Lesson15_Python_For_Data_Science_CaseStudies.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/paiml/python_for_datascience/blob/master/Lesson15_Python_For_Data_Science_CaseStudies.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "spdivf2TMnGC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Lesson 16: Case Studies"
      ]
    },
    {
      "metadata": {
        "id": "c_Id55m6Jsbu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pragmatic AI Labs\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "e5p96AqpSDZa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://paiml.com/images/logo_with_slogan_white_background.png)\n",
        "\n",
        "This notebook was produced by [Pragmatic AI Labs](https://paiml.com/).  You can continue learning about these topics by:\n",
        "\n",
        "*   Buying a copy of [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](http://www.informit.com/store/pragmatic-ai-an-introduction-to-cloud-based-machine-9780134863917)\n",
        "*   Reading an online copy of [Pragmatic AI:Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/)\n",
        "*  Watching video [Essential Machine Learning and AI with Python and Jupyter Notebook-Video-SafariOnline](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118) on Safari Books Online.\n",
        "* Watching video [AWS Certified Machine Learning-Speciality](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)\n",
        "* Purchasing video [Essential Machine Learning and AI with Python and Jupyter Notebook- Purchase Video](http://www.informit.com/store/essential-machine-learning-and-ai-with-python-and-jupyter-9780135261095)\n",
        "*   Viewing more content at [noahgift.com](https://noahgift.com/)\n"
      ]
    },
    {
      "metadata": {
        "id": "pBTeTbnRKG_k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NvoiEwiAWrWy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 16.4 Ludwig (Open Source AutoML)"
      ]
    },
    {
      "metadata": {
        "id": "jbnbFKcTXNOn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Github Project URL**:  https://uber.github.io/ludwig/\n",
        "\n",
        "![alt text](https://user-images.githubusercontent.com/58792/52920000-f78d8c00-32bc-11e9-8e5e-adf53f1b8a37.png)"
      ]
    },
    {
      "metadata": {
        "id": "Aa-KnZKcfvkV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Install Ludwig"
      ]
    },
    {
      "metadata": {
        "id": "Q3FrtesdfyV9",
        "colab_type": "code",
        "outputId": "1db2fd0d-8904-489e-ac1f-70bc70c9704a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install --upgrade numpy #must restart colab runtime\n",
        "!pip install --upgrade scikit-image\n",
        "!pip install -q ludwig\n",
        "!python -m spacy download en "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: numpy in /usr/local/lib/python3.6/dist-packages (1.16.1)\n",
            "Collecting scikit-image\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/24/06/d560630eb9e36d90d69fe57d9ff762d8f501664ce478b8a0ae132b3c3008/scikit_image-0.14.2-cp36-cp36m-manylinux1_x86_64.whl (25.3MB)\n",
            "\u001b[K    100% |████████████████████████████████| 25.3MB 1.9MB/s \n",
            "\u001b[?25hCollecting pillow>=4.3.0 (from scikit-image)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/5e/e91792f198bbc5a0d7d3055ad552bc4062942d27eaf75c3e2783cf64eae5/Pillow-5.4.1-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 2.0MB 18.3MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (3.0.2)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (1.11.0)\n",
            "Requirement already satisfied, skipping upgrade: cloudpickle>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (0.6.1)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=1.8 in /usr/local/lib/python3.6/dist-packages (from scikit-image) (2.2)\n",
            "Collecting dask[array]>=1.0.0 (from scikit-image)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7c/2b/cf9e5477bec3bd3b4687719876ea38e9d8c9dc9d3526365c74e836e6a650/dask-1.1.1-py2.py3-none-any.whl (701kB)\n",
            "\u001b[K    100% |████████████████████████████████| 706kB 25.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy>=0.17.0->scikit-image) (1.16.1)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->scikit-image) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->scikit-image) (2.3.1)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->scikit-image) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.0.0->scikit-image) (2.5.3)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=1.8->scikit-image) (4.3.2)\n",
            "Requirement already satisfied, skipping upgrade: toolz>=0.7.3; extra == \"array\" in /usr/local/lib/python3.6/dist-packages (from dask[array]>=1.0.0->scikit-image) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.0.0->scikit-image) (40.8.0)\n",
            "\u001b[31mfeaturetools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31malbumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.8 which is incompatible.\u001b[0m\n",
            "Installing collected packages: pillow, dask, scikit-image\n",
            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
            "      Successfully uninstalled Pillow-4.0.0\n",
            "  Found existing installation: dask 0.20.2\n",
            "    Uninstalling dask-0.20.2:\n",
            "      Successfully uninstalled dask-0.20.2\n",
            "  Found existing installation: scikit-image 0.13.1\n",
            "    Uninstalling scikit-image-0.13.1:\n",
            "      Successfully uninstalled scikit-image-0.13.1\n",
            "Successfully installed dask-1.1.1 pillow-5.4.1 scikit-image-0.14.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: en_core_web_sm==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm==2.0.0 in /usr/local/lib/python3.6/dist-packages (2.0.0)\n",
            "\n",
            "\u001b[93m    Linking successful\u001b[0m\n",
            "    /usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n",
            "    /usr/local/lib/python3.6/dist-packages/spacy/data/en\n",
            "\n",
            "    You can now load the model via spacy.load('en')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yVLSSAUViyiX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Basic Ideas"
      ]
    },
    {
      "metadata": {
        "id": "qHaRqAN5i1iV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* **Training Models**\n",
        "* **Prediction (Inference)**\n",
        "* **Datatypes**\n",
        " - binary\n",
        " - numerical\n",
        " - category\n",
        " - set\n",
        " - bag\n",
        " - sequence\n",
        " - text\n",
        " - timeseries\n",
        " - image\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "W3G5sZ3yo-yK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Topic Modeling Example"
      ]
    },
    {
      "metadata": {
        "id": "aIbXYrxU8ySd",
        "colab_type": "code",
        "outputId": "241c61f9-ad81-4c4d-82dd-42bef0502fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/uchidalab/book-dataset/master/Task1/book30-listing-train.csv\n",
        "!wget https://raw.githubusercontent.com/noahgift/recommendations/master/model_definition.yaml"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-18 02:44:21--  https://raw.githubusercontent.com/uchidalab/book-dataset/master/Task1/book30-listing-train.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9728786 (9.3M) [text/plain]\n",
            "Saving to: ‘book30-listing-train.csv.3’\n",
            "\n",
            "book30-listing-trai 100%[===================>]   9.28M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2019-02-18 02:44:23 (64.4 MB/s) - ‘book30-listing-train.csv.3’ saved [9728786/9728786]\n",
            "\n",
            "--2019-02-18 02:44:24--  https://raw.githubusercontent.com/noahgift/recommendations/master/model_definition.yaml\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 180 [text/plain]\n",
            "Saving to: ‘model_definition.yaml.2’\n",
            "\n",
            "model_definition.ya 100%[===================>]     180  --.-KB/s    in 0s      \n",
            "\n",
            "2019-02-18 02:44:25 (34.7 MB/s) - ‘model_definition.yaml.2’ saved [180/180]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v-w5Zkzcumoi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Ingest"
      ]
    },
    {
      "metadata": {
        "id": "Ef8dbaV4tHrz",
        "colab_type": "code",
        "outputId": "e7bbaff9-edcf-43df-f142-f8e5e916338f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://media.githubusercontent.com/media/noahgift/recommendations/master/data/book30-listing-train-with-headers.csv\")\n",
        "df = df.drop(\"Unnamed: 0\", axis=1)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>TITLE</th>\n",
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              "      <th>CATEGORYID</th>\n",
              "      <th>CATEGORY</th>\n",
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              "      <td>http://ecx.images-amazon.com/images/I/51UJnL3T...</td>\n",
              "      <td>Magnets: Pulling Together, Pushing Apart (Amaz...</td>\n",
              "      <td>Natalie M. Rosinsky</td>\n",
              "      <td>4</td>\n",
              "      <td>Children's Books</td>\n",
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              "      <th>1</th>\n",
              "      <td>1446276082</td>\n",
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              "      <td>http://ecx.images-amazon.com/images/I/51MGUKhk...</td>\n",
              "      <td>Energy Security (SAGE Library of International...</td>\n",
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              "      <td>http://ecx.images-amazon.com/images/I/51qKvjsi...</td>\n",
              "      <td>An Amish Gathering: Life in Lancaster County</td>\n",
              "      <td>Beth Wiseman</td>\n",
              "      <td>9</td>\n",
              "      <td>Christian Books &amp; Bibles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>970096410</td>\n",
              "      <td>0970096410.jpg</td>\n",
              "      <td>http://ecx.images-amazon.com/images/I/51qoUENb...</td>\n",
              "      <td>City of Rocks Idaho: A Climber's Guide (Region...</td>\n",
              "      <td>Dave Bingham</td>\n",
              "      <td>26</td>\n",
              "      <td>Sports &amp; Outdoors</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8436808053</td>\n",
              "      <td>8436808053.jpg</td>\n",
              "      <td>http://ecx.images-amazon.com/images/I/41aDW5pz...</td>\n",
              "      <td>Como vencer el insomnio. Tecnicas, reglas y co...</td>\n",
              "      <td>Choliz Montanes</td>\n",
              "      <td>11</td>\n",
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              "3   970096410  0970096410.jpg   \n",
              "4  8436808053  8436808053.jpg   \n",
              "\n",
              "                                           IMAGE URL  \\\n",
              "0  http://ecx.images-amazon.com/images/I/51UJnL3T...   \n",
              "1  http://ecx.images-amazon.com/images/I/51MGUKhk...   \n",
              "2  http://ecx.images-amazon.com/images/I/51qKvjsi...   \n",
              "3  http://ecx.images-amazon.com/images/I/51qoUENb...   \n",
              "4  http://ecx.images-amazon.com/images/I/41aDW5pz...   \n",
              "\n",
              "                                               TITLE               AUTHOR  \\\n",
              "0  Magnets: Pulling Together, Pushing Apart (Amaz...  Natalie M. Rosinsky   \n",
              "1  Energy Security (SAGE Library of International...                  NaN   \n",
              "2       An Amish Gathering: Life in Lancaster County         Beth Wiseman   \n",
              "3  City of Rocks Idaho: A Climber's Guide (Region...         Dave Bingham   \n",
              "4  Como vencer el insomnio. Tecnicas, reglas y co...      Choliz Montanes   \n",
              "\n",
              "   CATEGORYID                      CATEGORY  \n",
              "0           4              Children's Books  \n",
              "1          10  Engineering & Transportation  \n",
              "2           9      Christian Books & Bibles  \n",
              "3          26             Sports & Outdoors  \n",
              "4          11     Health, Fitness & Dieting  "
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "efw0icqJ_0Bq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df.to_csv(\"book30-listing-train-with-headers.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wR_L2OPkuqH4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### EDA"
      ]
    },
    {
      "metadata": {
        "id": "IUGz5U5duj-D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Columns**"
      ]
    },
    {
      "metadata": {
        "id": "KVYJIiwHuhiT",
        "colab_type": "code",
        "outputId": "c5a39f2f-e99f-4514-8bb8-ebe7c4eee1b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ASIN', 'FILENAME', 'IMAGE URL', 'TITLE', 'AUTHOR', 'CATEGORYID',\n",
              "       'CATEGORY'],\n",
              "      dtype='object')"
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          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "ir0Viy2zuyr1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Shape**"
      ]
    },
    {
      "metadata": {
        "id": "-kaJsKyruyAl",
        "colab_type": "code",
        "outputId": "f0e37663-1297-49c0-e000-ec0af6665d43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51299, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "JJNSA3n0u3Zf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Training w/Ludwig"
      ]
    },
    {
      "metadata": {
        "id": "bldBWuL2Nwmh",
        "colab_type": "code",
        "outputId": "1c6b54c6-35ec-4fa8-b1a5-dd9bcd42b013",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "cell_type": "code",
      "source": [
        "!head book30-listing-train-with-headers.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ",ASIN,FILENAME,IMAGE URL,TITLE,AUTHOR,CATEGORYID,CATEGORY\n",
            "0,1404803335,1404803335.jpg,http://ecx.images-amazon.com/images/I/51UJnL3Tx6L.jpg,\"Magnets: Pulling Together, Pushing Apart (Amazing Science)\",Natalie M. Rosinsky,4,Children's Books\n",
            "1,1446276082,1446276082.jpg,http://ecx.images-amazon.com/images/I/51MGUKhkyhL.jpg,Energy Security (SAGE Library of International Security),,10,Engineering & Transportation\n",
            "2,1491522666,1491522666.jpg,http://ecx.images-amazon.com/images/I/51qKvjsi3ML.jpg,An Amish Gathering: Life in Lancaster County,Beth Wiseman,9,Christian Books & Bibles\n",
            "3,970096410,0970096410.jpg,http://ecx.images-amazon.com/images/I/51qoUENb1CL.jpg,City of Rocks Idaho: A Climber's Guide (Regional Rock Climbing Series),Dave Bingham,26,Sports & Outdoors\n",
            "4,8436808053,8436808053.jpg,http://ecx.images-amazon.com/images/I/41aDW5pzZBL.jpg,\"Como vencer el insomnio. Tecnicas, reglas y consejos practicos para dormir mejor (BIBLIOTECA PRACTICA) (Spanish Edition)\",Choliz Montanes,11,\"Health, Fitness & Dieting\"\n",
            "5,1848291388,1848291388.jpg,http://ecx.images-amazon.com/images/I/51Lpg7xmrBL.jpg,John Martin Littlejohn: An Enigma of Osteopathy,John O'Brien,16,Medical Books\n",
            "6,73402656,0073402656.jpg,http://ecx.images-amazon.com/images/I/51WccSzFUrL.jpg,Chemistry: The Molecular Nature of Matter and Change,Martin Silberberg,23,Science & Math\n",
            "7,323045979,0323045979.jpg,http://ecx.images-amazon.com/images/I/51rJir5EpnL.jpg,\"Mosby's Oncology Nursing Advisor: A Comprehensive Guide to Clinical Practice, 1e\",Susan Newton MS  RN  AOCN  AOCNS,16,Medical Books\n",
            "8,1847176968,1847176968.jpg,http://ecx.images-amazon.com/images/I/61KoC743OzL.jpg,Ireland's Wild Atlantic Way,Carsten Krieger,29,Travel\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ous6EqC8Nocg",
        "colab_type": "code",
        "outputId": "398add9c-ef76-47e2-9fbb-219c8ff1af53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "cell_type": "code",
      "source": [
        "!cat model_definition.yaml"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_features:\n",
            "    -\n",
            "        name: TITLE\n",
            "        type: text\n",
            "        encoder: parallel_cnn\n",
            "        level: word\n",
            "\n",
            "output_features:\n",
            "    -\n",
            "        name: CATEGORY\n",
            "        type: category"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WpVA2fyXLRoK",
        "colab_type": "code",
        "outputId": "abfc3b5b-6f59-469f-cb7b-aff5e28cb8ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 20338
        }
      },
      "cell_type": "code",
      "source": [
        "!ludwig experiment --data_csv book30-listing-train-with-headers.csv --model_definition_file model_definition.yaml\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " _         _        _      \n",
            "| |_  _ __| |_ __ _(_)__ _ \n",
            "| | || / _` \\ V  V / / _` |\n",
            "|_|\\_,_\\__,_|\\_/\\_/|_\\__, |\n",
            "                     |___/ \n",
            "ludwig v0.1.0 - Experiment\n",
            "\n",
            "Experiment name: experiment\n",
            "Model name: run\n",
            "Output path: results/experiment_run_0\n",
            "\n",
            "ludwig_version: '0.1.0'\n",
            "command: ('/usr/local/bin/ludwig experiment --data_csv '\n",
            " 'book30-listing-train-with-headers.csv --model_definition_file '\n",
            " 'model_definition.yaml')\n",
            "dataset_type: 'book30-listing-train-with-headers.csv'\n",
            "model_definition: {   'combiner': {'type': 'concat'},\n",
            "    'input_features': [   {   'encoder': 'parallel_cnn',\n",
            "                              'level': 'word',\n",
            "                              'name': 'TITLE',\n",
            "                              'tied_weights': None,\n",
            "                              'type': 'text'}],\n",
            "    'output_features': [   {   'dependencies': [],\n",
            "                               'loss': {   'class_distance_temperature': 0,\n",
            "                                           'class_weights': 1,\n",
            "                                           'confidence_penalty': 0,\n",
            "                                           'distortion': 1,\n",
            "                                           'labels_smoothing': 0,\n",
            "                                           'negative_samples': 0,\n",
            "                                           'robust_lambda': 0,\n",
            "                                           'sampler': None,\n",
            "                                           'type': 'softmax_cross_entropy',\n",
            "                                           'unique': False,\n",
            "                                           'weight': 1},\n",
            "                               'name': 'CATEGORY',\n",
            "                               'reduce_dependencies': 'sum',\n",
            "                               'reduce_input': 'sum',\n",
            "                               'top_k': 3,\n",
            "                               'type': 'category'}],\n",
            "    'preprocessing': {   'bag': {   'fill_value': '',\n",
            "                                    'format': 'space',\n",
            "                                    'lowercase': 10000,\n",
            "                                    'missing_value_strategy': 'fill_with_const',\n",
            "                                    'most_common': False},\n",
            "                         'binary': {   'fill_value': 0,\n",
            "                                       'missing_value_strategy': 'fill_with_const'},\n",
            "                         'category': {   'fill_value': '<UNK>',\n",
            "                                         'lowercase': False,\n",
            "                                         'missing_value_strategy': 'fill_with_const',\n",
            "                                         'most_common': 10000},\n",
            "                         'force_split': False,\n",
            "                         'image': {'missing_value_strategy': 'backfill'},\n",
            "                         'numerical': {   'fill_value': 0,\n",
            "                                          'missing_value_strategy': 'fill_with_const'},\n",
            "                         'sequence': {   'fill_value': '',\n",
            "                                         'format': 'space',\n",
            "                                         'lowercase': False,\n",
            "                                         'missing_value_strategy': 'fill_with_const',\n",
            "                                         'most_common': 20000,\n",
            "                                         'padding': 'right',\n",
            "                                         'padding_symbol': '<PAD>',\n",
            "                                         'sequence_length_limit': 256,\n",
            "                                         'unknown_symbol': '<UNK>'},\n",
            "                         'set': {   'fill_value': '',\n",
            "                                    'format': 'space',\n",
            "                                    'lowercase': False,\n",
            "                                    'missing_value_strategy': 'fill_with_const',\n",
            "                                    'most_common': 10000},\n",
            "                         'split_probabilities': (0.7, 0.1, 0.2),\n",
            "                         'stratify': None,\n",
            "                         'text': {   'char_format': 'characters',\n",
            "                                     'char_most_common': 70,\n",
            "                                     'char_sequence_length_limit': 1024,\n",
            "                                     'fill_value': '',\n",
            "                                     'lowercase': True,\n",
            "                                     'missing_value_strategy': 'fill_with_const',\n",
            "                                     'padding': 'right',\n",
            "                                     'padding_symbol': '<PAD>',\n",
            "                                     'unknown_symbol': '<UNK>',\n",
            "                                     'word_format': 'space_punct',\n",
            "                                     'word_most_common': 20000,\n",
            "                                     'word_sequence_length_limit': 256},\n",
            "                         'timeseries': {   'fill_value': '',\n",
            "                                           'format': 'space',\n",
            "                                           'missing_value_strategy': 'fill_with_const',\n",
            "                                           'padding': 'right',\n",
            "                                           'padding_value': 0,\n",
            "                                           'timeseries_length_limit': 256}},\n",
            "    'training': {   'batch_size': 128,\n",
            "                    'bucketing_field': None,\n",
            "                    'decay': False,\n",
            "                    'decay_rate': 0.96,\n",
            "                    'decay_steps': 10000,\n",
            "                    'dropout_rate': 0.0,\n",
            "                    'early_stop': 3,\n",
            "                    'epochs': 200,\n",
            "                    'gradient_clipping': None,\n",
            "                    'increase_batch_size_on_plateau': 0,\n",
            "                    'increase_batch_size_on_plateau_max': 512,\n",
            "                    'increase_batch_size_on_plateau_patience': 5,\n",
            "                    'increase_batch_size_on_plateau_rate': 2,\n",
            "                    'learning_rate': 0.001,\n",
            "                    'learning_rate_warmup_epochs': 5,\n",
            "                    'optimizer': {   'beta1': 0.9,\n",
            "                                     'beta2': 0.999,\n",
            "                                     'epsilon': 1e-08,\n",
            "                                     'type': 'adam'},\n",
            "                    'reduce_learning_rate_on_plateau': 0,\n",
            "                    'reduce_learning_rate_on_plateau_patience': 5,\n",
            "                    'reduce_learning_rate_on_plateau_rate': 0.5,\n",
            "                    'regularization_lambda': 0,\n",
            "                    'regularizer': 'l2',\n",
            "                    'staircase': False,\n",
            "                    'validation_field': 'combined',\n",
            "                    'validation_measure': 'loss'}}\n",
            "\n",
            "Using full raw csv, no hdf5 and json file with the same name have been found\n",
            "Building dataset (it may take a while)\n",
            "Loading NLP pipeline\n",
            "Writing dataset\n",
            "Writing train set metadata with vocabulary\n",
            "Training set: 36059\n",
            "Validation set: 5042\n",
            "Test set: 10198\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/losses/losses_impl.py:209: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n",
            "From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n",
            "\n",
            "╒══════════╕\n",
            "│ TRAINING │\n",
            "╘══════════╛\n",
            "\n",
            "2019-02-18 01:21:33.899464: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2019-02-18 01:21:33.899801: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x318ac00 executing computations on platform Host. Devices:\n",
            "2019-02-18 01:21:33.899835: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "2019-02-18 01:21:34.055715: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2019-02-18 01:21:34.056285: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x318a100 executing computations on platform CUDA. Devices:\n",
            "2019-02-18 01:21:34.056320: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2019-02-18 01:21:34.056733: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "totalMemory: 11.17GiB freeMemory: 11.10GiB\n",
            "2019-02-18 01:21:34.056767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
            "2019-02-18 01:21:43.842054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-02-18 01:21:43.842116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
            "2019-02-18 01:21:43.842133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
            "2019-02-18 01:21:43.842364: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-02-18 01:21:43.842446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10752 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "\n",
            "Epoch   1\n",
            "Training:   0% 0/282 [00:00<?, ?it/s]2019-02-18 01:21:44.623868: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n",
            "Training: 100% 282/282 [00:20<00:00, 13.95it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.52it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 59.33it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 52.80it/s]\n",
            "Took 27.3456s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.3077 │     0.0791 │      0.1855 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.3093 │     0.0768 │      0.1813 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.3204 │     0.0757 │      0.1757 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   2\n",
            "Training: 100% 282/282 [00:17<00:00, 16.85it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.75it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 60.45it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 59.97it/s]\n",
            "Took 23.9509s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2823 │     0.0904 │      0.2009 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2871 │     0.0851 │      0.1962 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.3021 │     0.0828 │      0.1897 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   3\n",
            "Training: 100% 282/282 [00:17<00:00, 16.92it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.96it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 59.63it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.28it/s]\n",
            "Took 23.9664s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2728 │     0.0940 │      0.2102 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2773 │     0.0898 │      0.2071 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2966 │     0.0838 │      0.1968 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   4\n",
            "Training: 100% 282/282 [00:17<00:00, 16.86it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.63it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 61.04it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 59.69it/s]\n",
            "Took 23.9503s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2530 │     0.0970 │      0.2159 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2623 │     0.0926 │      0.2081 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2824 │     0.0884 │      0.2033 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   5\n",
            "Training: 100% 282/282 [00:17<00:00, 16.83it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 60.61it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 59.69it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.04it/s]\n",
            "Took 23.9652s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2445 │     0.0983 │      0.2182 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2562 │     0.0908 │      0.2130 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2762 │     0.0875 │      0.2024 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   6\n",
            "Training: 100% 282/282 [00:17<00:00, 16.85it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.89it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 60.76it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.00it/s]\n",
            "Took 23.9497s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2367 │     0.1004 │      0.2211 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2543 │     0.0898 │      0.2098 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2740 │     0.0868 │      0.2043 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   7\n",
            "Training: 100% 282/282 [00:17<00:00, 16.83it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 60.08it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 61.33it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.27it/s]\n",
            "Took 23.9176s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2357 │     0.1012 │      0.2220 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2567 │     0.0916 │      0.2108 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2771 │     0.0880 │      0.2010 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Last improvement of loss on combined happened 1 epoch ago\n",
            "\n",
            "\n",
            "Epoch   8\n",
            "Training: 100% 282/282 [00:17<00:00, 16.75it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.96it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 60.53it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.30it/s]\n",
            "Took 23.9056s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2256 │     0.1046 │      0.2259 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2541 │     0.0934 │      0.2114 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2751 │     0.0913 │      0.1995 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Validation loss on combined improved, model saved\n",
            "\n",
            "\n",
            "Epoch   9\n",
            "Training: 100% 282/282 [00:17<00:00, 16.76it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 60.44it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 61.39it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.05it/s]\n",
            "Took 23.9047s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2222 │     0.1041 │      0.2277 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2547 │     0.0962 │      0.2164 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2755 │     0.0917 │      0.2004 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Last improvement of loss on combined happened 1 epoch ago\n",
            "\n",
            "\n",
            "Epoch  10\n",
            "Training: 100% 282/282 [00:17<00:00, 16.97it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.94it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 61.11it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 59.82it/s]\n",
            "Took 23.9255s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2181 │     0.1053 │      0.2331 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2575 │     0.0958 │      0.2196 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2789 │     0.0886 │      0.2082 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Last improvement of loss on combined happened 2 epochs ago\n",
            "\n",
            "\n",
            "Epoch  11\n",
            "Training: 100% 282/282 [00:17<00:00, 16.88it/s]\n",
            "Evaluation train: 100% 282/282 [00:04<00:00, 59.83it/s]\n",
            "Evaluation vali : 100% 40/40 [00:00<00:00, 60.48it/s]\n",
            "Evaluation test : 100% 80/80 [00:01<00:00, 60.10it/s]\n",
            "Took 23.8798s\n",
            "╒════════════╤════════╤════════════╤═════════════╕\n",
            "│ CATEGORY   │   loss │   accuracy │   hits_at_k │\n",
            "╞════════════╪════════╪════════════╪═════════════╡\n",
            "│ train      │ 3.2211 │     0.1051 │      0.2338 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ vali       │ 3.2667 │     0.0936 │      0.2140 │\n",
            "├────────────┼────────┼────────────┼─────────────┤\n",
            "│ test       │ 3.2868 │     0.0891 │      0.2045 │\n",
            "╘════════════╧════════╧════════════╧═════════════╛\n",
            "Last improvement of loss on combined happened 3 epochs ago\n",
            "\n",
            "EARLY STOPPING due to lack of validation improvement, it has been 3 epochs since last validation accuracy improvement\n",
            "\n",
            "Best validation model epoch: 8\n",
            "Best validation model loss on validation set combined: 3.2541212318720016\n",
            "Best validation model loss on test set combined: 3.275094079606602\n",
            "\n",
            "╒═════════╕\n",
            "│ PREDICT │\n",
            "╘═════════╛\n",
            "\n",
            "Evaluation: 100% 80/80 [00:01<00:00, 57.96it/s]\n",
            "\n",
            "===== CATEGORY =====\n",
            "accuracy: 0.0891351245342224\n",
            "hits_at_k: 0.20445185330456953\n",
            "loss: 3.286845474856628\n",
            "overall_stats: { 'avg_f1_score_macro': 0.06812071846149517,\n",
            "  'avg_f1_score_micro': 0.0891351245342224,\n",
            "  'avg_f1_score_weighted': 0.06790552679270521,\n",
            "  'avg_precision_macro': 0.09177260758729153,\n",
            "  'avg_precision_micro': 0.0891351245342224,\n",
            "  'avg_precision_weighted': 0.0891351245342224,\n",
            "  'avg_recall_macro': 0.09056387599530688,\n",
            "  'avg_recall_micro': 0.0891351245342224,\n",
            "  'avg_recall_weighted': 0.0891351245342224,\n",
            "  'kappa_score': 0.058034041734078334,\n",
            "  'overall_accuracy': 0.0891351245342224}\n",
            "per_class_stats: {<UNK>: {   'accuracy': 1.0,\n",
            "    'f1_score': 0,\n",
            "    'fall_out': 0.0,\n",
            "    'false_discovery_rate': 1.0,\n",
            "    'false_negative_rate': 1.0,\n",
            "    'false_negatives': 0,\n",
            "    'false_omission_rate': 0.0,\n",
            "    'false_positive_rate': 0.0,\n",
            "    'false_positives': 0,\n",
            "    'hit_rate': 0,\n",
            "    'informedness': 0.0,\n",
            "    'markedness': 0.0,\n",
            "    'matthews_correlation_coefficient': 0,\n",
            "    'miss_rate': 1.0,\n",
            "    'negative_predictive_value': 1.0,\n",
            "    'positive_predictive_value': 0,\n",
            "    'precision': 0,\n",
            "    'recall': 0,\n",
            "    'sensitivity': 0,\n",
            "    'specificity': 1.0,\n",
            "    'true_negative_rate': 1.0,\n",
            "    'true_negatives': 10198,\n",
            "    'true_positive_rate': 0,\n",
            "    'true_positives': 0},\n",
            "  Children's Books: {   'accuracy': 0.9269464600902138,\n",
            "    'f1_score': 0.10991636798088411,\n",
            "    'fall_out': 0.02880446004542636,\n",
            "    'false_discovery_rate': 0.8584615384615385,\n",
            "    'false_negative_rate': 0.91015625,\n",
            "    'false_negatives': 466,\n",
            "    'false_omission_rate': 0.04719943279651573,\n",
            "    'false_positive_rate': 0.02880446004542636,\n",
            "    'false_positives': 279,\n",
            "    'hit_rate': 0.08984375,\n",
            "    'informedness': 0.06103928995457375,\n",
            "    'markedness': 0.09433902874194589,\n",
            "    'matthews_correlation_coefficient': 0.07588403869993006,\n",
            "    'miss_rate': 0.91015625,\n",
            "    'negative_predictive_value': 0.9528005672034843,\n",
            "    'positive_predictive_value': 0.14153846153846153,\n",
            "    'precision': 0.14153846153846153,\n",
            "    'recall': 0.08984375,\n",
            "    'sensitivity': 0.08984375,\n",
            "    'specificity': 0.9711955399545736,\n",
            "    'true_negative_rate': 0.9711955399545736,\n",
            "    'true_negatives': 9407,\n",
            "    'true_positive_rate': 0.08984375,\n",
            "    'true_positives': 46},\n",
            "  Engineering & Transportation: {   'accuracy': 0.963326142380859,\n",
            "    'f1_score': 0.04591836734693878,\n",
            "    'fall_out': 0.029946629768728972,\n",
            "    'false_discovery_rate': 0.9711538461538461,\n",
            "    'false_negative_rate': 0.8875,\n",
            "    'false_negatives': 71,\n",
            "    'false_omission_rate': 0.0071818733562614145,\n",
            "    'false_positive_rate': 0.029946629768728972,\n",
            "    'false_positives': 303,\n",
            "    'hit_rate': 0.1125,\n",
            "    'informedness': 0.08255337023127107,\n",
            "    'markedness': 0.02166428048989233,\n",
            "    'matthews_correlation_coefficient': 0.042290180516003875,\n",
            "    'miss_rate': 0.8875,\n",
            "    'negative_predictive_value': 0.9928181266437386,\n",
            "    'positive_predictive_value': 0.028846153846153848,\n",
            "    'precision': 0.028846153846153848,\n",
            "    'recall': 0.1125,\n",
            "    'sensitivity': 0.1125,\n",
            "    'specificity': 0.970053370231271,\n",
            "    'true_negative_rate': 0.970053370231271,\n",
            "    'true_negatives': 9815,\n",
            "    'true_positive_rate': 0.1125,\n",
            "    'true_positives': 9},\n",
            "  Christian Books & Bibles: {   'accuracy': 0.9656795450088252,\n",
            "    'f1_score': 0.005681818181818181,\n",
            "    'fall_out': 0.034229109454688156,\n",
            "    'false_discovery_rate': 0.9971428571428571,\n",
            "    'false_negative_rate': 0.5,\n",
            "    'false_negatives': 1,\n",
            "    'false_omission_rate': 0.00010154346060109454,\n",
            "    'false_positive_rate': 0.034229109454688156,\n",
            "    'false_positives': 349,\n",
            "    'hit_rate': 0.5,\n",
            "    'informedness': 0.46577089054531173,\n",
            "    'markedness': 0.002755599396541797,\n",
            "    'matthews_correlation_coefficient': 0.035825660983621235,\n",
            "    'miss_rate': 0.5,\n",
            "    'negative_predictive_value': 0.9998984565393989,\n",
            "    'positive_predictive_value': 0.002857142857142857,\n",
            "    'precision': 0.002857142857142857,\n",
            "    'recall': 0.5,\n",
            "    'sensitivity': 0.5,\n",
            "    'specificity': 0.9657708905453118,\n",
            "    'true_negative_rate': 0.9657708905453118,\n",
            "    'true_negatives': 9847,\n",
            "    'true_positive_rate': 0.5,\n",
            "    'true_positives': 1},\n",
            "  Sports & Outdoors: {   'accuracy': 0.963424200823691,\n",
            "    'f1_score': 0,\n",
            "    'fall_out': 0.03297244094488194,\n",
            "    'false_discovery_rate': 1.0,\n",
            "    'false_negative_rate': 1.0,\n",
            "    'false_negatives': 38,\n",
            "    'false_omission_rate': 0.0038527831288655,\n",
            "    'false_positive_rate': 0.03297244094488194,\n",
            "    'false_positives': 335,\n",
            "    'hit_rate': 0.0,\n",
            "    'informedness': -0.03297244094488194,\n",
            "    'markedness': -0.0038527831288655,\n",
            "    'matthews_correlation_coefficient': -0.011271009901067143,\n",
            "    'miss_rate': 1.0,\n",
            "    'negative_predictive_value': 0.9961472168711345,\n",
            "    'positive_predictive_value': 0.0,\n",
            "    'precision': 0.0,\n",
            "    'recall': 0.0,\n",
            "    'sensitivity': 0.0,\n",
            "    'specificity': 0.9670275590551181,\n",
            "    'true_negative_rate': 0.9670275590551181,\n",
            "    'true_negatives': 9825,\n",
            "    'true_positive_rate': 0.0,\n",
            "    'true_positives': 0},\n",
            "  Health, Fitness & Dieting: {   'accuracy': 0.9297901549323396,\n",
            "    'f1_score': 0.0427807486631016,\n",
            "    'fall_out': 0.03329248366013071,\n",
            "    'false_discovery_rate': 0.9532163742690059,\n",
            "    'false_negative_rate': 0.9605911330049262,\n",
            "    'false_negatives': 390,\n",
            "    'false_omission_rate': 0.03956980519480524,\n",
            "    'false_positive_rate': 0.03329248366013071,\n",
            "    'false_positives': 326,\n",
            "    'hit_rate': 0.03940886699507389,\n",
            "    'informedness': 0.006116383334943132,\n",
            "    'markedness': 0.0072138205361889085,\n",
            "    'matthews_correlation_coefficient': 0.0066424763235420695,\n",
            "    'miss_rate': 0.9605911330049262,\n",
            "    'negative_predictive_value': 0.9604301948051948,\n",
            "    'positive_predictive_value': 0.04678362573099415,\n",
            "    'precision': 0.04678362573099415,\n",
            "    'recall': 0.03940886699507389,\n",
            "    'sensitivity': 0.03940886699507389,\n",
            "    'specificity': 0.9667075163398693,\n",
            "    'true_negative_rate': 0.9667075163398693,\n",
            "    'true_negatives': 9466,\n",
            "    'true_positive_rate': 0.03940886699507389,\n",
            "    'true_positives': 16},\n",
            "  Medical Books: {   'accuracy': 0.9540105903118259,\n",
            "    'f1_score': 0.07495069033530573,\n",
            "    'fall_out': 0.0315180530620387,\n",
            "    'false_discovery_rate': 0.9432835820895522,\n",
            "    'false_negative_rate': 0.8895348837209303,\n",
            "    'false_negatives': 153,\n",
            "    'false_omission_rate': 0.0155125215451688,\n",
            "    'false_positive_rate': 0.0315180530620387,\n",
            "    'false_positives': 316,\n",
            "    'hit_rate': 0.11046511627906977,\n",
            "    'informedness': 0.07894706321703104,\n",
            "    'markedness': 0.04120389636527899,\n",
            "    'matthews_correlation_coefficient': 0.05703443355673547,\n",
            "    'miss_rate': 0.8895348837209303,\n",
            "    'negative_predictive_value': 0.9844874784548312,\n",
            "    'positive_predictive_value': 0.056716417910447764,\n",
            "    'precision': 0.056716417910447764,\n",
            "    'recall': 0.11046511627906977,\n",
            "    'sensitivity': 0.11046511627906977,\n",
            "    'specificity': 0.9684819469379613,\n",
            "    'true_negative_rate': 0.9684819469379613,\n",
            "    'true_negatives': 9710,\n",
            "    'true_positive_rate': 0.11046511627906977,\n",
            "    'true_positives': 19},\n",
            "  Science & Math: {   'accuracy': 0.9558737007256325,\n",
            "    'f1_score': 0.030172413793103446,\n",
            "    'fall_out': 0.036212525972098564,\n",
            "    'false_discovery_rate': 0.9812332439678284,\n",
            "    'false_negative_rate': 0.9230769230769231,\n",
            "    'false_negatives': 84,\n",
            "    'false_omission_rate': 0.008549618320610741,\n",
            "    'false_positive_rate': 0.036212525972098564,\n",
            "    'false_positives': 366,\n",
            "    'hit_rate': 0.07692307692307693,\n",
            "    'informedness': 0.04071055095097842,\n",
            "    'markedness': 0.010217137711560742,\n",
            "    'matthews_correlation_coefficient': 0.020394737198102416,\n",
            "    'miss_rate': 0.9230769230769231,\n",
            "    'negative_predictive_value': 0.9914503816793893,\n",
            "    'positive_predictive_value': 0.01876675603217158,\n",
            "    'precision': 0.01876675603217158,\n",
            "    'recall': 0.07692307692307693,\n",
            "    'sensitivity': 0.07692307692307693,\n",
            "    'specificity': 0.9637874740279014,\n",
            "    'true_negative_rate': 0.9637874740279014,\n",
            "    'true_negatives': 9741,\n",
            "    'true_positive_rate': 0.07692307692307693,\n",
            "    'true_positives': 7},\n",
            "  Travel: {   'accuracy': 0.9540105903118259,\n",
            "    'f1_score': 0.016771488469601678,\n",
            "    'fall_out': 0.030505433157212658,\n",
            "    'false_discovery_rate': 0.9870967741935484,\n",
            "    'false_negative_rate': 0.9760479041916168,\n",
            "    'false_negatives': 163,\n",
            "    'false_omission_rate': 0.016484627831715226,\n",
            "    'false_positive_rate': 0.030505433157212658,\n",
            "    'false_positives': 306,\n",
            "    'hit_rate': 0.023952095808383235,\n",
            "    'informedness': -0.006553337348829458,\n",
            "    'markedness': -0.0035814020252635803,\n",
            "    'matthews_correlation_coefficient': -0.004844598606007852,\n",
            "    'miss_rate': 0.9760479041916168,\n",
            "    'negative_predictive_value': 0.9835153721682848,\n",
            "    'positive_predictive_value': 0.012903225806451613,\n",
            "    'precision': 0.012903225806451613,\n",
            "    'recall': 0.023952095808383235,\n",
            "    'sensitivity': 0.023952095808383235,\n",
            "    'specificity': 0.9694945668427873,\n",
            "    'true_negative_rate': 0.9694945668427873,\n",
            "    'true_negatives': 9725,\n",
            "    'true_positive_rate': 0.023952095808383235,\n",
            "    'true_positives': 4},\n",
            "  Business & Money: {   'accuracy': 0.9681310060796234,\n",
            "    'f1_score': 0,\n",
            "    'fall_out': 0.030823598704230903,\n",
            "    'false_discovery_rate': 1.0,\n",
            "    'false_negative_rate': 1.0,\n",
            "    'false_negatives': 11,\n",
            "    'false_omission_rate': 0.0011129097531363819,\n",
            "    'false_positive_rate': 0.030823598704230903,\n",
            "    'false_positives': 314,\n",
            "    'hit_rate': 0.0,\n",
            "    'informedness': -0.030823598704230903,\n",
            "    'markedness': -0.0011129097531363819,\n",
            "    'matthews_correlation_coefficient': -0.00585695173487886,\n",
            "    'miss_rate': 1.0,\n",
            "    'negative_predictive_value': 0.9988870902468636,\n",
            "    'positive_predictive_value': 0.0,\n",
            "    'precision': 0.0,\n",
            "    'recall': 0.0,\n",
            "    'sensitivity': 0.0,\n",
            "    'specificity': 0.9691764012957691,\n",
            "    'true_negative_rate': 0.9691764012957691,\n",
            "    'true_negatives': 9873,\n",
            "    'true_positive_rate': 0.0,\n",
            "    'true_positives': 0},\n",
            "  Cookbooks, Food & Wine: {   'accuracy': 0.9595018631104139,\n",
            "    'f1_score': 0.019002375296912115,\n",
            "    'fall_out': 0.03492846571287622,\n",
            "    'false_discovery_rate': 0.9888268156424581,\n",
            "    'false_negative_rate': 0.9365079365079365,\n",
            "    'false_negatives': 59,\n",
            "    'false_omission_rate': 0.00599593495934958,\n",
            "    'false_positive_rate': 0.03492846571287622,\n",
            "    'false_positives': 354,\n",
            "    'hit_rate': 0.06349206349206349,\n",
            "    'informedness': 0.028563597779187155,\n",
            "    'markedness': 0.005177249398192307,\n",
            "    'matthews_correlation_coefficient': 0.012160627837924515,\n",
            "    'miss_rate': 0.9365079365079365,\n",
            "    'negative_predictive_value': 0.9940040650406504,\n",
            "    'positive_predictive_value': 0.0111731843575419,\n",
            "    'precision': 0.0111731843575419,\n",
            "    'recall': 0.06349206349206349,\n",
            "    'sensitivity': 0.06349206349206349,\n",
            "    'specificity': 0.9650715342871238,\n",
            "    'true_negative_rate': 0.9650715342871238,\n",
            "    'true_negatives': 9781,\n",
            "    'true_positive_rate': 0.06349206349206349,\n",
            "    'true_positives': 4},\n",
            "  Politics & Social Sciences: {   'accuracy': 0.928025102961365,\n",
            "    'f1_score': 0.0516795865633075,\n",
            "    'fall_out': 0.035834609494640124,\n",
            "    'false_discovery_rate': 0.9460916442048517,\n",
            "    'false_negative_rate': 0.9503722084367245,\n",
            "    'false_negatives': 383,\n",
            "    'false_omission_rate': 0.03897425460466064,\n",
            "    'false_positive_rate': 0.035834609494640124,\n",
            "    'false_positives': 351,\n",
            "    'hit_rate': 0.04962779156327544,\n",
            "    'informedness': 0.013793182068635224,\n",
            "    'markedness': 0.014934101190487548,\n",
            "    'matthews_correlation_coefficient': 0.01435230910870509,\n",
            "    'miss_rate': 0.9503722084367245,\n",
            "    'negative_predictive_value': 0.9610257453953394,\n",
            "    'positive_predictive_value': 0.05390835579514825,\n",
            "    'precision': 0.05390835579514825,\n",
            "    'recall': 0.04962779156327544,\n",
            "    'sensitivity': 0.04962779156327544,\n",
            "    'specificity': 0.9641653905053599,\n",
            "    'true_negative_rate': 0.9641653905053599,\n",
            "    'true_negatives': 9444,\n",
            "    'true_positive_rate': 0.04962779156327544,\n",
            "    'true_positives': 20},\n",
            "  Crafts, Hobbies & Home: {   'accuracy': 0.9681310060796234,\n",
            "    'f1_score': 0,\n",
            "    'fall_out': 0.0312990580847724,\n",
            "    'false_discovery_rate': 1.0,\n",
            "    'false_negative_rate': 1.0,\n",
            "    'false_negatives': 6,\n",
            "    'false_omission_rate': 0.000607348921955686,\n",
            "    'false_positive_rate': 0.0312990580847724,\n",
            "    'false_positives': 319,\n",
            "    'hit_rate': 0.0,\n",
            "    'informedness': -0.0312990580847724,\n",
            "    'markedness': -0.000607348921955686,\n",
            "    'matthews_correlation_coefficient': -0.004359982704783838,\n",
            "    'miss_rate': 1.0,\n",
            "    'negative_predictive_value': 0.9993926510780443,\n",
            "    'positive_predictive_value': 0.0,\n",
            "    'precision': 0.0,\n",
            "    'recall': 0.0,\n",
            "    'sensitivity': 0.0,\n",
            "    'specificity': 0.9687009419152276,\n",
            "    'true_negative_rate': 0.9687009419152276,\n",
            "    'true_negatives': 9873,\n",
            "    'true_positive_rate': 0.0,\n",
            "    'true_positives': 0},\n",
            "  Religion & Spirituality: {   'accuracy': 0.957834869582271,\n",
            "    'f1_score': 0.009216589861751152,\n",
            "    'fall_out': 0.03517091483896462,\n",
            "    'false_discovery_rate': 0.994413407821229,\n",
            "    'false_negative_rate': 0.9736842105263158,\n",
            "    'false_negatives': 74,\n",
            "    'false_omission_rate': 0.007520325203252076,\n",
            "    'false_positive_rate': 0.03517091483896462,\n",
            "    'false_positives': 356,\n",
            "    'hit_rate': 0.02631578947368421,\n",
            "    'informedness': -0.008855125365280436,\n",
            "    'markedness': -0.0019337330244810769,\n",
            "    'matthews_correlation_coefficient': -0.004138048858431092,\n",
            "    'miss_rate': 0.9736842105263158,\n",
            "    'negative_predictive_value': 0.9924796747967479,\n",
            "    'positive_predictive_value': 0.00558659217877095,\n",
            "    'precision': 0.00558659217877095,\n",
            "    'recall': 0.02631578947368421,\n",
            "    'sensitivity': 0.02631578947368421,\n",
            "    'specificity': 0.9648290851610354,\n",
            "    'true_negative_rate': 0.9648290851610354,\n",
            "    'true_negatives': 9766,\n",
            "    'true_positive_rate': 0.02631578947368421,\n",
            "    'true_positives': 2},\n",
            "  Literature & Fiction: {   'accuracy': 0.9111590507942734,\n",
            "    'f1_score': 0.12884615384615386,\n",
            "    'fall_out': 0.03088559722659945,\n",
            "    'false_discovery_rate': 0.814404432132964,\n",
            "    'false_negative_rate': 0.9013254786450663,\n",
            "    'false_negatives': 612,\n",
            "    'false_omission_rate': 0.06221408966148212,\n",
            "    'false_positive_rate': 0.03088559722659945,\n",
            "    'false_positives': 294,\n",
            "    'hit_rate': 0.09867452135493372,\n",
            "    'informedness': 0.06778892412833426,\n",
            "    'markedness': 0.12338147820555401,\n",
            "    'matthews_correlation_coefficient': 0.09145434743585469,\n",
            "    'miss_rate': 0.9013254786450663,\n",
            "    'negative_predictive_value': 0.9377859103385179,\n",
            "    'positive_predictive_value': 0.18559556786703602,\n",
            "    'precision': 0.18559556786703602,\n",
            "    'recall': 0.09867452135493372,\n",
            "    'sensitivity': 0.09867452135493372,\n",
            "    'specificity': 0.9691144027734006,\n",
            "    'true_negative_rate': 0.9691144027734006,\n",
            "    'true_negatives': 9225,\n",
            "    'true_positive_rate': 0.09867452135493372,\n",
            "    'true_positives': 67},\n",
            "  Humor & Entertainment: {   'accuracy': 0.9680329476367915,\n",
            "    'f1_score': 0,\n",
            "    'fall_out': 0.031492200529775305,\n",
            "    'false_discovery_rate': 1.0,\n",
            "    'false_negative_rate': 1.0,\n",
            "    'false_negatives': 5,\n",
            "    'false_omission_rate': 0.0005062265870203753,\n",
            "    'false_positive_rate': 0.031492200529775305,\n",
            "    'false_positives': 321,\n",
            "    'hit_rate': 0.0,\n",
            "    'informedness': -0.031492200529775305,\n",
            "    'markedness': -0.0005062265870203753,\n",
            "    'matthews_correlation_coefficient': -0.003992767109655738,\n",
            "    'miss_rate': 1.0,\n",
            "    'negative_predictive_value': 0.9994937734129796,\n",
            "    'positive_predictive_value': 0.0,\n",
            "    'precision': 0.0,\n",
            "    'recall': 0.0,\n",
            "    'sensitivity': 0.0,\n",
            "    'specificity': 0.9685077994702247,\n",
            "    'true_negative_rate': 0.9685077994702247,\n",
            "    'true_negatives': 9872,\n",
            "    'true_positive_rate': 0.0,\n",
            "    'true_positives': 0},\n",
            "  Law: {   'accuracy': 0.9264561678760541,\n",
            "    'f1_score': 0.05778894472361809,\n",
            "    'fall_out': 0.03343246846477288,\n",
            "    'false_discovery_rate': 0.9340974212034384,\n",
            "    'false_negative_rate': 0.9485458612975392,\n",
            "    'false_negatives': 424,\n",
            "    'false_omission_rate': 0.04305005584323285,\n",
            "    'false_positive_rate': 0.03343246846477288,\n",
            "    'false_positives': 326,\n",
            "    'hit_rate': 0.05145413870246085,\n",
            "    'informedness': 0.01802167023768808,\n",
            "    'markedness': 0.022852522953328736,\n",
            "    'matthews_correlation_coefficient': 0.020293857020391354,\n",
            "    'miss_rate': 0.9485458612975392,\n",
            "    'negative_predictive_value': 0.9569499441567672,\n",
            "    'positive_predictive_value': 0.0659025787965616,\n",
            "    'precision': 0.0659025787965616,\n",
            "    'recall': 0.05145413870246085,\n",
            "    'sensitivity': 0.05145413870246085,\n",
            "    'specificity': 0.9665675315352271,\n",
            "    'true_negative_rate': 0.9665675315352271,\n",
            "    'true_negatives': 9425,\n",
            "    'true_positive_rate': 0.05145413870246085,\n",
            "    'true_positives': 23},\n",
            "  Computers & Technology: {   'accuracy': 0.9531280643263385,\n",
            "    'f1_score': 0.047808764940239036,\n",
            "    'fall_out': 0.03508597554915016,\n",
            "    'false_discovery_rate': 0.9671232876712329,\n",
            "    'false_negative_rate': 0.9124087591240876,\n",
            "    'false_negatives': 125,\n",
            "    'false_omission_rate': 0.01271229533204521,\n",
            "    'false_positive_rate': 0.03508597554915016,\n",
            "    'false_positives': 353,\n",
            "    'hit_rate': 0.08759124087591241,\n",
            "    'informedness': 0.05250526532676236,\n",
            "    'markedness': 0.02016441699672189,\n",
            "    'matthews_correlation_coefficient': 0.03253825540148643,\n",
            "    'miss_rate': 0.9124087591240876,\n",
            "    'negative_predictive_value': 0.9872877046679548,\n",
            "    'positive_predictive_value': 0.03287671232876712,\n",
            "    'precision': 0.03287671232876712,\n",
            "    'recall': 0.08759124087591241,\n",
            "    'sensitivity': 0.08759124087591241,\n",
            "    'specificity': 0.9649140244508498,\n",
            "    'true_negative_rate': 0.9649140244508498,\n",
            "    'true_negatives': 9708,\n",
            "    'true_positive_rate': 0.08759124087591241,\n",
            "    'true_positives': 12},\n",
            "  Test Preparation: {   'accuracy': 0.9327319082172975,\n",
            "    'f1_score': 0.15099009900990099,\n",
            "    'fall_out': 0.027274598600247058,\n",
            "    'false_discovery_rate': 0.8128834355828221,\n",
            "    'false_negative_rate': 0.8734439834024896,\n",
            "    'false_negatives': 421,\n",
            "    'false_omission_rate': 0.04264586709886553,\n",
            "    'false_positive_rate': 0.027274598600247058,\n",
            "    'false_positives': 265,\n",
            "    'hit_rate': 0.12655601659751037,\n",
            "    'informedness': 0.09928141799726342,\n",
            "    'markedness': 0.1444706973183123,\n",
            "    'matthews_correlation_coefficient': 0.11976333198778118,\n",
            "    'miss_rate': 0.8734439834024896,\n",
            "    'negative_predictive_value': 0.9573541329011345,\n",
            "    'positive_predictive_value': 0.18711656441717792,\n",
            "    'precision': 0.18711656441717792,\n",
            "    'recall': 0.12655601659751037,\n",
            "    'sensitivity': 0.12655601659751037,\n",
            "    'specificity': 0.9727254013997529,\n",
            "    'true_negative_rate': 0.9727254013997529,\n",
            "    'true_negatives': 9451,\n",
            "    'true_positive_rate': 0.12655601659751037,\n",
            "    'true_positives': 61},\n",
            "  Arts & Photography: {   'accuracy': 0.941557168072171,\n",
            "    'f1_score': 0.04487179487179488,\n",
            "    'fall_out': 0.03171076550191876,\n",
            "    'false_discovery_rate': 0.9573170731707317,\n",
            "    'false_negative_rate': 0.9527027027027027,\n",
            "    'false_negatives': 282,\n",
            "    'false_omission_rate': 0.02857142857142858,\n",
            "    'false_positive_rate': 0.03171076550191876,\n",
            "    'false_positives': 314,\n",
            "    'hit_rate': 0.0472972972972973,\n",
            "    'informedness': 0.0155865317953785,\n",
            "    'markedness': 0.014111498257839639,\n",
            "    'matthews_correlation_coefficient': 0.01483068832779676,\n",
            "    'miss_rate': 0.9527027027027027,\n",
            "    'negative_predictive_value': 0.9714285714285714,\n",
            "    'positive_predictive_value': 0.042682926829268296,\n",
            "    'precision': 0.042682926829268296,\n",
            "    'recall': 0.0472972972972973,\n",
            "    'sensitivity': 0.0472972972972973,\n",
            "    'specificity': 0.9682892344980812,\n",
            "    'true_negative_rate': 0.9682892344980812,\n",
            "    'true_negatives': 9588,\n",
            "    'true_positive_rate': 0.0472972972972973,\n",
            "    'true_positives': 14},\n",
            "  Parenting & Relationships: {   'accuracy': 0.9494018434987253,\n",
            "    'f1_score': 0.0851063829787234,\n",
            "    'fall_out': 0.035068438405435054,\n",
            "    'false_discovery_rate': 0.9359999999999999,\n",
            "    'false_negative_rate': 0.873015873015873,\n",
            "    'false_negatives': 165,\n",
            "    'false_omission_rate': 0.016797312430011146,\n",
            "    'false_positive_rate': 0.035068438405435054,\n",
            "    'false_positives': 351,\n",
            "    'hit_rate': 0.12698412698412698,\n",
            "    'informedness': 0.09191568857869203,\n",
            "    'markedness': 0.04720268756998891,\n",
            "    'matthews_correlation_coefficient': 0.0658685625375291,\n",
            "    'miss_rate': 0.873015873015873,\n",
            "    'negative_predictive_value': 0.9832026875699889,\n",
            "    'positive_predictive_value': 0.064,\n",
            "    'precision': 0.064,\n",
            "    'recall': 0.12698412698412698,\n",
            "    'sensitivity': 0.12698412698412698,\n",
            "    'specificity': 0.964931561594565,\n",
            "    'true_negative_rate': 0.964931561594565,\n",
            "    'true_negatives': 9658,\n",
            "    'true_positive_rate': 0.12698412698412698,\n",
            "    'true_positives': 24},\n",
            "  Romance: {   'accuracy': 0.9173367326926848,\n",
            "    'f1_score': 0.11542497376705142,\n",
            "    'fall_out': 0.030138700594431134,\n",
            "    'false_discovery_rate': 0.8401162790697674,\n",
            "    'false_negative_rate': 0.909688013136289,\n",
            "    'false_negatives': 554,\n",
            "    'false_omission_rate': 0.05622082403085038,\n",
            "    'false_positive_rate': 0.030138700594431134,\n",
            "    'false_positives': 289,\n",
            "    'hit_rate': 0.090311986863711,\n",
            "    'informedness': 0.06017328626927987,\n",
            "    'markedness': 0.10366289689938224,\n",
            "    'matthews_correlation_coefficient': 0.07897934648140213,\n",
            "    'miss_rate': 0.909688013136289,\n",
            "    'negative_predictive_value': 0.9437791759691496,\n",
            "    'positive_predictive_value': 0.15988372093023256,\n",
            "    'precision': 0.15988372093023256,\n",
            "    'recall': 0.090311986863711,\n",
            "    'sensitivity': 0.090311986863711,\n",
            "    'specificity': 0.9698612994055689,\n",
            "    'true_negative_rate': 0.9698612994055689,\n",
            "    'true_negatives': 9300,\n",
            "    'true_positive_rate': 0.090311986863711,\n",
            "    'true_positives': 55},\n",
            "  History: {   'accuracy': 0.9323396744459698,\n",
            "    'f1_score': 0.07999999999999999,\n",
            "    'fall_out': 0.030978427563643773,\n",
            "    'false_discovery_rate': 0.9099099099099099,\n",
            "    'false_negative_rate': 0.9280575539568345,\n",
            "    'false_negatives': 387,\n",
            "    'false_omission_rate': 0.0392295995945261,\n",
            "    'false_positive_rate': 0.030978427563643773,\n",
            "    'false_positives': 303,\n",
            "    'hit_rate': 0.07194244604316546,\n",
            "    'informedness': 0.04096401847952169,\n",
            "    'markedness': 0.05086049049556407,\n",
            "    'matthews_correlation_coefficient': 0.04564482525476266,\n",
            "    'miss_rate': 0.9280575539568345,\n",
            "    'negative_predictive_value': 0.9607704004054739,\n",
            "    'positive_predictive_value': 0.09009009009009009,\n",
            "    'precision': 0.09009009009009009,\n",
            "    'recall': 0.07194244604316546,\n",
            "    'sensitivity': 0.07194244604316546,\n",
            "    'specificity': 0.9690215724363562,\n",
            "    'true_negative_rate': 0.9690215724363562,\n",
            "    'true_negatives': 9478,\n",
            "    'true_positive_rate': 0.07194244604316546,\n",
            "    'true_positives': 30},\n",
            "  Comics & Graphic Novels: {   'accuracy': 0.9580309864679349,\n",
            "    'f1_score': 0.218978102189781,\n",
            "    'fall_out': 0.028708612583775106,\n",
            "    'false_discovery_rate': 0.8270893371757925,\n",
            "    'false_negative_rate': 0.7014925373134329,\n",
            "    'false_negatives': 141,\n",
            "    'false_omission_rate': 0.014313267688559561,\n",
            "    'false_positive_rate': 0.028708612583775106,\n",
            "    'false_positives': 287,\n",
            "    'hit_rate': 0.29850746268656714,\n",
            "    'informedness': 0.2697988501027919,\n",
            "    'markedness': 0.15859739513564786,\n",
            "    'matthews_correlation_coefficient': 0.20685597607247405,\n",
            "    'miss_rate': 0.7014925373134329,\n",
            "    'negative_predictive_value': 0.9856867323114404,\n",
            "    'positive_predictive_value': 0.1729106628242075,\n",
            "    'precision': 0.1729106628242075,\n",
            "    'recall': 0.29850746268656714,\n",
            "    'sensitivity': 0.29850746268656714,\n",
            "    'specificity': 0.9712913874162249,\n",
            "    'true_negative_rate': 0.9712913874162249,\n",
            "    'true_negatives': 9710,\n",
            "    'true_positive_rate': 0.29850746268656714,\n",
            "    'true_positives': 60},\n",
            "  Reference: {   'accuracy': 0.9581290449107668,\n",
            "    'f1_score': 0.027334851936218676,\n",
            "    'fall_out': 0.034220156265453494,\n",
            "    'false_discovery_rate': 0.9829545454545454,\n",
            "    'false_negative_rate': 0.9310344827586207,\n",
            "    'false_negatives': 81,\n",
            "    'false_omission_rate': 0.00822669104204754,\n",
            "    'false_positive_rate': 0.034220156265453494,\n",
            "    'false_positives': 346,\n",
            "    'hit_rate': 0.06896551724137931,\n",
            "    'informedness': 0.03474536097592584,\n",
            "    'markedness': 0.008818763503406934,\n",
            "    'matthews_correlation_coefficient': 0.01750460286002505,\n",
            "    'miss_rate': 0.9310344827586207,\n",
            "    'negative_predictive_value': 0.9917733089579525,\n",
            "    'positive_predictive_value': 0.017045454545454544,\n",
            "    'precision': 0.017045454545454544,\n",
            "    'recall': 0.06896551724137931,\n",
            "    'sensitivity': 0.06896551724137931,\n",
            "    'specificity': 0.9657798437345465,\n",
            "    'true_negative_rate': 0.9657798437345465,\n",
            "    'true_negatives': 9765,\n",
            "    'true_positive_rate': 0.06896551724137931,\n",
            "    'true_positives': 6},\n",
            "  Teen & Young Adult: {   'accuracy': 0.9515591292410277,\n",
            "    'f1_score': 0.04263565891472868,\n",
            "    'fall_out': 0.034176962933439636,\n",
            "    'false_discovery_rate': 0.9689265536723164,\n",
            "    'false_negative_rate': 0.9320987654320988,\n",
            "    'false_negatives': 151,\n",
            "    'false_omission_rate': 0.015339292970337315,\n",
            "    'false_positive_rate': 0.034176962933439636,\n",
            "    'false_positives': 343,\n",
            "    'hit_rate': 0.06790123456790123,\n",
            "    'informedness': 0.03372427163446168,\n",
            "    'markedness': 0.015734153357346292,\n",
            "    'matthews_correlation_coefficient': 0.023035252587315484,\n",
            "    'miss_rate': 0.9320987654320988,\n",
            "    'negative_predictive_value': 0.9846607070296627,\n",
            "    'positive_predictive_value': 0.031073446327683617,\n",
            "    'precision': 0.031073446327683617,\n",
            "    'recall': 0.06790123456790123,\n",
            "    'sensitivity': 0.06790123456790123,\n",
            "    'specificity': 0.9658230370665604,\n",
            "    'true_negative_rate': 0.9658230370665604,\n",
            "    'true_negatives': 9693,\n",
            "    'true_positive_rate': 0.06790123456790123,\n",
            "    'true_positives': 11},\n",
            "  Self-Help: {   'accuracy': 0.8456560109825456,\n",
            "    'f1_score': 0.11173814898419863,\n",
            "    'fall_out': 0.025380130330398987,\n",
            "    'false_discovery_rate': 0.691588785046729,\n",
            "    'false_negative_rate': 0.9317711922811854,\n",
            "    'false_negatives': 1352,\n",
            "    'false_omission_rate': 0.1368836691303027,\n",
            "    'false_positive_rate': 0.025380130330398987,\n",
            "    'false_positives': 222,\n",
            "    'hit_rate': 0.06822880771881461,\n",
            "    'informedness': 0.04284867738841558,\n",
            "    'markedness': 0.17152754582296836,\n",
            "    'matthews_correlation_coefficient': 0.08573055741213308,\n",
            "    'miss_rate': 0.9317711922811854,\n",
            "    'negative_predictive_value': 0.8631163308696973,\n",
            "    'positive_predictive_value': 0.308411214953271,\n",
            "    'precision': 0.308411214953271,\n",
            "    'recall': 0.06822880771881461,\n",
            "    'sensitivity': 0.06822880771881461,\n",
            "    'specificity': 0.974619869669601,\n",
            "    'true_negative_rate': 0.974619869669601,\n",
            "    'true_negatives': 8525,\n",
            "    'true_positive_rate': 0.06822880771881461,\n",
            "    'true_positives': 99},\n",
            "  Calendars: {   'accuracy': 0.8434006667974112,\n",
            "    'f1_score': 0.19627579265223954,\n",
            "    'fall_out': 0.014421385860007074,\n",
            "    'false_discovery_rate': 0.3867924528301887,\n",
            "    'false_negative_rate': 0.8831635710005992,\n",
            "    'false_negatives': 1474,\n",
            "    'false_omission_rate': 0.14919028340080975,\n",
            "    'false_positive_rate': 0.014421385860007074,\n",
            "    'false_positives': 123,\n",
            "    'hit_rate': 0.11683642899940083,\n",
            "    'informedness': 0.10241504313939376,\n",
            "    'markedness': 0.46401726376900143,\n",
            "    'matthews_correlation_coefficient': 0.21799621117424445,\n",
            "    'miss_rate': 0.8831635710005992,\n",
            "    'negative_predictive_value': 0.8508097165991902,\n",
            "    'positive_predictive_value': 0.6132075471698113,\n",
            "    'precision': 0.6132075471698113,\n",
            "    'recall': 0.11683642899940083,\n",
            "    'sensitivity': 0.11683642899940083,\n",
            "    'specificity': 0.9855786141399929,\n",
            "    'true_negative_rate': 0.9855786141399929,\n",
            "    'true_negatives': 8406,\n",
            "    'true_positive_rate': 0.11683642899940083,\n",
            "    'true_positives': 195},\n",
            "  Science Fiction & Fantasy: {   'accuracy': 0.9561678760541282,\n",
            "    'f1_score': 0.11485148514851486,\n",
            "    'fall_out': 0.027994401119776025,\n",
            "    'false_discovery_rate': 0.9061488673139159,\n",
            "    'false_negative_rate': 0.8520408163265306,\n",
            "    'false_negatives': 167,\n",
            "    'false_omission_rate': 0.016887450702801066,\n",
            "    'false_positive_rate': 0.027994401119776025,\n",
            "    'false_positives': 280,\n",
            "    'hit_rate': 0.14795918367346939,\n",
            "    'informedness': 0.11996478255369336,\n",
            "    'markedness': 0.07696368198328307,\n",
            "    'matthews_correlation_coefficient': 0.09608814377255998,\n",
            "    'miss_rate': 0.8520408163265306,\n",
            "    'negative_predictive_value': 0.9831125492971989,\n",
            "    'positive_predictive_value': 0.09385113268608414,\n",
            "    'precision': 0.09385113268608414,\n",
            "    'recall': 0.14795918367346939,\n",
            "    'sensitivity': 0.14795918367346939,\n",
            "    'specificity': 0.972005598880224,\n",
            "    'true_negative_rate': 0.972005598880224,\n",
            "    'true_negatives': 9722,\n",
            "    'true_positive_rate': 0.14795918367346939,\n",
            "    'true_positives': 29},\n",
            "  Mystery, Thriller & Suspense: {   'accuracy': 0.9433222200431457,\n",
            "    'f1_score': 0.12158054711246201,\n",
            "    'fall_out': 0.030069859269008847,\n",
            "    'false_discovery_rate': 0.8813056379821959,\n",
            "    'false_negative_rate': 0.8753894080996885,\n",
            "    'false_negatives': 281,\n",
            "    'false_omission_rate': 0.028496095730656146,\n",
            "    'false_positive_rate': 0.030069859269008847,\n",
            "    'false_positives': 297,\n",
            "    'hit_rate': 0.12461059190031153,\n",
            "    'informedness': 0.09454073263130258,\n",
            "    'markedness': 0.09019826628714811,\n",
            "    'matthews_correlation_coefficient': 0.09234397748018172,\n",
            "    'miss_rate': 0.8753894080996885,\n",
            "    'negative_predictive_value': 0.9715039042693439,\n",
            "    'positive_predictive_value': 0.11869436201780416,\n",
            "    'precision': 0.11869436201780416,\n",
            "    'recall': 0.12461059190031153,\n",
            "    'sensitivity': 0.12461059190031153,\n",
            "    'specificity': 0.9699301407309912,\n",
            "    'true_negative_rate': 0.9699301407309912,\n",
            "    'true_negatives': 9580,\n",
            "    'true_positive_rate': 0.12461059190031153,\n",
            "    'true_positives': 40},\n",
            "  Biographies & Memoirs: {   'accuracy': 0.8951755246126691,\n",
            "    'f1_score': 0.09329940627650551,\n",
            "    'fall_out': 0.03210666666666662,\n",
            "    'false_discovery_rate': 0.8455056179775281,\n",
            "    'false_negative_rate': 0.9331713244228432,\n",
            "    'false_negatives': 768,\n",
            "    'false_omission_rate': 0.0780329201381833,\n",
            "    'false_positive_rate': 0.03210666666666662,\n",
            "    'false_positives': 301,\n",
            "    'hit_rate': 0.06682867557715674,\n",
            "    'informedness': 0.03472200891049004,\n",
            "    'markedness': 0.0764614618842887,\n",
            "    'matthews_correlation_coefficient': 0.05152567865497131,\n",
            "    'miss_rate': 0.9331713244228432,\n",
            "    'negative_predictive_value': 0.9219670798618167,\n",
            "    'positive_predictive_value': 0.1544943820224719,\n",
            "    'precision': 0.1544943820224719,\n",
            "    'recall': 0.06682867557715674,\n",
            "    'sensitivity': 0.06682867557715674,\n",
            "    'specificity': 0.9678933333333334,\n",
            "    'true_negative_rate': 0.9678933333333334,\n",
            "    'true_negatives': 9074,\n",
            "    'true_positive_rate': 0.06682867557715674,\n",
            "    'true_positives': 55}}\n",
            "\n",
            "Finished: experiment_run\n",
            "Saved to: results/experiment_run_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qTL1K5SSXD1U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 16.5 Sklearn Algorithm Cheatsheet"
      ]
    },
    {
      "metadata": {
        "id": "T3d3iRE6XN1M",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Sklearn model selection"
      ]
    },
    {
      "metadata": {
        "id": "A8oBgYpGTR4S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "url:  https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html\n",
        "\n",
        "![cheat-sheet](https://scikit-learn.org/stable/_static/ml_map.png)"
      ]
    },
    {
      "metadata": {
        "id": "-EDmZcGjCBP1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model Explainability with SHAP\n"
      ]
    },
    {
      "metadata": {
        "id": "tc7GQk3NCF-b",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "[https://github.com/slundberg/shap](https://github.com/slundberg/shap):  *A unified approach to explain the output of any machine learning model.*"
      ]
    },
    {
      "metadata": {
        "id": "WqFiPokws8nN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Install SHAP"
      ]
    },
    {
      "metadata": {
        "id": "PiPf95catAHk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q shap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-Sokh2GCsra1",
        "colab_type": "code",
        "outputId": "7a8354ee-752b-4dad-eb5a-bdb3daa0e768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 42
        }
      },
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "import shap\n",
        "\n",
        "shap.initjs()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TYUXsHf65HUU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Load Census Data"
      ]
    },
    {
      "metadata": {
        "id": "NO1iYErvBluS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "[Adult datasets](https://archive.ics.uci.edu/ml/datasets/Adult)\n",
        "\n",
        "* *Predict whether income exceeds $50K/yr based on census data. Also known as \"Census Income\" dataset.*"
      ]
    },
    {
      "metadata": {
        "id": "hD_xS-tV5Gp_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X,y = shap.datasets.adult()\n",
        "X_display,y_display = shap.datasets.adult(display=True)\n",
        "X_train, X_valid, y_train, y_valid = sklearn.model_selection.train_test_split(X, y, test_size=0.2, random_state=7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rKBTIdWgS7Vx",
        "colab_type": "code",
        "outputId": "73a92dc0-1557-47fd-c546-6f7ff38854af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((26048, 12), (26048,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "tfuP7W1N9t1D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Train a k-nearest neighbor Classifier"
      ]
    },
    {
      "metadata": {
        "id": "slogcMRd9yX2",
        "colab_type": "code",
        "outputId": "fa3fd6f8-cf88-452b-c488-5df0ef827b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "cell_type": "code",
      "source": [
        "knn = sklearn.neighbors.KNeighborsClassifier()\n",
        "knn.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "           weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "wtq8kfB192KS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Explain predictions"
      ]
    },
    {
      "metadata": {
        "id": "kLuiobE595g3",
        "colab_type": "code",
        "outputId": "809d39ec-3bdd-4976-c558-2709aff46cf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "cell_type": "code",
      "source": [
        "f = lambda x: knn.predict_proba(x)[:,1]\n",
        "med = X_train.median().values.reshape((1,X_train.shape[1]))\n",
        "explainer = shap.KernelExplainer(f, med)\n",
        "shap_values_single = explainer.shap_values(X.iloc[0,:], nsamples=1000)\n",
        "\n",
        "#Plot\n",
        "shap.initjs()\n",
        "shap.force_plot(explainer.expected_value, shap_values_single, X_display.iloc[0,:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/shap/explainers/kernel.py:535: UserWarning: l1_reg=\"auto\" is deprecated and in the next version (v0.29) the behavior will change from a conditional use of AIC to simply \"num_features(10)\"!\n",
            "  \"l1_reg=\\\"auto\\\" is deprecated and in the next version (v0.29) the behavior will change from a \" \\\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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strict\";function r(t){this._root=t,this._startText=this.getText(),this._fallbackText=null}var i=n(3),o=n(18),a=n(172);i(r.prototype,{destructor:function(){this._root=null,this._startText=null,this._fallbackText=null},getText:function(){return\"value\"in this._root?this._root.value:this._root[a()]},getData:function(){if(this._fallbackText)return this._fallbackText;var t,e,n=this._startText,r=n.length,i=this.getText(),o=i.length;for(t=0;t<r&&n[t]===i[t];t++);var a=r-t;for(e=1;e<=a&&n[r-e]===i[o-e];e++);var u=e>1?1-e:void 0;return this._fallbackText=i.slice(t,u),this._fallbackText}}),o.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";var r=n(21),i=r.injection.MUST_USE_PROPERTY,o=r.injection.HAS_BOOLEAN_VALUE,a=r.injection.HAS_NUMERIC_VALUE,u=r.injection.HAS_POSITIVE_NUMERIC_VALUE,c=r.injection.HAS_OVERLOADED_BOOLEAN_VALUE,s={isCustomAttribute:RegExp.prototype.test.bind(new RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"),null!=e.dangerouslySetInnerHTML&&(null!=e.children&&g(\"60\"),\"object\"==typeof e.dangerouslySetInnerHTML&&z in e.dangerouslySetInnerHTML||g(\"61\")),null!=e.style&&\"object\"!=typeof e.style&&g(\"62\",r(t)))}function o(t,e,n,r){if(!(r instanceof D)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===q,u=o?i._node:i._ownerDocument;B(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;k.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;N.postMountWrapper(t)}function c(){var t=this;O.postMountWrapper(t)}function s(){var t=this;A.postMountWrapper(t)}function l(){L.track(this)}function f(){var t=this;t._rootNodeID||g(\"63\");var e=j(t);switch(e||g(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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              "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
              "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
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      ]
    },
    {
      "metadata": {
        "id": "SEIH6ESVXKOb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 16.6 Recommendations"
      ]
    },
    {
      "metadata": {
        "id": "ANeUczzxXOf2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Install Surprise"
      ]
    },
    {
      "metadata": {
        "id": "0R_rvBACYbp9",
        "colab_type": "code",
        "outputId": "6ff83a68-19f5-4bcc-89cf-ad0fde5f058e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        " !pip install -q scikit-surprise"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K    100% |████████████████████████████████| 3.3MB 10.9MB/s \n",
            "\u001b[?25h  Building wheel for scikit-surprise (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "48NhoRpWYmc_",
        "colab_type": "code",
        "outputId": "1c1c121f-12af-4612-caae-5550bab8e28a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "cell_type": "code",
      "source": [
        "from surprise import SVD\n",
        "from surprise import Dataset\n",
        "from surprise.model_selection import cross_validate\n",
        "\n",
        "# Load the movielens-100k dataset (download it if needed).\n",
        "data = Dataset.load_builtin('ml-100k')\n",
        "\n",
        "# Use the famous SVD algorithm.\n",
        "algo = SVD()\n",
        "\n",
        "# Run 5-fold cross-validation and print results.\n",
        "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
            "\n",
            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
            "RMSE (testset)    0.9460  0.9371  0.9344  0.9300  0.9354  0.9366  0.0053  \n",
            "MAE (testset)     0.7441  0.7390  0.7338  0.7312  0.7397  0.7376  0.0045  \n",
            "Fit time          5.30    5.22    5.25    5.23    5.23    5.24    0.03    \n",
            "Test time         0.16    0.26    0.16    0.15    0.16    0.18    0.04    \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'fit_time': (5.302802085876465,\n",
              "  5.2162816524505615,\n",
              "  5.2515764236450195,\n",
              "  5.2256152629852295,\n",
              "  5.226689577102661),\n",
              " 'test_mae': array([0.74405382, 0.73902602, 0.73379062, 0.73123877, 0.73968219]),\n",
              " 'test_rmse': array([0.94601002, 0.93705768, 0.93435584, 0.93001856, 0.93540059]),\n",
              " 'test_time': (0.16068744659423828,\n",
              "  0.26168084144592285,\n",
              "  0.1584162712097168,\n",
              "  0.1538381576538086,\n",
              "  0.16183090209960938)}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "bRKFiJVvPyZi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Handcoded Similarity Engine"
      ]
    },
    {
      "metadata": {
        "id": "aUwh2jQMP2UD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```python\n",
        "\"\"\"Data Science Algorithms\"\"\"\n",
        "\n",
        "\n",
        "def tanimoto(list1, list2):\n",
        "    \"\"\"tanimoto coefficient\n",
        "    In [2]: list2=['39229', '31995', '32015']\n",
        "    In [3]: list1=['31936', '35989', '27489', '39229', '15468', '31993', '26478']\n",
        "    In [4]: tanimoto(list1,list2)\n",
        "    Out[4]: 0.1111111111111111\n",
        "    Uses intersection of two sets to determine numerical score\n",
        "    \"\"\"\n",
        "\n",
        "    intersection = set(list1).intersection(set(list2))\n",
        "    return float(len(intersection))/(len(list1) + len(list2) - len(intersection))\n",
        "```\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "3b1-VTl8jubf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Collaborative Filtering Recommendation Exploration\n"
      ]
    },
    {
      "metadata": {
        "id": "xrWJwi5pQLPa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Knn Exploration of MovieLens with Surprise"
      ]
    },
    {
      "metadata": {
        "id": "-GaS8y05jubj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import io  # needed because of weird encoding of u.item file\n",
        "from surprise import KNNBaseline\n",
        "from surprise import Dataset\n",
        "from surprise import get_dataset_dir"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wQ8dueD5jubm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Helper Function to Convert IDS to Names"
      ]
    },
    {
      "metadata": {
        "id": "ReVmJK2Tjubn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def read_item_names():\n",
        "    \"\"\"Read the u.item file from MovieLens 100-k dataset and return two\n",
        "    mappings to convert raw ids into movie names and movie names into raw ids.\n",
        "    \"\"\"\n",
        "\n",
        "    file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'\n",
        "    rid_to_name = {}\n",
        "    name_to_rid = {}\n",
        "    with io.open(file_name, 'r', encoding='ISO-8859-1') as f:\n",
        "        for line in f:\n",
        "            line = line.split('|')\n",
        "            rid_to_name[line[0]] = line[1]\n",
        "            name_to_rid[line[1]] = line[0]\n",
        "\n",
        "    return rid_to_name, name_to_rid"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nxPSuxe3jubp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Train KNN based model"
      ]
    },
    {
      "metadata": {
        "id": "tAyBaE_Cjubq",
        "colab_type": "code",
        "outputId": "1e219365-5b00-42d1-8561-a9d8ac0a4f1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "# First, train the algorithm to compute the similarities between items\n",
        "data = Dataset.load_builtin('ml-100k')\n",
        "trainset = data.build_full_trainset()\n",
        "sim_options = {'name': 'pearson_baseline', 'user_based': False}\n",
        "algo = KNNBaseline(sim_options=sim_options)\n",
        "algo.fit(trainset)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Estimating biases using als...\n",
            "Computing the pearson_baseline similarity matrix...\n",
            "Done computing similarity matrix.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.prediction_algorithms.knns.KNNBaseline at 0x7f596007c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "wv5b_Nhljubx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Recommendations"
      ]
    },
    {
      "metadata": {
        "id": "1QXutzAkjubx",
        "colab_type": "code",
        "outputId": "57f0cbe3-2e1e-4760-f7ed-3ff1471b9401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "cell_type": "code",
      "source": [
        "# Read the mappings raw id <-> movie name\n",
        "rid_to_name, name_to_rid = read_item_names()\n",
        "\n",
        "# Retrieve inner id of the movie Toy Story\n",
        "toy_story_raw_id = name_to_rid['Toy Story (1995)']\n",
        "toy_story_inner_id = algo.trainset.to_inner_iid(toy_story_raw_id)\n",
        "\n",
        "# Retrieve inner ids of the nearest neighbors of Toy Story.\n",
        "toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)\n",
        "\n",
        "# Convert inner ids of the neighbors into names.\n",
        "toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)\n",
        "                       for inner_id in toy_story_neighbors)\n",
        "toy_story_neighbors = (rid_to_name[rid]\n",
        "                       for rid in toy_story_neighbors)\n",
        "\n",
        "for movie in toy_story_neighbors:\n",
        "  print(movie)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Beauty and the Beast (1991)\n",
            "Raiders of the Lost Ark (1981)\n",
            "That Thing You Do! (1996)\n",
            "Lion King, The (1994)\n",
            "Craft, The (1996)\n",
            "Liar Liar (1997)\n",
            "Aladdin (1992)\n",
            "Cool Hand Luke (1967)\n",
            "Winnie the Pooh and the Blustery Day (1968)\n",
            "Indiana Jones and the Last Crusade (1989)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Igt__Lo_ZF4k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}