MPS_RNN_Data_Modeling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MPS: RNN Data Modeling",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "language_info": {
      "name": "python",
      "version": "3.6.4",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "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/jasoriya/Mercari-Price-Suggestion-Challenge/blob/master/MPS_RNN_Data_Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YcLNOTSq2C5",
        "colab_type": "code",
        "outputId": "f2955405-4579-42b7-a5e6-70dc0783c02d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Don't run if not running in Google Colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VY3FPpopbdeD",
        "colab_type": "text"
      },
      "source": [
        "### Pre-requisite to install before running the code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2tsvIQYF05zM",
        "colab_type": "text"
      },
      "source": [
        "If you are installing any dependent libraries, please specify here as pre-req \n",
        "to install before running the notebook\n",
        "\n",
        "\n",
        "1.   scikit-learn\n",
        "2.   Keras\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siMd4TKR-LZk",
        "colab_type": "code",
        "outputId": "0501b887-19a6-4f3b-b98f-f615338780d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "#run to install pre-req libraries\n",
        "!pip install textstat\n",
        "!pip install -U scikit-learn"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: textstat in /usr/local/lib/python3.6/dist-packages (0.5.6)\n",
            "Requirement already satisfied: repoze.lru in /usr/local/lib/python3.6/dist-packages (from textstat) (0.7)\n",
            "Requirement already satisfied: pyphen in /usr/local/lib/python3.6/dist-packages (from textstat) (0.9.5)\n",
            "Requirement already up-to-date: scikit-learn in /usr/local/lib/python3.6/dist-packages (0.21.3)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.16.4)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (0.13.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJtWJrIfXNAS",
        "colab_type": "code",
        "outputId": "f19b03be-62cf-4e8b-b2c6-840ab6fbe164",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from datetime import datetime \n",
        "start_real = datetime.now()\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pydot_ng as pydot\n",
        "from textstat import textstat\n",
        "import dask.dataframe as dd\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import RidgeCV\n",
        "from sklearn.pipeline import FeatureUnion\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Input, Dropout, Dense, concatenate, GRU, Embedding, Flatten, Activation\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import History\n",
        "from keras.models import Model\n",
        "from keras.utils import plot_model\n",
        "from keras import backend as K\n",
        "from nltk.corpus import stopwords\n",
        "import math\n",
        "# set seed\n",
        "np.random.seed(123)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMELkOwnXrLs",
        "colab_type": "text"
      },
      "source": [
        "### Store the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "bEC0wv0PqqK2",
        "colab_type": "code",
        "outputId": "1f3a92fa-c109-4eda-c5c1-b1471c31112e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Input data files are available in the \"../mercari-price-suggestion-challenge/\" directory.\n",
        "# NOTE: Please modify the absolute path before /mercari-price-suggestion-challenge accordingly\n",
        "\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the mercari-price-suggestion-challenge directory\n",
        "\n",
        "import os\n",
        "print(os.listdir(\"/content/gdrive/My Drive/DMT/mercari-price-suggestion-challenge\"))\n",
        "input_folder = '/content/gdrive/My Drive/DMT/mercari-price-suggestion-challenge/'\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['sample_submission.csv.7z', 'sample_submission_stg2.csv', 'train.tsv', 'test.tsv', 'train.pkl', 'wiki.en.bin.bin', 'model.best.weights.hdf5', 'train_word2vec.pkl', 'model.png', 'dumps', 'model_word2vec.best.weights.hdf5', 'val_xgboost.pkl', 'test_xgboost.pkl', 'train_xgboost.pkl']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNmzm7ylSjW1",
        "colab_type": "text"
      },
      "source": [
        "## 1. Data Preprocesing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeTaec-qVAfN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load saved preprocessed data\n",
        "processed_train_data = pd.read_pickle(input_folder + 'dumps/train_data.pkl')\n",
        "processed_val_data = pd.read_pickle(input_folder + 'dumps/validation_data.pkl')\n",
        "processed_test_data = pd.read_pickle(input_folder + 'dumps/test_data.pkl')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0Ew6EVnb9BF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = processed_train_data.copy()\n",
        "dev_df = processed_val_data.copy()\n",
        "test_df = processed_test_data.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLKUJCvAcjh8",
        "colab_type": "code",
        "outputId": "4316ef5f-5fbe-4eb2-afb3-e818e2437c74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "n_trains = train_df.shape[0]\n",
        "n_devs = dev_df.shape[0]\n",
        "n_tests = test_df.shape[0]\n",
        "print(\"Training on\", n_trains, \"examples\")\n",
        "print(\"Validating on\", n_devs, \"examples\")\n",
        "print(\"Testing on\", n_tests, \"examples\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training on 1333492 examples\n",
            "Validating on 74083 examples\n",
            "Testing on 74083 examples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acs5_cxyoHPj",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "### Convert Categorical features into continuous features\n",
        "\n",
        "\n",
        "The columns name: category, cat_1, cat_2, cat_3 and brand_name have categorical values. Categorical values can be converted to One Hot Encoding to feed into a machine learning model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cHTO_1YcI5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import bisect\n",
        "\n",
        "\n",
        "def label_encoding(col):\n",
        "  le = LabelEncoder()\n",
        "  train_df[col] = train_df[col].astype(str)\n",
        "  le.fit(train_df[col])\n",
        "\n",
        "  le_classes = le.classes_.tolist()\n",
        "  bisect.insort_left(le_classes, '<unknown>')\n",
        "  le.classes_ = le_classes\n",
        "  #transform on train data\n",
        "  train_df[col] = le.transform(train_df[col])\n",
        "  #transform on test data\n",
        "  test_df[col] = test_df[col].map(lambda s: '<unknown>' if s not in le.classes_ else s)\n",
        "  test_df[col] = le.transform(test_df[col])\n",
        "  #transform on dev data\n",
        "  dev_df[col] = dev_df[col].map(lambda s: '<unknown>' if s not in le.classes_ else s)\n",
        "  dev_df[col] = le.transform(dev_df[col])\n",
        "  \n",
        "  del le\n",
        "  return train_df[col], test_df[col], dev_df[col]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHMRFOuCgFH8",
        "colab_type": "code",
        "outputId": "304611c4-ed24-42f6-fc66-6269a2eb928f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "%%time\n",
        "\n",
        "train_df['category'] = train_df.category_name\n",
        "test_df['category'] = test_df.category_name\n",
        "dev_df['category'] = dev_df.category_name\n",
        "\n",
        "train_df['category'],test_df['category'], dev_df['category']= label_encoding('category')\n",
        "train_df['cat_1'],test_df['cat_1'], dev_df['cat_1'] = label_encoding('cat_1')\n",
        "train_df['cat_2'],test_df['cat_2'], dev_df['cat_2'] = label_encoding('cat_2')\n",
        "train_df['cat_3'],test_df['cat_3'], dev_df['cat_3'] = label_encoding('cat_3')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 5.32 s, sys: 52.2 ms, total: 5.37 s\n",
            "Wall time: 5.37 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhMVDZHw47Xb",
        "colab_type": "text"
      },
      "source": [
        "### Process text data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOPAEancpfvd",
        "colab_type": "code",
        "outputId": "b6119f07-c945-4f8a-bd6b-a0bd444ddaf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "\n",
        "%%time\n",
        "\n",
        "print(\"Transforming text data to sequences...\")\n",
        "raw_text = np.hstack([train_df.item_description.str.lower(), train_df.name.str.lower(), train_df.category_name.str.lower()])\n",
        "\n",
        "print(\"   Fitting tokenizer...\")\n",
        "tok_raw = Tokenizer()\n",
        "tok_raw.fit_on_texts(raw_text)\n",
        "\n",
        "print(\"   Transforming text to sequences...\")\n",
        "train_df['seq_item_description'] = tok_raw.texts_to_sequences(train_df.item_description.str.lower())\n",
        "train_df['seq_name'] = tok_raw.texts_to_sequences(train_df.name.str.lower())\n",
        "\n",
        "test_df['seq_item_description'] = tok_raw.texts_to_sequences(test_df.item_description.str.lower())\n",
        "test_df['seq_name'] = tok_raw.texts_to_sequences(test_df.name.str.lower())\n",
        "\n",
        "dev_df['seq_item_description'] = tok_raw.texts_to_sequences(dev_df.item_description.str.lower())\n",
        "dev_df['seq_name'] = tok_raw.texts_to_sequences(dev_df.name.str.lower())\n",
        "\n",
        "del tok_raw"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transforming text data to sequences...\n",
            "   Fitting tokenizer...\n",
            "   Transforming text to sequences...\n",
            "CPU times: user 2min 47s, sys: 1.33 s, total: 2min 48s\n",
            "Wall time: 2min 48s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJGoeU1-49QG",
        "colab_type": "code",
        "outputId": "32e05f19-b2da-42bb-e3cc-8b344fd2b169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "train_df['seq_name'][:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "958469           [513, 1916, 61, 729, 61]\n",
              "693778       [145, 3746, 1465, 779, 2070]\n",
              "210050                 [61, 906, 283, 61]\n",
              "447294     [5012, 71420, 319, 1546, 1271]\n",
              "1480041           [1728, 2155, 821, 1973]\n",
              "Name: seq_name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIpc1J9A5Fta",
        "colab_type": "text"
      },
      "source": [
        "#### constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBeKlxUort1J",
        "colab_type": "code",
        "outputId": "a47fce67-2f8c-4a62-d08c-45b17b722e2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "MAX_NAME_SEQ = 10 #17\n",
        "MAX_ITEM_DESC_SEQ = 75 #269\n",
        "MAX_CATEGORY_SEQ = 8\n",
        "MAX_TEXT = np.max([\n",
        "    np.max(train_df.seq_name.max()),\n",
        "    np.max(train_df.seq_item_description.max()),\n",
        "#     np.max(train_df.seq_category.max()),\n",
        "]) + 100\n",
        "MAX_CATEGORY = np.max(train_df.category.max()) + 1\n",
        "MAX_DESC_LEN = int(np.max(train_df.desc_len.max()) + 1)\n",
        "MAX_NAME_LEN = int(np.max(train_df.name_len.max()) + 1)\n",
        "MAX_CAT_1 = np.max(train_df.cat_1.max()) + 1\n",
        "MAX_CAT_2 = np.max(train_df.cat_2.max()) + 1\n",
        "MAX_CAT_3 = np.max(train_df.cat_3.max()) + 1\n",
        "\n",
        "\n",
        "print(MAX_CATEGORY)\n",
        "print(MAX_TEXT)\n",
        "print(MAX_DESC_LEN)\n",
        "print(MAX_NAME_LEN)\n",
        "print(MAX_CAT_1)\n",
        "print(MAX_CAT_2)\n",
        "print(MAX_CAT_3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1281\n",
            "244455\n",
            "246\n",
            "18\n",
            "12\n",
            "115\n",
            "872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtuZu3b05J4e",
        "colab_type": "code",
        "outputId": "32e9bbe1-5378-4963-ec47-5af2ac6b8356",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "## GET DATA FOR RNN\n",
        "\n",
        "%%time\n",
        "\n",
        "def get_rnn_data(dataset):\n",
        "    X = {\n",
        "        'name': pad_sequences(dataset.seq_name, maxlen=MAX_NAME_SEQ),\n",
        "        'item_desc': pad_sequences(dataset.seq_item_description, maxlen=MAX_ITEM_DESC_SEQ),\n",
        "        'category': np.array(dataset.category),\n",
        "#         'category_name': pad_sequences(dataset.seq_category, maxlen=MAX_CATEGORY_SEQ),\n",
        "#         'item_condition': np.array(dataset.item_condition_id),\n",
        "        'shipping': np.array(dataset[[\"shipping\"]]),\n",
        "        'desc_len': np.array(dataset[[\"desc_len\"]]),\n",
        "        'name_len': np.array(dataset[[\"name_len\"]]),\n",
        "        'cat_1': np.array(dataset.cat_1),\n",
        "        'cat_2': np.array(dataset.cat_2),\n",
        "        'cat_3': np.array(dataset.cat_3),\n",
        "        'item_condition_id_1': np.array(dataset.item_condition_id_1),\n",
        "        'item_condition_id_2': np.array(dataset.item_condition_id_2),\n",
        "        'item_condition_id_3': np.array(dataset.item_condition_id_3),\n",
        "        'item_condition_id_4': np.array(dataset.item_condition_id_4),\n",
        "        'item_condition_id_5': np.array(dataset.item_condition_id_5),\n",
        "        'item_description_readability_score': np.array(dataset.item_description_readability_score),\n",
        "    }\n",
        "    return X\n",
        "\n",
        "\n",
        "X_train = get_rnn_data(train_df)\n",
        "Y_train = train_df.price.values.reshape(-1, 1)\n",
        "\n",
        "X_dev = get_rnn_data(dev_df)\n",
        "Y_dev = dev_df.price.values.reshape(-1, 1)\n",
        "\n",
        "X_test = get_rnn_data(test_df)\n",
        "Y_test = test_df.price.values.reshape(-1, 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 15.7 s, sys: 355 ms, total: 16 s\n",
            "Wall time: 16 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12C6EXcZt7WW",
        "colab_type": "code",
        "outputId": "7bbd2ebe-cd00-465d-b937-9206212f58fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "features = ['seq_name','category','seq_item_description',\n",
        "            'desc_len','name_len','shipping','cat_1','cat_2','cat_3','item_description_readability_score','item_condition_id_1',\n",
        "            'item_condition_id_2','item_condition_id_3','item_condition_id_4','item_condition_id_5','price']\n",
        "\n",
        "\n",
        "train_df[features].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>seq_name</th>\n",
              "      <th>category</th>\n",
              "      <th>seq_item_description</th>\n",
              "      <th>desc_len</th>\n",
              "      <th>name_len</th>\n",
              "      <th>shipping</th>\n",
              "      <th>cat_1</th>\n",
              "      <th>cat_2</th>\n",
              "      <th>cat_3</th>\n",
              "      <th>item_description_readability_score</th>\n",
              "      <th>item_condition_id_1</th>\n",
              "      <th>item_condition_id_2</th>\n",
              "      <th>item_condition_id_3</th>\n",
              "      <th>item_condition_id_4</th>\n",
              "      <th>item_condition_id_5</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>958469</th>\n",
              "      <td>[513, 1916, 61, 729, 61]</td>\n",
              "      <td>697</td>\n",
              "      <td>[15, 2024, 34, 729, 3077, 1, 177, 4178, 5, 463...</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>438</td>\n",
              "      <td>6.34</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.305263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>693778</th>\n",
              "      <td>[145, 3746, 1465, 779, 2070]</td>\n",
              "      <td>731</td>\n",
              "      <td>[49, 18, 705, 682, 709, 99]</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>58</td>\n",
              "      <td>189</td>\n",
              "      <td>9.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.227319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210050</th>\n",
              "      <td>[61, 906, 283, 61]</td>\n",
              "      <td>1232</td>\n",
              "      <td>[82, 7, 94, 1059, 127, 906, 283, 184, 18, 52, ...</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>97</td>\n",
              "      <td>410</td>\n",
              "      <td>5.84</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.045449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447294</th>\n",
              "      <td>[5012, 71420, 319, 1546, 1271]</td>\n",
              "      <td>1278</td>\n",
              "      <td>[16, 6, 46, 38, 117, 18]</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>112</td>\n",
              "      <td>695</td>\n",
              "      <td>9.07</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.071430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1480041</th>\n",
              "      <td>[1728, 2155, 821, 1973]</td>\n",
              "      <td>3</td>\n",
              "      <td>[27, 11, 3, 6, 82, 475, 2220, 1111, 1, 5860, 1...</td>\n",
              "      <td>120</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>191</td>\n",
              "      <td>11.99</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.357226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               seq_name  ...     price\n",
              "958469         [513, 1916, 61, 729, 61]  ... -0.305263\n",
              "693778     [145, 3746, 1465, 779, 2070]  ... -0.227319\n",
              "210050               [61, 906, 283, 61]  ... -0.045449\n",
              "447294   [5012, 71420, 319, 1546, 1271]  ... -0.071430\n",
              "1480041         [1728, 2155, 821, 1973]  ... -0.357226\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPxsmKe5nu7s",
        "colab_type": "text"
      },
      "source": [
        "## 4. Data modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roY791msZOub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def rmsle(y_true, y_pred):\n",
        "    assert K.int_shape(y_true)[0] == K.int_shape(y_pred)[0]\n",
        "    return K.sqrt(K.mean(K.square(K.log(y_pred+1) - K.log(y_true+1))))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99yV1J9-n2y_",
        "colab_type": "text"
      },
      "source": [
        "### RNN Model\n",
        "\n",
        "This section will use RNN Model to solve the competition with following steps:\n",
        "\n",
        "1. Build RNN model\n",
        "1. Fitting RNN model on training data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZ2p9dk-5mTJ",
        "colab_type": "text"
      },
      "source": [
        "### BUILD RNN MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WS3XhqE15iCT",
        "colab_type": "code",
        "outputId": "1e5d0ecd-9311-43ec-a8a4-4d4373bc3e52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# set seed again in case testing models adjustments by looping next 2 blocks\n",
        "np.random.seed(123)\n",
        "\n",
        "def new_rnn_model(lr=0.001, decay=0.0):\n",
        "  \n",
        "    # Passing Features from dataset into Input Layer\n",
        "    name = Input(shape=[X_train[\"name\"].shape[1]], name=\"name\")\n",
        "    item_desc = Input(shape=[X_train[\"item_desc\"].shape[1]], name=\"item_desc\")\n",
        "\n",
        "#     item_condition = Input(shape=[1], name=\"item_condition\")\n",
        "    item_condition_id_1 = Input(shape=[1], name=\"item_condition_id_1\")\n",
        "    item_condition_id_2 = Input(shape=[1], name=\"item_condition_id_2\")\n",
        "    item_condition_id_3 = Input(shape=[1], name=\"item_condition_id_3\")\n",
        "    item_condition_id_4 = Input(shape=[1], name=\"item_condition_id_4\")\n",
        "    item_condition_id_5 = Input(shape=[1], name=\"item_condition_id_5\")\n",
        "    item_description_readability_score = Input(shape=[1], name=\"item_description_readability_score\") \n",
        "    \n",
        "    shipping = Input(shape=[X_train[\"shipping\"].shape[1]], name=\"shipping\")\n",
        "    desc_len = Input(shape=[1], name=\"desc_len\")\n",
        "    name_len = Input(shape=[1], name=\"name_len\")\n",
        "    cat_1    = Input(shape=[1], name=\"cat_1\")\n",
        "    cat_2    = Input(shape=[1], name=\"cat_2\")\n",
        "    cat_3    = Input(shape=[1], name=\"cat_3\")\n",
        "    \n",
        "    \n",
        "    # Embeddings layers\n",
        "    emb_name = Embedding(MAX_TEXT, 20)(name)\n",
        "    emb_item_desc = Embedding(MAX_TEXT, 60)(item_desc)\n",
        "    \n",
        "    #emb_item_condition = Embedding(MAX_CONDITION, 5)(item_condition)\n",
        "    emb_desc_len = Embedding(MAX_DESC_LEN, 5)(desc_len)\n",
        "    emb_name_len = Embedding(MAX_NAME_LEN, 5)(name_len)\n",
        "    emb_cat_1 = Embedding(MAX_CAT_1, 10)(cat_1)\n",
        "    emb_cat_2 = Embedding(MAX_CAT_2, 10)(cat_2)\n",
        "    emb_cat_3 = Embedding(MAX_CAT_3, 10)(cat_3)\n",
        "    \n",
        "\n",
        "    # rnn layers (GRUs are faster than LSTMs)\n",
        "    rnn_layer1 = GRU(16) (emb_item_desc)\n",
        "    rnn_layer2 = GRU(8) (emb_name)\n",
        "\n",
        "\n",
        "    # main layers\n",
        "    main_l = concatenate([\n",
        "#         Flatten() (emb_item_condition),\n",
        "#         , Flatten() (emb_category)\n",
        "#         ,Flatten() (emb_brand_name)\n",
        "        Flatten() (emb_desc_len)\n",
        "        , Flatten() (emb_name_len)\n",
        "        , Flatten() (emb_cat_1)\n",
        "        , Flatten() (emb_cat_2)\n",
        "        , Flatten() (emb_cat_3)\n",
        "        , rnn_layer1\n",
        "        , rnn_layer2\n",
        "        , shipping\n",
        "        , item_condition_id_1\n",
        "        , item_condition_id_2\n",
        "        , item_condition_id_3\n",
        "        , item_condition_id_4\n",
        "        , item_condition_id_5\n",
        "        , item_description_readability_score\n",
        "    ])\n",
        "\n",
        "    main_l = Dropout(0.1)(Dense(512,kernel_initializer='normal',activation='relu') (main_l))\n",
        "    main_l = Dropout(0.1)(Dense(256,kernel_initializer='normal',activation='relu') (main_l))\n",
        "    main_l = Dropout(0.1)(Dense(128,kernel_initializer='normal',activation='relu') (main_l))\n",
        "    main_l = Dropout(0.1)(Dense(64,kernel_initializer='normal',activation='relu') (main_l))\n",
        "\n",
        "    # the output layer.\n",
        "    output = Dense(1, activation=\"linear\") (main_l)\n",
        "\n",
        "\n",
        "    model = Model([name, item_desc, item_condition_id_1,item_condition_id_2,item_condition_id_3,item_condition_id_4,item_condition_id_5, \n",
        "                   shipping, desc_len, name_len, cat_1, cat_2, cat_3, item_description_readability_score], output)\n",
        "  \n",
        "    optimizer = Adam(lr=lr, decay=decay)\n",
        " \n",
        "    model.compile(loss = 'mse', optimizer = optimizer)\n",
        "\n",
        "    return model\n",
        "\n",
        "model = new_rnn_model()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0808 20:11:48.810801 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0808 20:11:48.840131 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0808 20:11:48.842005 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0808 20:11:49.333421 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4115: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "W0808 20:11:49.349936 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0808 20:11:49.358272 140520030812032 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "W0808 20:11:49.502078 140520030812032 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "desc_len (InputLayer)           (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "name_len (InputLayer)           (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "cat_1 (InputLayer)              (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "cat_2 (InputLayer)              (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "cat_3 (InputLayer)              (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_desc (InputLayer)          (None, 75)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "name (InputLayer)               (None, 10)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_3 (Embedding)         (None, 1, 5)         1230        desc_len[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "embedding_4 (Embedding)         (None, 1, 5)         90          name_len[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "embedding_5 (Embedding)         (None, 1, 10)        120         cat_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "embedding_6 (Embedding)         (None, 1, 10)        1150        cat_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "embedding_7 (Embedding)         (None, 1, 10)        8720        cat_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "embedding_2 (Embedding)         (None, 75, 60)       14667300    item_desc[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 10, 20)       4889100     name[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 5)            0           embedding_3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 5)            0           embedding_4[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_3 (Flatten)             (None, 10)           0           embedding_5[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_4 (Flatten)             (None, 10)           0           embedding_6[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_5 (Flatten)             (None, 10)           0           embedding_7[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "gru_1 (GRU)                     (None, 16)           3696        embedding_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "gru_2 (GRU)                     (None, 8)            696         embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "shipping (InputLayer)           (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_condition_id_1 (InputLayer (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_condition_id_2 (InputLayer (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_condition_id_3 (InputLayer (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_condition_id_4 (InputLayer (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_condition_id_5 (InputLayer (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "item_description_readability_sc (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 71)           0           flatten_1[0][0]                  \n",
            "                                                                 flatten_2[0][0]                  \n",
            "                                                                 flatten_3[0][0]                  \n",
            "                                                                 flatten_4[0][0]                  \n",
            "                                                                 flatten_5[0][0]                  \n",
            "                                                                 gru_1[0][0]                      \n",
            "                                                                 gru_2[0][0]                      \n",
            "                                                                 shipping[0][0]                   \n",
            "                                                                 item_condition_id_1[0][0]        \n",
            "                                                                 item_condition_id_2[0][0]        \n",
            "                                                                 item_condition_id_3[0][0]        \n",
            "                                                                 item_condition_id_4[0][0]        \n",
            "                                                                 item_condition_id_5[0][0]        \n",
            "                                                                 item_description_readability_scor\n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 512)          36864       concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 512)          0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 256)          131328      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 256)          0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 128)          32896       dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 128)          0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 64)           8256        dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 64)           0           dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 1)            65          dropout_4[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 19,781,511\n",
            "Trainable params: 19,781,511\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuswkYbTJ3C9",
        "colab_type": "code",
        "outputId": "6a8a8da1-58c6-4925-d87d-f7554bec5a1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        }
      },
      "source": [
        "#display model architecture\n",
        "import pydot_ng as pydot\n",
        "plot_model(model, show_shapes=True, show_layer_names=True, to_file= input_folder + 'rnn_model.png')\n",
        "from IPython.display import Image\n",
        "Image(retina=True, filename=input_folder + 'model.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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aWl1ZhONgLxiQzyjwyyhwwC3Ida7x+13h5qPVATtda/WKm1O3fu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\ndP3110uyd43GLedaAGpn53hu27at0tPTddppp+mnP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73VGGPMsGHDTMOGDU1OTo7p1auX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\nUE2OjIcMYtrcuXMNuxlO6Nmzp+nZs6et94waNcqkpKSY2bNnmwMHDpjVq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X9NqAWTNwxi7IYGRkRG1tbU0+GwBYX3VwwXe/+12lUil1dHToyJEjOnHihO68807dcccduu66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\nAT/4wQ/w6KOP4qmnnoLX60Umk0FTUxO+853v4H3ve9+K/Nz5+Xn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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 3092,
              "height": 700
            }
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEQ_I9NqJ7My",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvIIcnQwFPmS",
        "colab_type": "text"
      },
      "source": [
        "### Fitting RNN model to train data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky-7qlXUGHJt",
        "colab_type": "text"
      },
      "source": [
        "keeping 2 epochs with smaller batches tends to do better than more epochs with larger batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3ZBcotCViLG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = History()\n",
        "\n",
        "callbacks = [history]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ud7MJENB5pK-",
        "colab_type": "code",
        "outputId": "bb21d08e-e4e0-477e-d89f-b60f1cbef4d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "%%time\n",
        "\n",
        "# Set hyper parameters for the model.\n",
        "BATCH_SIZE = 512 * 3\n",
        "epochs = 2\n",
        "\n",
        "# Calculate learning rate decay.\n",
        "exp_decay = lambda init, fin, steps: (init/fin)**(1/(steps-1)) - 1\n",
        "steps = int(len(X_train['name']) / BATCH_SIZE) * epochs\n",
        "lr_init, lr_fin = 0.005, 0.001\n",
        "lr_decay = exp_decay(lr_init, lr_fin, steps)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create model and fit it with training dataset.\n",
        "rnn_model = new_rnn_model(lr=lr_init, decay=lr_decay)\n",
        "history  =  rnn_model.fit(\n",
        "        X_train, Y_train, epochs=epochs, batch_size=BATCH_SIZE,\n",
        "        validation_data=(X_dev, Y_dev), verbose=1, callbacks=callbacks\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0808 20:11:51.943603 140520030812032 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 1333492 samples, validate on 74083 samples\n",
            "Epoch 1/2\n",
            "1333492/1333492 [==============================] - 163s 122us/step - loss: 0.5721 - val_loss: 0.4301\n",
            "Epoch 2/2\n",
            "1333492/1333492 [==============================] - 160s 120us/step - loss: 0.4042 - val_loss: 0.4088\n",
            "CPU times: user 7min 59s, sys: 1min 30s, total: 9min 30s\n",
            "Wall time: 5min 26s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnJ4gJflFTKz",
        "colab_type": "text"
      },
      "source": [
        "## 5.  Evaluation\n",
        "\n",
        "1. Evaluating RNN model\n",
        "1. Make prediction for test data using RNN model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNv6NPe7aaV8",
        "colab_type": "text"
      },
      "source": [
        "### RMSL Error Helper Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63ADyogcZxwN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def rmsle(Y, Y_pred):\n",
        "#     assert Y.shape == Y_pred.shape\n",
        "#     return np.sqrt(np.mean(np.square(Y_pred - Y )))\n",
        "\n",
        "def rmsle(y_true, y_pred):\n",
        "    assert y_true.shape == y_pred.shape\n",
        "    return np.sqrt(np.mean(np.square(np.log1p(y_pred) - np.log1p(y_true))))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAnLIXY1WlXt",
        "colab_type": "text"
      },
      "source": [
        " ### Evaluate RNN model on dev data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fok1FIoOFT4z",
        "colab_type": "code",
        "outputId": "d1549405-2b3a-42ca-a337-c374ccfb424a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "%%time\n",
        "\n",
        "print(\"Evaluating the model on validation data...\")\n",
        "y_pred = rnn_model.predict(X_dev, batch_size=BATCH_SIZE)\n",
        "print(\" RMSLE Validation error:\", rmsle(Y_dev, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating the model on validation data...\n",
            " RMSLE Validation error: 0.33794264996734025\n",
            "CPU times: user 5.27 s, sys: 820 ms, total: 6.09 s\n",
            "Wall time: 3.36 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwPo0h5yRLdW",
        "colab_type": "text"
      },
      "source": [
        "Hypertuning parameter to improve validation error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXx4ZXaPRDRI",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate RNN model on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGWJjGAvc2B4",
        "colab_type": "code",
        "outputId": "b954b459-2118-4cb5-f014-d32d01c53535",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "%%time\n",
        "Y_test = test_df.price.values.reshape(-1, 1)\n",
        "print(\"Evaluating the model on test data...\")\n",
        "y_test_pred = rnn_model.predict(X_test, batch_size=BATCH_SIZE, verbose=1)\n",
        "print(\" RMSLE test error:\", rmsle(Y_test, y_test_pred))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating the model on test data...\n",
            "74083/74083 [==============================] - 3s 41us/step\n",
            " RMSLE test error: 0.33659991547614904\n",
            "CPU times: user 4.94 s, sys: 862 ms, total: 5.81 s\n",
            "Wall time: 3.03 s\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}