DataLoaderBaseLine.ipynb

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  "metadata": {
    "colab": {
      "name": "DataLoaderBaseLine.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/DataLoaderBaseLine.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('1-23K4u93cFCcVa19EH38N2Tu0R1YhNt3', 'dataBaseline.parquet')\n",
        "\n",
        "    def getLabelFeature(self):\n",
        "        df_1 = pd.read_parquet('dataBaseline.parquet')\n",
        "        df_1 = df_1[['label', 'averagePooledValues']].copy()\n",
        "        returnData = [tuple(r) for r in df_1.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",
        "outputId": "0ce18e86-8aa5-46a4-826f-834170cab344",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "traindata[0:20]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         -3.86239514e-02, -2.88554598e-02, -3.84788401e-02, -2.03863662e-02,\n",
              "          2.24893838e-02,  8.73308033e-02, -2.96745710e-02, -2.77398229e-02,\n",
              "         -6.58484772e-02,  3.13746370e-02,  5.52915782e-02, -1.99140422e-02,\n",
              "          6.44990429e-02,  1.70273811e-01,  7.95747116e-02,  7.48075619e-02,\n",
              "          6.90834746e-02,  3.30736190e-02, -3.69517915e-02, -1.76781900e-02,\n",
              "         -6.25253171e-02,  5.51285073e-02,  1.04490723e-02, -3.28960061e-01,\n",
              "         -3.46385837e-02, -1.35000646e-02,  1.12754414e-02,  5.48879653e-02,\n",
              "         -2.50791176e-03,  8.33418593e-02, -1.91891268e-01, -1.79460734e-01,\n",
              "         -7.33031705e-02,  1.12663358e-01, -3.47603001e-02,  2.00509459e-01,\n",
              "          1.17558539e-01,  3.31594981e-02,  6.03400031e-03, -3.46228592e-02,\n",
              "          9.52567682e-02,  1.33748353e-01, -2.56552380e-02,  1.38365468e-02,\n",
              "         -5.43432720e-02, -2.39647478e-02, -1.62537117e-02, -1.71386421e-01,\n",
              "          8.91348869e-02, -6.16901442e-02,  5.40091954e-02,  3.75129096e-02,\n",
              "          1.86637342e-02, -4.52856459e-02, -4.10131775e-02, -7.63054863e-02,\n",
              "          8.37477744e-02, -1.15827747e-01,  3.11745610e-03,  6.38975110e-03,\n",
              "          3.15593295e-02, -6.88955188e-02, -2.33574901e-02,  7.98457768e-03,\n",
              "          3.30822133e-02,  4.01200950e-02,  1.39446810e-01,  5.56214377e-02,\n",
              "          1.59918424e-02, -7.40759540e-03, -3.23763378e-02,  3.31893601e-02,\n",
              "          1.05767129e-02, -2.04750840e-02, -8.95611271e-02, -4.44726825e-01,\n",
              "          7.03739226e-02,  1.67383000e-01, -4.09381129e-02,  6.47989511e-02,\n",
              "          7.25618750e-02, -8.39479342e-02, -9.51898918e-02, -7.43938535e-02,\n",
              "         -7.96048641e-02,  1.38780447e-02,  8.86804890e-03, -8.35030675e-02,\n",
              "         -9.20292363e-02, -5.49399219e-02,  1.16900587e-02,  8.97032097e-02,\n",
              "          8.25642496e-02,  1.01146311e-01, -1.70550235e-02,  3.46689299e-02,\n",
              "          1.00475103e-01, -7.66946524e-02,  2.21469961e-02,  1.98918745e-01,\n",
              "         -7.13416636e-02, -1.22248381e-02,  7.48840272e-02,  2.87610963e-02,\n",
              "          7.09542185e-02, -2.52924152e-02,  3.73034808e-03,  9.29658767e-03,\n",
              "         -4.95951343e-03,  7.77368918e-02, -1.13300323e-01, -5.63970283e-02],\n",
              "        dtype=float32))]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crHDNrSYOvzh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}