hotel_rating.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hotel_rating.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP0HVRZ+qbao7g38GSqz3Ro",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/flydragon2018/Analytics_Vidhya/blob/master/hotel_rating.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZ_lS5T4eur9",
        "outputId": "530014e4-8053-4474-f90a-468df9ad0fb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive') #, force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFjsQ-Q2e8wv",
        "outputId": "d5b72b8d-a4b3-498c-e1d5-3e33b34b8175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!unzip -q /content/drive/\"My Drive\"/hotel/Hotel_rating.zip\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "replace test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imQbyIbJhBlQ",
        "outputId": "36861109-4ff4-4ee3-e2dc-2fd38746eff9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!git clone https://github.com/Shemka/ReviewsScoring.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'ReviewsScoring' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqG0a-PLh16g",
        "outputId": "9154045c-3a68-427a-ef5f-6e44fa381b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "!python -m spacy download en_core_web_sm"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (50.3.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.5)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.2.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKnApC9vi-bu"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiWPBViOi-fk"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jd7a73JxiApr"
      },
      "source": [
        "!cat  /content/ReviewsScoring/requirements.txt"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHBGNu9FhKZM"
      },
      "source": [
        "!sed 's/en-core-web-sm==2.1.0/en-core-web-sm/' /content/ReviewsScoring/requirements.txt > /content/ReviewsScoring/requirements.txt"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzSlkyBHjPYj"
      },
      "source": [
        "!pip install -r /content/ReviewsScoring/requirements.txt"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdL74w8NjPsT"
      },
      "source": [
        ""
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCqST1lre84B"
      },
      "source": [
        "#!wget \"https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\"\n",
        "#!gunzip GoogleNews-vectors-negative300.bin.gz"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tybrMQ7sjnlR"
      },
      "source": [
        "#!cp -rf /content/ReviewsScoring/* ."
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0xrc6WyjnpU"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IB3KwJSGe87f"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOT13RDge8-o",
        "outputId": "1f58e73a-ebaa-4d23-a74a-af8bad92a2a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# MAIN\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from collections import Counter\n",
        "import gc\n",
        "import pickle\n",
        "import joblib\n",
        "from scripts.storing import save_model, load_model\n",
        "from scripts.cleanup import CleanUpText\n",
        "\n",
        "# NLP\n",
        "import spacy\n",
        "import string\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from gensim.models import KeyedVectors\n",
        "import gensim\n",
        "from gensim.models import Word2Vec\n",
        "\n",
        "# SKLEARN\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, mean_squared_error, r2_score\n",
        "\n",
        "# TENSORFLOW\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Input, Embedding, Reshape, Flatten, Conv1D, SpatialDropout1D, MaxPooling1D, Dense, GRU, LSTM, Dropout, BatchNormalization, Bidirectional\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.optimizers import Adam, SGD, RMSprop\n",
        "from keras.callbacks import LearningRateScheduler, EarlyStopping"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccwm1EBqj0Bj",
        "outputId": "6252c296-5edd-4da6-cbbc-9ea87faa1b37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "reviews = pd.read_csv('train.csv') \n",
        "#reviews = reviews[['Reviewer_Score', 'review']]\n",
        "reviews.head(5)"
      ],
      "execution_count": 12,
      "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>review</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>good place stay check rainforest biobay vieque...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>great firstly did n't enjoy hong kong, 3 days ...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>clean convenient hotel catedral ideally locate...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>transport good class high excellent communicat...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>stay happy la quinta, used stay travelodge str...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review  rating\n",
              "0  good place stay check rainforest biobay vieque...       4\n",
              "1  great firstly did n't enjoy hong kong, 3 days ...       5\n",
              "2  clean convenient hotel catedral ideally locate...       4\n",
              "3  transport good class high excellent communicat...       3\n",
              "4  stay happy la quinta, used stay travelodge str...       4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OihVAq8j0E6",
        "outputId": "2c58e194-7d01-4a72-f288-aa792779f147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "text = CleanUpText().fit_transform(reviews['review'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 16392/16392 [00:21<00:00, 746.69it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqA6sFz7j0IO",
        "outputId": "f5b3a746-46ad-4354-87ad-439f85c0b0d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Data splitting and tokenizing \n",
        "X_train, X_val, y_train, y_val = train_test_split(text, reviews['rating']/5, random_state=99, test_size=0.1)\n",
        "# Vocabulary len\n",
        "NUM_WORDS = 50000\n",
        "\n",
        "tokenizer = Tokenizer(num_words=NUM_WORDS)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_val = tokenizer.texts_to_sequences(X_val)\n",
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word_index))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 35461 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOSq8Vc0j0LB",
        "outputId": "7d165487-8c44-405f-f3ed-8b9bf2e94649",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "\n",
        "# Save tokenizer object\n",
        "save_model(tokenizer, 'my_tokenizer.pkl')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model was saved in my_tokenizer.pkl\n",
            "Use load_model to load model from file.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOSqQt4Lj0OA",
        "outputId": "1ac3de08-66ab-4796-b2a1-7b9de8644ba4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "%%time\n",
        "# Sequence cropping\n",
        "X_train = pad_sequences(X_train)\n",
        "X_val = pad_sequences(X_val, maxlen=X_train.shape[1])\n",
        "print('Shape of X train and X validation tensor:', X_train.shape, X_val.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of X train and X validation tensor: (14752, 1505) (1640, 1505)\n",
            "CPU times: user 217 ms, sys: 16.1 ms, total: 233 ms\n",
            "Wall time: 234 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z24i7TnzlZWC",
        "outputId": "7e483d30-b223-4c1c-f944-a12b74fbb1ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "%%time\n",
        "MAX_LEN = X_train.shape[1]\n",
        "gwv = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
        "EMBEDDING_DIM=300\n",
        "vocabulary_size=min(len(word_index)+1,NUM_WORDS)\n",
        "embedding_matrix = np.zeros((vocabulary_size, EMBEDDING_DIM))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 33.9 s, sys: 4.13 s, total: 38 s\n",
            "Wall time: 1min 38s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMgnDwtZlZZM"
      },
      "source": [
        ""
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Kc8eQ2Yj0Qo",
        "outputId": "55945879-4034-4377-c9be-ab99afa23824",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "%%time\n",
        "# Create embedding matrix\n",
        "count_null = 0 \n",
        "for word, i in word_index.items():\n",
        "    if i>=NUM_WORDS:\n",
        "        continue\n",
        "    try:\n",
        "        embedding_vector = gwv[word]\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "    except KeyError:\n",
        "        count_null += 1\n",
        "        embedding_matrix[i]=np.random.normal(0,np.sqrt(0.25),EMBEDDING_DIM)\n",
        "print('Count of string that not in word2vec vocabulary:', count_null)\n",
        "print('From %d words' % (vocabulary_size))\n",
        "del(gwv); gc.collect()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Count of string that not in word2vec vocabulary: 14157\n",
            "From 35462 words\n",
            "CPU times: user 790 ms, sys: 54.9 ms, total: 845 ms\n",
            "Wall time: 844 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i50Oj7iSj0Tf"
      },
      "source": [
        "embedding_layer = Embedding(vocabulary_size,\n",
        "                            EMBEDDING_DIM,\n",
        "                            weights=[embedding_matrix],\n",
        "                            input_shape=(MAX_LEN,),\n",
        "                            trainable=False)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMMgO1vtmEtU"
      },
      "source": [
        "optimizer_adam = Adam(lr = 0.0005)\n",
        "\n",
        "callbacks = [\n",
        "    EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=10, verbose=1, restore_best_weights=True)\n",
        "]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DM1hALBZj0WS",
        "outputId": "22ee8e98-dcd5-4d23-daa9-732572979a6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(embedding_layer)\n",
        "model.add(SpatialDropout1D(0.2))\n",
        "model.add(Bidirectional(GRU(32, return_sequences=True)))\n",
        "model.add(Conv1D(32, 11, activation='relu'))\n",
        "model.add(MaxPooling1D(5))\n",
        "model.add(SpatialDropout1D(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='mean_absolute_error', optimizer=optimizer_adam, metrics=['mae', 'mse'])\n",
        "model.summary()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 1505, 300)         10638600  \n",
            "_________________________________________________________________\n",
            "spatial_dropout1d_2 (Spatial (None, 1505, 300)         0         \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 1505, 64)          64128     \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 1495, 32)          22560     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 299, 32)           0         \n",
            "_________________________________________________________________\n",
            "spatial_dropout1d_3 (Spatial (None, 299, 32)           0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 9568)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 9569      \n",
            "=================================================================\n",
            "Total params: 10,734,857\n",
            "Trainable params: 96,257\n",
            "Non-trainable params: 10,638,600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5QTc9rnmQ52",
        "outputId": "210b8784-2e26-4748-fab5-06cee6c2eb72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# It's just an example of training a model. Real model have another loss\n",
        "model.fit(X_train, np.asarray(y_train), batch_size=512, epochs=120, verbose=1, \n",
        "          callbacks=callbacks, validation_data=(X_val, np.asarray(y_val)))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/120\n",
            "29/29 [==============================] - 16s 566ms/step - loss: 0.2333 - mae: 0.2333 - mse: 0.0979 - val_loss: 0.2083 - val_mae: 0.2083 - val_mse: 0.1003\n",
            "Epoch 2/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.2088 - mae: 0.2088 - mse: 0.1041 - val_loss: 0.2084 - val_mae: 0.2084 - val_mse: 0.1007\n",
            "Epoch 3/120\n",
            "29/29 [==============================] - 16s 552ms/step - loss: 0.2088 - mae: 0.2088 - mse: 0.1038 - val_loss: 0.2081 - val_mae: 0.2081 - val_mse: 0.0999\n",
            "Epoch 4/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.2079 - mae: 0.2079 - mse: 0.0998 - val_loss: 0.1985 - val_mae: 0.1985 - val_mse: 0.0794\n",
            "Epoch 5/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.1825 - mae: 0.1825 - mse: 0.0585 - val_loss: 0.1526 - val_mae: 0.1526 - val_mse: 0.0406\n",
            "Epoch 6/120\n",
            "29/29 [==============================] - 16s 543ms/step - loss: 0.1456 - mae: 0.1456 - mse: 0.0392 - val_loss: 0.1336 - val_mae: 0.1336 - val_mse: 0.0349\n",
            "Epoch 7/120\n",
            "29/29 [==============================] - 16s 542ms/step - loss: 0.1359 - mae: 0.1359 - mse: 0.0372 - val_loss: 0.1297 - val_mae: 0.1297 - val_mse: 0.0338\n",
            "Epoch 8/120\n",
            "29/29 [==============================] - 16s 542ms/step - loss: 0.1313 - mae: 0.1313 - mse: 0.0345 - val_loss: 0.1284 - val_mae: 0.1284 - val_mse: 0.0321\n",
            "Epoch 9/120\n",
            "29/29 [==============================] - 16s 543ms/step - loss: 0.1292 - mae: 0.1292 - mse: 0.0336 - val_loss: 0.1248 - val_mae: 0.1248 - val_mse: 0.0329\n",
            "Epoch 10/120\n",
            "29/29 [==============================] - 16s 542ms/step - loss: 0.1228 - mae: 0.1228 - mse: 0.0316 - val_loss: 0.1183 - val_mae: 0.1183 - val_mse: 0.0294\n",
            "Epoch 11/120\n",
            "29/29 [==============================] - 16s 544ms/step - loss: 0.1190 - mae: 0.1190 - mse: 0.0296 - val_loss: 0.1158 - val_mae: 0.1158 - val_mse: 0.0280\n",
            "Epoch 12/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.1161 - mae: 0.1161 - mse: 0.0284 - val_loss: 0.1136 - val_mae: 0.1136 - val_mse: 0.0263\n",
            "Epoch 13/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.1129 - mae: 0.1129 - mse: 0.0271 - val_loss: 0.1126 - val_mae: 0.1126 - val_mse: 0.0255\n",
            "Epoch 14/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.1113 - mae: 0.1113 - mse: 0.0265 - val_loss: 0.1109 - val_mae: 0.1109 - val_mse: 0.0253\n",
            "Epoch 15/120\n",
            "29/29 [==============================] - 16s 544ms/step - loss: 0.1094 - mae: 0.1094 - mse: 0.0253 - val_loss: 0.1095 - val_mae: 0.1095 - val_mse: 0.0238\n",
            "Epoch 16/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.1074 - mae: 0.1074 - mse: 0.0247 - val_loss: 0.1074 - val_mae: 0.1074 - val_mse: 0.0244\n",
            "Epoch 17/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.1057 - mae: 0.1057 - mse: 0.0242 - val_loss: 0.1079 - val_mae: 0.1079 - val_mse: 0.0236\n",
            "Epoch 18/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.1047 - mae: 0.1047 - mse: 0.0240 - val_loss: 0.1057 - val_mae: 0.1057 - val_mse: 0.0232\n",
            "Epoch 19/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.1032 - mae: 0.1032 - mse: 0.0234 - val_loss: 0.1055 - val_mae: 0.1055 - val_mse: 0.0242\n",
            "Epoch 20/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.1014 - mae: 0.1014 - mse: 0.0228 - val_loss: 0.1033 - val_mae: 0.1033 - val_mse: 0.0224\n",
            "Epoch 21/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.1008 - mae: 0.1008 - mse: 0.0227 - val_loss: 0.1028 - val_mae: 0.1028 - val_mse: 0.0225\n",
            "Epoch 22/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0987 - mae: 0.0987 - mse: 0.0216 - val_loss: 0.1049 - val_mae: 0.1049 - val_mse: 0.0244\n",
            "Epoch 23/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0987 - mae: 0.0987 - mse: 0.0218 - val_loss: 0.1013 - val_mae: 0.1013 - val_mse: 0.0226\n",
            "Epoch 24/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0978 - mae: 0.0978 - mse: 0.0215 - val_loss: 0.1029 - val_mae: 0.1029 - val_mse: 0.0229\n",
            "Epoch 25/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0971 - mae: 0.0971 - mse: 0.0208 - val_loss: 0.1010 - val_mae: 0.1010 - val_mse: 0.0224\n",
            "Epoch 26/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0957 - mae: 0.0957 - mse: 0.0208 - val_loss: 0.0993 - val_mae: 0.0993 - val_mse: 0.0214\n",
            "Epoch 27/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0945 - mae: 0.0945 - mse: 0.0204 - val_loss: 0.0998 - val_mae: 0.0998 - val_mse: 0.0221\n",
            "Epoch 28/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0935 - mae: 0.0935 - mse: 0.0201 - val_loss: 0.0998 - val_mae: 0.0998 - val_mse: 0.0213\n",
            "Epoch 29/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0932 - mae: 0.0932 - mse: 0.0198 - val_loss: 0.0996 - val_mae: 0.0996 - val_mse: 0.0213\n",
            "Epoch 30/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0932 - mae: 0.0932 - mse: 0.0204 - val_loss: 0.0979 - val_mae: 0.0979 - val_mse: 0.0208\n",
            "Epoch 31/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0935 - mae: 0.0935 - mse: 0.0197 - val_loss: 0.1000 - val_mae: 0.1000 - val_mse: 0.0215\n",
            "Epoch 32/120\n",
            "29/29 [==============================] - 16s 550ms/step - loss: 0.0933 - mae: 0.0933 - mse: 0.0198 - val_loss: 0.0997 - val_mae: 0.0997 - val_mse: 0.0222\n",
            "Epoch 33/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0914 - mae: 0.0914 - mse: 0.0193 - val_loss: 0.0981 - val_mae: 0.0981 - val_mse: 0.0209\n",
            "Epoch 34/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0906 - mae: 0.0906 - mse: 0.0188 - val_loss: 0.0989 - val_mae: 0.0989 - val_mse: 0.0221\n",
            "Epoch 35/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0912 - mae: 0.0912 - mse: 0.0192 - val_loss: 0.0980 - val_mae: 0.0980 - val_mse: 0.0216\n",
            "Epoch 36/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.0900 - mae: 0.0900 - mse: 0.0187 - val_loss: 0.0979 - val_mae: 0.0979 - val_mse: 0.0214\n",
            "Epoch 37/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0882 - mae: 0.0882 - mse: 0.0180 - val_loss: 0.0997 - val_mae: 0.0997 - val_mse: 0.0213\n",
            "Epoch 38/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0891 - mae: 0.0891 - mse: 0.0185 - val_loss: 0.0981 - val_mae: 0.0981 - val_mse: 0.0207\n",
            "Epoch 39/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0875 - mae: 0.0875 - mse: 0.0176 - val_loss: 0.0974 - val_mae: 0.0974 - val_mse: 0.0212\n",
            "Epoch 40/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0867 - mae: 0.0867 - mse: 0.0174 - val_loss: 0.0967 - val_mae: 0.0967 - val_mse: 0.0211\n",
            "Epoch 41/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0866 - mae: 0.0866 - mse: 0.0174 - val_loss: 0.0962 - val_mae: 0.0962 - val_mse: 0.0205\n",
            "Epoch 42/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0877 - mae: 0.0877 - mse: 0.0179 - val_loss: 0.0975 - val_mae: 0.0975 - val_mse: 0.0216\n",
            "Epoch 43/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0850 - mae: 0.0850 - mse: 0.0171 - val_loss: 0.0975 - val_mae: 0.0975 - val_mse: 0.0206\n",
            "Epoch 44/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0848 - mae: 0.0848 - mse: 0.0168 - val_loss: 0.0964 - val_mae: 0.0964 - val_mse: 0.0213\n",
            "Epoch 45/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0858 - mae: 0.0858 - mse: 0.0172 - val_loss: 0.0962 - val_mae: 0.0962 - val_mse: 0.0212\n",
            "Epoch 46/120\n",
            "29/29 [==============================] - 16s 548ms/step - loss: 0.0842 - mae: 0.0842 - mse: 0.0167 - val_loss: 0.0966 - val_mae: 0.0966 - val_mse: 0.0214\n",
            "Epoch 47/120\n",
            "29/29 [==============================] - 16s 550ms/step - loss: 0.0842 - mae: 0.0842 - mse: 0.0167 - val_loss: 0.0955 - val_mae: 0.0955 - val_mse: 0.0201\n",
            "Epoch 48/120\n",
            "29/29 [==============================] - 16s 551ms/step - loss: 0.0834 - mae: 0.0834 - mse: 0.0164 - val_loss: 0.0958 - val_mae: 0.0958 - val_mse: 0.0207\n",
            "Epoch 49/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0831 - mae: 0.0831 - mse: 0.0162 - val_loss: 0.0960 - val_mae: 0.0960 - val_mse: 0.0207\n",
            "Epoch 50/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0815 - mae: 0.0815 - mse: 0.0159 - val_loss: 0.0958 - val_mae: 0.0958 - val_mse: 0.0204\n",
            "Epoch 51/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0815 - mae: 0.0815 - mse: 0.0157 - val_loss: 0.0962 - val_mae: 0.0962 - val_mse: 0.0210\n",
            "Epoch 52/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.0801 - mae: 0.0801 - mse: 0.0153 - val_loss: 0.0962 - val_mae: 0.0962 - val_mse: 0.0206\n",
            "Epoch 53/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0811 - mae: 0.0811 - mse: 0.0156 - val_loss: 0.0951 - val_mae: 0.0951 - val_mse: 0.0206\n",
            "Epoch 54/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0797 - mae: 0.0797 - mse: 0.0153 - val_loss: 0.0976 - val_mae: 0.0976 - val_mse: 0.0209\n",
            "Epoch 55/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0800 - mae: 0.0800 - mse: 0.0151 - val_loss: 0.0958 - val_mae: 0.0958 - val_mse: 0.0209\n",
            "Epoch 56/120\n",
            "29/29 [==============================] - 16s 546ms/step - loss: 0.0796 - mae: 0.0796 - mse: 0.0150 - val_loss: 0.0955 - val_mae: 0.0955 - val_mse: 0.0213\n",
            "Epoch 57/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0783 - mae: 0.0783 - mse: 0.0147 - val_loss: 0.0953 - val_mae: 0.0953 - val_mse: 0.0208\n",
            "Epoch 58/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0785 - mae: 0.0785 - mse: 0.0147 - val_loss: 0.0958 - val_mae: 0.0958 - val_mse: 0.0211\n",
            "Epoch 59/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0779 - mae: 0.0779 - mse: 0.0146 - val_loss: 0.0971 - val_mae: 0.0971 - val_mse: 0.0221\n",
            "Epoch 60/120\n",
            "29/29 [==============================] - 16s 545ms/step - loss: 0.0770 - mae: 0.0770 - mse: 0.0143 - val_loss: 0.1008 - val_mae: 0.1008 - val_mse: 0.0216\n",
            "Epoch 61/120\n",
            "29/29 [==============================] - 16s 547ms/step - loss: 0.0775 - mae: 0.0775 - mse: 0.0143 - val_loss: 0.0957 - val_mae: 0.0957 - val_mse: 0.0213\n",
            "Epoch 62/120\n",
            "29/29 [==============================] - 16s 549ms/step - loss: 0.0770 - mae: 0.0770 - mse: 0.0144 - val_loss: 0.0975 - val_mae: 0.0975 - val_mse: 0.0218\n",
            "Epoch 63/120\n",
            "29/29 [==============================] - ETA: 0s - loss: 0.0756 - mae: 0.0756 - mse: 0.0138Restoring model weights from the end of the best epoch.\n",
            "29/29 [==============================] - 16s 551ms/step - loss: 0.0756 - mae: 0.0756 - mse: 0.0138 - val_loss: 0.0965 - val_mae: 0.0965 - val_mse: 0.0204\n",
            "Epoch 00063: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f919c5c1e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NqPxOBCmQ9G",
        "outputId": "7cd07537-5dee-4129-d7ad-213de4a70337",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "test=pd.read_csv('test.csv')\n",
        "test.head()"
      ],
      "execution_count": 28,
      "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>review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>ehhh better punta cana twice compared hotel st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4 n't think, decided book atenea night stay de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>awesome time just returned vacation fantastic ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>grand oasis wonderful second time, group 20 fr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>not bad stay stayed hotel family attending jav...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id                                             review\n",
              "0   0  ehhh better punta cana twice compared hotel st...\n",
              "1   1  4 n't think, decided book atenea night stay de...\n",
              "2   2  awesome time just returned vacation fantastic ...\n",
              "3   3  grand oasis wonderful second time, group 20 fr...\n",
              "4   4  not bad stay stayed hotel family attending jav..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXcJlnz2mQ_6"
      },
      "source": [
        "import tensorflow as tf\n",
        "from scripts.review_scoring import ReviewScorer"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xmb6x4dsnJSL"
      },
      "source": [
        "!cp *.pkl /content/models/"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IypAwZiyvm8Z",
        "outputId": "216f645d-e8b8-43ad-ab4d-3701fd6502c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "scorer = ReviewScorer('models/model.h5', 'models/my_tokenizer.pkl')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model was loaded from models/my_tokenizer.pkl\n",
            "WARNING:tensorflow:Layer gru_9 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
            "WARNING:tensorflow:Layer gru_9 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n",
            "WARNING:tensorflow:Layer gru_9 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2l5F_L7xnJXU"
      },
      "source": [
        "p=scorer.predict(['It\\'s the best hotel I ever see'])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMB-TblB0hav",
        "outputId": "e868d522-1a43-4550-dad0-9af2788a059b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "p[0]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8338482"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buK3exQdnJbS",
        "outputId": "d24683be-74f6-4136-e6b0-fd11b15a5fa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "test['rating']=0\n",
        "for i in test.index:\n",
        "  text=test['review'][i]\n",
        "  score=scorer.predict([text])\n",
        "  s=int(round(score[0]*5))\n",
        "  test['rating'][i]=s\n",
        "  print(i,s)\n",
        "  \n",
        "  #if i==10:\n",
        "  #  break\n",
        "\n",
        "test.to_csv(\"rating_sub.csv\",columns=['id','rating'],header=None,index=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
        }
      ]
    }
  ]
}