doc/karura_notebook_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## karura demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Render our plots inline\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(os.path.join(os.getcwd(), \"../\"))\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Read the dataset to dataframe\n",
    "FILE_NAME = \"../tests/data/titanic_train.csv\"\n",
    "\n",
    "df = pd.read_csv(FILE_NAME)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from karura.core.auto_run import AutoRun\n",
    "import karura.core.insights as I\n",
    "\n",
    "\n",
    "# Now, construct the pipeline of insights\n",
    "insights = [\n",
    "    I.NAFrequencyCheckInsight(),\n",
    "    I.CategoricalItemInsight(),\n",
    "    I.DatetimeToCategoricalInsight(),\n",
    "    I.CategoryReductionInsight(),\n",
    "    I.NumericalScalingInsight(),\n",
    "    I.CategoricalToDummyInsight(),\n",
    "    I.LabelFormatInsight(),\n",
    "    I.FeatureSelectionInsight(),\n",
    "    I.ModelSelectionInsight()\n",
    "]\n",
    "\n",
    "karura = AutoRun(df, insights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "karura.set_target(\"Survived\")\n",
    "karura.ignore([\"PassengerId\", \"Ticket\", \"Name\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Execute Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabin は欠損値が多い項目のため、除外されました\n",
      "Sex,Embarked,Pclass,SibSp,Parch,Survived は分類項目として処理しました\n",
      "項目Age,Fare,Pclass_1,Pclass_2,Pclass_3,Sex_female,Sex_male,SibSp_0,SibSp_1,Parch_0,Parch_2,Embarked_Cは予測に有効な項目です。これらを利用し、モデルを構築します。\n",
      "モデルの精度は0.755です(利用モデル:RandomForestClassifier)。各項目の貢献度は図のようになっています。\n"
     ]
    },
    {
     "data": {
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cAKBSqaBSqXpss2XLFowePRpXXnmlz32YTCaYTCZp2W63/9K8Pun1+j7fp1yYVXlK/0zB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\nj4iIhMSCIiIiIbGgiIhISCwoIiIS0v8DS+IDEgRjBu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27e9c962828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GXMRb4rnvss4TlphMJmJMMcSYY7DF2EJaju6kpqYas1e6Pe5OSVyru9XYFpjU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27ea0396668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "descriptions = karura.execute()\n",
    "for d in descriptions:\n",
    "    print(d)\n",
    "        \n",
    "result = karura.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}