predicting_bike_sharing_data_with_tune_for_hyperparameter_tuning.ipynb

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  "metadata": {
    "colab": {
      "name": "predicting_bike_sharing_data_with_tune_for_hyperparameter_tuning.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TobiasSunderdiek/hyperparameter-tuning-with-tune/blob/master/predicting_bike_sharing_data_with_tune_for_hyperparameter_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9z0eKFKr8neG",
        "colab_type": "text"
      },
      "source": [
        "# Predicting bike sharing data with tune for hyperparameter tuning\n",
        "\n",
        "This notebook does a regression task on the bikesharing dataset, which I know from the udacity project [1]. I build a pytorch model similar to the numpy/pandas-based version in the udacity project and tune the hyperparameters with tune [2].\n",
        "\n",
        "[1] https://github.com/udacity/deep-learning-v2-pytorch/blob/master/project-bikesharing/Predicting_bike_sharing_data.ipynb\n",
        "\n",
        "[2] https://ray.readthedocs.io/en/latest/tune.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85c1-HDvsrmn",
        "colab_type": "text"
      },
      "source": [
        "## Prepare for using tune\n",
        "For use in colab, pyarrow has to be uninstalled. After that, colab runtime has be be restarted.\n",
        "\n",
        "Additionally, clean-up log dir for tune logs and install hyperopt."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lci88lLVTmd5",
        "colab_type": "code",
        "outputId": "12845578-84d5-4e87-819e-2d8d0044bf58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "# preparing for using tune\n",
        "!pip install ray\n",
        "!pip uninstall -y pyarrow\n",
        "# cleanup tune log dir if exists\n",
        "!rm -rf tune_logs"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ray in /usr/local/lib/python3.6/dist-packages (0.7.4)\n",
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            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->ray) (19.1.0)\n",
            "\u001b[33mWARNING: Skipping pyarrow as it is not installed.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9O7_R1ZyTKuD",
        "colab_type": "code",
        "outputId": "80c5a119-a4e6-4f72-b792-925ecef4d6e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "!pip uninstall -y albumentations\n",
        "!pip install --upgrade git+git://github.com/hyperopt/hyperopt.git\n",
        "!pip install setproctitle"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Skipping albumentations as it is not installed.\u001b[0m\n",
            "Collecting git+git://github.com/hyperopt/hyperopt.git\n",
            "  Cloning git://github.com/hyperopt/hyperopt.git to /tmp/pip-req-build-kf2hr58l\n",
            "  Running command git clone -q git://github.com/hyperopt/hyperopt.git /tmp/pip-req-build-kf2hr58l\n",
            "  Running command git submodule update --init --recursive -q\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (1.16.5)\n",
            "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (1.3.1)\n",
            "Requirement already satisfied, skipping upgrade: pymongo in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (3.9.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: networkx==2.2 in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (2.2)\n",
            "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from hyperopt==0.1.2) (4.28.1)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx==2.2->hyperopt==0.1.2) (4.4.0)\n",
            "Building wheels for collected packages: hyperopt\n",
            "  Building wheel for hyperopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hyperopt: filename=hyperopt-0.1.2-cp36-none-any.whl size=115643 sha256=b010b167ab60a1685ff015c9322d26b1efea9e70e53785a90554bf36b11aef11\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-u6evxymw/wheels/c9/13/71/0d7783dc440af384cb42aff4d1f3b1ca565115fcda10588c9c\n",
            "Successfully built hyperopt\n",
            "Installing collected packages: hyperopt\n",
            "  Found existing installation: hyperopt 0.1.2\n",
            "    Uninstalling hyperopt-0.1.2:\n",
            "      Successfully uninstalled hyperopt-0.1.2\n",
            "Successfully installed hyperopt-0.1.2\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.6/dist-packages (1.1.10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jb5V5Sb09V4W",
        "colab_type": "text"
      },
      "source": [
        "## Loading dataset from github\n",
        "The original dataset is located here:\n",
        "\n",
        "https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/project-bikesharing/Bike-Sharing-Dataset/hour.csv\n",
        "\n",
        "which originaly came from [1].\n",
        "\n",
        "[1] Fanaee-T, Hadi, and Gama, Joao, \"Event labeling combining ensemble detectors and background knowledge\", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT4j5Qg2o9Yg",
        "colab_type": "code",
        "outputId": "d29c1c30-aed5-48ca-808a-6160eff3fc33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Fetch a single file using the raw GitHub URL.\n",
        "!curl --remote-name \\\n",
        "     -H 'Accept: application/vnd.github.v3.raw' \\\n",
        "     --location https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/project-bikesharing/Bike-Sharing-Dataset/hour.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100 1129k  100 1129k    0     0  7737k      0 --:--:-- --:--:-- --:--:-- 7790k\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVMh2OG7rGpx",
        "colab_type": "code",
        "outputId": "eb6f1db4-57ba-49bc-b716-ab229c93a86d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "rides = pd.read_csv(\"/content/hour.csv\")\n",
        "rides_origin = rides\n",
        "rides.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
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              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
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              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
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              "      <th>cnt</th>\n",
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            "text/plain": [
              "   instant      dteday  season  yr  ...  windspeed  casual  registered  cnt\n",
              "0        1  2011-01-01       1   0  ...        0.0       3          13   16\n",
              "1        2  2011-01-01       1   0  ...        0.0       8          32   40\n",
              "2        3  2011-01-01       1   0  ...        0.0       5          27   32\n",
              "3        4  2011-01-01       1   0  ...        0.0       3          10   13\n",
              "4        5  2011-01-01       1   0  ...        0.0       0           1    1\n",
              "\n",
              "[5 rows x 17 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 4
        }
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      "cell_type": "markdown",
      "metadata": {
        "id": "segnb5Shu-1S",
        "colab_type": "text"
      },
      "source": [
        "### Convert data\n",
        "I do nearly the same converting as in the udacity project mentioned in [1].\n",
        "- hot-encode categorical features *season*, *weathersit*, *mnth*, *hr*, *weekday* and drop origin of this features\n",
        "- drop fields *instant*, *dteday*, *atemp* and *workingday* as in the udacity project\n",
        "- additionally drop fields *casual* and *registered*, focus on overall output *cnt*\n",
        "- shift and scale continuous features *cnt*, *temp*, *hum*, *windspeed* so they have zero mean and standard deviation of 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rqqljiastz8",
        "colab_type": "code",
        "outputId": "9b42f909-3242-47cd-8967-0ff3c3113437",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "for feature in ['season', 'weathersit', 'mnth', 'hr', 'weekday']:\n",
        "  hot_encoded_features = pd.get_dummies(rides[feature], prefix=feature, drop_first=False)\n",
        "  rides = pd.concat([rides, hot_encoded_features], axis=1)\n",
        "  rides = rides.drop(feature, axis=1)\n",
        "rides = rides.drop(['instant', 'dteday', 'atemp', 'workingday', 'casual', 'registered'], axis=1)\n",
        "\n",
        "feature_scaling_store = {}\n",
        "\n",
        "for feature in ['cnt', 'temp', 'hum', 'windspeed']:\n",
        "  mean, std = rides[feature].mean(), rides[feature].std()\n",
        "  feature_scaling_store[feature] = [mean, std]\n",
        "  rides.loc[:, feature] = (rides[feature] - mean) / std\n",
        "\n",
        "rides.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>holiday</th>\n",
              "      <th>temp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>cnt</th>\n",
              "      <th>season_1</th>\n",
              "      <th>season_2</th>\n",
              "      <th>season_3</th>\n",
              "      <th>season_4</th>\n",
              "      <th>weathersit_1</th>\n",
              "      <th>weathersit_2</th>\n",
              "      <th>weathersit_3</th>\n",
              "      <th>weathersit_4</th>\n",
              "      <th>mnth_1</th>\n",
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              "      <th>mnth_3</th>\n",
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              "      <th>mnth_10</th>\n",
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              "      <th>hr_0</th>\n",
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              "      <th>hr_21</th>\n",
              "      <th>hr_22</th>\n",
              "      <th>hr_23</th>\n",
              "      <th>weekday_0</th>\n",
              "      <th>weekday_1</th>\n",
              "      <th>weekday_2</th>\n",
              "      <th>weekday_3</th>\n",
              "      <th>weekday_4</th>\n",
              "      <th>weekday_5</th>\n",
              "      <th>weekday_6</th>\n",
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              "      <td>-0.956312</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.438475</td>\n",
              "      <td>0.895513</td>\n",
              "      <td>-1.553844</td>\n",
              "      <td>-0.823998</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.438475</td>\n",
              "      <td>0.895513</td>\n",
              "      <td>-1.553844</td>\n",
              "      <td>-0.868103</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.334609</td>\n",
              "      <td>0.636351</td>\n",
              "      <td>-1.553844</td>\n",
              "      <td>-0.972851</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.334609</td>\n",
              "      <td>0.636351</td>\n",
              "      <td>-1.553844</td>\n",
              "      <td>-1.039008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   yr  holiday      temp       hum  ...  weekday_3  weekday_4  weekday_5  weekday_6\n",
              "0   0        0 -1.334609  0.947345  ...          0          0          0          1\n",
              "1   0        0 -1.438475  0.895513  ...          0          0          0          1\n",
              "2   0        0 -1.438475  0.895513  ...          0          0          0          1\n",
              "3   0        0 -1.334609  0.636351  ...          0          0          0          1\n",
              "4   0        0 -1.334609  0.636351  ...          0          0          0          1\n",
              "\n",
              "[5 rows x 57 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFXhUrU9895a",
        "colab_type": "text"
      },
      "source": [
        "### Split into training,  testing and validation set\n",
        "I do the same converting as in the udacity project mentioned in [1].\n",
        "\n",
        "The data consists of entries of how many bikes are rented at one specific hour of the day. The total number of entries in the hour.csv is 17.379, which means divided by 24 there are datapoints for approximatly 724 days.\n",
        "\n",
        "The last 21 days (3%) are used as testing data.\n",
        "\n",
        "Of the remaining days, 60 days (8.5%) are used as validation data.\n",
        "\n",
        "The training data consists of 643 days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckJMWC_GmfUM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = rides[-21*24:]\n",
        "rides = rides[:-21*24]\n",
        "\n",
        "validation_data = rides[-60*24:]\n",
        "rides = rides[:-60*24]\n",
        "\n",
        "train_data = rides\n",
        "\n",
        "target_fields = ['cnt']\n",
        "\n",
        "features_train, targets_train = train_data.drop(target_fields, axis=1), train_data[target_fields]\n",
        "features_validation, targets_validation = validation_data.drop(target_fields, axis=1), validation_data[target_fields]\n",
        "features_test, targets_test = test_data.drop(target_fields, axis=1), test_data[target_fields]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFzFb1t_BqB1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "\n",
        "from torch.utils.data import TensorDataset\n",
        "\n",
        "train_set = TensorDataset(torch.tensor(features_train.values).float(),\n",
        "                          torch.tensor(targets_train.values).float())\n",
        "\n",
        "valid_set = TensorDataset(torch.tensor(features_validation.values).float(),\n",
        "                          torch.tensor(targets_validation.values).float())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiIjayNCsbK3",
        "colab_type": "text"
      },
      "source": [
        "## Network architecture\n",
        "I define the necessary parts of the architecture and try them out before handing over to tune.\n",
        "\n",
        "I observed, that there is a difference in using float or double and bias/no bias in the network. At least, the losses oscilatte much more with floats than with doubles. I compared the original numpy/panda-based version of the network from udacity with a pytorch version of my own in https://github.com/TobiasSunderdiek/predicting-bike-sharing-patterns-with-pytorch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQqLWSXY44AG",
        "colab_type": "text"
      },
      "source": [
        "### Hyperparameter\n",
        "\n",
        "defined as map in `hyperparameter`, e.g.:\n",
        "\n",
        " `hyperparameter = {'learning_rate': 0.01, 'hidden_nodes': 25, 'epochs': 4000, 'batch_size': 1}`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9EJaIYW2czS",
        "colab_type": "text"
      },
      "source": [
        "### Model\n",
        "\n",
        "Size of input node is equal to columns in feature data\n",
        "```\n",
        "input_nodes = features_train.shape[1]\n",
        "\n",
        "bikeSharingModel = BikeSharingModel(input_nodes, hyperparameter['hidden_nodes'])\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PT-x4JQl6eM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import nn\n",
        "\n",
        "class BikeSharingModel(nn.Module):\n",
        "  def __init__(self, input_nodes, hidden_nodes):\n",
        "    super(BikeSharingModel, self).__init__()\n",
        "    self.fc_1 = nn.Linear(input_nodes, hidden_nodes, bias=False)\n",
        "    self.fc_2 = nn.Linear(hidden_nodes, 1, bias=False)\n",
        " \n",
        "  def forward(self, x):\n",
        "    x = self.fc_1(x)\n",
        "    x = torch.sigmoid(x)\n",
        "    x = self.fc_2(x)\n",
        " \n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fo4exhg2SUE",
        "colab_type": "text"
      },
      "source": [
        "### Loss-function\n",
        "\n",
        "MSE is used as loss function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjB51OjZqJ-Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuqGV4br2vfA",
        "colab_type": "text"
      },
      "source": [
        "### Optimizer\n",
        "To first get a result which is similar to the udacity project, I choose SGD as optimizer.\n",
        "\n",
        "```\n",
        "optimizer = optim.SGD(bikeSharingModel.parameters(), lr=hyperparameter['learning_rate'])\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKSR0diz_zVO",
        "colab_type": "text"
      },
      "source": [
        "## Helper"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oERur5zlPGeQ",
        "colab_type": "code",
        "outputId": "99d7a946-7ccd-41e5-fc13-9fb7d9bb0ebd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# check gpu\n",
        "train_on_gpu = torch.cuda.is_available()\n",
        "if train_on_gpu:\n",
        "  print(\"Train on GPU\")\n",
        "else:\n",
        "  print(\"Train on CPU\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmcszdFmXhc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot(train_losses, validation_losses):\n",
        "  plt.plot(train_losses, label='Training loss')\n",
        "  plt.plot(validation_losses, label='Validation loss')\n",
        "  plt.legend()\n",
        "  _ = plt.ylim(0, 0.75)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eg8BvTPN21Zn",
        "colab_type": "text"
      },
      "source": [
        "## Train, validate and test/inference\n",
        "Training is done with a batch size of random training data. Batch size is configured within `hyperparameter['batch_size']`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIw-zHyYBKOU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import sys\n",
        "from ray import tune\n",
        "from torch import optim\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "def train_validate_with_tune_tracking(train_set, valid_set, hyperparameter, with_tune_tracking=False):\n",
        "\n",
        "  train_loader = DataLoader(train_set, shuffle=True, batch_size=hyperparameter['batch_size'])\n",
        "  valid_loader = DataLoader(valid_set, shuffle=True, batch_size=hyperparameter['batch_size'])\n",
        "  input_nodes = iter(train_loader).next()[0].shape[1]\n",
        "  bikeSharingModel = BikeSharingModel(input_nodes, hyperparameter['hidden_nodes'])\n",
        "  optimizer = optim.SGD(bikeSharingModel.parameters(), lr=hyperparameter['learning_rate'])\n",
        "  \n",
        "  if train_on_gpu:\n",
        "    bikeSharingModel.cuda()\n",
        "\n",
        "  train_losses, validation_losses = [], []\n",
        "  for epoch in range(1, hyperparameter['epochs']+1):\n",
        "    \n",
        "    features_batch, targets_batch = iter(train_loader).next() # train with one batch only to get similar results to implementation without pytorch\n",
        "\n",
        "    if train_on_gpu:\n",
        "      features_batch, targets_batch = features_batch.cuda(), targets_batch.cuda()\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    output = bikeSharingModel(features_batch)\n",
        "\n",
        "    loss = criterion(output, targets_batch)\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "\n",
        "    train_losses.append(loss.item())\n",
        "\n",
        "    # validate\n",
        "    loss_validation = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "      bikeSharingModel.eval()\n",
        "      for features_valid, targets_valid in valid_loader:\n",
        "\n",
        "        if train_on_gpu:\n",
        "          features_valid, targets_valid = features_valid.cuda(), targets_valid.cuda()\n",
        "\n",
        "        output_validation = bikeSharingModel(features_valid)\n",
        "        loss_validation += criterion(output_validation, targets_valid).item()\n",
        "\n",
        "    bikeSharingModel.train()\n",
        "\n",
        "    loss_validation = loss_validation / (len(valid_loader.dataset) / hyperparameter['batch_size'])\n",
        "    validation_losses.append(loss_validation)\n",
        "\n",
        "    # log results\n",
        "    if with_tune_tracking:\n",
        "      tune.track.log(validation_loss_metric=loss_validation)\n",
        "    else:\n",
        "      sys.stdout.write(\"\\rProgress: {:2.1f}% Training loss: {:2.3f} Validation loss: {:2.3f} \".format(100*epoch/(hyperparameter['epochs']+1), loss, loss_validation))\n",
        "      sys.stdout.flush()\n",
        "\n",
        "  if not with_tune_tracking:\n",
        "    plot(train_losses, validation_losses)\n",
        "\n",
        "  return bikeSharingModel"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuRF9VcCm7oo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(model_to_test, features, targets):\n",
        "\n",
        "  with torch.no_grad():\n",
        "    model_to_test.eval()\n",
        "    if train_on_gpu:\n",
        "      features, targets = features.cuda(), targets.cuda()\n",
        "      \n",
        "    output = model_to_test(features)\n",
        "    test_loss = criterion(output, targets)\n",
        "    model_to_test.train()\n",
        "\n",
        "    print(\"\\nTest loss \", test_loss.item())\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8,4))\n",
        "    mean, std = feature_scaling_store['cnt']\n",
        "    ax.plot(output.cpu().numpy()*std + mean, label='Prediction')\n",
        "    ax.plot((targets_test['cnt']*std + mean).values, label='Data')\n",
        "    ax.set_xlim(right=len(output))\n",
        "    ax.legend()\n",
        "    dates = pd.to_datetime(rides_origin.iloc[test_data.index]['dteday'])\n",
        "    dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
        "    ax.set_xticks(np.arange(len(dates))[12::24])\n",
        "    _ = ax.set_xticklabels(dates[12::24], rotation=45)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0JgM0Oq3gX8",
        "colab_type": "text"
      },
      "source": [
        "## Manual hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFe-RycNCorU",
        "colab_type": "code",
        "outputId": "e5116a16-99b8-4103-d147-c402f0b3d780",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        }
      },
      "source": [
        "hyperparameter = {'learning_rate': 0.5, 'hidden_nodes': 25, 'epochs': 4000, 'batch_size': 128}\n",
        "\n",
        "trained_model = train_validate_with_tune_tracking(train_set, valid_set, hyperparameter)\n",
        "test(trained_model, torch.tensor(features_test.values).float(), torch.tensor(targets_test.values).float())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Progress: 100.0% Training loss: 0.555 Validation loss: 1.270 \n",
            "Test loss  0.45897312528739265\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y8CLw42CFRGSxiGwTkW2HDh1y6NDKn93aUHM5E3Df9nGiK55w0cafl5O8XsCRrk/06RK+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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qYU3CrtolXmHhGISwfTeNsa+7jCFsA4O+CF/CFkK8H9glpXw1zBeWUt4qpZwn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fLuu+9e1nH00EMPPTzVeNkHf8g9+6a59+3PIuWzu1sPTw+cFBa2lLIqhPgN4CtAEPjn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\nhQB1wy6KoigKAeqGXRRFURQC/AWw4IGt+1hXSQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCovkuS8Q7Ug",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparamter tuning with tune\n",
        "\n",
        "### Search algorithm\n",
        "Choosing hyperopt\n",
        "\n",
        "Tune's HyperOptSearch uses hyperopt with TPE (Tree structured parzen estimators) see:\n",
        "\n",
        "https://ray.readthedocs.io/en/latest/tune-searchalg.html#hyperopt-search-tree-structured-parzen-estimators\n",
        "\n",
        "### Scheduler\n",
        "Choosing PBT, see:\n",
        "\n",
        "https://ray.readthedocs.io/en/latest/tune-schedulers.html#population-based-training-pbt"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF08qStlWP0A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from ray.tune.suggest.hyperopt import HyperOptSearch\n",
        "from hyperopt import hp, tpe\n",
        "from ray.tune.schedulers import PopulationBasedTraining\n",
        "\n",
        "def tune_it(config):\n",
        "  train_validate_with_tune_tracking(train_set, valid_set, config, with_tune_tracking=True)\n",
        "\n",
        "search_space = {'learning_rate': hp.choice('learning_rate', [0.00001, 0.0001, 0.001, 0.01, 0.1, 0.5, 0.7]),\n",
        "                'hidden_nodes': hp.choice('hidden_nodes', [10, 25, 30]),\n",
        "                'batch_size': hp.choice('batch_size', [128, 256, 10, 1])\n",
        "}\n",
        "\n",
        "hyperopt = HyperOptSearch(search_space,\n",
        "                          metric=\"validation_loss_metric\",\n",
        "                          mode=\"min\")\n",
        "\n",
        "pbt = PopulationBasedTraining(metric='validation_loss_metric',\n",
        "                              mode='min',\n",
        "                              hyperparam_mutations= search_space)\n",
        "\n",
        "tune_result = tune.run(tune_it,\n",
        "                   config={'epochs': 4000},\n",
        "                   local_dir='tune_logs',\n",
        "                   verbose=0,\n",
        "                   resources_per_trial={\"cpu\": 2, \"gpu\": 1},\n",
        "                   search_alg=hyperopt,\n",
        "                   num_samples = 50,\n",
        "                   scheduler = pbt,\n",
        "                   reuse_actors = False,\n",
        "                   resume = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeWOTgTXj4zn",
        "colab_type": "text"
      },
      "source": [
        "## Tune result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tr2qnmJgYFge",
        "colab_type": "code",
        "outputId": "b47020a1-e7cf-4596-d18f-302713e24059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "from ray.tune import Analysis\n",
        "\n",
        "def print_config(tune_report):\n",
        "  batch_size = tune_report['config/batch_size'].item()\n",
        "  hidden_nodes = tune_report['config/hidden_nodes'].item()\n",
        "  learning_rate = tune_report['config/learning_rate'].item()\n",
        "  validation_loss_metric = tune_report['validation_loss_metric'].item()\n",
        "  print(\"Best config: 'batch_size': {}, 'hidden_nodes': {}, 'learning_rate': {}, validation loss: {}\".format(batch_size, hidden_nodes, learning_rate, validation_loss_metric))\n",
        "\n",
        "# best config access by directory\n",
        "report_0 = Analysis(\"/content/tune_logs/tune_it/\").dataframe().sort_values('validation_loss_metric').head(1)\n",
        "print_config(report_0)\n",
        "\n",
        "# best config access by function\n",
        "print(\"Best config: {} out of {} trials\".format(tune_result.get_best_config(metric='validation_loss_metric'), len(trials)))\n",
        "\n",
        "# best config access by dataframe - variant #1\n",
        "report_1 = tune_result.dataframe().sort_values('validation_loss_metric').head(1)\n",
        "print_config(report_1)\n",
        "\n",
        "# best config access by dataframe - variant #2\n",
        "report_2= tune_result.dataframe(metric=\"validation_loss_metric\", mode=\"min\").sort_values('validation_loss_metric').head(1)\n",
        "print_config(report_2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best config: 'batch_size': 256, 'hidden_nodes': 10, 'learning_rate': 0.7, validation loss: 0.13463438223072796\n",
            "Best config: 'batch_size': 256, 'hidden_nodes': 10, 'learning_rate': 0.7, validation loss: 0.13463438223072796\n",
            "Best config: 'batch_size': 256, 'hidden_nodes': 10, 'learning_rate': 0.7, validation loss: 0.1222448822866021\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7K04-YyjXToq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir tune_logs/tune_it"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zngicL0337N-",
        "colab_type": "text"
      },
      "source": [
        "### Test tune result\n",
        "Actual tune results `batch_size: 256, hidden_nodes: 10, learning_rate: 0.7 validation loss: 0.13463438223072796\n",
        "` for minimal validation loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGpL_oe54eog",
        "colab_type": "code",
        "outputId": "72943c36-0a15-4240-cffb-99430b9caf01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        }
      },
      "source": [
        "hyperparameter = {'learning_rate': 0.7, 'hidden_nodes': 10, 'epochs': 4000, 'batch_size': 256}\n",
        "\n",
        "trained_model = train_validate_with_tune_tracking(train_set, valid_set, hyperparameter)\n",
        "test(trained_model, torch.tensor(features_test.values).float(), torch.tensor(targets_test.values).float())"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Progress: 100.0% Training loss: 0.070 Validation loss: 0.175 \n",
            "Test loss  0.19735859334468842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tBUI4vyN/vgnH9oev/WLAknIXQvQXQmwRQmwTQowxOH6HEGKdEGKNEGKJECLF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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CI/QUJA1IaWVmFBN6AwGqCLlr1zzVrRU65Sj0ZAjf2uVeJoc+Ysjd5NABYLzG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\nuWPrAC6eVECnP8DmdhfU6t/JY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            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9-dmXi33EE0",
        "colab_type": "text"
      },
      "source": [
        "## Further steps/TODO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7WTb-tW2HQ-",
        "colab_type": "text"
      },
      "source": [
        "### Features and targets\n",
        "- test with adding field *workingday*\n",
        "- test with making field *holiday* categorical\n",
        "- test with delete field *year*\n",
        "- test with adding field *atemp*\n",
        "- check standard deviation\n",
        "- drop header in data\n",
        "- try different bag sizes for test/validation/train sets\n",
        "\n",
        "### Model\n",
        "- add dropout\n",
        "- build fcn best practice by Andrej Karpathy\n",
        "\n",
        "### Hyperparameter tuning\n",
        "- write documentation of used search algo and scheduler\n",
        "- save best model\n",
        "- hand different optimizer and model over to tune (in tune docs: models should be handed over as state_dict: https://ray.readthedocs.io/en/latest/using-ray-with-pytorch.html)\n",
        "- log multiple metrices at once\n",
        "- better understand tune result\n",
        "- does my combination of hyperopt and pbt make sense?\n",
        "- resume on error\n",
        "- example error\n",
        "```\n",
        "Traceback (most recent call last):\n",
        "  File \"/usr/local/lib/python3.6/dist-packages/ray/tune/trial_runner.py\", line 521, in _process_trial\n",
        "    self, trial, flat_result)\n",
        "  File \"/usr/local/lib/python3.6/dist-packages/ray/tune/schedulers/pbt.py\", line 245, in on_trial_result\n",
        "    self._exploit(trial_runner.trial_executor, trial, trial_to_clone)\n",
        "  File \"/usr/local/lib/python3.6/dist-packages/ray/tune/schedulers/pbt.py\", line 310, in _exploit\n",
        "    trial_to_clone, new_config)\n",
        "  File \"/usr/local/lib/python3.6/dist-packages/ray/tune/schedulers/pbt.py\", line 279, in _log_config_on_step\n",
        "    f.write(json.dumps(policy) + \"\\n\")\n",
        "  File \"/usr/lib/python3.6/json/__init__.py\", line 231, in dumps\n",
        "    return _default_encoder.encode(obj)\n",
        "  File \"/usr/lib/python3.6/json/encoder.py\", line 199, in encode\n",
        "    chunks = self.iterencode(o, _one_shot=True)\n",
        "  File \"/usr/lib/python3.6/json/encoder.py\", line 257, in iterencode\n",
        "    return _iterencode(o, 0)\n",
        "  File \"/usr/lib/python3.6/json/encoder.py\", line 180, in default\n",
        "    o.__class__.__name__)\n",
        "TypeError: Object of type 'Apply' is not JSON serializable\n",
        "```"
      ]
    }
  ]
}