LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"index.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns = [\"Creditability\"])\n",
    "y = df['Creditability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x=sc.fit_transform(X_train)\n",
    "test_x=sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_x\n",
    "X_test = test_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv1D, MaxPool1D, LSTM\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 2\n",
    "model = Sequential()\n",
    "model.add(LSTM(32, activation='relu', input_shape = X_train[0].shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 32)                4352      \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 5,569\n",
      "Trainable params: 5,505\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=0.0001), loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 670 samples, validate on 330 samples\n",
      "Epoch 1/2\n",
      "670/670 [==============================] - 3s 5ms/sample - loss: 0.7176 - accuracy: 0.5328 - val_loss: 0.6907 - val_accuracy: 0.5758\n",
      "Epoch 2/2\n",
      "670/670 [==============================] - 0s 498us/sample - loss: 0.7194 - accuracy: 0.5224 - val_loss: 0.6890 - val_accuracy: 0.6576\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f86c0643cf8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = (Y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_lstm = confusion_matrix(y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.imshow(cm_lstm, interpolation='nearest', cmap=plt.cm.Wistia)\n",
    "classNames = ['Negative','Positive']\n",
    "plt.title('LSTM')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "tick_marks = np.arange(len(classNames))\n",
    "plt.xticks(tick_marks, classNames, rotation=45)\n",
    "plt.yticks(tick_marks, classNames)\n",
    "s = [['TN','FP'], ['FN', 'TP']]\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        plt.text(j,i, str(s[i][j])+\" = \"+str(cm_lstm[i][j]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision = 0.7003367003367004\n",
      "recall = 0.896551724137931\n",
      "accuracy = 0.6575757575757576\n",
      "F1 Score = 0.7863894139886579\n"
     ]
    }
   ],
   "source": [
    "precision_ann = precision_score(y_test, Y_pred)\n",
    "print(\"precision = \"+str(precision_ann))\n",
    "recall_ann = recall_score(y_test, Y_pred)\n",
    "print(\"recall = \"+ str(recall_ann))\n",
    "accuracy_ann = accuracy_score(y_test, Y_pred)\n",
    "print(\"accuracy = \"+ str(accuracy_ann))\n",
    "F1_score_ann = f1_score(y_test, Y_pred)\n",
    "print(\"F1 Score = \"+ str(F1_score_ann))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn, fp, fn, tp = cm_lstm.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "specificity =0.09183673469387756\n"
     ]
    }
   ],
   "source": [
    "specificity = (tn)/(tn+fp)\n",
    "print(\"specificity =\"+ str(specificity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"index.csv\")\n",
    "\n",
    "df.head()\n",
    "\n",
    "X = df.drop(columns = [\"Creditability\"])\n",
    "y = df['Creditability']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "\n",
    "train_x=sc.fit_transform(X_train)\n",
    "test_x=sc.fit_transform(X_test)\n",
    "\n",
    "X_train = train_x\n",
    "X_test = test_x\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
    "\n",
    "X_train.shape\n",
    "\n",
    "y_train = y_train.to_numpy()\n",
    "\n",
    "y_train\n",
    "\n",
    "y_test = y_test.to_numpy()\n",
    "\n",
    "y_test\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv1D, MaxPool1D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "print(tf.__version__)\n",
    "\n",
    "#BatchNormalization() -> DIVIDE INTO MINI BATCHED AND STANDARDISES FOR TRAINING\n",
    "#DROPOUT -> DROPPING THE NUMBER OF % OF NEURONS WHICH ARE NOT UPDATING THERE WEIGHTS DURING TRAINING\n",
    "#FLATTEN -> FULLY CONNECTED LAYER \n",
    "epochs = 50\n",
    "model = Sequential()\n",
    "model.add(Conv1D(32, 2, activation='relu', input_shape = X_train[0].shape))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv1D(64, 2, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer=Adam(lr=0.0001), loss = 'binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), verbose=1)\n",
    "\n",
    "Y_pred = model.predict(X_test)\n",
    "\n",
    "Y_pred = (Y_pred > 0.5)\n",
    "\n",
    "Y_pred\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(Y_pred,y_test)\n",
    "\n",
    "cm_cnn = confusion_matrix(y_test, Y_pred)\n",
    "\n",
    "precision_ann = precision_score(y_test, Y_pred)\n",
    "print(\"precision = \"+str(precision_ann))\n",
    "recall_ann = recall_score(y_test, Y_pred)\n",
    "print(\"recall = \"+ str(recall_ann))\n",
    "accuracy_ann = accuracy_score(y_test, Y_pred)\n",
    "print(\"accuracy = \"+ str(accuracy_ann))\n",
    "F1_score_ann = f1_score(y_test, Y_pred)\n",
    "print(\"F1 Score = \"+ str(F1_score_ann))\n",
    "\n",
    "tn, fp, fn, tp = cm_cnn.ravel()\n",
    "\n",
    "specificity = (tn)/(tn+fp)\n",
    "print(\"specificity =\"+ str(specificity))\n",
    "\n"
   ]
  }
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