Sample code using judas library.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jude's Automated System (JUDAS)\n",
    "\n",
    "Author: Jude Michael 2019, MSDS 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.167080Z",
     "start_time": "2019-07-24T06:09:46.972242Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.175850Z",
     "start_time": "2019-07-24T06:09:49.170303Z"
    }
   },
   "outputs": [],
   "source": [
    "# df = pd.read_excel('titanic.xls')\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.245380Z",
     "start_time": "2019-07-24T06:09:49.180410Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.270425Z",
     "start_time": "2019-07-24T06:09:49.250090Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_clean = df.drop(['name', 'cabin', 'ticket'], axis=1)\n",
    "df_clean = df.drop(['embark_town', 'alive'], axis=1)\n",
    "df_clean = df_clean.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.307280Z",
     "start_time": "2019-07-24T06:09:49.273449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population per class:      0\n",
      "1  123\n",
      "0   59\n",
      "\n",
      "1.25 * Proportion Chance Criterion: 70.22853520106267%\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# state_counts = Counter(df_clean['target'])\n",
    "state_counts = Counter(df_clean['survived'])\n",
    "df_state = pd.DataFrame.from_dict(state_counts, orient='index')\n",
    "\n",
    "num=(df_state[0]/df_state[0].sum())**2\n",
    "print(\"Population per class: {}\\n\".format(df_state))\n",
    "print(\"1.25 * Proportion Chance Criterion: {}%\".format(1.25*100*num.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.562734Z",
     "start_time": "2019-07-24T06:09:49.533312Z"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.280593</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.967694</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.873565</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>G</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.316003</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass     sex   age  sibsp  parch      fare embarked  class  \\\n",
       "1          1       1  female  38.0      1      0  4.280593        C  First   \n",
       "3          1       1  female  35.0      1      0  3.990834        S  First   \n",
       "6          0       1    male  54.0      0      0  3.967694        S  First   \n",
       "10         1       3  female   4.0      1      1  2.873565        S  Third   \n",
       "11         1       1  female  58.0      0      0  3.316003        S  First   \n",
       "\n",
       "      who  adult_male deck  alone  \n",
       "1   woman       False    C  False  \n",
       "3   woman       False    C  False  \n",
       "6     man        True    E   True  \n",
       "10  child       False    G  False  \n",
       "11  woman       False    C   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed = df_clean.copy()\n",
    "log_features = ['fare']\n",
    "for feature in log_features:\n",
    "    df_transformed[feature] = np.log(df_transformed[feature]+1)\n",
    "    \n",
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:49.789321Z",
     "start_time": "2019-07-24T06:09:49.741226Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 20)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.get_dummies(df_transformed.drop('survived', axis=1), drop_first=True)\n",
    "y = df_clean['survived']\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:09:53.502356Z",
     "start_time": "2019-07-24T06:09:52.595344Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass       age     sibsp  parch      fare  adult_male  alone  sex_male  \\\n",
       "0     0.0  0.468892  0.333333   0.00  0.685892         0.0    0.0       0.0   \n",
       "1     0.0  0.430956  0.333333   0.00  0.639463         0.0    0.0       0.0   \n",
       "2     0.0  0.671219  0.000000   0.00  0.635755         1.0    1.0       1.0   \n",
       "3     1.0  0.038948  0.333333   0.25  0.460439         0.0    0.0       0.0   \n",
       "4     0.0  0.721801  0.000000   0.00  0.531333         0.0    1.0       0.0   \n",
       "\n",
       "   embarked_Q  embarked_S  class_Second  class_Third  who_man  who_woman  \\\n",
       "0         0.0         0.0           0.0          0.0      0.0        1.0   \n",
       "1         0.0         1.0           0.0          0.0      0.0        1.0   \n",
       "2         0.0         1.0           0.0          0.0      1.0        0.0   \n",
       "3         0.0         1.0           0.0          1.0      0.0        0.0   \n",
       "4         0.0         1.0           0.0          0.0      0.0        1.0   \n",
       "\n",
       "   deck_B  deck_C  deck_D  deck_E  deck_F  deck_G  \n",
       "0     0.0     1.0     0.0     0.0     0.0     0.0  \n",
       "1     0.0     1.0     0.0     0.0     0.0     0.0  \n",
       "2     0.0     0.0     0.0     1.0     0.0     0.0  \n",
       "3     0.0     0.0     0.0     0.0     0.0     1.0  \n",
       "4     0.0     1.0     0.0     0.0     0.0     0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_norm = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "X_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:11:09.584574Z",
     "start_time": "2019-07-24T06:10:00.057538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn, n neighbors=20\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 11.06s\n",
      "logistic, reg=l1\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 5.10s\n",
      "logistic, reg=l2\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 2.34s\n",
      "svm, reg=l1\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 3.32s\n",
      "svm, reg=l2\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 3.97s\n",
      "nsvm-rbf\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 3.86s\n",
      "ensemble-decisiontree, max depth=range(1, 20)\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 2.43s\n",
      "ensemble-randomforest, n estimators=range(1, 20)\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 9.00s\n",
      "ensemble-gbm, max depth=range(1, 10)\n",
      "Computing: [########################################] Seed 20/20 | Execution time: 27.99s\n"
     ]
    }
   ],
   "source": [
    "from judas.classification.automate import Judas as JudasClassifier\n",
    "\n",
    "trials = 20\n",
    "judasc = JudasClassifier()\n",
    "params = [\n",
    "    ('knn', trials, range(1,30)),\n",
    "    ('logistic', 'l1', trials),\n",
    "    ('logistic', 'l2', trials),\n",
    "    ('svm', 'l1', trials),\n",
    "    ('svm', 'l2', trials),\n",
    "    ('nsvm-rbf', trials),\n",
    "    ('ensemble-decisiontree', trials, range(1,20)),\n",
    "    ('ensemble-randomforest', trials, range(1,20)),\n",
    "    ('ensemble-gbm', trials, range(1,10)),\n",
    "]\n",
    "\n",
    "judasc.automate(X_norm,y,params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:11:09.641851Z",
     "start_time": "2019-07-24T06:11:09.587750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Machine Learning Method</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Best Parameter</th>\n",
       "      <th>Top Predictor Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kNN</td>\n",
       "      <td>76.41%</td>\n",
       "      <td>N_Neighbor = 24</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic (l1)</td>\n",
       "      <td>74.89%</td>\n",
       "      <td>C = 0.4</td>\n",
       "      <td>who_woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic (l2)</td>\n",
       "      <td>74.35%</td>\n",
       "      <td>C = 1</td>\n",
       "      <td>who_woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVM (l1)</td>\n",
       "      <td>75.65%</td>\n",
       "      <td>C = 0.2</td>\n",
       "      <td>fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Linear SVM (l2)</td>\n",
       "      <td>75.22%</td>\n",
       "      <td>C = 0.1</td>\n",
       "      <td>fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nonlinear SVM (RBF)</td>\n",
       "      <td>77.17%</td>\n",
       "      <td>gamma = 0.01</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>79.02%</td>\n",
       "      <td>depth = 1</td>\n",
       "      <td>adult_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>75.65%</td>\n",
       "      <td>n-estimator = 10</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gradient Boosting Method</td>\n",
       "      <td>75.87%</td>\n",
       "      <td>depth = 5</td>\n",
       "      <td>fare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Machine Learning Method Test Accuracy    Best Parameter  \\\n",
       "0                       kNN        76.41%   N_Neighbor = 24   \n",
       "1             Logistic (l1)        74.89%           C = 0.4   \n",
       "2             Logistic (l2)        74.35%             C = 1   \n",
       "3           Linear SVM (l1)        75.65%           C = 0.2   \n",
       "4           Linear SVM (l2)        75.22%           C = 0.1   \n",
       "5       Nonlinear SVM (RBF)        77.17%      gamma = 0.01   \n",
       "6            Decision Trees        79.02%         depth = 1   \n",
       "7             Random Forest        75.65%  n-estimator = 10   \n",
       "8  Gradient Boosting Method        75.87%         depth = 5   \n",
       "\n",
       "  Top Predictor Variable  \n",
       "0                     NA  \n",
       "1              who_woman  \n",
       "2              who_woman  \n",
       "3                   fare  \n",
       "4                   fare  \n",
       "5                     NA  \n",
       "6             adult_male  \n",
       "7                    age  \n",
       "8                   fare  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "judasc.score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I know that this is a classification problem, but let's use the regression library to show that the library works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:12:29.030776Z",
     "start_time": "2019-07-24T06:11:09.645961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn, n neighbors=5\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 1.44s\n",
      "linear\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 0.06s\n",
      "lasso\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 0.63s\n",
      "ridge\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 0.72s\n",
      "svm\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 68.76s\n",
      "svm-rbf\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 1.24s\n",
      "svm-poly\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 0.74s\n",
      "ensemble-decisiontree, max depth=range(1, 20)\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 0.58s\n",
      "ensemble-randomforest, n estimators=range(1, 20)\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 2.12s\n",
      "ensemble-gbm, max depth=range(1, 10)\n",
      "Computing: [########################################] Seed 5/5 | Execution time: 2.97s\n"
     ]
    }
   ],
   "source": [
    "from judas.regression.automate import Judas as JudasRegressor\n",
    "\n",
    "trials = 5\n",
    "judasr = JudasRegressor()\n",
    "params = [\n",
    "    ('knn', trials, range(1,30)),\n",
    "    ('linear', trials),\n",
    "    ('lasso', trials),\n",
    "    ('ridge', trials),\n",
    "    ('svm', trials), # too slow\n",
    "    ('svm-rbf', trials), # too slow\n",
    "    ('svm-poly', trials), # too slow\n",
    "    ('ensemble-decisiontree', trials, range(1,20)),\n",
    "    ('ensemble-randomforest', trials, range(1,20)),\n",
    "    ('ensemble-gbm', trials, range(1,10)),\n",
    "]\n",
    "\n",
    "judasr.automate(X_norm,y,params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:12:29.096792Z",
     "start_time": "2019-07-24T06:12:29.034187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Machine Learning Method</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Best Parameter</th>\n",
       "      <th>Top Predictor Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kNN</td>\n",
       "      <td>22.43%</td>\n",
       "      <td>N_Neighbor = 7</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear</td>\n",
       "      <td>18.39%</td>\n",
       "      <td>NA</td>\n",
       "      <td>pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>21.73%</td>\n",
       "      <td>alpha = 0.01</td>\n",
       "      <td>pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>23.61%</td>\n",
       "      <td>alpha = 20</td>\n",
       "      <td>pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVR (linear)</td>\n",
       "      <td>12.12%</td>\n",
       "      <td>C = 0.01</td>\n",
       "      <td>adult_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVR (rbf)</td>\n",
       "      <td>14.00%</td>\n",
       "      <td>C = 0.1</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVR (poly)</td>\n",
       "      <td>18.66%</td>\n",
       "      <td>C = 3</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>20.03%</td>\n",
       "      <td>depth = 1</td>\n",
       "      <td>adult_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>16.95%</td>\n",
       "      <td>n-estimator = 16</td>\n",
       "      <td>fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Method</td>\n",
       "      <td>23.36%</td>\n",
       "      <td>depth = 2</td>\n",
       "      <td>adult_male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Machine Learning Method Test Accuracy    Best Parameter  \\\n",
       "0                       kNN        22.43%    N_Neighbor = 7   \n",
       "1                    Linear        18.39%                NA   \n",
       "2                     Lasso        21.73%      alpha = 0.01   \n",
       "3                     Ridge        23.61%        alpha = 20   \n",
       "4              SVR (linear)        12.12%          C = 0.01   \n",
       "5                 SVR (rbf)        14.00%           C = 0.1   \n",
       "6                SVR (poly)        18.66%             C = 3   \n",
       "7            Decision Trees        20.03%         depth = 1   \n",
       "8             Random Forest        16.95%  n-estimator = 16   \n",
       "9  Gradient Boosting Method        23.36%         depth = 2   \n",
       "\n",
       "  Top Predictor Variable  \n",
       "0                     NA  \n",
       "1                 pclass  \n",
       "2                 pclass  \n",
       "3                 pclass  \n",
       "4             adult_male  \n",
       "5                     NA  \n",
       "6                     NA  \n",
       "7             adult_male  \n",
       "8                   fare  \n",
       "9             adult_male  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "judasr.score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test again using another 'dataset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:12:29.128844Z",
     "start_time": "2019-07-24T06:12:29.100865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col0</th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "      <th>col5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>45.627404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>82.114706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>44.463121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>17.514817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>58.984383</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col0       col1  col2  col3  col4  col5\n",
       "0   0.0  45.627404   0.0   0.0   0.0   0.0\n",
       "1   1.0  82.114706   0.0   0.0   0.0   0.0\n",
       "2   2.0  44.463121   0.0   0.0   0.0   0.0\n",
       "3   3.0  17.514817   0.0   0.0   0.0   0.0\n",
       "4   4.0  58.984383   0.0   0.0   0.0   0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = np.arange(100)\n",
    "X2 = np.random.uniform(0,100,100)\n",
    "X = [X1, X2]\n",
    "y = X1 + X2\n",
    "# y = pd.Series(y,name='Target')\n",
    "\n",
    "X = np.concatenate([X, [np.zeros(100)]*4], axis=0).T\n",
    "X = pd.DataFrame(X, columns=['col{}'.format(i) for i in range(X.shape[1])])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:12:29.155151Z",
     "start_time": "2019-07-24T06:12:29.131870Z"
    }
   },
   "outputs": [
    {
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       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "      <th>col5</th>\n",
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       "    <tr>\n",
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       "      <td>0.030303</td>\n",
       "      <td>0.173637</td>\n",
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       "       col0      col1  col2  col3  col4  col5\n",
       "0  0.000000  0.455486   0.0   0.0   0.0   0.0\n",
       "1  0.010101  0.821298   0.0   0.0   0.0   0.0\n",
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      ]
     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_norm = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "X_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:13:50.514973Z",
     "start_time": "2019-07-24T06:12:29.159551Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn, n neighbors=50\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 10.13s\n",
      "linear\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 0.52s\n",
      "lasso\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 5.21s\n",
      "ridge\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 3.57s\n",
      "svm\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 4.55s\n",
      "svm-rbf\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 7.62s\n",
      "svm-poly\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 6.04s\n",
      "ensemble-decisiontree, max depth=range(1, 20)\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 5.30s\n",
      "ensemble-randomforest, n estimators=range(1, 20)\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 18.60s\n",
      "ensemble-gbm, max depth=range(1, 10)\n",
      "Computing: [########################################] Seed 50/50 | Execution time: 19.68s\n"
     ]
    }
   ],
   "source": [
    "trials = 50\n",
    "judasr = JudasRegressor()\n",
    "params = [\n",
    "    ('knn', trials, range(1,30)),\n",
    "    ('linear', trials),\n",
    "    ('lasso', trials),\n",
    "    ('ridge', trials),\n",
    "    ('svm', trials), # too slow\n",
    "    ('svm-rbf', trials), # too slow\n",
    "    ('svm-poly', trials), # too slow\n",
    "    ('ensemble-decisiontree', trials, range(1,20)),\n",
    "    ('ensemble-randomforest', trials, range(1,20)),\n",
    "    ('ensemble-gbm', trials, range(1,10)),\n",
    "]\n",
    "\n",
    "judasr.automate(X_norm,y,params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-24T06:13:50.601048Z",
     "start_time": "2019-07-24T06:13:50.519292Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Machine Learning Method</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Best Parameter</th>\n",
       "      <th>Top Predictor Variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kNN</td>\n",
       "      <td>97.24%</td>\n",
       "      <td>N_Neighbor = 2</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>NA</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>alpha = 1e-08</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>alpha = 1e-08</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVR (linear)</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>C = 300</td>\n",
       "      <td>col1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVR (rbf)</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>C = 5000</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVR (poly)</td>\n",
       "      <td>84.78%</td>\n",
       "      <td>C = 5000</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>91.80%</td>\n",
       "      <td>depth = 9</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>95.67%</td>\n",
       "      <td>n-estimator = 19</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gradient Boosting Method</td>\n",
       "      <td>79.40%</td>\n",
       "      <td>depth = 7</td>\n",
       "      <td>col0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Machine Learning Method Test Accuracy    Best Parameter  \\\n",
       "0                       kNN        97.24%    N_Neighbor = 2   \n",
       "1                    Linear       100.00%                NA   \n",
       "2                     Lasso       100.00%     alpha = 1e-08   \n",
       "3                     Ridge       100.00%     alpha = 1e-08   \n",
       "4              SVR (linear)       100.00%           C = 300   \n",
       "5                 SVR (rbf)       100.00%          C = 5000   \n",
       "6                SVR (poly)        84.78%          C = 5000   \n",
       "7            Decision Trees        91.80%         depth = 9   \n",
       "8             Random Forest        95.67%  n-estimator = 19   \n",
       "9  Gradient Boosting Method        79.40%         depth = 7   \n",
       "\n",
       "  Top Predictor Variable  \n",
       "0                     NA  \n",
       "1                   col0  \n",
       "2                   col0  \n",
       "3                   col0  \n",
       "4                   col1  \n",
       "5                     NA  \n",
       "6                     NA  \n",
       "7                   col0  \n",
       "8                   col0  \n",
       "9                   col0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "judasr.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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