GRU_sentiment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GRU_sentiment.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "36ZYOuGsQWOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr2D3fdQQafs",
        "colab_type": "code",
        "outputId": "8c854305-f1c3-4653-ad6c-27a5300b5aa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1OoZjWRQeE1",
        "colab_type": "code",
        "outputId": "26c288a9-2b18-4461-9978-ecf85abb10bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "!pwd\n",
        "%cd /content/drive/My Drive/Term5/NLP/Project"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "/content/drive/My Drive/Term5/NLP/Project\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcH74oGLXNNO",
        "colab_type": "code",
        "outputId": "c52a4f1c-a7df-4a37-fd3b-d55287efb622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import pandas as pd\n",
        "from keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5DJ4gCaXDTg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def readFile(fileName):\n",
        "  filedata = pd.read_csv(fileName, delimiter='\\t', header = None, skiprows=1)\n",
        "  filedata.columns = [\"id\", \"drugName\", \"condition\", \"review\", \"rating\", \"date\", \"usefulCount\", \"clean_review\", \"sentiment\"]\n",
        "  filedata.drop(['review'], axis=1, inplace = True)\n",
        "  filedata.dropna(axis=0, inplace=True)\n",
        "  return filedata"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MBVvilSQeqI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataPath = '/content/drive/My Drive/Term5/NLP/Project/Dataset'\n",
        "outputPath = '/content/drive/My Drive/Term5/NLP/Project/Model_output/GRU/'\n",
        "glove_vectors_file = dataPath + '/../word_embeddings/glove.840B.300d.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QC83lC8W5Ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drug_train = dataPath + '/cleaned_Train_data.csv'\n",
        "drug_test = dataPath + '/cleaned_Test_data.csv'\n",
        "drug_valid = dataPath + '/cleaned_Valid_data.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1N8F32cW928",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = readFile(drug_train)\n",
        "valid_data = readFile(drug_valid)\n",
        "test_data = readFile(drug_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63_uF3AWYxSL",
        "colab_type": "code",
        "outputId": "d0fe180e-7d6c-4d8b-fd5a-925f6d7180bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "train_data.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>drugName</th>\n",
              "      <th>condition</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>usefulCount</th>\n",
              "      <th>clean_review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>204048</td>\n",
              "      <td>Xerac AC</td>\n",
              "      <td>Hyperhidrosis</td>\n",
              "      <td>10.0</td>\n",
              "      <td>May 15, 2010</td>\n",
              "      <td>10</td>\n",
              "      <td>wow struggl excess underarm sweat sever year r...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52764</td>\n",
              "      <td>Carbidopa / levodopa</td>\n",
              "      <td>Parkinson's Disease</td>\n",
              "      <td>4.0</td>\n",
              "      <td>December 30, 2016</td>\n",
              "      <td>17</td>\n",
              "      <td>rytari month first work well pill mg mg time d...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>210591</td>\n",
              "      <td>Mirtazapine</td>\n",
              "      <td>Post Traumatic Stress Disorde</td>\n",
              "      <td>7.0</td>\n",
              "      <td>September 1, 2017</td>\n",
              "      <td>6</td>\n",
              "      <td>hey least one make manic suicid like zoloft pr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id  ... sentiment\n",
              "0  204048  ...         1\n",
              "1   52764  ...         0\n",
              "2  210591  ...         1\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uc_0I3LNQOEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMvspDcaRY2R",
        "colab_type": "code",
        "outputId": "992a49ef-ab71-4a1e-d70e-0ada8be1aac2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#http://nlp.stanford.edu/data/glove.840B.300d.zip\n",
        "glove_embeddings = {}\n",
        "\n",
        "print('Loading '+ glove_vectors_file)\n",
        "with open(glove_vectors_file, 'r') as f:\n",
        "  for line in f:\n",
        "    values = line.split(' ')\n",
        "    word = values[0]\n",
        "    embedding = np.asarray(values[1:], dtype='float32')\n",
        "    glove_embeddings[word] = embedding\n",
        "        \n",
        "print('Embeddings size: %d' % len(glove_embeddings))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading /content/drive/My Drive/Term5/NLP/Project/Dataset/../word_embeddings/glove.840B.300d.txt\n",
            "Embeddings size: 2196016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06vLHnysReAD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_dim = 300"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcjTJusKRexy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convertSentencesToList(traindata, validdata):\n",
        "  train_sentence = traindata['clean_review'].values.tolist()\n",
        "  valid_sentence = validdata['clean_review'].values.tolist()\n",
        "  return train_sentence, valid_sentence"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4zqDEVHSzKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def converToWordSequences(embeddinglength, training_data, t_s, v_s):\n",
        "  tokenizer = Tokenizer(num_words = embeddinglength)\n",
        "  tokenizer.fit_on_texts(training_data)\n",
        "\n",
        "  t_s_word_sequences = tokenizer.texts_to_sequences(t_s)\n",
        "\n",
        "  v_s_word_sequences = tokenizer.texts_to_sequences(v_s)\n",
        "  return tokenizer, t_s_word_sequences, v_s_word_sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPTzNdm0TREF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_sent, valid_sent = convertSentencesToList(train_data, valid_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuNmdPgKTbLe",
        "colab_type": "code",
        "outputId": "ebffe3f5-fa92-48e9-fc41-6a064ebace03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "glove_length = len(glove_embeddings)\n",
        "\n",
        "print('Found '+ str(len(train_sent)) + ' in training sentence')\n",
        "\n",
        "training_sent = train_sent\n",
        "valid_sent = valid_sent\n",
        "\n",
        "tokenizer_g, t_s_word_sequences_g, v_s_word_sequences_g = converToWordSequences(glove_length, training_sent, train_sent, valid_sent)\n",
        "word_index_g = tokenizer_g.word_index\n",
        "\n",
        "print('Found '+str(len(word_index_g))+' unique tokens for glove sequencing.')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 127122 in training sentence\n",
            "Found 29822 unique tokens for glove sequencing.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4B1GxZwETyxj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import defaultdict"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlAUgGggTzjx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def useWordEmbeddings(word_index, NUM_WORDS, embeddings, sentence_word_sequences):\n",
        "  words_len = min(NUM_WORDS, len(word_index))\n",
        "  # word_embedding_matrix = np.zeros((words_len + 1, embedding_dim))\n",
        "  word_embedding_matrix = np.random.random((words_len + 1, embedding_dim))\n",
        "  k = 0\n",
        "  for word, i in word_index.items():\n",
        "    if i >= NUM_WORDS:\n",
        "      continue\n",
        "    embedding_vector = embeddings.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      word_embedding_matrix[i] = embedding_vector\n",
        "      k += 1\n",
        "\n",
        "  return word_embedding_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_1aboY5Um-z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "word_embedding_matrix_g = useWordEmbeddings(word_index_g, glove_length, glove_embeddings, t_s_word_sequences_g)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdxssUK_VAQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_seq_length = 300"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W59j9a5CVBh7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LByN8bABVFnl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convertToPadSequences(sentence_word_sequences, valid_sentence_word_sequences, max_seq_length):\n",
        "  s1_data = pad_sequences(sentence_word_sequences, maxlen = max_seq_length)\n",
        "  s1_dataValid = pad_sequences(valid_sentence_word_sequences, maxlen = max_seq_length)\n",
        "  return s1_data, s1_dataValid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PE6xd1LlVLRz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def printSentenceInfo(s1_data, labels):\n",
        "  print('Shape of sentence1 tensor:', s1_data.shape)\n",
        "  print('Shape of label tensor:', labels.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPe_k-Y0VLvv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s1_data_g, s1_dataValid_g = convertToPadSequences(t_s_word_sequences_g, v_s_word_sequences_g, max_seq_length)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DiioFVKezVo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils import np_utils, plot_model\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAPy-bzRfbGb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convertToCategorical(data):\n",
        "  labels = np_utils.to_categorical(le.fit_transform(data[\"sentiment\"].values)).astype(\"int64\")\n",
        "  return labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--LiUorfVYBh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels = convertToCategorical(train_data)\n",
        "valid_labels = convertToCategorical(valid_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngw8VJvhVYtE",
        "colab_type": "code",
        "outputId": "cbee0084-8b4b-4629-f8ff-dc3c639cb901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "printSentenceInfo(s1_data_g, train_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of sentence1 tensor: (127122, 300)\n",
            "Shape of label tensor: (127122, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiEvFcqfVazo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Embedding, concatenate, Dense, PReLU, Dropout, Activation, Conv1D, MaxPooling1D, Flatten, GRU\n",
        "from keras.models import Sequential\n",
        "from keras.initializers import Constant\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from IPython.display import Image\n",
        "import time\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3woEneNVfCZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_rnn = np.random.randint(175, 275)\n",
        "num_dense = np.random.randint(100, 150)\n",
        "rate_drop_rnn = 0.15 + np.random.rand() * 0.25\n",
        "rate_drop_dense = 0.15 + np.random.rand() * 0.25"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHPGKygMgAMw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 128"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dz8wDqmugDM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def trainModel(model, model_name, s1_data, labels, batch_size, s1_data_valid, labels_valid):\n",
        "  checkpoint = ModelCheckpoint(outputPath+ model_name + '-checkpoint-weights.{epoch:02d}-{val_accuracy:.2f}.hdf5', monitor='val_accuracy', save_best_only=True)\n",
        "  history = model.fit([s1_data],\n",
        "                        labels,\n",
        "                        epochs = 10,\n",
        "                        batch_size = batch_size,\n",
        "                        validation_data=([s1_data_valid], labels_valid),\n",
        "                        shuffle = False,\n",
        "                        verbose = 2,\n",
        "                        callbacks = [checkpoint])\n",
        "  return history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NPQ7EPkgF5Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plotModelPicture(model, model_name):\n",
        "  plot_model(model, to_file = outputPath + model_name + '-model.png', show_shapes=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8l7bXjfqgGZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plotMetricsGraph(modelhistory, model_name):\n",
        "  # summarize history for accuracy\n",
        "  plt.plot(modelhistory.history['accuracy'])\n",
        "  plt.plot(modelhistory.history['val_accuracy'])\n",
        "  plt.title('model accuracy')\n",
        "  plt.ylabel('accuracy')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'valid'], loc='upper left')\n",
        "  plt.show()\n",
        "  plt.savefig(model_name + '-acc.png', dpi = 300)\n",
        "\n",
        "  # summarize history for loss\n",
        "  plt.plot(modelhistory.history['loss'])\n",
        "  plt.plot(modelhistory.history['val_loss'])\n",
        "  plt.title('model loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'valid'], loc='upper left')\n",
        "  plt.show()\n",
        "  plt.savefig(model_name + '-loss.png', dpi = 300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcGsRyCUEsKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluateModel(model, test_s_data, test_labels):\n",
        "  loss, accuracy = model.evaluate([test_s_data], test_labels)\n",
        "  print('loss     = ', loss)\n",
        "  print('accuracy = ', accuracy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEh3hj_DCmHn",
        "colab_type": "code",
        "outputId": "496f08a7-a9d1-465e-e4f3-6d9c36091461",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plotConfusionMatrix(test_y, y_pred):\n",
        "  #y_pred=model.predict(test_x,batch_size=15)\n",
        "  #cm =confusion_matrix(test_y.argmax(axis=1), y_pred.argmax(axis=1))\n",
        "  cm =confusion_matrix(test_y, y_pred)\n",
        "  print(cm)\n",
        "  index = ['Negative','Positive']\n",
        "  columns = ['Negative','Positive']\n",
        "  cm_df = pd.DataFrame(cm,columns,index)             \n",
        "  plt.figure(figsize=(10,6))\n",
        "  sns.heatmap(cm_df, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5v8GhA34b9O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def GRUModel(word_index, EMBEDDING_DIM, embedding_matrix, MAX_SEQUENCE_LENGTH, p_drop=0.0):\n",
        "  encoder_1 = Sequential()\n",
        "  encoder_1.add(Embedding(len(word_index) + 1, EMBEDDING_DIM,\n",
        "                                    embeddings_initializer=Constant(embedding_matrix),\n",
        "                                    input_length=MAX_SEQUENCE_LENGTH,\n",
        "                                    trainable=False))\n",
        "  \n",
        "  encoder_1.add(GRU(64,dropout=0.4,return_sequences=True))\n",
        "  encoder_1.add(GRU(32,dropout=0.5,return_sequences=False))\n",
        "  encoder_1.add(Dense(2,activation='sigmoid'))\n",
        "  encoder_1.compile(loss='binary_crossentropy',optimizer=Adam(lr = 0.001),metrics=['accuracy'])\n",
        "  return encoder_1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8sb0LRoohEH4",
        "colab_type": "code",
        "outputId": "c4562ddc-0865-458f-ef20-a30d16d6589d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "model_gru_g = GRUModel(word_index_g, embedding_dim, word_embedding_matrix_g, max_seq_length, p_drop=0.25)\n",
        "model_gru_g.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 300, 300)          8946900   \n",
            "_________________________________________________________________\n",
            "gru_1 (GRU)                  (None, 300, 64)           70080     \n",
            "_________________________________________________________________\n",
            "gru_2 (GRU)                  (None, 32)                9312      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 9,026,358\n",
            "Trainable params: 79,458\n",
            "Non-trainable params: 8,946,900\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPa-pu43hohU",
        "colab_type": "code",
        "outputId": "f060c3dd-e74b-4713-e348-9fc2fcec7b92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "model_name = 'GRU_Glove'\n",
        "plotModelPicture(model_gru_g, model_name)\n",
        "Image(filename = outputPath + model_name + '-model.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeIxQDj68L5Z",
        "colab_type": "code",
        "outputId": "cd780fb0-e160-433b-e417-79a494e6ef45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "history_gru_g = trainModel(model_gru_g, model_name, s1_data_g, train_labels, batch_size, s1_dataValid_g, valid_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 127122 samples, validate on 31607 samples\n",
            "Epoch 1/10\n",
            " - 1020s - loss: 0.4964 - accuracy: 0.7626 - val_loss: 0.4276 - val_accuracy: 0.8114\n",
            "Epoch 2/10\n",
            " - 1019s - loss: 0.4242 - accuracy: 0.8071 - val_loss: 0.4031 - val_accuracy: 0.8280\n",
            "Epoch 3/10\n",
            " - 1003s - loss: 0.3964 - accuracy: 0.8221 - val_loss: 0.3702 - val_accuracy: 0.8373\n",
            "Epoch 4/10\n",
            " - 1014s - loss: 0.3815 - accuracy: 0.8301 - val_loss: 0.3604 - val_accuracy: 0.8451\n",
            "Epoch 5/10\n",
            " - 1009s - loss: 0.3687 - accuracy: 0.8376 - val_loss: 0.3444 - val_accuracy: 0.8511\n",
            "Epoch 6/10\n",
            " - 1006s - loss: 0.3585 - accuracy: 0.8422 - val_loss: 0.3390 - val_accuracy: 0.8544\n",
            "Epoch 7/10\n",
            " - 1009s - loss: 0.3513 - accuracy: 0.8454 - val_loss: 0.3362 - val_accuracy: 0.8552\n",
            "Epoch 8/10\n",
            " - 1010s - loss: 0.3459 - accuracy: 0.8497 - val_loss: 0.3262 - val_accuracy: 0.8593\n",
            "Epoch 9/10\n",
            " - 1016s - loss: 0.3400 - accuracy: 0.8522 - val_loss: 0.3272 - val_accuracy: 0.8603\n",
            "Epoch 10/10\n",
            " - 1028s - loss: 0.3348 - accuracy: 0.8547 - val_loss: 0.3217 - val_accuracy: 0.8616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYhyWc2w-IEC",
        "colab_type": "code",
        "outputId": "72a0868e-42ce-467b-d91b-d3d4cdd5ac9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "plotMetricsGraph(history_gru_g, model_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGn7R8iKDspw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getTestDataSequences(tokenizer):\n",
        "  test_s_word_sequences = tokenizer.texts_to_sequences(test_data['clean_review'].values.tolist())\n",
        "\n",
        "  test_s_data = pad_sequences(test_s_word_sequences, maxlen = max_seq_length)\n",
        "  test_labels = np_utils.to_categorical(le.fit_transform(test_data[\"sentiment\"].values)).astype(\"int64\")\n",
        "\n",
        "  print('Shape of test sentence1 tensor:', test_s_data.shape)\n",
        "  print('Shape of test label tensor:', test_labels.shape)\n",
        "  return test_s_data, test_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EBxNREdv9KK",
        "colab_type": "code",
        "outputId": "453b9a77-7d43-48b6-ddb1-0a4a538ea90c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "test_s_data_g, test_labels_g = getTestDataSequences(tokenizer_g)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of test sentence1 tensor: (52856, 300)\n",
            "Shape of test label tensor: (52856, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMuAHTZ_v_b3",
        "colab_type": "code",
        "outputId": "ef0d9cd7-271a-460d-897a-b327fcb84e56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "evaluateModel(model_gru_g, test_s_data_g, test_labels_g)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "52856/52856 [==============================] - 147s 3ms/step\n",
            "loss     =  0.32220633674609744\n",
            "accuracy =  0.8632605671882629\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbO6otG7wCqy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = model_gru_g"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Id-vzpwwFeQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_pred = model.predict([test_s_data_g], batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfDcf-f0wH6S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output = list()\n",
        "output_score = list()\n",
        "for x in test_pred:\n",
        "  #print(type(x))\n",
        "  output_score.append(np.amax(x))\n",
        "  output.append(np.argmax(x))\n",
        "\n",
        "test_data[\"Actual Output\"] = output\n",
        "test_data[\"Predicted Score\"] = output_score\n",
        "\n",
        "test_data.to_excel(outputPath+'output_gru_glove.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0B_0DmjxwOvO",
        "colab_type": "code",
        "outputId": "5e12f52d-4bfa-465e-9cd7-bbe77a8c3e3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "predicted = np.argmax(test_pred, axis=1)\n",
        "report = classification_report(test_data[\"sentiment\"], test_data[\"Actual Output\"])\n",
        "print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.73      0.76     15962\n",
            "           1       0.89      0.92      0.90     36894\n",
            "\n",
            "    accuracy                           0.86     52856\n",
            "   macro avg       0.84      0.83      0.83     52856\n",
            "weighted avg       0.86      0.86      0.86     52856\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrfJo7KWwQ1w",
        "colab_type": "code",
        "outputId": "3345f310-f865-46ef-ab49-e1d078583e62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "plotConfusionMatrix(test_data[\"sentiment\"], test_data[\"Actual Output\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[11652  4310]\n",
            " [ 2911 33983]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yc6XJPry5yq5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(model_name+'.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}