ex4_analysis_Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff\n",
    "from scipy.spatial.distance import euclidean\n",
    "import pandas as pd\n",
    "import joblib\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder , StandardScaler , PolynomialFeatures , MinMaxScaler\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",
    "\n",
    "from keras.regularizers import l1_l2\n",
    "import keras.backend as K\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import LeakyReLU, Activation, Input, Dense, Dropout, Concatenate, BatchNormalization\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = 'diabetes' # diabetes , german_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load\n",
    "if data_set == 'diabetes':\n",
    "    BB_clf = joblib.load(\"/home/odedrot/DL_ex_4/diabetes_trained_rf_scaled.joblib\")\n",
    "    GAN_gen = load_model('/home/odedrot/DL_ex_4/part2_Diabetes_generator_3ok.h5', compile=False)\n",
    "    data = arff.loadarff('/home/odedrot/DL_ex_4/Assignment 4 files/diabetes.arff')\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",
    "    X = pd.DataFrame()\n",
    "    y = pd.DataFrame()\n",
    "    X = df.drop(['class'], axis=1)\n",
    "    y = df['class']  \n",
    "    X = X.to_numpy()\n",
    "    labelencoder = LabelEncoder()\n",
    "    y = labelencoder.fit_transform(y)\n",
    "    y = np.hstack(y)\n",
    "    # # normalize feature space \n",
    "    sc = StandardScaler() # MinMaxScaler()  StandardScaler()\n",
    "    X = sc.fit_transform(X) # use training data to fit and transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1000\n",
    "noise_dim = 10\n",
    "noise_batch      = np.random.normal(0, 1, (batch_size, noise_dim))    \n",
    "c_batch      = np.random.uniform(0, 1, (batch_size, 1))\n",
    "c_batch = c_batch[:,0]\n",
    "generated_features = GAN_gen.predict([noise_batch,c_batch]) # (32, 8)\n",
    "y_label = BB_clf.predict_proba(generated_features)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         0.98,  1.  ], dtype=float32),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(generated_features.flatten(), 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in range(generated_features.shape[1]):\n",
    "    plt.hist(generated_features[:,i], 100)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(c_batch.flatten(),'bo', label=\"c\")\n",
    "plt.plot(y_label.flatten(),'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "\n",
    "c_sorted_idx = np.argsort(c_batch, axis=0) # c indices sorted\n",
    "sorted_c = c_batch[c_sorted_idx] # c sorted 0-1\n",
    "sorted_y = y_label[c_sorted_idx] # sort y according to c indices\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(sorted_c.flatten(),'bo', label=\"c\")\n",
    "plt.plot(sorted_y.flatten(),'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "# split by 0.5\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(np.round(sorted_c.flatten()),'bo', label=\"c\")\n",
    "plt.plot(np.round(sorted_y.flatten())+0.1,'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "# split by y_mean\n",
    "sorted_y_ = np.copy(sorted_y)\n",
    "y_mean = np.mean(sorted_y)\n",
    "sorted_y_[sorted_y>y_mean] = 1\n",
    "sorted_y_[sorted_y<y_mean] = 0\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(np.round(sorted_c.flatten()),'bo', label=\"c\")\n",
    "plt.plot(np.round(sorted_y_.flatten())+0.1,'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "526 474 501 499\n",
      "1.0527426160337552 0.9524714828897338\n"
     ]
    }
   ],
   "source": [
    "# accuracy per class\n",
    "n_c1 = len(sorted_c[sorted_c>0.5])\n",
    "n_c0 = len(sorted_c[sorted_c<0.5])\n",
    "n_y1 = len(sorted_y_[sorted_y_>y_mean])\n",
    "n_y0 = len(sorted_y_[sorted_y_<y_mean])\n",
    "\n",
    "print( n_c1, n_c0, n_y1, n_y0 )\n",
    "acc0 = 1- (n_c0 - n_y0) / n_c0\n",
    "acc1 = 1- (n_c1 - n_y1) / n_c1\n",
    "print(acc0 , acc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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.flatten(), 100)\n",
    "# plt.hist(generated_features.flatten(), 100)\n",
    "\n",
    "plt.title('distributions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Are there any conclusions you can draw?\n",
    "# Were you more successful for a specific class of samples than another? \n",
    "# Were you more successful for a specific range of confidence scores?\n",
    "# Did your model suffer from mode collapse or other problems that may have prevented it from generating more effective samples?\n",
    "# If so, in what cases? Elaborate\n",
    "\n",
    "# Answers \n",
    "# The random Forrest clf on diabetes data provided a 72% on test (82% on class 0 and 52% on class 1) and 100% on train (overfit)\n",
    "# This low clf accuracy score impacts the ability of the gan to generate good data \n",
    "# even with low accuracy scores we can see the results of the adversarial gan in this scenario did quite well.\n",
    "# we generated 1000 random values U(0,1) to recieve 1000 vectors from the trained generator and fed them to the disc to get their y values from the BB\n",
    "# To compare the c (input) and y (output) we first sorted c from 0 to 1 and the corresponding y values accordingly. \n",
    "# We can see a trend where y follows c in a low/high value \n",
    "# since we use \"tanh\" in the generator we are not accurately sythesizing the distribution of the training data which is N(0,1) due to the standard scaler we inforced. \n",
    "# Therefore, we rounded the c and y probabilities to 0 and 1 to calculate the accuracy of each class which is more interesting in the classification task rather than probabilities.\n",
    "# we can see that in the diabetes case we are getting 96% accuracy in class 1 and 100% in class 0 \n",
    "#\n",
    "#\n",
    "#\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}