P4_CNN on Drug-Disease interaction-Train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-25 11:53:52.162428: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-06-25 11:53:52.162460: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# import modin.pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import confusion_matrix_pretty_print\n",
    "from confusion_matrix_pretty_print import plot_confusion_matrix_from_data\n",
    "\n",
    "from sklearn.metrics import confusion_matrix,classification_report,precision_score,\\\n",
    "auc, precision_recall_curve, roc_curve\n",
    "# from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "import tensorflow as tf\n",
    "# import keras\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Softmax, Dropout, MaxPooling2D\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras import metrics as kmetr\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "import pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### test & train split\n",
    "data = pd.read_csv('../F_Drug-Disease_SMOTE.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.iloc[:int(0.9*len(data.iloc[:,2])),:].to_csv('../trainSaved F(Drug-Disease).csv',index=False)\n",
    "# data.iloc[int(0.9*len(data.iloc[:,2])):,:].to_csv('../testSaved F(Drug-Disease).csv',index=False)\n",
    "                 \n",
    "# del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataTrain = pd.read_csv('../trainSaved F(Drug-Disease).csv')\n",
    "# dataTest = pd.read_csv('../testSaved F(Drug-Disease).csv')\n",
    "# dataTest = pd.read_csv('../../triple_cosineSNF(zeros).csv')\n",
    "# print(dataTest.shape,dataTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrain = data.copy()\n",
    "dataTest = pd.read_csv('../Drug_disease_testData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(330618, 907)"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataTrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18560, 909)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataTest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "906/2/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = dataTrain.values[:,:-1]\n",
    "y_train = dataTrain['y'].values.astype(int)\n",
    "trainNum = len(X_train)\n",
    "del dataTrain\n",
    "\n",
    "X_test = dataTest.values[:,:-1]\n",
    "y_test = dataTest['y'].values.astype(int)\n",
    "testNum = len(X_test)\n",
    "del data\n",
    "\n",
    "transformer = Normalizer().fit(X_train)  # fit does nothing.\n",
    "X_train = transformer.transform(X_train)\n",
    "X_test = transformer.transform(X_test)\n",
    "\n",
    "\n",
    "#reshape data to fit model\n",
    "X_train = X_train.reshape(trainNum,6,151,1)\n",
    "X_test = X_test.reshape(testNum,6,151,1)\n",
    "\n",
    "# y_train = y_train + 1\n",
    "# y_test  = y_test + 1\n",
    "# y_train = y_train / 2\n",
    "# y_test  = y_test / 2\n",
    "# print(y_train[0:5], y_test[0:5])\n",
    "\n",
    "#one-hot encode target column\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "# y_test[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 6, 151, 64)        1088      \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 6, 151, 32)        32800     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 6, 151, 16)        8208      \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 6, 151, 8)         2056      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 3, 75, 8)         0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1800)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                57632     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 2)                 66        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 101,850\n",
      "Trainable params: 101,850\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-05 13:31:47.038612: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-04-05 13:31:47.038652: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-04-05 13:31:47.038688: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (a-ZenBook): /proc/driver/nvidia/version does not exist\n",
      "2022-04-05 13:31:47.040457: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/home/aminuninnova/.local/lib/python3.8/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# #create model\n",
    "# model = Sequential()\n",
    "# #add model layers\n",
    "# model.add(Conv2D(128, kernel_size=4, activation='relu', input_shape=(16,71,1)))\n",
    "# # model.add(Conv2D(64, kernel_size=2, activation='relu'))\n",
    "\n",
    "# model.add(Conv2D(32, kernel_size=4, activation='relu'))\n",
    "# # model.add(Conv2D(16, kernel_size=2, activation='relu'))\n",
    "# model.add(Conv2D(8, kernel_size=4, activation='relu'))\n",
    "# model.add(Flatten())\n",
    "# # model.add(Dense( 64, activation='relu'))\n",
    "# model.add(Dense( 32, activation='relu'))\n",
    "# # model.add(Dropout(0.2))\n",
    "# model.add(Dropout(0.4))\n",
    "# # model.add(Dense( 16, activation='relu'))\n",
    "# model.add(Dense( 8, activation='relu'))\n",
    "# model.add(Dropout(0.4))\n",
    "# model.add(Dense( 2, activation='sigmoid'))\n",
    "# # model.add(Softmax(128))\n",
    "# model.summary()\n",
    "\n",
    "#create model\n",
    "model = Sequential()\n",
    "#add model layers\n",
    "model.add(Conv2D(64, kernel_size=4, activation='relu', padding='same',\\\n",
    "                 input_shape=(6,151,1)))\n",
    "# model.add(Conv2D(64, kernel_size=4, activation='relu', padding='same'))\n",
    "# model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=4, activation='relu', padding='same'))\n",
    "model.add(Conv2D(16, kernel_size=4, activation='relu', padding='same'))\n",
    "\n",
    "model.add(Conv2D(8, kernel_size=4, activation='relu', padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense( 32, activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "# model.add(Dense( 16, activation='relu'))\n",
    "model.add(Dense(2, activation='relu'))\n",
    "# model.add(Softmax(128))\n",
    "model.summary()\n",
    "\n",
    "\n",
    "adam = tf.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999)\n",
    "# model.compile(loss='hinge', optimizer=adam, metrics=[kmetr.categorical_accuracy])\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy',\\\n",
    "              metrics=['categorical_accuracy']) ## Minist\n",
    "\n",
    "### Load the model's saved weights.\n",
    "# model.load_weights('cnn_4_epoch.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #### plotting model\n",
    "plot_model(model,show_shapes = True, to_file='model .png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "   1/7233 [..............................] - ETA: 14:05 - loss: nan - categorical_accuracy: 0.2812"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-05 13:34:40.454293: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 838713192 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 203/7233 [..............................] - ETA: 12:26 - loss: nan - categorical_accuracy: 0.2882"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#### train the model\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#compile model using accuracy to measure model performance\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m#                 1: 95\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m#                }\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1377\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1378\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   1379\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   1380\u001b[0m     step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m   1381\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   1382\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m   1383\u001b[0m   callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m   tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1385\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1386\u001b[0m     context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2956\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2953\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m   2954\u001b[0m   (graph_function,\n\u001b[1;32m   2955\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2957\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:1853\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1849\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1850\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1851\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1852\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1853\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1854\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1855\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m     args,\n\u001b[1;32m   1857\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1858\u001b[0m     executing_eagerly)\n\u001b[1;32m   1859\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    498\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    505\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    506\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    507\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    508\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    511\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    512\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#### train the model\n",
    "\n",
    "#compile model using accuracy to measure model performance\n",
    "# from sklearn.utils import class_weight\n",
    "# class_weights = class_weight.compute_class_weight('balanced',\n",
    "#                                                  np.unique(y_train),\n",
    "#                                                  y_train)\n",
    "# class_weight = {0: 5,\n",
    "#                 1: 95\n",
    "#                }\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2)\n",
    "#                     , class_weight=class_weight)\n",
    "# history = model.fit(X_train, y_train, epochs=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### پایین‌تر ترو\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saveing the Model\n",
    "model.save_weights('cnn_2_epoch.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predit = model.predict(X_test)\n",
    "#actual results for first 4 images in test set\n",
    "print(predit[:4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #from sklearn.metrics import precision_recall_curve, roc_curve\n",
    "\n",
    "prec, rec, thr = precision_recall_curve(y_test[:,1], predit[:,1])\n",
    "aupr_val = auc(rec, prec)\n",
    "fpr, tpr, thr = roc_curve(y_test[:,1], predit[:,1])\n",
    "auc_val = auc(fpr, tpr)\n",
    "print(aupr_val,auc_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(list(range(1,5)),history.history['categorical_accuracy'])\n",
    "plt.plot(list(range(1,5)),history.history['val_categorical_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(list(range(1,5)),history.history['loss'])\n",
    "plt.plot(list(range(1,5)),history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # predit\n",
    "# predit[:,0].shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "predicts = []\n",
    "for a,b in predit:\n",
    "    if a >=b:\n",
    "        predicts.append(0)\n",
    "    else:\n",
    "        predicts.append(1)\n",
    "len(predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicts1 = []\n",
    "# e = d = z = 0\n",
    "\n",
    "# for a,b in predit:\n",
    "#     if a >=0.90:\n",
    "#         predicts1.append(0)\n",
    "#         d += 1\n",
    "#     elif b>=0.95:\n",
    "#         predicts1.append(2)\n",
    "#         e += 1\n",
    "#     elif a<=0.05 and b<=0.1:\n",
    "#         predicts1.append(1)\n",
    "#         z += 1\n",
    "# print('degrassive', d, 'enhancive', e, 'zeros', z)\n",
    "# print(\"\"\"\n",
    "# Epoch04: degrassive 224 enhancive 2939 zeros 40\n",
    "# Epoch05: degrassive 280 enhancive 2823 zeros 39\n",
    "# Epoch06: degrassive 233 enhancive 2879 zeros 79\n",
    "# Epoch07: degrassive 203 enhancive 2926 zeros 134\n",
    "# Epoch08: degrassive 224 enhancive 2895 zeros 180\n",
    "# Epoch09: degrassive 191 enhancive 2856 zeros 191\n",
    "# Epoch10: degrassive 189 enhancive 2821 zeros 246\n",
    "# Epoch11: degrassive 164 enhancive 2581 zeros 235\n",
    "# Epoch12: degrassive 166 enhancive 2454 zeros 266\n",
    "# \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# max(list((dataTest.values[:,2]+1)/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "cm = confusion_matrix(list(predicts), list((dataTest.values[:,2])))\n",
    "print(cm)\n",
    "\n",
    "CR = classification_report(list((dataTest.values[:,2])),list(predicts))\n",
    "print(CR)\n",
    "# print(145/4702)\n",
    "# i=0\n",
    "# for j in list(data.values[9500:,2]+1):\n",
    "#     if j==1:\n",
    "#         i +=1\n",
    "# print(i)\n",
    "\n",
    "# plt.show()\n",
    "plot_confusion_matrix_from_data(list((dataTest.values[:,2])), list(predicts))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd.DataFrame(predit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(predit).plot.density()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(predit).iloc[:,0].plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(predit).iloc[:,1].plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16, 8)\n",
    "\n",
    "# matplotlib histogram\n",
    "# plt.hist(pd.DataFrame(predit).iloc[:,1], color = 'blue', edgecolor = 'black',\n",
    "#          bins = int(200))\n",
    "\n",
    "# seaborn histogram\n",
    "sns.distplot(pd.DataFrame(predit).iloc[:,1], hist=True, kde=False, \n",
    "             bins=int(100), color = 'blue',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "\n",
    "# sns.distplot(pd.DataFrame(predit).iloc[:,0], hist=True, kde=True, \n",
    "#              bins=int(200), color = 'darkblue', \n",
    "#              hist_kws={'edgecolor':'black'},\n",
    "#              kde_kws={'linewidth': 4})\n",
    "# Add labels\n",
    "plt.title('frequency Histogram of Drugs')\n",
    "plt.xlabel('Interaction drugs Probability')\n",
    "plt.ylabel('frequency distribution')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16,8)\n",
    "\n",
    "# matplotlib histogram\n",
    "# plt.hist(pd.DataFrame(predit).iloc[:,1], color = 'blue', edgecolor = 'black',\n",
    "#          bins = int(200))\n",
    "\n",
    "# seaborn histogram\n",
    "\n",
    "sns.distplot(pd.DataFrame(predit).iloc[:,0], hist=True, kde=False, \n",
    "             bins=int(100), color = 'red',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "# sns.distplot(pd.DataFrame(predit).iloc[:,0], hist=True, kde=True, \n",
    "#              bins=int(200), color = 'darkblue', \n",
    "#              hist_kws={'edgecolor':'black'},\n",
    "#              kde_kws={'linewidth': 4})\n",
    "# Add labels\n",
    "plt.title('frequency Histogram of Drugs')\n",
    "plt.xlabel('None Interaction drugs Probability')\n",
    "plt.ylabel('frequency distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16,8)\n",
    "\n",
    "# matplotlib histogram\n",
    "# plt.hist(pd.DataFrame(predit).iloc[:,1], color = 'blue', edgecolor = 'black',\n",
    "#          bins = int(200))\n",
    "\n",
    "# seaborn histogram\n",
    "sns.distplot(pd.DataFrame(predit).iloc[:,1], hist=True, kde=False, \n",
    "             bins=int(100), color = 'blue',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "\n",
    "sns.distplot(pd.DataFrame(predit).iloc[:,0], hist=True, kde=False, \n",
    "             bins=int(100), color = 'red',\n",
    "             hist_kws={'edgecolor':'black'})\n",
    "# sns.distplot(pd.DataFrame(predit).iloc[:,0], hist=True, kde=True, \n",
    "#              bins=int(200), color = 'darkblue', \n",
    "#              hist_kws={'edgecolor':'black'},\n",
    "#              kde_kws={'linewidth': 4})\n",
    "# Add labels\n",
    "plt.title('frequency Histogram of Drugs')\n",
    "plt.xlabel('all drugs Probability')\n",
    "plt.ylabel('frequency distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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