CNN Sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Docs prep....\n",
      "Vectorize...\n",
      "Vectorization done\n",
      "(25000, 200)\n",
      "(25000, 200)\n",
      "torch.Size([18750, 2])\n",
      "Epoch 0 loss 0.6933985948562622\n",
      "Test accuracy 0.524\n",
      "----------\n",
      "Epoch 100 loss 0.6237034797668457\n",
      "Test accuracy 0.671\n",
      "----------\n",
      "Epoch 200 loss 0.5984214544296265\n",
      "Test accuracy 0.686\n",
      "----------\n",
      "Epoch 300 loss 0.5863625407218933\n",
      "Test accuracy 0.693\n",
      "----------\n",
      "Epoch 400 loss 0.5813255906105042\n",
      "Test accuracy 0.695\n",
      "----------\n",
      "Epoch 500 loss 0.5797422528266907\n",
      "Test accuracy 0.698\n",
      "----------\n",
      "Epoch 600 loss 0.5793668031692505\n",
      "Test accuracy 0.699\n",
      "----------\n",
      "Epoch 700 loss 0.5786646604537964\n",
      "Test accuracy 0.701\n",
      "----------\n",
      "Epoch 800 loss 0.5760718584060669\n",
      "Test accuracy 0.699\n",
      "----------\n",
      "Epoch 900 loss 0.5751960873603821\n",
      "Test accuracy 0.700\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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RM32BstlQ198edKZ/Nr1sCaa9OXrZc1BRgQb4zlBs901v1DiFEHUnRV8AoL4/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf94278cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test Acc\n",
      "Test accuracy 0.699\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zs2bx4x//mPLycubPnx+hNAfHVFfC1BlYcY5ohyIiEjG9dhlVVFSQnp5OWloa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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf9447fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive\n",
      "Positive\n",
      "Negative\n",
      "Positive\n",
      "Positive\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import os\n",
    "\n",
    "style.use('ggplot')\n",
    "\n",
    "POS_TRAIN_PATH = 'datasets/aclImdb/train/pos/'\n",
    "NEG_TRAIN_PATH = 'datasets//aclImdb/train/neg/'\n",
    "pos_files = os.listdir(POS_TRAIN_PATH)\n",
    "neg_files = os.listdir(NEG_TRAIN_PATH)\n",
    "print('Docs prep....')\n",
    "pos_lines = []\n",
    "neg_lines = []\n",
    "for file in pos_files:\n",
    "    with open(POS_TRAIN_PATH + file) as f:\n",
    "        lines = f.readlines()\n",
    "        lines = [line.strip() for line in lines if line.strip()]\n",
    "        pos_lines.extend(lines)\n",
    "for file in neg_files:\n",
    "    with open(NEG_TRAIN_PATH + file) as f:\n",
    "        lines = f.readlines()\n",
    "        lines = [line.strip() for line in lines if line.strip()]\n",
    "        neg_lines.extend(lines)\n",
    "\n",
    "docs = []\n",
    "docs.extend(pos_lines)\n",
    "docs.extend(neg_lines)\n",
    "print('Vectorize...')\n",
    "vectorizer2 = TfidfVectorizer(stop_words=ENGLISH_STOP_WORDS, max_features=200)\n",
    "vectorizer2.fit(docs)\n",
    "print('Vectorization done')\n",
    "\n",
    "input_data2 = vectorizer2.transform(docs).todense()\n",
    "print(input_data2.shape)\n",
    "df2 = pd.DataFrame(input_data2)\n",
    "\n",
    "pos_labels = np.zeros(len(pos_lines))\n",
    "neg_labels = np.ones(len(neg_lines))\n",
    "\n",
    "labels2 = []\n",
    "labels2 = np.append(pos_labels, neg_labels)\n",
    "df2['labels'] = labels2\n",
    "# df2.to_csv('data/aclImdb/processed_input_labelled3.csv')\n",
    "\n",
    "pos_labels = np.zeros(len(pos_lines))\n",
    "neg_labels = np.ones(len(neg_lines))\n",
    "\n",
    "X = df2.drop('labels', axis=1)\n",
    "X = np.array(X)\n",
    "print(X.shape)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df2['labels'], random_state=20)\n",
    "test_accuracies = []\n",
    "def evaluate(cnn):\n",
    "    cnn.train(False)\n",
    "    total = len(X_test)\n",
    "    correct = 0.0\n",
    "\n",
    "    for val_x, val_y in zip(X_test, y_test):\n",
    "\n",
    "        t_x = np.array([[val_x]]).astype(np.float32)\n",
    "\n",
    "        output = cnn(Variable(torch.from_numpy(np.array(t_x))))\n",
    "\n",
    "        output = output[0]\n",
    "        out = output.data.numpy()\n",
    "\n",
    "        if val_y == 0:\n",
    "\n",
    "            if not np.argmax(out):\n",
    "                correct += 1\n",
    "\n",
    "        if val_y == 1:\n",
    "\n",
    "            if np.argmax(out):\n",
    "                correct += 1\n",
    "\n",
    "    acc = correct / total\n",
    "    print('Test accuracy %.3f' % (acc))\n",
    "    test_accuracies.append(acc)\n",
    "\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self,output_size):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(1, 1, 3, stride=2)\n",
    "\n",
    "        self.linear = nn.Linear(49, 10)\n",
    "        self.linear2 = nn.Linear(10, output_size)\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "\n",
    "        output = F.max_pool1d(self.conv1(x), kernel_size=2)\n",
    "\n",
    "        output = output.transpose(0,1)\n",
    "        output = self.dropout(F.relu(self.linear(output[0])))\n",
    "\n",
    "        output = F.softmax(self.linear2(output))\n",
    "\n",
    "        return output\n",
    "\n",
    "cnn = CNN(2)\n",
    "epochs = 1000\n",
    "learning_rate = 5e-3\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n",
    "losses = []\n",
    "for epoch in range(epochs):\n",
    "    cnn.train(True)\n",
    "    inputs = Variable(torch.from_numpy(np.array([X_train]).astype(np.float32)).transpose(0, 1))\n",
    "    targets = Variable(torch.from_numpy(np.array(y_train).astype(np.float32)).long())\n",
    "    optimizer.zero_grad()\n",
    "    outputs = cnn(inputs)\n",
    "\n",
    "    loss = criterion(outputs, targets)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if epoch == 0:\n",
    "        print(outputs.size())\n",
    "    if epoch % 100 == 0:\n",
    "        \n",
    "        print('Epoch %d loss %s' % (epoch, loss.data[0]))\n",
    "        evaluate(cnn)\n",
    "        print('-'*10)\n",
    "        \n",
    "    losses.append(loss.data[0])\n",
    "plt.title('Loss')\n",
    "plt.plot(losses)\n",
    "plt.show()\n",
    "print('Final Test Acc')\n",
    "evaluate(cnn)\n",
    "\n",
    "plt.title('Test Acc.')\n",
    "plt.plot(test_accuracies)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def test_line(line):\n",
    "    t_x = vectorizer2.transform([line]).todense().astype(np.float32)\n",
    "\n",
    "    t_x = np.array([t_x])\n",
    "    cnn.eval()\n",
    "    out = cnn(Variable(torch.from_numpy(t_x)))[0]\n",
    "    val = out.data.numpy()\n",
    "\n",
    "    \n",
    "    if not np.argmax(val):\n",
    "        return 'Positive'\n",
    "    elif np.argmax(val):\n",
    "        return 'Negative'\n",
    "test_neg = \"\"\" It was plain boring.\"\"\"\n",
    "test_pos = \"\"\" The movie was good the actors performed amazingly\"\"\"\n",
    "print(test_line(test_neg))\n",
    "print(test_line(test_pos))\n",
    "print(test_line('it was bad '))\n",
    "\n",
    "print(test_line('I liked it '))\n",
    "\n",
    "print(test_line('It was good'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Mini Batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Docs prep....\n",
      "Vectorize...\n",
      "Vectorization done\n",
      "(25000, 200)\n",
      "(25000, 200)\n",
      "Epoch 0 Total loss 130.3626847267151\n",
      "Test accuracy 0.527\n",
      "----------\n",
      "Epoch 1 Total loss 126.28806030750275\n",
      "Test accuracy 0.672\n",
      "----------\n",
      "Epoch 2 Total loss 115.56674021482468\n",
      "Test accuracy 0.681\n",
      "----------\n",
      "Epoch 3 Total loss 112.82626733183861\n",
      "Test accuracy 0.685\n",
      "----------\n",
      "Epoch 4 Total loss 111.8749241232872\n",
      "Test accuracy 0.686\n",
      "----------\n"
     ]
    },
    {
     "data": {
      "image/png": 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7oXUo1kXJ6LNnjI4jhKgDUgiiUsqzCaZJf4OTheiVC42OI4SoA1II4pJUx86oETejP/0I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\n7qBg2zUXFye9m7rH0YXSWuu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46294bb898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test Acc\n",
      "Test accuracy 0.686\n"
     ]
    },
    {
     "data": {
      "image/png": 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20dDQQE5ODo8//jiZmZnMmDGDmTNn8tWvfhVjDEuXLu3xHcJwYELVEBgPk6c5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f463148cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n",
      "Positive\n",
      "Negative\n",
      "Positive\n",
      "Positive\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "import os\n",
    "\n",
    "style.use('ggplot')\n",
    "\n",
    "POS_TRAIN_PATH = 'datasets/aclImdb/train/pos/'\n",
    "NEG_TRAIN_PATH = 'datasets//aclImdb/train/neg/'\n",
    "pos_files = os.listdir(POS_TRAIN_PATH)\n",
    "neg_files = os.listdir(NEG_TRAIN_PATH)\n",
    "print('Docs prep....')\n",
    "pos_lines = []\n",
    "neg_lines = []\n",
    "for file in pos_files:\n",
    "    with open(POS_TRAIN_PATH + file) as f:\n",
    "        lines = f.readlines()\n",
    "        lines = [line.strip() for line in lines if line.strip()]\n",
    "        pos_lines.extend(lines)\n",
    "for file in neg_files:\n",
    "    with open(NEG_TRAIN_PATH + file) as f:\n",
    "        lines = f.readlines()\n",
    "        lines = [line.strip() for line in lines if line.strip()]\n",
    "        neg_lines.extend(lines)\n",
    "\n",
    "docs = []\n",
    "docs.extend(pos_lines)\n",
    "docs.extend(neg_lines)\n",
    "print('Vectorize...')\n",
    "vectorizer2 = TfidfVectorizer(stop_words=ENGLISH_STOP_WORDS, max_features=200)\n",
    "vectorizer2.fit(docs)\n",
    "print('Vectorization done')\n",
    "\n",
    "input_data2 = vectorizer2.transform(docs).todense()\n",
    "print(input_data2.shape)\n",
    "df2 = pd.DataFrame(input_data2)\n",
    "\n",
    "pos_labels = np.zeros(len(pos_lines))\n",
    "neg_labels = np.ones(len(neg_lines))\n",
    "\n",
    "labels2 = []\n",
    "labels2 = np.append(pos_labels, neg_labels)\n",
    "df2['labels'] = labels2\n",
    "# df2.to_csv('data/aclImdb/processed_input_labelled3.csv')\n",
    "\n",
    "pos_labels = np.zeros(len(pos_lines))\n",
    "neg_labels = np.ones(len(neg_lines))\n",
    "\n",
    "X = df2.drop('labels', axis=1)\n",
    "X = np.array(X)\n",
    "print(X.shape)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df2['labels'], random_state=20)\n",
    "test_accuracies = []\n",
    "def evaluate(cnn):\n",
    "    cnn.train(False)\n",
    "    total = len(X_test)\n",
    "    correct = 0.0\n",
    "\n",
    "    for val_x, val_y in zip(X_test, y_test):\n",
    "\n",
    "        t_x = np.array([[val_x]]).astype(np.float32)\n",
    "\n",
    "        output = cnn(Variable(torch.from_numpy(np.array(t_x))))\n",
    "\n",
    "        output = output[0]\n",
    "        out = output.data.numpy()\n",
    "\n",
    "        if val_y == 0:\n",
    "\n",
    "            if not np.argmax(out):\n",
    "                correct += 1\n",
    "\n",
    "        if val_y == 1:\n",
    "\n",
    "            if np.argmax(out):\n",
    "                correct += 1\n",
    "\n",
    "    acc = correct / total\n",
    "    print('Test accuracy %.3f' % (acc))\n",
    "    test_accuracies.append(acc)\n",
    "\n",
    "class TextLoader(Dataset):\n",
    "    def __init__(self,X_train, y_train):\n",
    "        self.X_train =  np.array(X_train)\n",
    "        self.y_train = np.array(y_train)\n",
    "   \n",
    "    def __getitem__(self, index):\n",
    "        return self.X_train[index], self.y_train[index]\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.X_train.tolist())\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self,output_size):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(1, 1, 3, stride=2)\n",
    "\n",
    "        self.linear = nn.Linear(49, 10)\n",
    "        self.linear2 = nn.Linear(10, output_size)\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "\n",
    "        output = F.max_pool1d(self.conv1(x), kernel_size=2)\n",
    "\n",
    "        output = output.transpose(0,1)\n",
    "        output =self.dropout( F.relu(self.linear(output[0])))\n",
    "\n",
    "        output = F.softmax(self.linear2(output))\n",
    "\n",
    "        return output\n",
    "\n",
    "cnn = CNN(2)\n",
    "epochs = 5\n",
    "learning_rate = 5e-3\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n",
    "losses = []\n",
    "dset = TextLoader(X_train, y_train)  \n",
    "\n",
    "trainLoader = DataLoader(dset, batch_size=100, shuffle=True, num_workers=4)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    loss_val = 0\n",
    "    for x_train, y_train in trainLoader:\n",
    "\n",
    "        cnn.train(True)\n",
    "        inputs = Variable(x_train.unsqueeze(0).transpose(0,1).float())\n",
    "        targets = Variable(y_train.long())\n",
    "        optimizer.zero_grad()\n",
    "        outputs = cnn(inputs)\n",
    "\n",
    "        loss = criterion(outputs, targets)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        loss_val += loss.data[0]\n",
    "\n",
    "    \n",
    "    if epoch % 1 == 0:\n",
    "\n",
    "        print('Epoch %d Total loss %s' % (epoch, loss_val))\n",
    "        evaluate(cnn)\n",
    "        print('-'*10)\n",
    "\n",
    "    losses.append(loss_val)\n",
    "\n",
    "plt.title('Loss')\n",
    "plt.plot(losses)\n",
    "plt.show()\n",
    "print('Final Test Acc')\n",
    "evaluate(cnn)\n",
    "\n",
    "plt.title('Test Acc.')\n",
    "plt.plot(test_accuracies)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def test_line(line):\n",
    "    t_x = vectorizer2.transform([line]).todense().astype(np.float32)\n",
    "\n",
    "    t_x = np.array([t_x])\n",
    "    cnn.eval()\n",
    "    out = cnn(Variable(torch.from_numpy(t_x)))[0]\n",
    "    val = out.data.numpy()\n",
    "\n",
    "    \n",
    "    if not np.argmax(val):\n",
    "        return 'Positive'\n",
    "    elif np.argmax(val):\n",
    "        return 'Negative'\n",
    "test_neg = \"\"\" It was plain boring.\"\"\"\n",
    "test_pos = \"\"\" The movie was good the actors performed amazingly\"\"\"\n",
    "print(test_line(test_neg))\n",
    "print(test_line(test_pos))\n",
    "print(test_line('it was bad '))\n",
    "\n",
    "print(test_line('I liked it '))\n",
    "\n",
    "print(test_line('It was good'))\n"
   ]
  }
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