Word2vec/Categorical_Embeddings.ipynb

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    "colab": {
      "name": "Categorical_Embeddings.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFX28FqSWdsg"
      },
      "source": [
        "### Word Embeddings for Categorical Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNdYlmw5XWIe"
      },
      "source": [
        "This snippet of bash script will link your kaggle api key to the library and download the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yW5ahPhdWKsq",
        "outputId": "63029fb8-f4d0-45e3-d536-3b6418777418"
      },
      "source": [
        "!echo \"Installing dependencies\"\n",
        "!pip3 install --upgrade --force-reinstall --no-deps kaggle\n",
        "# This snippet will install kaggle api and connect your api-key to it\n",
        "!mkdir -p ~/.kaggle\n",
        "!echo \"Setting up your Kaggle key to API...\"\n",
        "!cp '/content/drive/MyDrive/Kaggle/kaggle.json' ~/.kaggle/\n",
        "!cat ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!echo \"Kaggle API Key successfully linked !!!\"\n",
        "!kaggle competitions download -c cat-in-the-dat-ii\n",
        "!cd ~/.\n",
        "!unzip cat-in-the-dat-ii.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing dependencies\n",
            "Processing /root/.cache/pip/wheels/a1/6a/26/d30b7499ff85a4a4593377a87ecf55f7d08af42f0de9b60303/kaggle-1.5.12-cp37-none-any.whl\n",
            "Installing collected packages: kaggle\n",
            "  Found existing installation: kaggle 1.5.12\n",
            "    Uninstalling kaggle-1.5.12:\n",
            "      Successfully uninstalled kaggle-1.5.12\n",
            "Successfully installed kaggle-1.5.12\n",
            "Setting up your Kaggle key to API...\n",
            "{\"username\":\"atharvaingle\",\"key\":\"2d3cebc4a4637012b24ba6d11e73f5ce\"}Kaggle API Key successfully linked !!!\n",
            "cat-in-the-dat-ii.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  cat-in-the-dat-ii.zip\n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYs-91MXZcIF"
      },
      "source": [
        "#### Let' start"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXQw6Y3JWk11"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from sklearn import preprocessing\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.models import Model, load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfC2tChPZiGS"
      },
      "source": [
        "train = pd.read_csv(\"/content/train.csv\")\n",
        "test = pd.read_csv(\"/content/test.csv\")\n",
        "sample = pd.read_csv(\"/content/sample_submission.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
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        "id": "Mrahd5VlZzMM",
        "outputId": "1cac28a2-2dac-4f34-87a2-a5f49aefd8bf"
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      "source": [
        "train.head()"
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              "   id  bin_0  bin_1  bin_2 bin_3 bin_4  ... ord_3 ord_4 ord_5  day month target\n",
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      "outputs": []
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        "data = pd.concat([train, test]).reset_index(drop=True)"
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      "source": [
        "features = [f for f in train.columns if f not in [\"id\", \"target\"]]"
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      "outputs": []
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      "source": [
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              "      <td>170</td>\n",
              "      <td>2167</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>556</td>\n",
              "      <td>361</td>\n",
              "      <td>182</td>\n",
              "      <td>222</td>\n",
              "      <td>1747</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  bin_0  bin_1  bin_2  bin_3  ...  ord_4  ord_5  day  month  target\n",
              "0   0      0      0      0      0  ...     20     56    5      5       0\n",
              "1   1      1      1      0      0  ...     23    151    6      9       0\n",
              "2   2      0      1      0      0  ...     15    105    4     11       0\n",
              "3   3      2      0      0      0  ...      2    140    2      5       0\n",
              "4   4      0      2      0      1  ...      2     50    4      3       0\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NOFSx-fa2ke"
      },
      "source": [
        "train = data[data.target != -1].reset_index(drop=True)\n",
        "test = data[data.target == -1].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5Aik_MgbFsb",
        "outputId": "a352419f-4d4c-49e8-9253-084742f5cfbf"
      },
      "source": [
        "train.shape, test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((600000, 25), (400000, 25))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUd0iKnSbM1A"
      },
      "source": [
        "def get_model(df, categorical_columns):\n",
        "    inputs = []\n",
        "    outputs = []\n",
        "    for c in categorical_columns:\n",
        "        num_unique_vals = int(df[c].nunique())\n",
        "        embed_dim = int(min(np.ceil(num_unique_vals / 2), 50))\n",
        "        inp = layers.Input(shape=(1,))\n",
        "        out = layers.Embedding(num_unique_vals + 1, embed_dim, name=c)(inp)\n",
        "        out = layers.Reshape(target_shape=(embed_dim, ))(out)\n",
        "        inputs.append(inp)\n",
        "        outputs.append(out)\n",
        "    x = layers.Concatenate()(outputs)\n",
        "    x = layers.Dense(300, activation='relu')(x)\n",
        "    x = layers.Dropout(0.3)(x)\n",
        "    y = layers.Dense(1, activation='sigmoid')(x)\n",
        "    model = Model(inputs=inputs, outputs=y)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLgJtVQnevDS"
      },
      "source": [
        "model = get_model(train, features)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ohxcu950ezDL",
        "outputId": "dabf9b03-36fc-4a60-c8da-7608ea294dfc"
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "model.fit([train.loc[:, f].values for f in features], train.target.values)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18750/18750 [==============================] - 173s 9ms/step - loss: 0.4012\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fcaf7daa910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SM_EHx5ZfegB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}