTfK9gWwHnlCsrMql976fZm0dxY4y3bMhAz6zgausbVItPIEkkDSQY4r9VQFhE3lCsoM6t+HaJF2EDSOcAwkkT4e+DrwCOAE6GZrVb1pMFsd42/RfKszjsRcTwwCOha1qjMrKpJUFujTEt7kKVrvDwiVkmqk9STZDzfVmWOy8yqXIfqGgPTJa0HXE1yJ3kp8FQ5gzKz6ldFeTDTWON/S7/+UtL9QM+ImFnesMysmgl1jPcaSxrc3LaImFGekMys6mWfWaZdaK5FeHEz2wLYt8SxZLL9Nptyy90XVKJqK9KUOe9UOgSrgA5xjTAi9mnLQMys4xBQ2xESoZnZmmgnT8Zk4kRoZmXhRGhmuZZMw189mTDLe40l6RhJ/5Guby7pK+UPzcyqWTXNR5hliN1VwFDgO+n6EuDKskVkZh1CCV/eVHZZusa7RcRgSc8CRMTi9LWeZmZNEtCpvWS5DLIkwpWSakmn55e0IbCqrFGZWdWrojyYKRFeBvwW6CNpAslsND8ua1RmVtWkDjLErkFE3CzpGZKpuAQcHhEvlD0yM6tqVZQHM03MujnwCXBvYVlEvFHOwMysurWXO8JZZOka/47PX+K0FrAl8DdgQBnjMrMqJmg3k65mkaVrvFPhejorzfdWs7uZGbSjZwSzaPXIkoiYIWnXcgRjZh2HquitJVmuEf6gYLUGGAy8V7aIzKzqdcTXefYo+F5Hcs3wzvKEY2YdRYdJhOmD1N0j4t/bKB4z6yCqadKF5qbq7xQRdc1N2W9m1pTkdZ6VjiK75lqET5FcD3xO0j3A7cCyho0RcVeZYzOzKlbKkSXpmzSvAQaSPM53AsljfLcBWwDzgCMjYnEx58+Ss3sB75O8o+QQ4J/TTzOzJjXcLCnhNFyXAvdHxPbAIOAFYBwwNSK2Baam60VprkXYJ71jPIvPH6huEMVWaGb5UKoGoaSewN7AcQAR8SnwqaTDgGHpbpOAacCZxdTRXCKsBbpDkw8DORGaWTNETfbnCHtLml6wPjEiJhasb0XyyN51kgYBzwCnAhtFxAKAiFggqU+x0TaXCBdExPhiT2xm+SVa1SJcFBFDmtneieR+xSkR8aSkS1mDbnBTmrtGWD33vs2sfRF0qlGmJYP5wPyIeDJdv4MkMb4rqS9A+rmw2HCbS4TDiz2pmeVbQ4uwFFP1R8Q7wJuS+qdFw4E5wD3AyLRsJHB3sfE294L3D4o9qZlZiSdmPQW4OX1NyGvA8SQNucmSRgFvAEcUe3K/ztPMyqKUeTAingOauo5Ykp6rE6GZlZzI9pBye+FEaGalp5J3jcvKidDMSi4ZWeJEaGY5Vz1p0InQzMqkihqEToRmVg7qGPMRmpkVy3eNzczwzRIzyzt1kKn6zcyK5a6xmRluEZqZ+TlCM8s3AbVuEZpZ3lVRHnQiNLNyEKqizrEToZmVhVuEZpZryeMz1ZMJnQjNrPQyvo+kvXAiNLOy8BA7M8u1ZGLWSkeRnROhmZWF7xqbWe5VUc/YibASVny6kn/90URWrqyjvn4V++wxkO8etT8vvfY2F/3if/l0ZR21NTWcMeYwBmy3WaXDtdTJp19Ot7W6UFNTQ21NDef/dBRPPDWHO377MG8tWMR/nnMCW2+5caXDbDdy3yJUMtr6L8CEiPhDWnYkcEJEHFiOOqtJl86duOK8E1m7W1fq6ur53rhfMvTL/bn6lgcZNWI4Q7/cn8emv8iVk/7AVRNGVzpcK/CTccfSs8fan61vtmkffjD2CK6+/ncVjKr98TVCICJC0hjgdkkPAbXABCD3SRCSWTnW7tYVgLr6eurqVyGSf0GXfbICgKWf/J3evXpWMErLYpONe1c6hPZJ8l1jgIiYJele4ExgHeAm4GxJO6X1nhsRd0saAFwHdCGZwuybEfFyueJqL+rrV3H86Vcwf8H7fPOg3RnQf3O+f+IhfP/ca7n8ut+zKoKJ/zWm0mFaAQHn/+wWBAzfZzD77TO40iG1a9WTBst/jfCnwAzgU+A+4E8RcYKk9YCnJP0RGANcGhE3S+pC0nr8AkmjgdEAfTfpGNfMamtruOGSsSxZupxxF9zEq6+/w91TnuLUUYewzx4D+eMjMzn/8ju5/LwTKx2qpX764+PotX4PPvp4GRMuuplN+m7ADtv3q3RY7VK1vde4rJPIRsQy4DbgRmB/YJyk54BpwFrA5sDjwI8knQn0i4jlTZxnYkQMiYgh6/XqWF2RHt27MXinLXlixkv8/qEZDBs6AIDhe+7EnJfnVzg6K9Rr/R4ArNtzHXb9cn9eee3tCkfUvinj0h60xWzaq9JFJN3eXdJl84h4ISJuAQ4FlgNTJO3bBjFV1OKPlrJkaZLv/75iJU//9VX6bbohvXv15NlZcwGYPvNVNtt4g0qGaQX+vuJTli9f8dn3mbPmstmmfSocVTtXRZmwLR+fmQKcIumU9GbKlyLiWUlbAa9FxGXp952BP7VhXG3u/cVLGH/J7axaFUQE++65E3vtugM91unG/1xzL/X1q+jSuRPj/u0blQ7VUh99tIyLL7sdgFX1q9hz6EB22Xlrnpr+ItffNIWPl3zCRT+/jX6bb8SP/v2oCkfbPlRT11gRUd4KpHOBpcCVwCXAHiT/DsyLiEMknQUcA6wE3gGOiogPVne+HXceHLfc9+eyxmyl9fqHyyodgrXS4YP6PhMRQ4o9foedvhQ33D0t075f2Xq9THVJqgWmA2+luaMXyaW3LYB5wJERsbiYeMveIoyIcwtWv9fE9guAC8odh5m1sdI3CE8FXgAanisbB0yNiAsljUvXzyzmxNX0xj0zqxINz8Vm+S/T+aRNgYOBawqKDwMmpd8nAYcXG6+H2JlZ6bVuPsLekqYXrE+MiImN9rkE+CHQo6Bso4hYABARCyQVfffKidDMyqIVPeNFzV0jlHQIsDAinpE0bI0Da4IToZmVgUr5gvc9gUMlHUTy/HFPSTcB70rqm7YG+wILi63A1wjNrCykbEtLIuKsiNg0IrYARpCMUDsGuAcYme42Eri72FjdIjSzkmujZ6UvBCZLGgW8ARxR7ImcCM2sPMqQCSNiGskQXSLifWB4Kc7rRGhmZZH7iVnNzKpohJ0ToZmVgd9rbGbmrrGZ5Zxwi9DMrIrag06EZlYuVZQJnQjNrCyqaWJWJ0IzK4vqSYNOhGZWLlWUCZ0IzazkGiZmrRZOhGZWen6g2sysqnrGToRmVg4lnZi17JwIzawsqigPOhGaWem10cSsJeNEaGblUUWZ0InQzMrCj8+YWe75GqGZ5ZugxonQzKx6MqEToZmVnCdmNTOjmtqDToRmViZuEZpZ7nmInZnlXvWkQSdCMysDeRouMzOPLDEzq6q+sROhmZVFFeVBJ0IzKwdV1es8ayodgJl1PA0jS7IsLZ5L2kzSQ5JekDRb0qlpeS9JD0p6Of1cv9h4nQjNrL2rA06PiB2A3YGTJO0IjAOmRsS2wNR0vShOhGZWFqVqEUbEgoiYkX5fArwAbAIcBkxKd5sEHF5srL5GaGZl0YrHZ3pLml6wPjEiJjZ5TmkL4EvAk8BGEbEAkmQpqU+xsToRmlnpte6B6kURMaTFU0rdgTuB70fEx6UcwueusZmVXClvlgBI6kySBG+OiLvS4ncl9U239wUWFhuvE6GZlYUy/tfieZKm36+BFyLi5wWb7gFGpt9HAncXG6u7xmZWFiXsue4JHAs8L+m5tOxHwIXAZEmjgDeAI4qtwInQzMqiVHkwIh5p5nTDS1GHE6GZlUf1DCxxIjSz0hNU1RA7RUSlY2gVSe8Br1c6jjLpDSyqdBCWWUf+e/WLiA2LPVjS/SS/nywWRcSBxdZVClWXCDsySdOzPE9l7YP/Xh2HH58xs9xzIjSz3HMibF+aHF9p7Zb/Xh2ErxGaWe65RWhmuedEaGa550RYAZJC0sUF62dIOreCIVkTlHhE0tcLyo5Mn5GzDsSJsDJWAN+QlPWBU6uASC6gjwF+LmktSesAE4CTKhuZlZoTYWXUkdxxPK3xBkn9JE2VNDP93Lztw7MGETELuBc4EzgHuAk4W9LTkp6VdBiApAGSnpL0XPq327aCYVsr+a5xBUhaCmwMzAQGAd8FukfEuZLuBe6IiEmSTgAOjYjDKxetpS3BGcCnwH3A7Ii4SdJ6wFMkU8dfCDwRETdL6gLURsTySsVsreNEWAGSlkZEd0njgZXAcj5PhIuAvhGxMp2Vd0FEuAtdYenfailwJLAWSaseoBdwAEkyPBu4AbgrIl6uRJxWHM8+U1mXkLQ0rmtmH/9L1T6sShcB34yIvzXa/oKkJ4GDgSmSToyIP7V1kFYcXyOsoIj4AJgMjCoofgwYkX4/GnikreOyZk0BTkmnj0fSl9LPrYDXIuIykinkd65ciNZaToSVdzFfnK5oLHC8pJkk05OfWpGobHXOAzoDMyXNStcBvg3MSqeS356ki2xVwtcIzSz33CI0s9xzIjSz3HMiNLPccyI0s9xzIjSz3HMi7IAk1adjXmdJul3S2mtwruslfSv9fo2kHZvZd5ikPYqoY15TE1CsrrzRPktbWde5ks5obYzWsTkRdkzLI2KXiBhIMj52TOFGSbXFnDQiToyIOc3sMgxodSI0qzQnwo7vL8A2aWvtIUm3AM9LqpX0s3QWlZmSvgefzcF3haQ5kn4H9Gk4kaRpkoak3w+UNEPSX9NZcrYgSbinpa3Rr0raUNKdaR1PS9ozPXYDSQ+ks7f8imTYWrMk/a+kZyTNljS60baL01imStowLdta0v3pMX+RtH1JfpvWIXmscQcmqRPwdaBhItGvAAMjYm6aTD6KiF0ldQUelfQAyeQB/YGdgI2AOcC1jc67IXA1sHd6rl4R8YGkXwJLI+K/0/1uAf4nIh5JpxObAuxAMp3VIxExXtLBwBcS22qckNbRDXha0p0R8T6wDjAjIk6X9B/puU8mmeZsTES8LGk34Cpg3yJ+jZYDToQdU7d0qBckLcJfk3RZn4qIuWn514CdG67/AesC2wJ7A7dGRD3wtqSmJg7YHXi44VzpmOmm7AfsmA7LBegpqUdaxzfSY38naXGGn2mspH9Jv2+Wxvo+yUQIt6XlNwF3Seqe/ry3F9TdNUMdllNOhB3T8ojYpbAgTQjLCouAUyJiSqP9DqLlGW+UYR9ILr0MbTwvXxpL5rGdkoaRJNWhEfGJpGkkU2E1JdJ6P2z8OzBbHV8jzK8pwL+mcx4iabt0AtKHgRHpNcS+wD5NHPs48P8kbZke2ystXwL0KNjvAZJuKul+u6RfHyaZWQcl7wNZv4VY1wUWp0lwe5IWaYMaoKFVexRJl/tjYK6kI9I6JGlQC3VYjjkR5tc1JNf/ZqSzqPyKpIfwW+Bl4HngF8CfGx8YEe+RXNe7S9Jf+bxrei/wLw03S0hm0hmS3oyZw+d3r38K7C1pBkkX/Y0WYr0f6JTOyHMe8ETBtmXAAEnPkFwDHJ+WHw2MSuObDRyW4XdiOeXZZ8ws99wiNLPccyI0s9xzIjSz3HMiNLPccyI0s9xzIjSz3HMiNLPc+z/DzlCX4sNucgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(rf_model, X_test, y_test, display_labels=['No', 'Yes'], cmap=plt.cm.Blues,normalize=None)\n",
    "disp.ax_.set_title('rf test set')\n",
    "# 0 - 86%   1 - 68%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.07\n",
      "0.6527705627705629\n"
     ]
    }
   ],
   "source": [
    "# test probabilities score distribution\n",
    "predictions = rf_model.predict_proba(X_test)\n",
    "_ = plt.hist(predictions.flatten(), bins=150)\n",
    "plt.show()\n",
    "print(np.max(predictions[:,0]))\n",
    "print(np.min(predictions[:,0]))\n",
    "print(np.mean(predictions[:,0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random 8dim arrays\n",
    "rand_features  = np.random.uniform(0, 1, (1000, 8))\n",
    "y_label_rand = rf_model.predict(rand_features)\n",
    "plt.hist(y_label_rand)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib.dump(rf_model, \"/home/odedrot/DL_ex_4/credit_trained_rf_standardscaler.joblib\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "odedenv",
   "language": "python",
   "name": "odedenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}