DataLoader.ipynb

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  "metadata": {
    "colab": {
      "name": "DataLoader.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/andrewk1/pytorch-deep-bayesian-bandits/blob/master/DataLoader.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqfb57heiJJC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "class textVecData():\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"Initialize model parameters.\n",
        "        Apply for two embedding layers.\n",
        "        Initialize layer weight\n",
        "        Args:\n",
        "            emb_size: Embedding size.\n",
        "            emb_dimention: Embedding dimention, typically from 50 to 500.\n",
        "        Returns:\n",
        "            None\n",
        "        \"\"\"\n",
        "\n",
        "        def download_file_from_google_drive(id, destination):\n",
        "            URL = \"https://docs.google.com/uc?export=download\"\n",
        "\n",
        "            session = requests.Session()\n",
        "\n",
        "            response = session.get(URL, params = { 'id' : id }, stream = True)\n",
        "            token = get_confirm_token(response)\n",
        "\n",
        "            if token:\n",
        "                params = { 'id' : id, 'confirm' : token }\n",
        "                response = session.get(URL, params = params, stream = True)\n",
        "\n",
        "            save_response_content(response, destination)    \n",
        "\n",
        "        def get_confirm_token(response):\n",
        "            for key, value in response.cookies.items():\n",
        "                if key.startswith('download_warning'):\n",
        "                    return value\n",
        "\n",
        "            return None\n",
        "\n",
        "        def save_response_content(response, destination):\n",
        "            CHUNK_SIZE = 32768\n",
        "\n",
        "            with open(destination, \"wb\") as f:\n",
        "                for chunk in response.iter_content(CHUNK_SIZE):\n",
        "                    if chunk: # filter out keep-alive new chunks\n",
        "                        f.write(chunk)\n",
        "\n",
        "        download_file_from_google_drive('14GNjfpVWeSDMEtqZHnXNeyY0hQIqeFO6', 'data1.parquet')\n",
        "        download_file_from_google_drive('1l59vzX-D0UeC-BgHsachau26sgBAT77O', 'data2.parquet')\n",
        "\n",
        "    def getLabelFeature(self):\n",
        "        df_1 = pd.read_parquet('data1.parquet')\n",
        "        df_2 = pd.read_parquet('data2.parquet')\n",
        "        finaldf = pd.concat([df_1, df_2])\n",
        "        finaldf = finaldf[['label', 'averagePooledValues']].copy()\n",
        "        returnData = [tuple(r) for r in finaldf.values]\n",
        "        return returnData\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9S6XoqTmNXs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "getdata  = textVecData()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ep_D1OkUmdnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "traindata = getdata.getLabelFeature()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EV1hLgKTm5f6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fa845bc8-0279-40ba-f4bc-15ba4b368cda"
      },
      "source": [
        "traindata[0:2]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        dtype=float32))]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    }
  ]
}