BankMarketCampaignOperationalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Market Campaign Prediction NoteBook\n",
    "\n",
    "## Summary\n",
    "Market campaign prediction aims to predict the success of telemarketing.\n",
    "\n",
    "## Description\n",
    "\n",
    "### Use Case Description\n",
    "A company, such as bank, wants to do market campaign prediction. The bank collects customer demographic data, bank account information, history telemarketing activity record from various data sources. The task is to build a pipeline that automatically analyze the bank market dataset, to predict the success of telemarketing calls for selling bank long-term deposits. The aim is to provide market intelligence for the bank and better target valuable customers and hence reduce marketing cost.\n",
    "\n",
    "#### Use Case Data\n",
    "The data used in this use case is [BankMarket dataset](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing), a publicly available data set collected from UCI Machine Learning repository. The data contains 17 variables and 4521 rows. \n",
    "\n",
    "We shared the market data in the data folder. You can use this shared data to follow the steps in this template, or you can access the full dataset from UCI website.\n",
    "\n",
    "Each instance in the data set has 17 fields:\n",
    "\n",
    "* 1 - age (numeric)\n",
    "* 2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\", \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n",
    "* 3 - marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n",
    "* 4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n",
    "* 5 - default: has credit in default? (binary: \"yes\",\"no\")\n",
    "* 6 - balance: average yearly balance, in euros (numeric) \n",
    "* 7 - housing: has housing loan? (binary: \"yes\",\"no\")\n",
    "* 8 - loan: has personal loan? (binary: \"yes\",\"no\")\n",
    "* 9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "* 10 - day: last contact day of the month (numeric)\n",
    "* 11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "* 12 - duration: last contact duration, in seconds (numeric)\n",
    "* 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "* 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n",
    "* 15 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* 16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n",
    "\n",
    "Target variable:\n",
    "* 17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Market Campaign Operationalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Schema Generation\n",
    "\n",
    "In order to deploy the model as a web-service, we need first define functions to generate schema file for the service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This script generates the scoring and schema files\n",
    "# necessary to operaitonalize the Market Campaign prediction sample\n",
    "# Init and run functions\n",
    "\n",
    "from azureml.api.schema.dataTypes import DataTypes\n",
    "from azureml.api.schema.sampleDefinition import SampleDefinition\n",
    "from azureml.api.realtime.services import generate_schema\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "executed\n"
     ]
    }
   ],
   "source": [
    "# Prepare the web service definition by authoring\n",
    "# init() and run() functions. Test the fucntions\n",
    "# before deploying the web service.\n",
    "\n",
    "def init():\n",
    "    from sklearn.externals import joblib\n",
    "\n",
    "    # load the model file\n",
    "    global model\n",
    "    model = joblib.load('./code/marketcampaign/dt.pkl')\n",
    "\n",
    "def run(input_df):\n",
    "    import json\n",
    "    df = df1.append(input_df, ignore_index=True)\n",
    "    columns_to_encode = list(df.select_dtypes(include=['category','object']))\n",
    "    for column_to_encode in columns_to_encode:\n",
    "        dummies = pd.get_dummies(df[column_to_encode])\n",
    "        one_hot_col_names = []\n",
    "        for col_name in list(dummies.columns):\n",
    "            one_hot_col_names.append(column_to_encode + '_' + col_name)\n",
    "        dummies.columns = one_hot_col_names\n",
    "        df = df.drop(column_to_encode, axis=1)\n",
    "        df = df.join(dummies)\n",
    "    pred = model.predict(df)\n",
    "    return json.dumps(str(pred[12]))\n",
    "    #return pred[12]\n",
    "print('executed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>jan</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>retired</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>747</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>23</td>\n",
       "      <td>feb</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>176</td>\n",
       "      <td>3</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>mar</td>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>147</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>jul</td>\n",
       "      <td>151</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41</td>\n",
       "      <td>student</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>221</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>14</td>\n",
       "      <td>aug</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>-88</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>sep</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>39</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>9374</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>20</td>\n",
       "      <td>nov</td>\n",
       "      <td>273</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>unknown</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>264</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>dec</td>\n",
       "      <td>113</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age            job   marital  education default  balance housing loan  \\\n",
       "0    30         admin.  divorced    unknown     yes     1787      no   no   \n",
       "1    33    blue-collar   married  secondary      no     4789     yes  yes   \n",
       "2    35   entrepreneur    single   tertiary      no     1350     yes   no   \n",
       "3    30      housemaid   married   tertiary      no     1476     yes  yes   \n",
       "4    59     management   married  secondary      no        0     yes   no   \n",
       "5    35        retired    single   tertiary      no      747      no   no   \n",
       "6    36  self-employed   married   tertiary      no      307     yes   no   \n",
       "7    39       services   married  secondary      no      147     yes   no   \n",
       "8    41        student   married   tertiary      no      221     yes   no   \n",
       "9    43     technician   married    primary      no      -88     yes  yes   \n",
       "10   39     unemployed   married  secondary      no     9374     yes   no   \n",
       "11   43        unknown   married  secondary      no      264     yes   no   \n",
       "\n",
       "      contact  day month  duration  campaign  pdays  previous poutcome  \n",
       "0   telephone   19   oct        79         1     -1         0  unknown  \n",
       "1    cellular   11   may       220         1    339         4  success  \n",
       "2    cellular   16   apr       185         1    330         1  failure  \n",
       "3     unknown    3   jun       199         4     -1         0  unknown  \n",
       "4     unknown    5   jan       226         1     -1         0  unknown  \n",
       "5    cellular   23   feb       141         2    176         3  failure  \n",
       "6    cellular   14   mar       341         1    330         2    other  \n",
       "7    cellular    6   jul       151         2     -1         0  unknown  \n",
       "8     unknown   14   aug        57         2     -1         0  unknown  \n",
       "9    cellular   17   sep       313         1    147         2  failure  \n",
       "10    unknown   20   nov       273         1     -1         0  unknown  \n",
       "11   cellular   17   dec       113         2     -1         0  unknown  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(data=[[30,'admin.','divorced','unknown','yes',1787,'no','no','telephone',19,'oct',79,1,-1,0,'unknown'],[33,'blue-collar','married','secondary','no',4789,'yes','yes','cellular',11,'may',220,1,339,4,'success'],[35,'entrepreneur','single','tertiary','no',1350,'yes','no','cellular',16,'apr',185,1,330,1,'failure'],[30,'housemaid','married','tertiary','no',1476,'yes','yes','unknown',3,'jun',199,4,-1,0,'unknown'],[59,'management','married','secondary','no',0,'yes','no','unknown',5,'jan',226,1,-1,0,'unknown'],[35,'retired','single','tertiary','no',747,'no','no','cellular',23,'feb',141,2,176,3,'failure'],[36,'self-employed','married','tertiary','no',307,'yes','no','cellular',14,'mar',341,1,330,2,'other'],[39,'services','married','secondary','no',147,'yes','no','cellular',6,'jul',151,2,-1,0,'unknown'],[41,'student','married','tertiary','no',221,'yes','no','unknown',14,'aug',57,2,-1,0,'unknown'],[43,'technician','married','primary','no',-88,'yes','yes','cellular',17,'sep',313,1,147,2,'failure'],[39,'unemployed','married','secondary','no',9374,'yes','no','unknown',20,'nov',273,1,-1,0,'unknown'],[43,'unknown','married','secondary','no',264,'yes','no','cellular',17,'dec',113,2,-1,0,'unknown']], columns=['age','job','marital','education','default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome'])\n",
    "\n",
    "df1.dtypes\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital education default  balance housing loan   contact  \\\n",
       "0   30  unemployed  married   primary      no     1787      no   no  cellular   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome  \n",
       "0   19   oct        79         1     -1         0  unknown  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[30,'unemployed','married','primary','no',1787,'no','no','cellular',19,'oct',79,1,-1,0,'unknown']], columns=['age','job','marital','education','default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome'])\n",
    "df.dtypes\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital education default  balance housing loan   contact  \\\n",
       "0   30  unemployed  married   primary      no     1787      no   no  cellular   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome  \n",
       "0   19   oct        79         1     -1         0  unknown  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init()\n",
    "input1 = pd.DataFrame([[30,'unemployed','married','primary','no',1787,'no','no','cellular',19,'oct',79,1,-1,0,'unknown']], columns=['age','job','marital','education','default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome'])\n",
    "input1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"0\"'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run(input1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': {'input_df': {'internal': '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',\n",
       "   'swagger': {'example': [{'age': 30,\n",
       "      'balance': 1787,\n",
       "      'campaign': 1,\n",
       "      'contact': 'cellular',\n",
       "      'day': 19,\n",
       "      'default': 'no',\n",
       "      'duration': 79,\n",
       "      'education': 'primary',\n",
       "      'housing': 'no',\n",
       "      'job': 'unemployed',\n",
       "      'loan': 'no',\n",
       "      'marital': 'married',\n",
       "      'month': 'oct',\n",
       "      'pdays': -1,\n",
       "      'poutcome': 'unknown',\n",
       "      'previous': 0}],\n",
       "    'items': {'properties': {'age': {'format': 'int64', 'type': 'integer'},\n",
       "      'balance': {'format': 'int64', 'type': 'integer'},\n",
       "      'campaign': {'format': 'int64', 'type': 'integer'},\n",
       "      'contact': {'type': 'string'},\n",
       "      'day': {'format': 'int64', 'type': 'integer'},\n",
       "      'default': {'type': 'string'},\n",
       "      'duration': {'format': 'int64', 'type': 'integer'},\n",
       "      'education': {'type': 'string'},\n",
       "      'housing': {'type': 'string'},\n",
       "      'job': {'type': 'string'},\n",
       "      'loan': {'type': 'string'},\n",
       "      'marital': {'type': 'string'},\n",
       "      'month': {'type': 'string'},\n",
       "      'pdays': {'format': 'int64', 'type': 'integer'},\n",
       "      'poutcome': {'type': 'string'},\n",
       "      'previous': {'format': 'int64', 'type': 'integer'}},\n",
       "     'type': 'object'},\n",
       "    'type': 'array'},\n",
       "   'type': 3}}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = {\"input_df\": SampleDefinition(DataTypes.PANDAS, df)}\n",
    "\n",
    "# The prepare statement writes the scoring file (main.py) and\n",
    "# the schema file (service_schema.json) the the output folder.\n",
    "\n",
    "generate_schema(run_func=run, inputs=inputs, filepath='market_service_schema.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scoring Function\n",
    "\n",
    "Then, we will need to define a scoring function to score on the new instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def init():\n",
    "\n",
    "    from sklearn.externals import joblib\n",
    "    # load the model file\n",
    "    global model\n",
    "    model = joblib.load('./code/marketcampaign/dt.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "executed\n"
     ]
    }
   ],
   "source": [
    "def run(input_df):\n",
    "    import json\n",
    "    df = df1.append(input_df, ignore_index=True)\n",
    "    columns_to_encode = list(df.select_dtypes(include=['category','object']))\n",
    "    for column_to_encode in columns_to_encode:\n",
    "        dummies = pd.get_dummies(df[column_to_encode])\n",
    "        one_hot_col_names = []\n",
    "        for col_name in list(dummies.columns):\n",
    "            one_hot_col_names.append(column_to_encode + '_' + col_name)\n",
    "        dummies.columns = one_hot_col_names\n",
    "        df = df.drop(column_to_encode, axis=1)\n",
    "        df = df.join(dummies)\n",
    "    pred = model.predict(df)\n",
    "    return json.dumps(str(pred[12]))\n",
    "    #return pred[12]\n",
    "print('executed')\n",
    "\n",
    "df1 = pd.DataFrame(data=[[30,'admin.','divorced','unknown','yes',1787,'no','no','telephone',19,'oct',79,1,-1,0,'unknown'],[33,'blue-collar','married','secondary','no',4789,'yes','yes','cellular',11,'may',220,1,339,4,'success'],[35,'entrepreneur','single','tertiary','no',1350,'yes','no','cellular',16,'apr',185,1,330,1,'failure'],[30,'housemaid','married','tertiary','no',1476,'yes','yes','unknown',3,'jun',199,4,-1,0,'unknown'],[59,'management','married','secondary','no',0,'yes','no','unknown',5,'jan',226,1,-1,0,'unknown'],[35,'retired','single','tertiary','no',747,'no','no','cellular',23,'feb',141,2,176,3,'failure'],[36,'self-employed','married','tertiary','no',307,'yes','no','cellular',14,'mar',341,1,330,2,'other'],[39,'services','married','secondary','no',147,'yes','no','cellular',6,'jul',151,2,-1,0,'unknown'],[41,'student','married','tertiary','no',221,'yes','no','unknown',14,'aug',57,2,-1,0,'unknown'],[43,'technician','married','primary','no',-88,'yes','yes','cellular',17,'sep',313,1,147,2,'failure'],[39,'unemployed','married','secondary','no',9374,'yes','no','unknown',20,'nov',273,1,-1,0,'unknown'],[43,'unknown','married','secondary','no',264,'yes','no','cellular',17,'dec',113,2,-1,0,'unknown']], columns=['age','job','marital','education','default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"0\"\n"
     ]
    }
   ],
   "source": [
    "# Implement test code to run in IDE or Azure ML Workbench\n",
    "if __name__ == '__main__':\n",
    "    init()\n",
    "    input = input1\n",
    "    print(run(input))\n",
    "    #input = \"{}\"\n",
    "    #run(input)"
   ]
  }
 ],
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