Sentiment_Analysis_04.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = open('reviews.txt','r')\n",
    "reviews = list(map(lambda x:x[:-1], g.readlines()))\n",
    "g.close()\n",
    "\n",
    "g = open('labels.txt','r')\n",
    "labels = list(map(lambda x:x[:-1].lower(), g.readlines()))\n",
    "g.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_counts = Counter()\n",
    "negative_counts = Counter()\n",
    "total_counts = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(reviews)):\n",
    "    if(labels[i] == 'positive'):\n",
    "        for word in reviews[i].split(\" \"):\n",
    "            positive_counts[word] += 1\n",
    "            total_counts[word] += 1\n",
    "            \n",
    "    else:\n",
    "        for word in reviews[i].split(\" \"):\n",
    "            negative_counts[word] += 1\n",
    "            total_counts[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_neg_ratios = Counter()\n",
    "\n",
    "for term,cnt in list(total_counts.most_common()):\n",
    "    if(cnt > 100):\n",
    "        pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n",
    "        pos_neg_ratios[term] = pos_neg_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab size : 74074\n"
     ]
    }
   ],
   "source": [
    "vocab = set(total_counts.keys())\n",
    "vocab_size = len(vocab)\n",
    "print(\"vocab size :\",len(vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 74074)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_0 = np.zeros((1, vocab_size))\n",
    "layer_0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_map_index = {}\n",
    "for i,word in enumerate(vocab):\n",
    "    word_map_index[word] = i\n",
    "    \n",
    "#word_map_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74073"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_map_index[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_input_layer(review):\n",
    "    global layer_0\n",
    "    \n",
    "    layer_0 *= 0\n",
    "    #print(layer_0)\n",
    "    for word in review.split(\" \"):\n",
    "        layer_0[0][word_map_index[word]] += 1\n",
    "        #print(layer_0[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from the array you can find how many times each word has appeared in the given review, For example \" \" has appeared 18 times\n",
    "#in the second review\n",
    "update_input_layer(reviews[1])\n",
    "layer_0\n",
    "#reviews[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target_for_label(label):\n",
    "    if(label == 'positive'):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_target_for_label(labels[0])\n",
    "#print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import sys\n",
    "import numpy as np\n",
    "\n",
    "# Encapsulate our neural network in a class\n",
    "class SentimentNetwork:\n",
    "    def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n",
    "        \"\"\"Create a SentimenNetwork with the given settings\n",
    "        Args:\n",
    "            reviews(list) - List of reviews used for training\n",
    "            labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n",
    "            hidden_nodes(int) - Number of nodes to create in the hidden layer\n",
    "            learning_rate(float) - Learning rate to use while training\n",
    "        \n",
    "        \"\"\"\n",
    "        # Assign a seed to our random number generator to ensure we get\n",
    "        # reproducable results during development \n",
    "        np.random.seed(1)\n",
    "\n",
    "        # process the reviews and their associated labels so that everything\n",
    "        # is ready for training\n",
    "        self.pre_process_data(reviews, labels)\n",
    "        \n",
    "        # Build the network to have the number of hidden nodes and the learning rate that\n",
    "        # were passed into this initializer. Make the same number of input nodes as\n",
    "        # there are vocabulary words and create a single output node.\n",
    "        self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n",
    "\n",
    "    def pre_process_data(self, reviews, labels):\n",
    "        \n",
    "        # populate review_vocab with all of the words in the given reviews\n",
    "        review_vocab = set()\n",
    "        for review in reviews:\n",
    "            for word in review.split(\" \"):\n",
    "                review_vocab.add(word)\n",
    "\n",
    "        # Convert the vocabulary set to a list so we can access words via indices\n",
    "        self.review_vocab = list(review_vocab)\n",
    "        \n",
    "        # populate label_vocab with all of the words in the given labels.\n",
    "        label_vocab = set()\n",
    "        for label in labels:\n",
    "            label_vocab.add(label)\n",
    "        \n",
    "        # Convert the label vocabulary set to a list so we can access labels via indices\n",
    "        self.label_vocab = list(label_vocab)\n",
    "        \n",
    "        # Store the sizes of the review and label vocabularies.\n",
    "        self.review_vocab_size = len(self.review_vocab)\n",
    "        self.label_vocab_size = len(self.label_vocab)\n",
    "        \n",
    "        # Create a dictionary of words in the vocabulary mapped to index positions\n",
    "        self.word2index = {}\n",
    "        for i, word in enumerate(self.review_vocab):\n",
    "            self.word2index[word] = i\n",
    "        \n",
    "        # Create a dictionary of labels mapped to index positions\n",
    "        self.label2index = {}\n",
    "        for i, label in enumerate(self.label_vocab):\n",
    "            self.label2index[label] = i\n",
    "        \n",
    "    def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Store the learning rate\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "        # Initialize weights\n",
    "\n",
    "        # These are the weights between the input layer and the hidden layer.\n",
    "        self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n",
    "    \n",
    "        # These are the weights between the hidden layer and the output layer.\n",
    "        self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n",
    "                                                (self.hidden_nodes, self.output_nodes))\n",
    "        \n",
    "        # The input layer, a two-dimensional matrix with shape 1 x input_nodes\n",
    "        self.layer_0 = np.zeros((1,input_nodes))\n",
    "    \n",
    "        \n",
    "    def update_input_layer(self,review):\n",
    "\n",
    "        # clear out previous state, reset the layer to be all 0s\n",
    "        self.layer_0 *= 0\n",
    "        \n",
    "        for word in review.split(\" \"):\n",
    "            # NOTE: This if-check was not in the version of this method created in Project 2,\n",
    "            #       and it appears in Andrew's Project 3 solution without explanation. \n",
    "            #       It simply ensures the word is actually a key in word2index before\n",
    "            #       accessing it, which is important because accessing an invalid key\n",
    "            #       with raise an exception in Python. This allows us to ignore unknown\n",
    "            #       words encountered in new reviews.\n",
    "            if(word in self.word2index.keys()):\n",
    "                ## New for Project 4: changed to set to 1 instead of add 1\n",
    "                self.layer_0[0][self.word2index[word]] = 1\n",
    "                \n",
    "    def get_target_for_label(self,label):\n",
    "        if(label == 'positive'):\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "        \n",
    "    def sigmoid(self,x):\n",
    "        return 1 / (1 + np.exp(-x))\n",
    "    \n",
    "    def sigmoid_output_2_derivative(self,output):\n",
    "        return output * (1 - output)\n",
    "    \n",
    "    def train(self, training_reviews, training_labels):\n",
    "        \n",
    "        # make sure out we have a matching number of reviews and labels\n",
    "        assert(len(training_reviews) == len(training_labels))\n",
    "        \n",
    "        # Keep track of correct predictions to display accuracy during training \n",
    "        correct_so_far = 0\n",
    "\n",
    "        # Remember when we started for printing time statistics\n",
    "        start = time.time()\n",
    "        \n",
    "        # loop through all the given reviews and run a forward and backward pass,\n",
    "        # updating weights for every item\n",
    "        for i in range(len(training_reviews)):\n",
    "            \n",
    "            # Get the next review and its correct label\n",
    "            review = training_reviews[i]\n",
    "            label = training_labels[i]\n",
    "            \n",
    "            #### Implement the forward pass here ####\n",
    "            ### Forward pass ###\n",
    "\n",
    "            # Input Layer\n",
    "            self.update_input_layer(review)\n",
    "\n",
    "            # Hidden layer\n",
    "            layer_1 = self.layer_0.dot(self.weights_0_1)\n",
    "\n",
    "            # Output layer\n",
    "            layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
    "            \n",
    "            #### Implement the backward pass here ####\n",
    "            ### Backward pass ###\n",
    "\n",
    "            # Output error\n",
    "            layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n",
    "            layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n",
    "\n",
    "            # Backpropagated error\n",
    "            layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n",
    "            layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n",
    "\n",
    "            # Update the weights\n",
    "            self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n",
    "            self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n",
    "\n",
    "            # Keep track of correct predictions.\n",
    "            if(layer_2 >= 0.5 and label == 'positive'):\n",
    "                correct_so_far += 1\n",
    "            elif(layer_2 < 0.5 and label == 'negative'):\n",
    "                correct_so_far += 1\n",
    "            \n",
    "            # For debug purposes, print out our prediction accuracy and speed \n",
    "            # throughout the training process. \n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n",
    "            \n",
    "            sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n",
    "                             + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
    "                             + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n",
    "                             + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n",
    "            if(i % 2500 == 0):\n",
    "                print(\"\")\n",
    "    \n",
    "    def test(self, testing_reviews, testing_labels):\n",
    "        \"\"\"\n",
    "        Attempts to predict the labels for the given testing_reviews,\n",
    "        and uses the test_labels to calculate the accuracy of those predictions.\n",
    "        \"\"\"\n",
    "        \n",
    "        # keep track of how many correct predictions we make\n",
    "        correct = 0\n",
    "\n",
    "        # we'll time how many predictions per second we make\n",
    "        start = time.time()\n",
    "\n",
    "        # Loop through each of the given reviews and call run to predict\n",
    "        # its label. \n",
    "        for i in range(len(testing_reviews)):\n",
    "            pred = self.run(testing_reviews[i])\n",
    "            if(pred == testing_labels[i]):\n",
    "                correct += 1\n",
    "            \n",
    "            # For debug purposes, print out our prediction accuracy and speed \n",
    "            # throughout the prediction process. \n",
    "\n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n",
    "            \n",
    "            sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n",
    "                             + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n",
    "                             + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n",
    "                             + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n",
    "    \n",
    "    def run(self, review):\n",
    "        \"\"\"\n",
    "        Returns a POSITIVE or NEGATIVE prediction for the given review.\n",
    "        \"\"\"\n",
    "        # Run a forward pass through the network, like in the \"train\" function.\n",
    "        \n",
    "        # Input Layer\n",
    "        self.update_input_layer(review.lower())\n",
    "\n",
    "        # Hidden layer\n",
    "        layer_1 = self.layer_0.dot(self.weights_0_1)\n",
    "\n",
    "        # Output layer\n",
    "        layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n",
    "        \n",
    "        # Return POSITIVE for values above greater-than-or-equal-to 0.5 in the output layer;\n",
    "        # return NEGATIVE for other values\n",
    "        if(layer_2[0] >= 0.5):\n",
    "            return \"positive\"\n",
    "        else:\n",
    "            return \"negative\"\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n",
      "Progress:10.4% Speed(reviews/sec):67.24 #Correct:1962 #Trained:2501 Training Accuracy:78.4%\n",
      "Progress:20.8% Speed(reviews/sec):69.17 #Correct:4002 #Trained:5001 Training Accuracy:80.0%\n",
      "Progress:31.2% Speed(reviews/sec):69.73 #Correct:6120 #Trained:7501 Training Accuracy:81.5%\n",
      "Progress:41.6% Speed(reviews/sec):70.05 #Correct:8271 #Trained:10001 Training Accuracy:82.7%\n",
      "Progress:52.0% Speed(reviews/sec):70.22 #Correct:10431 #Trained:12501 Training Accuracy:83.4%\n",
      "Progress:62.5% Speed(reviews/sec):70.39 #Correct:12565 #Trained:15001 Training Accuracy:83.7%\n",
      "Progress:72.9% Speed(reviews/sec):70.46 #Correct:14670 #Trained:17501 Training Accuracy:83.8%\n",
      "Progress:83.3% Speed(reviews/sec):70.23 #Correct:16833 #Trained:20001 Training Accuracy:84.1%\n",
      "Progress:93.7% Speed(reviews/sec):70.16 #Correct:19015 #Trained:22501 Training Accuracy:84.5%\n",
      "Progress:99.9% Speed(reviews/sec):69.90 #Correct:20335 #Trained:24000 Training Accuracy:84.7%"
     ]
    }
   ],
   "source": [
    "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)\n",
    "mlp.train(reviews[:-1000],labels[:-1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r",
      "Progress:0.1% Speed(reviews/sec):200.0 #Correct:1 #Tested:2 Testing Accuracy:50.0%\r",
      "Progress:0.2% Speed(reviews/sec):285.7 #Correct:2 #Tested:3 Testing Accuracy:66.6%\r",
      "Progress:0.3% Speed(reviews/sec):332.9 #Correct:3 #Tested:4 Testing Accuracy:75.0%\r",
      "Progress:0.4% Speed(reviews/sec):363.4 #Correct:4 #Tested:5 Testing Accuracy:80.0%\r",
      "Progress:0.5% Speed(reviews/sec):384.5 #Correct:5 #Tested:6 Testing Accuracy:83.3%\r",
      "Progress:0.6% Speed(reviews/sec):400.0 #Correct:6 #Tested:7 Testing Accuracy:85.7%\r",
      "Progress:0.7% Speed(reviews/sec):411.7 #Correct:7 #Tested:8 Testing Accuracy:87.5%\r",
      "Progress:0.8% Speed(reviews/sec):421.1 #Correct:8 #Tested:9 Testing Accuracy:88.8%\r",
      "Progress:0.9% Speed(reviews/sec):391.2 #Correct:9 #Tested:10 Testing Accuracy:90.0%\r",
      "Progress:1.0% Speed(reviews/sec):399.9 #Correct:10 #Tested:11 Testing Accuracy:90.9%\r",
      "Progress:1.1% Speed(reviews/sec):407.4 #Correct:11 #Tested:12 Testing Accuracy:91.6%\r",
      "Progress:1.2% Speed(reviews/sec):428.7 #Correct:12 #Tested:13 Testing Accuracy:92.3%\r",
      "Progress:1.3% Speed(reviews/sec):433.5 #Correct:13 #Tested:14 Testing Accuracy:92.8%\r",
      "Progress:1.4% Speed(reviews/sec):437.6 #Correct:14 #Tested:15 Testing Accuracy:93.3%\r",
      "Progress:1.5% Speed(reviews/sec):441.2 #Correct:15 #Tested:16 Testing Accuracy:93.7%\r",
      "Progress:1.6% Speed(reviews/sec):432.5 #Correct:16 #Tested:17 Testing Accuracy:94.1%\r",
      "Progress:1.7% Speed(reviews/sec):436.0 #Correct:17 #Tested:18 Testing Accuracy:94.4%\r",
      "Progress:1.8% Speed(reviews/sec):450.2 #Correct:18 #Tested:19 Testing Accuracy:94.7%\r",
      "Progress:1.9% Speed(reviews/sec):442.1 #Correct:18 #Tested:20 Testing Accuracy:90.0%\r",
      "Progress:2.0% Speed(reviews/sec):444.6 #Correct:19 #Tested:21 Testing Accuracy:90.4%\r",
      "Progress:2.1% Speed(reviews/sec):446.9 #Correct:20 #Tested:22 Testing Accuracy:90.9%\r",
      "Progress:2.2% Speed(reviews/sec):449.2 #Correct:21 #Tested:23 Testing Accuracy:91.3%\r",
      "Progress:2.3% Speed(reviews/sec):451.2 #Correct:22 #Tested:24 Testing Accuracy:91.6%\r",
      "Progress:2.4% Speed(reviews/sec):452.9 #Correct:23 #Tested:25 Testing Accuracy:92.0%\r",
      "Progress:2.5% Speed(reviews/sec):463.1 #Correct:24 #Tested:26 Testing Accuracy:92.3%\r",
      "Progress:2.6% Speed(reviews/sec):464.4 #Correct:25 #Tested:27 Testing Accuracy:92.5%\r",
      "Progress:2.7% Speed(reviews/sec):442.7 #Correct:26 #Tested:28 Testing Accuracy:92.8%\r",
      "Progress:2.8% Speed(reviews/sec):437.7 #Correct:27 #Tested:29 Testing Accuracy:93.1%\r",
      "Progress:2.9% Speed(reviews/sec):426.6 #Correct:28 #Tested:30 Testing Accuracy:93.3%\r",
      "Progress:3.0% Speed(reviews/sec):416.8 #Correct:29 #Tested:31 Testing Accuracy:93.5%\r",
      "Progress:3.1% Speed(reviews/sec):419.0 #Correct:30 #Tested:32 Testing Accuracy:93.7%\r",
      "Progress:3.2% Speed(reviews/sec):421.2 #Correct:31 #Tested:33 Testing Accuracy:93.9%\r",
      "Progress:3.3% Speed(reviews/sec):423.2 #Correct:32 #Tested:34 Testing Accuracy:94.1%\r",
      "Progress:3.4% Speed(reviews/sec):425.2 #Correct:33 #Tested:35 Testing Accuracy:94.2%\r",
      "Progress:3.5% Speed(reviews/sec):427.0 #Correct:34 #Tested:36 Testing Accuracy:94.4%\r",
      "Progress:3.6% Speed(reviews/sec):428.8 #Correct:35 #Tested:37 Testing Accuracy:94.5%\r",
      "Progress:3.7% Speed(reviews/sec):435.5 #Correct:36 #Tested:38 Testing Accuracy:94.7%\r",
      "Progress:3.8% Speed(reviews/sec):437.0 #Correct:37 #Tested:39 Testing Accuracy:94.8%\r",
      "Progress:3.9% Speed(reviews/sec):433.5 #Correct:37 #Tested:40 Testing Accuracy:92.5%\r",
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      "Progress:99.9% Speed(reviews/sec):567.9 #Correct:856 #Tested:1000 Testing Accuracy:85.6%"
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