10. Keras Wide and Deep as TFData [experimental].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Loading in the Data\n",
    "In this example, we are going to use crossed columns and embedding columns inside of a tensorflow object created with keras.\n",
    "\n",
    "However, we will start the process by loading up a dataset with a mix of categorical data and numeric data. This dataset is quite old and has been used many times in machine learning examples: the census data from 1990's. We will use it to predict if a person will earn over or under 50k per year.\n",
    "\n",
    "- https://archive.ics.uci.edu/ml/datasets/Census-Income+(KDD)\n",
    "- This data goes by various name, including the \"Adult\" dataset and 1996 census data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              32561 non-null  int64 \n",
      " 1   workclass        32561 non-null  object\n",
      " 2   fnlwgt           32561 non-null  int64 \n",
      " 3   education        32561 non-null  object\n",
      " 4   edu_num          32561 non-null  int64 \n",
      " 5   marital_status   32561 non-null  object\n",
      " 6   occupation       32561 non-null  object\n",
      " 7   relationship     32561 non-null  object\n",
      " 8   race             32561 non-null  object\n",
      " 9   sex              32561 non-null  object\n",
      " 10  cap_gain         32561 non-null  int64 \n",
      " 11  cap_loss         32561 non-null  int64 \n",
      " 12  work_hrs_weekly  32561 non-null  int64 \n",
      " 13  country          32561 non-null  object\n",
      " 14  income           32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "headers = ['age','workclass','fnlwgt','education','edu_num','marital_status',\n",
    "           'occupation','relationship','race','sex','cap_gain','cap_loss','work_hrs_weekly','country','income']\n",
    "df_train_orig = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',names=headers)\n",
    "df_test_orig = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test',names=headers)\n",
    "df_test_orig = df_test_orig.iloc[1:]\n",
    "print(df_train_orig.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "df_train = deepcopy(df_train_orig)\n",
    "df_test = deepcopy(df_test_orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is organized as follows: \n",
    "\n",
    "|Variable | description|\n",
    "|----|--------|\n",
    "|age: | continuous|\n",
    "|workclass:      |Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, ...|\n",
    "|fnlwgt:         |continuous.|\n",
    "|education:      |Bachelors, Some-college, 11th, HS-grad, Prof-school, ...|\n",
    "|education-num:  |continuous.|\n",
    "|marital-status: |Married-civ-spouse, Divorced, Never-married, Separated, Widowed, ... |\n",
    "|occupation:     |Tech-support, Craft-repair, Other-service, ...|\n",
    "|relationship:   | Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.|\n",
    "|race:           |White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.|\n",
    "|sex:            |Female, Male.|\n",
    "|capital-gain:   |continuous.|\n",
    "|capital-loss:   |continuous.|\n",
    "|hours-per-week: |continuous.|\n",
    "|native-country: |United-States, Cambodia, England, ... |\n",
    "|**income (target):**|<50k, >=50k| "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>edu_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>cap_gain</th>\n",
       "      <th>cap_loss</th>\n",
       "      <th>work_hrs_weekly</th>\n",
       "      <th>country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693.0</td>\n",
       "      <td>10th</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age   workclass    fnlwgt      education  edu_num       marital_status  \\\n",
       "1  25     Private  226802.0           11th      7.0        Never-married   \n",
       "2  38     Private   89814.0        HS-grad      9.0   Married-civ-spouse   \n",
       "3  28   Local-gov  336951.0     Assoc-acdm     12.0   Married-civ-spouse   \n",
       "4  44     Private  160323.0   Some-college     10.0   Married-civ-spouse   \n",
       "6  34     Private  198693.0           10th      6.0        Never-married   \n",
       "\n",
       "           occupation    relationship    race    sex  cap_gain  cap_loss  \\\n",
       "1   Machine-op-inspct       Own-child   Black   Male       0.0       0.0   \n",
       "2     Farming-fishing         Husband   White   Male       0.0       0.0   \n",
       "3     Protective-serv         Husband   White   Male       0.0       0.0   \n",
       "4   Machine-op-inspct         Husband   Black   Male    7688.0       0.0   \n",
       "6       Other-service   Not-in-family   White   Male       0.0       0.0   \n",
       "\n",
       "   work_hrs_weekly         country   income  \n",
       "1             40.0   United-States   <=50K.  \n",
       "2             50.0   United-States   <=50K.  \n",
       "3             40.0   United-States    >50K.  \n",
       "4             40.0   United-States    >50K.  \n",
       "6             30.0   United-States   <=50K.  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# let's just get rid of rows with any missing data\n",
    "# and then reset the indices of the dataframe so it corresponds to row number\n",
    "df_train.replace(to_replace=' ?',value=np.nan, inplace=True)\n",
    "df_train.dropna(inplace=True)\n",
    "df_train.reset_index()\n",
    "\n",
    "df_test.replace(to_replace=' ?',value=np.nan, inplace=True)\n",
    "df_test.dropna(inplace=True)\n",
    "df_test.reset_index()\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing\n",
    "For preprocessing, we are going to fix a few issues in the dataset. \n",
    "\n",
    "- This first includes the use of \"50K.\" instead of \"50K\" in the test set. \n",
    "- Next, we will encode the categorical features as integers (eventually we will deal with these integers like a one-hot encoding)\n",
    "- Finally, we will make certain all the continuous data is scaled properly with zero mean and unit standard deviaiton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<=50K    11360\n",
      ">50K      3700\n",
      "Name: income, dtype: int64\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>edu_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>...</th>\n",
       "      <th>country</th>\n",
       "      <th>income</th>\n",
       "      <th>workclass_int</th>\n",
       "      <th>education_int</th>\n",
       "      <th>marital_status_int</th>\n",
       "      <th>occupation_int</th>\n",
       "      <th>relationship_int</th>\n",
       "      <th>race_int</th>\n",
       "      <th>sex_int</th>\n",
       "      <th>country_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.023104</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.033340</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.794697</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.423474</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.337883</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693.0</td>\n",
       "      <td>10th</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  workclass    fnlwgt     education  edu_num      marital_status  \\\n",
       "1 -1.023104    Private  226802.0          11th      7.0       Never-married   \n",
       "2 -0.033340    Private   89814.0       HS-grad      9.0  Married-civ-spouse   \n",
       "3 -0.794697  Local-gov  336951.0    Assoc-acdm     12.0  Married-civ-spouse   \n",
       "4  0.423474    Private  160323.0  Some-college     10.0  Married-civ-spouse   \n",
       "6 -0.337883    Private  198693.0          10th      6.0       Never-married   \n",
       "\n",
       "          occupation   relationship   race   sex  ...        country  income  \\\n",
       "1  Machine-op-inspct      Own-child  Black  Male  ...  United-States       0   \n",
       "2    Farming-fishing        Husband  White  Male  ...  United-States       0   \n",
       "3    Protective-serv        Husband  White  Male  ...  United-States       1   \n",
       "4  Machine-op-inspct        Husband  Black  Male  ...  United-States       1   \n",
       "6      Other-service  Not-in-family  White  Male  ...  United-States       0   \n",
       "\n",
       "   workclass_int education_int  marital_status_int  occupation_int  \\\n",
       "1              3             2                   5               7   \n",
       "2              3            12                   3               5   \n",
       "3              2             8                   3              11   \n",
       "4              3            16                   3               7   \n",
       "6              3             1                   5               8   \n",
       "\n",
       "   relationship_int  race_int  sex_int  country_int  \n",
       "1                 4         3        2           39  \n",
       "2                 1         5        2           39  \n",
       "3                 1         5        2           39  \n",
       "4                 1         3        2           39  \n",
       "6                 2         5        2           39  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# replace weird strings inconsistency\n",
    "if df_test.income.dtype=='object':\n",
    "    df_test.income.replace(to_replace=[' <=50K.',' >50K.'],\n",
    "                           value=['<=50K','>50K'],\n",
    "                           inplace=True)\n",
    "    print(df_test.income.value_counts())\n",
    "\n",
    "# ========================================================\n",
    "# define objects that can encode each variable as integer    \n",
    "encoders = dict() # save each encoder in dictionary\n",
    "categorical_headers = ['workclass','education','marital_status',\n",
    "                       'occupation','relationship','race','sex','country']\n",
    "\n",
    "# train all encoders (special case the target 'income')\n",
    "for col in categorical_headers+['income']:\n",
    "    df_train[col] = df_train[col].str.strip()\n",
    "    df_test[col] = df_test[col].str.strip()\n",
    "    \n",
    "    if col==\"income\":\n",
    "        # special case the target, just replace the column\n",
    "        tmp = LabelEncoder()\n",
    "        df_train[col] = tmp.fit_transform(df_train[col])\n",
    "        df_test[col] = tmp.transform(df_test[col])\n",
    "    else:\n",
    "        # integer encode strings that are features\n",
    "        encoders[col] = LabelEncoder() # save the encoder\n",
    "        df_train[col+'_int'] = encoders[col].fit_transform(df_train[col])+1\n",
    "        df_test[col+'_int'] = encoders[col].transform(df_test[col])+1\n",
    "\n",
    "# ========================================================\n",
    "# scale the numeric, continuous variables\n",
    "numeric_headers = [\"age\", \"cap_gain\", \"cap_loss\",\"work_hrs_weekly\"]\n",
    "\n",
    "ss = StandardScaler()\n",
    "df_train[numeric_headers] = ss.fit_transform(df_train[numeric_headers].values)\n",
    "df_test[numeric_headers] = ss.transform(df_test[numeric_headers].values)\n",
    "\n",
    "# Code above does the same as the following, for each column:\n",
    "# for col in numeric_headers:\n",
    "#     df_train[col] = df_train[col].astype(np.float)\n",
    "#     df_test[col] = df_test[col].astype(np.float)\n",
    "    \n",
    "#     ss = StandardScaler()\n",
    "#     df_train[col] = ss.fit_transform(df_train[col].values.reshape(-1, 1))\n",
    "#     df_test[col] = ss.transform(df_test[col].values.reshape(-1, 1))\n",
    "    \n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will use the following 12 features:\n",
      "[   'workclass_int',\n",
      "    'education_int',\n",
      "    'marital_status_int',\n",
      "    'occupation_int',\n",
      "    'relationship_int',\n",
      "    'race_int',\n",
      "    'sex_int',\n",
      "    'country_int',\n",
      "    'age',\n",
      "    'cap_gain',\n",
      "    'cap_loss',\n",
      "    'work_hrs_weekly']\n"
     ]
    }
   ],
   "source": [
    "# let's start as simply as possible, without any feature preprocessing\n",
    "categorical_headers_ints = [x+'_int' for x in categorical_headers]\n",
    "\n",
    "# we will forego one-hot encoding right now and instead just use all inputs as-is\n",
    "#   this is just to get an example running in Keras (its not a good idea)\n",
    "feature_columns = categorical_headers_ints+numeric_headers\n",
    "\n",
    "import pprint\n",
    "pp = pprint.PrettyPrinter(indent=4)\n",
    "\n",
    "print(f\"We will use the following {len(feature_columns)} features:\")\n",
    "pp.pprint(feature_columns)\n",
    "\n",
    "# combine the features into a single large matrix\n",
    "X_train = df_train[feature_columns].to_numpy()\n",
    "X_test = df_test[feature_columns].to_numpy()\n",
    "\n",
    "y_train = df_train['income'].values.astype(np.int32)\n",
    "y_test = df_test['income'].values.astype(np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![keras](https://blog.keras.io/img/keras-tensorflow-logo.jpg)\n",
    "\n",
    "\n",
    "# An example similar to Sklearn, Keras\n",
    "- We will start with creating a model that is similar to what we have already written. \n",
    "- Let's not worry about pre-processing the data right now. Let's just get familiar with Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n",
      "2.9.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "print(tf.__version__)\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Activation, Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.utils import plot_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a tensorflow dataset, for ease of use later\n",
    "batch_size = 50\n",
    "\n",
    "\n",
    "df = df_train.copy()\n",
    "labels = df['income']\n",
    "# \n",
    "df = {key: value.values[:,np.newaxis] for key, value in df_train[categorical_headers].items()}\n",
    "# df['numeric'] = tf.convert_to_tensor(df_train[numeric_headers].values)\n",
    "\n",
    "ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n",
    "\n",
    "ds = ds.shuffle(buffer_size=len(df_train))\n",
    "ds = ds.batch(batch_size)\n",
    "ds = ds.prefetch(batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['workclass', 'education', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'country'])\n",
      "{'workclass': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Federal-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Local-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Local-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Self-emp-not-inc'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Federal-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'State-gov'],\n",
      "       [b'Federal-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Local-gov'],\n",
      "       [b'Self-emp-not-inc'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Federal-gov'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private'],\n",
      "       [b'Private']], dtype=object)>, 'education': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Assoc-acdm'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Assoc-acdm'],\n",
      "       [b'HS-grad'],\n",
      "       [b'10th'],\n",
      "       [b'Some-college'],\n",
      "       [b'Bachelors'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Some-college'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Assoc-acdm'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Some-college'],\n",
      "       [b'10th'],\n",
      "       [b'Bachelors'],\n",
      "       [b'12th'],\n",
      "       [b'1st-4th'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Doctorate'],\n",
      "       [b'Masters'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'9th'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Bachelors'],\n",
      "       [b'Bachelors'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'7th-8th'],\n",
      "       [b'5th-6th'],\n",
      "       [b'Assoc-voc'],\n",
      "       [b'Bachelors'],\n",
      "       [b'11th'],\n",
      "       [b'Some-college'],\n",
      "       [b'Assoc-voc'],\n",
      "       [b'HS-grad'],\n",
      "       [b'7th-8th'],\n",
      "       [b'Bachelors'],\n",
      "       [b'Bachelors'],\n",
      "       [b'Some-college'],\n",
      "       [b'HS-grad'],\n",
      "       [b'HS-grad'],\n",
      "       [b'Some-college'],\n",
      "       [b'Some-college'],\n",
      "       [b'Some-college'],\n",
      "       [b'Some-college']], dtype=object)>, 'marital_status': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Never-married'],\n",
      "       [b'Separated'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Never-married'],\n",
      "       [b'Divorced'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Divorced'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Separated'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-spouse-absent'],\n",
      "       [b'Divorced'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Divorced'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Divorced'],\n",
      "       [b'Never-married'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Divorced'],\n",
      "       [b'Widowed'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse'],\n",
      "       [b'Never-married'],\n",
      "       [b'Never-married'],\n",
      "       [b'Married-civ-spouse']], dtype=object)>, 'occupation': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Exec-managerial'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Machine-op-inspct'],\n",
      "       [b'Adm-clerical'],\n",
      "       [b'Handlers-cleaners'],\n",
      "       [b'Adm-clerical'],\n",
      "       [b'Exec-managerial'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Protective-serv'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Other-service'],\n",
      "       [b'Handlers-cleaners'],\n",
      "       [b'Adm-clerical'],\n",
      "       [b'Handlers-cleaners'],\n",
      "       [b'Prof-specialty'],\n",
      "       [b'Transport-moving'],\n",
      "       [b'Other-service'],\n",
      "       [b'Exec-managerial'],\n",
      "       [b'Sales'],\n",
      "       [b'Other-service'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Prof-specialty'],\n",
      "       [b'Adm-clerical'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Exec-managerial'],\n",
      "       [b'Handlers-cleaners'],\n",
      "       [b'Machine-op-inspct'],\n",
      "       [b'Prof-specialty'],\n",
      "       [b'Prof-specialty'],\n",
      "       [b'Farming-fishing'],\n",
      "       [b'Handlers-cleaners'],\n",
      "       [b'Sales'],\n",
      "       [b'Other-service'],\n",
      "       [b'Transport-moving'],\n",
      "       [b'Tech-support'],\n",
      "       [b'Tech-support'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Other-service'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Transport-moving'],\n",
      "       [b'Other-service'],\n",
      "       [b'Transport-moving'],\n",
      "       [b'Sales'],\n",
      "       [b'Sales'],\n",
      "       [b'Sales'],\n",
      "       [b'Craft-repair'],\n",
      "       [b'Exec-managerial'],\n",
      "       [b'Sales'],\n",
      "       [b'Adm-clerical'],\n",
      "       [b'Other-service']], dtype=object)>, 'relationship': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Not-in-family'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Husband'],\n",
      "       [b'Husband'],\n",
      "       [b'Other-relative'],\n",
      "       [b'Wife'],\n",
      "       [b'Husband'],\n",
      "       [b'Own-child'],\n",
      "       [b'Own-child'],\n",
      "       [b'Unmarried'],\n",
      "       [b'Husband'],\n",
      "       [b'Other-relative'],\n",
      "       [b'Unmarried'],\n",
      "       [b'Husband'],\n",
      "       [b'Husband'],\n",
      "       [b'Husband'],\n",
      "       [b'Own-child'],\n",
      "       [b'Husband'],\n",
      "       [b'Other-relative'],\n",
      "       [b'Wife'],\n",
      "       [b'Unmarried'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Husband'],\n",
      "       [b'Unmarried'],\n",
      "       [b'Husband'],\n",
      "       [b'Own-child'],\n",
      "       [b'Husband'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Wife'],\n",
      "       [b'Other-relative'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Wife'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Wife'],\n",
      "       [b'Other-relative'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Own-child'],\n",
      "       [b'Husband'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Husband'],\n",
      "       [b'Husband'],\n",
      "       [b'Husband'],\n",
      "       [b'Own-child'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Husband'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Not-in-family'],\n",
      "       [b'Husband']], dtype=object)>, 'race': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Other'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Asian-Pac-Islander'],\n",
      "       [b'Black'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Black'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Asian-Pac-Islander'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Black'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'Black'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White'],\n",
      "       [b'White']], dtype=object)>, 'sex': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Male'],\n",
      "       [b'Male'],\n",
      "       [b'Female'],\n",
      "       [b'Female'],\n",
      "       [b'Male']], dtype=object)>, 'country': <tf.Tensor: shape=(50, 1), dtype=string, numpy=\n",
      "array([[b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'China'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'Vietnam'],\n",
      "       [b'Mexico'],\n",
      "       [b'United-States'],\n",
      "       [b'Italy'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'Mexico'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States'],\n",
      "       [b'United-States']], dtype=object)>}\n"
     ]
    }
   ],
   "source": [
    "[(feat,label)] = ds.take(1)\n",
    "print(feat.keys())\n",
    "print(feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_17\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " workclass (InputLayer)         [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " education (InputLayer)         [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " marital_status (InputLayer)    [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " occupation (InputLayer)        [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " relationship (InputLayer)      [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " race (InputLayer)              [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " sex (InputLayer)               [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " country (InputLayer)           [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " string_lookup_33 (StringLookup  (None, 1)           0           ['workclass[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_34 (StringLookup  (None, 1)           0           ['education[0][0]']              \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_35 (StringLookup  (None, 1)           0           ['marital_status[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_36 (StringLookup  (None, 1)           0           ['occupation[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_37 (StringLookup  (None, 1)           0           ['relationship[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_38 (StringLookup  (None, 1)           0           ['race[0][0]']                   \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_39 (StringLookup  (None, 1)           0           ['sex[0][0]']                    \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " string_lookup_40 (StringLookup  (None, 1)           0           ['country[0][0]']                \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " category_encoding_25 (Category  (None, 8)           0           ['string_lookup_33[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_26 (Category  (None, 17)          0           ['string_lookup_34[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_27 (Category  (None, 8)           0           ['string_lookup_35[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_28 (Category  (None, 15)          0           ['string_lookup_36[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_29 (Category  (None, 7)           0           ['string_lookup_37[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_30 (Category  (None, 6)           0           ['string_lookup_38[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_31 (Category  (None, 3)           0           ['string_lookup_39[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " category_encoding_32 (Category  (None, 42)          0           ['string_lookup_40[0][0]']       \n",
      " Encoding)                                                                                        \n",
      "                                                                                                  \n",
      " workclass_embed (Dense)        (None, 2)            16          ['category_encoding_25[0][0]']   \n",
      "                                                                                                  \n",
      " education_embed (Dense)        (None, 4)            68          ['category_encoding_26[0][0]']   \n",
      "                                                                                                  \n",
      " marital_status_embed (Dense)   (None, 2)            16          ['category_encoding_27[0][0]']   \n",
      "                                                                                                  \n",
      " occupation_embed (Dense)       (None, 3)            45          ['category_encoding_28[0][0]']   \n",
      "                                                                                                  \n",
      " relationship_embed (Dense)     (None, 2)            14          ['category_encoding_29[0][0]']   \n",
      "                                                                                                  \n",
      " race_embed (Dense)             (None, 2)            12          ['category_encoding_30[0][0]']   \n",
      "                                                                                                  \n",
      " sex_embed (Dense)              (None, 1)            3           ['category_encoding_31[0][0]']   \n",
      "                                                                                                  \n",
      " country_embed (Dense)          (None, 6)            252         ['category_encoding_32[0][0]']   \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " concat_1 (Concatenate)         (None, 22)           0           ['workclass_embed[0][0]',        \n",
      "                                                                  'education_embed[0][0]',        \n",
      "                                                                  'marital_status_embed[0][0]',   \n",
      "                                                                  'occupation_embed[0][0]',       \n",
      "                                                                  'relationship_embed[0][0]',     \n",
      "                                                                  'race_embed[0][0]',             \n",
      "                                                                  'sex_embed[0][0]',              \n",
      "                                                                  'country_embed[0][0]']          \n",
      "                                                                                                  \n",
      " combined (Dense)               (None, 1)            23          ['concat_1[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 449\n",
      "Trainable params: 449\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/12\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f82e99c23b0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f82e99c23b0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "604/604 [==============================] - 2s 2ms/step - loss: 0.1991 - accuracy: 0.7051\n",
      "Epoch 2/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1670 - accuracy: 0.7505\n",
      "Epoch 3/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1579 - accuracy: 0.7524\n",
      "Epoch 4/12\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1525 - accuracy: 0.7575\n",
      "Epoch 5/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1489 - accuracy: 0.7634\n",
      "Epoch 6/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1462 - accuracy: 0.7731\n",
      "Epoch 7/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1439 - accuracy: 0.7856\n",
      "Epoch 8/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1419 - accuracy: 0.7905\n",
      "Epoch 9/12\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1401 - accuracy: 0.7945\n",
      "Epoch 10/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1385 - accuracy: 0.8007\n",
      "Epoch 11/12\n",
      "604/604 [==============================] - 1s 2ms/step - loss: 0.1371 - accuracy: 0.8046\n",
      "Epoch 12/12\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1358 - accuracy: 0.8067\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f82fb79ee50>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.layers import StringLookup, CategoryEncoding\n",
    "from tensorflow.keras.layers import Embedding, Concatenate\n",
    "from tensorflow.keras.layers import concatenate, Flatten\n",
    "\n",
    "# start by getting only the categorical variables\n",
    "# these matrices are all integers\n",
    "# X_train = df_train[categorical_headers_ints].to_numpy() \n",
    "# X_test = df_test[categorical_headers_ints].to_numpy() \n",
    "\n",
    "all_inputs = []\n",
    "embed_branches = []\n",
    "all_branch_outputs = [] # this is where we will keep track of output of each branch\n",
    "\n",
    "# # feed in the entire matrix of categorical variables\n",
    "# inputs_num = Input(shape=(len(numeric_headers),),  \n",
    "#                      name='numeric_data1')\n",
    "\n",
    "# x_dense = Dense(units=10, activation='relu', name='num_1')(inputs_num)\n",
    "    \n",
    "# all_branch_outputs.append(x_dense)\n",
    "# all_inputs.append(input_num)\n",
    "\n",
    "# for each categorical variable\n",
    "for idx,col in enumerate(categorical_headers):\n",
    "    \n",
    "    # what the maximum integer value for this variable?\n",
    "    # which is the same as the number of categories\n",
    "    vocab = list(set(np.hstack((df_train[col].unique(),df_test[col].unique()))))\n",
    "    N = len(vocab)+1 # reserve \"0\" for not included\n",
    "    \n",
    "    #N = max(df_train[col].max(),df_test[col].max())+1 \n",
    "    \n",
    "    slook_layer = StringLookup(vocabulary=vocab)\n",
    "    \n",
    "    inp = Input(shape=(1,), dtype='string', \n",
    "                     name=col)\n",
    "    \n",
    "    # seriously? we need to categiry encode this to make it work... UGH\n",
    "    x = CategoryEncoding(num_tokens=N)(slook_layer(inp))\n",
    "    \n",
    "    #x = slook_layer(inp)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    # UGH,  the look ups do not interface with Embedding layers!!!\n",
    "#     x = Embedding(input_dim=N, \n",
    "#                   output_dim=int(np.sqrt(N)), \n",
    "#                   input_length=1, name=col+'_embed')(inp)\n",
    "    x = Dense(units=int(np.sqrt(N)), use_bias=False,\n",
    "                     activation='linear', \n",
    "                     name=col+'_embed')(x)\n",
    "    #x = Flatten()(x)\n",
    "    \n",
    "    # save these outputs in list to concatenate later\n",
    "    all_inputs.append(inp)\n",
    "    all_branch_outputs.append(x)\n",
    "    \n",
    "    \n",
    "# now concatenate the outputs and add a fully connected layer\n",
    "final_branch = concatenate(all_branch_outputs, name='concat_1')\n",
    "final_branch = Dense(units=1,\n",
    "                     activation='sigmoid', \n",
    "                     name='combined')(final_branch)\n",
    "\n",
    "model = Model(inputs=all_inputs, outputs=final_branch)\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.fit(ds, epochs=12, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Adding more customization\n",
    "For this particular optimization, we could go beyond SGD and use a different optimizer. There are many excellent explanations of different optimizers, for instance:\n",
    "- http://sebastianruder.com/optimizing-gradient-descent/\n",
    "\n",
    "Now that we have defined the model, its as simple as using it in a very familiar syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# you will need to install pydot properly on your machine to get this running\n",
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# now lets see how well the model performed\n",
    "# from sklearn import metrics as mt\n",
    "# yhat_proba = model.predict(X_test) # sigmoid output probabilities\n",
    "# yhat = np.round(yhat_proba) # round to get binary class\n",
    "\n",
    "# print(mt.confusion_matrix(y_test,yhat))\n",
    "# print(mt.classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ouch! It didn't work very well at all! Sometimes is will not even train better than chance. **But we are not handling categorical data properly here...** Let's fix that.\n",
    "\n",
    "**[back to slides]**\n",
    "\n",
    "## Embedding Categorical Data\n",
    "In order to add one-hot encoding, we need to separate the categorical features that are currently saved as integers and place them into Embedding layers. An embedding layer deals with integers as if they were one-hot encoded. To start, let's only create a model from these categorical variables. \n",
    "\n",
    "- The first decision to make is about the size of the dense feature embeddings. This is essentially a dimensionality reduction step. \n",
    " - This can be difficult to set, but one common setting is $\\log_2(N)$ or $\\sqrt{N}$ where $N$ is the total number of uniques values.\n",
    "- When using an Embedding, we can leave the variables represented as integers. That is, keras will use the integer representation to figure out how to one-hot-encode the variable. This is great because it means we don't need to change the variable in memory.\n",
    "- We will feed all the categorical variables to the model and, internally, the model will separate out the columns and apply a separate embedding. The function we will use to get each column is the `gather` function that is part of tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_7\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " categorical (InputLayer)       [(None, 8)]          0           []                               \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_16 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_17 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_18 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_19 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_20 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_21 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_22 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " tf.compat.v1.gather_23 (TFOpLa  (None,)             0           ['categorical[0][0]']            \n",
      " mbda)                                                                                            \n",
      "                                                                                                  \n",
      " workclass_int_embed (Embedding  (None, 2)           16          ['tf.compat.v1.gather_16[0][0]'] \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " education_int_embed (Embedding  (None, 4)           68          ['tf.compat.v1.gather_17[0][0]'] \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " marital_status_int_embed (Embe  (None, 2)           16          ['tf.compat.v1.gather_18[0][0]'] \n",
      " dding)                                                                                           \n",
      "                                                                                                  \n",
      " occupation_int_embed (Embeddin  (None, 3)           45          ['tf.compat.v1.gather_19[0][0]'] \n",
      " g)                                                                                               \n",
      "                                                                                                  \n",
      " relationship_int_embed (Embedd  (None, 2)           14          ['tf.compat.v1.gather_20[0][0]'] \n",
      " ing)                                                                                             \n",
      "                                                                                                  \n",
      " race_int_embed (Embedding)     (None, 2)            12          ['tf.compat.v1.gather_21[0][0]'] \n",
      "                                                                                                  \n",
      " sex_int_embed (Embedding)      (None, 1)            3           ['tf.compat.v1.gather_22[0][0]'] \n",
      "                                                                                                  \n",
      " country_int_embed (Embedding)  (None, 6)            252         ['tf.compat.v1.gather_23[0][0]'] \n",
      "                                                                                                  \n",
      " concat_1 (Concatenate)         (None, 22)           0           ['workclass_int_embed[0][0]',    \n",
      "                                                                  'education_int_embed[0][0]',    \n",
      "                                                                  'marital_status_int_embed[0][0]'\n",
      "                                                                 , 'occupation_int_embed[0][0]',  \n",
      "                                                                  'relationship_int_embed[0][0]', \n",
      "                                                                  'race_int_embed[0][0]',         \n",
      "                                                                  'sex_int_embed[0][0]',          \n",
      "                                                                  'country_int_embed[0][0]']      \n",
      "                                                                                                  \n",
      " combined (Dense)               (None, 1)            23          ['concat_1[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 449\n",
      "Trainable params: 449\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.layers import concatenate\n",
    "\n",
    "# start by getting only the categorical variables\n",
    "# these matrices are all integers\n",
    "X_train = df_train[categorical_headers_ints].to_numpy() \n",
    "X_test = df_test[categorical_headers_ints].to_numpy() \n",
    "\n",
    "embed_branches = []\n",
    "all_branch_outputs = [] # this is where we will keep track of output of each branch\n",
    "\n",
    "# feed in the entire matrix of categircal variables\n",
    "input_branch = Input(shape=(X_train.shape[1],), \n",
    "                     dtype='int64', \n",
    "                     name='categorical')\n",
    "\n",
    "# for each categorical variable\n",
    "for idx,col in enumerate(categorical_headers_ints):\n",
    "    \n",
    "    # what the maximum integer value for this variable?\n",
    "    # which is the same as the number of categories\n",
    "    N = max(df_train[col].max(),df_test[col].max())+1 \n",
    "    \n",
    "    # this line of code does this: input_branch[:,idx]\n",
    "    x = tf.gather(input_branch, idx, axis=1)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    x = Embedding(input_dim=N, \n",
    "                  output_dim=int(np.sqrt(N)), \n",
    "                  input_length=1, name=col+'_embed')(x)\n",
    "    \n",
    "    # save these outputs in list to concatenate later\n",
    "    all_branch_outputs.append(x)\n",
    "    \n",
    "# now concatenate the outputs and add a fully connected layer\n",
    "final_branch = concatenate(all_branch_outputs, name='concat_1')\n",
    "final_branch = Dense(units=1,\n",
    "                     activation='sigmoid', \n",
    "                     name='combined')(final_branch)\n",
    "\n",
    "model = Model(inputs=input_branch, outputs=final_branch)\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f831bb61170> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f831bb61170> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "604/604 [==============================] - 1s 872us/step - loss: 0.2050 - accuracy: 0.7489\n",
      "Epoch 2/10\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1814 - accuracy: 0.7511\n",
      "Epoch 3/10\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1736 - accuracy: 0.7511\n",
      "Epoch 4/10\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1665 - accuracy: 0.7511\n",
      "Epoch 5/10\n",
      "604/604 [==============================] - 1s 979us/step - loss: 0.1599 - accuracy: 0.7519\n",
      "Epoch 6/10\n",
      "604/604 [==============================] - 1s 858us/step - loss: 0.1545 - accuracy: 0.7539\n",
      "Epoch 7/10\n",
      "604/604 [==============================] - 1s 1ms/step - loss: 0.1504 - accuracy: 0.7529\n",
      "Epoch 8/10\n",
      "604/604 [==============================] - 1s 863us/step - loss: 0.1473 - accuracy: 0.7626\n",
      "Epoch 9/10\n",
      "604/604 [==============================] - 1s 842us/step - loss: 0.1449 - accuracy: 0.7866\n",
      "Epoch 10/10\n",
      "604/604 [==============================] - 0s 823us/step - loss: 0.1428 - accuracy: 0.7959\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f830c5538c0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f830c5538c0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "471/471 [==============================] - 0s 627us/step\n",
      "[[10793   567]\n",
      " [ 2427  1273]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.95      0.88     11360\n",
      "           1       0.69      0.34      0.46      3700\n",
      "\n",
      "    accuracy                           0.80     15060\n",
      "   macro avg       0.75      0.65      0.67     15060\n",
      "weighted avg       0.79      0.80      0.78     15060\n",
      "\n",
      "CPU times: user 8.23 s, sys: 1.25 s, total: 9.48 s\n",
      "Wall time: 6.93 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=50, verbose=1)\n",
    "\n",
    "from sklearn import metrics as mt\n",
    "yhat_proba = model.predict(X_test)\n",
    "yhat = np.round(yhat_proba)\n",
    "print(mt.confusion_matrix(y_test,yhat))\n",
    "print(mt.classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better!! Looks like the use of Embedding layers helped. Lets add some more of the features, using both the categorical and the integer features.\n",
    "\n",
    "## Multi-Modal Networks: Combine Branches, Sparse and Dense\n",
    "The keras functional API allows us to setup different branches of inputs. When we call the `fit` function, we can tell keras the input to each branch using a simple list (you can also use a dictionary to be more explicit).\n",
    "\n",
    "- Lets create two matrices: \n",
    " - one with the categorical data, `X_train_cat`\n",
    " - and another with the numeric data, `X_train_num`\n",
    " \n",
    "Now, we can feed both of these into the model as a list and concatenate their results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save categorical features\n",
    "X_train_cat = df_train[categorical_headers_ints].to_numpy() \n",
    "X_test_cat = df_test[categorical_headers_ints].to_numpy() \n",
    "\n",
    "# and save off the numeric features\n",
    "X_train_num =  df_train[numeric_headers].to_numpy()\n",
    "X_test_num = df_test[numeric_headers].to_numpy()\n",
    "\n",
    "all_branch_outputs = [] # this is where we will keep track of output of each branch\n",
    "\n",
    "# -----Like previous example, use embeddings from the integer data -----\n",
    "input_cat = Input(shape=(X_train_cat.shape[1],), dtype='int64', name='categorical')\n",
    "for idx,col in enumerate(categorical_headers_ints):\n",
    "    \n",
    "    # track what the maximum integer value will be for this variable\n",
    "    # which is the same as the number of categories\n",
    "    N = max(df_train[col].max(),df_test[col].max())+1\n",
    "    \n",
    "    \n",
    "    # this line of code does this: input_branch[:,idx]\n",
    "    x = tf.gather(input_cat, idx, axis=1)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    x = Embedding(input_dim=N, \n",
    "                  output_dim=int(np.sqrt(N)), \n",
    "                  input_length=1, name=col+'_embed')(x)\n",
    "    \n",
    "    # save these outputs to concatenate later\n",
    "    all_branch_outputs.append(x)\n",
    "    \n",
    "# -----HERE IS THE ADDING OF AN INPUT USING NUMERIC DATA-----\n",
    "# create dense input branch for numeric\n",
    "inputs_num = Input(shape=(X_train_num.shape[1],), name='numeric')\n",
    "x_dense = Dense(units=17, activation='relu', name='num_1')(inputs_num)\n",
    "    \n",
    "all_branch_outputs.append(x_dense)\n",
    "# -------------------------------------------------------------------\n",
    "\n",
    "# now concatenate the outputs and add a fully connected layer\n",
    "final_branch = concatenate(all_branch_outputs, name='concat_1')\n",
    "final_branch = Dense(units=10, activation='relu', name='combined_1')(final_branch)\n",
    "final_branch = Dense(units=1, activation='sigmoid', name='combined_2')(final_branch)\n",
    "\n",
    "model = Model(inputs=[input_cat,inputs_num], outputs=final_branch)\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit([ X_train_cat, X_train_num ], # inputs for each branch are a list\n",
    "          y_train, \n",
    "          epochs=10, \n",
    "          batch_size=50, \n",
    "          verbose=1)\n",
    "\n",
    "yhat = model.predict([X_test_cat,\n",
    "                      X_test_num]) # each branch has an input\n",
    "\n",
    "yhat = np.round(yhat)\n",
    "print(mt.confusion_matrix(y_test,yhat))\n",
    "print(mt.classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the confusion matrix is doing better! But we still are just using a simple neural network. We really want to take advantage of the embeddings and crossed columns that are possible with tensorflow. \n",
    "\n",
    "\n",
    "**[Back to Slides]**\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crossed Columns and Deep Embeddings\n",
    "Lets create the network below in steps. This network consists of a number of different paths for the input data to take.\n",
    "![asdfasfd](https://www.tensorflow.org/images/wide_n_deep.svg)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "___\n",
    "\n",
    "\n",
    "## Making Crossed Columns\n",
    "For this example, we are going to make embeddings for crossed columns. We will perform this step using `sklearn` \n",
    "\n",
    "Let's start simple with \n",
    "- branches of crossed categorical features as input \n",
    "- one branch for each crossed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sandbox for looking at different categorical variables\n",
    "for col in categorical_headers:\n",
    "    vals = df_train[col].unique()\n",
    "    print(col,'has', len(vals), 'unique values:')\n",
    "    print(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# a quick example of crossing some columns\n",
    "\n",
    "cols_list = ['marital_status','workclass','relationship']\n",
    "\n",
    "# 1. create crossed labels by string join operation\n",
    "X_crossed_train = df_train[cols_list].apply(lambda x: '_'.join(x), axis=1)\n",
    "X_crossed_test = df_test[cols_list].apply(lambda x: '_'.join(x), axis=1)\n",
    "\n",
    "# combine together for training\n",
    "all_vals = np.hstack((X_crossed_train.to_numpy(),  X_crossed_test.to_numpy()))\n",
    "print(np.unique(all_vals))\n",
    "    \n",
    "# 2. encode as integers, stacking all possibilities\n",
    "enc = LabelEncoder()\n",
    "enc.fit(all_vals)\n",
    "\n",
    "encoded_vals_train = enc.transform(X_crossed_train)\n",
    "encoded_vals_test  = enc.transform(X_crossed_test)\n",
    "\n",
    "print(np.min(encoded_vals_train), np.max(encoded_vals_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now let's create some different crossed values\n",
    "# possible crossing options:\n",
    "#   'workclass','education','marital_status',\n",
    "#   'occupation','relationship','race',\n",
    "#   'sex','country'\n",
    "\n",
    "# choose these as a class, what makes sense??\n",
    "cross_columns = [#['race','sex','education','occupation'],\n",
    "                 ['race','sex', 'marital_status','occupation'],\n",
    "                 ['workclass','occupation'],\n",
    "                 ['workclass','occupation','education'],\n",
    "                 #['occupation','race','education'],\n",
    "                 ['education','country']\n",
    "                ]\n",
    "\n",
    "# cross each set of columns in the list above\n",
    "cross_col_df_names = []\n",
    "for cols_list in cross_columns:\n",
    "    # encode as ints for the embedding\n",
    "    enc = LabelEncoder()\n",
    "    \n",
    "    # 1. create crossed labels by join operation\n",
    "    X_crossed_train = df_train[cols_list].apply(lambda x: '_'.join(x), axis=1)\n",
    "    X_crossed_test = df_test[cols_list].apply(lambda x: '_'.join(x), axis=1)\n",
    "    \n",
    "    # get a nice name for this new crossed column\n",
    "    cross_col_name = '_'.join(cols_list)\n",
    "    \n",
    "    # 2. encode as integers, stacking all possibilities\n",
    "    enc.fit(np.hstack((X_crossed_train.to_numpy(),  X_crossed_test.to_numpy())))\n",
    "    \n",
    "    # 3. Save into dataframe with new name\n",
    "    df_train[cross_col_name] = enc.transform(X_crossed_train)\n",
    "    df_test[cross_col_name] = enc.transform(X_crossed_test)\n",
    "    \n",
    "    # keep track of the new names of the crossed columns\n",
    "    cross_col_df_names.append(cross_col_name) \n",
    "    \n",
    "cross_col_df_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train a model only using crossed values\n",
    "# get crossed columns\n",
    "X_train_crossed = df_train[cross_col_df_names].to_numpy()\n",
    "X_test_crossed = df_test[cross_col_df_names].to_numpy()\n",
    "\n",
    "crossed_outputs = [] # this is where we will keep track of output of each branch\n",
    "\n",
    "input_crossed = Input(shape=(X_train_crossed.shape[1],), dtype='int64', name='categorical')\n",
    "for idx,col in enumerate(cross_col_df_names):\n",
    "    \n",
    "    # track what the maximum integer value will be for this variable\n",
    "    # which is the same as the number of categories\n",
    "    N = max(df_train[col].max(),df_test[col].max())+1\n",
    "    \n",
    "    # this line of code does this: input_branch[:,idx]\n",
    "    x = tf.gather(input_crossed, idx, axis=1)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    x = Embedding(input_dim=N, \n",
    "                  output_dim=int(np.sqrt(N)), \n",
    "                  input_length=1, name=col+'_embed')(x)\n",
    "    \n",
    "    # save these outputs to concatenate later\n",
    "    crossed_outputs.append(x)\n",
    "    \n",
    "\n",
    "# now concatenate the outputs and add a fully connected layer\n",
    "wide_branch = concatenate(crossed_outputs, name='concat_1')\n",
    "wide_branch = Dense(units=1,activation='sigmoid', name='combined')(wide_branch)\n",
    "\n",
    "model = Model(inputs=input_crossed, outputs=wide_branch)\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train_crossed,\n",
    "        y_train, epochs=10, batch_size=32, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bonus: we can keep training, picking up where we left of (no reinitialization)\n",
    "model.fit(X_train_crossed,\n",
    "        y_train, epochs=10, batch_size=32, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = np.round(model.predict(X_test_crossed))\n",
    "print(mt.confusion_matrix(y_test,yhat))\n",
    "print(mt.classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you will need to install pydot properly on your machine to get this running\n",
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! That is just using crossed columns and a one layer Linear Classifer! So memorization works fairly well here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "___\n",
    "\n",
    "\n",
    "## Combining Crossed Linear Classifier and Deep Embeddings\n",
    "Now its just a matter of setting the wide and deep columns for tensorflow. After which, we can use the combined classifier!\n",
    "\n",
    "Wide and deep models can have really interesting and useful properties so they are great to keep in mind when selecting an architecture. Some of the hyperparameters that are specific to this are:\n",
    "- which features to cross together, typically you only want to cross columns you think are important to be connected--they somehow might create new knowledge by combining.\n",
    "- the size of the dense feature embeddings. This can be difficult to set, but one common setting is $log_2(N)$ or $\\sqrt{N}$ where $N$ is the total number of uniques values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get crossed columns\n",
    "X_train_crossed = df_train[cross_col_df_names].to_numpy()\n",
    "X_test_crossed = df_test[cross_col_df_names].to_numpy()\n",
    "\n",
    "# save categorical features\n",
    "X_train_cat = df_train[categorical_headers_ints].to_numpy() \n",
    "X_test_cat = df_test[categorical_headers_ints].to_numpy() \n",
    "\n",
    "# and save off the numeric features\n",
    "X_train_num =  df_train[numeric_headers].to_numpy()\n",
    "X_test_num = df_test[numeric_headers].to_numpy()\n",
    "\n",
    "\n",
    "# we need to create separate lists for each branch\n",
    "crossed_outputs = []\n",
    "\n",
    "# CROSSED DATA INPUT\n",
    "input_crossed = Input(shape=(X_train_crossed.shape[1],), dtype='int64', name='wide_inputs')\n",
    "for idx,col in enumerate(cross_col_df_names):\n",
    "    \n",
    "    # track what the maximum integer value will be for this variable\n",
    "    # which is the same as the number of categories\n",
    "    N = max(df_train[col].max(),df_test[col].max())+1\n",
    "    \n",
    "    \n",
    "    # this line of code does this: input_branch[:,idx]\n",
    "    x = tf.gather(input_crossed, idx, axis=1)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    x = Embedding(input_dim=N, \n",
    "                  output_dim=int(np.sqrt(N)), \n",
    "                  input_length=1, name=col+'_embed')(x)\n",
    "    \n",
    "    # save these outputs to concatenate later\n",
    "    crossed_outputs.append(x)\n",
    "    \n",
    "\n",
    "# now concatenate the outputs and add a fully connected layer\n",
    "wide_branch = concatenate(crossed_outputs, name='wide_concat')\n",
    "\n",
    "# reset this input branch\n",
    "all_deep_branch_outputs = []\n",
    "\n",
    "# CATEGORICAL DATA INPUT\n",
    "input_cat = Input(shape=(X_train_cat.shape[1],), dtype='int64', name='categorical_input')\n",
    "for idx,col in enumerate(categorical_headers_ints):\n",
    "    \n",
    "    # track what the maximum integer value will be for this variable\n",
    "    # which is the same as the number of categories\n",
    "    N = max(df_train[col].max(),df_test[col].max())+1\n",
    "    \n",
    "    # this line of code does this: input_branch[:,idx]\n",
    "    x = tf.gather(input_cat, idx, axis=1)\n",
    "    \n",
    "    # now use an embedding to deal with integers as if they were one hot encoded\n",
    "    x = Embedding(input_dim=N, \n",
    "                  output_dim=int(np.sqrt(N)), \n",
    "                  input_length=1, name=col+'_embed')(x)\n",
    "    \n",
    "    # save these outputs to concatenate later\n",
    "    all_deep_branch_outputs.append(x)\n",
    "    \n",
    "# NUMERIC DATA INPUT\n",
    "# create dense input branch for numeric\n",
    "input_num = Input(shape=(X_train_num.shape[1],), name='numeric')\n",
    "x_dense = Dense(units=22, activation='relu',name='num_1')(input_num)\n",
    "    \n",
    "all_deep_branch_outputs.append(x_dense)\n",
    "\n",
    "\n",
    "# merge the deep branches together\n",
    "deep_branch = concatenate(all_deep_branch_outputs,name='concat_embeds')\n",
    "deep_branch = Dense(units=50,activation='relu', name='deep1')(deep_branch)\n",
    "deep_branch = Dense(units=25,activation='relu', name='deep2')(deep_branch)\n",
    "deep_branch = Dense(units=10,activation='relu', name='deep3')(deep_branch)\n",
    "    \n",
    "# merge the deep and wide branch\n",
    "final_branch = concatenate([wide_branch, deep_branch],\n",
    "                           name='concat_deep_wide')\n",
    "final_branch = Dense(units=1,activation='sigmoid',\n",
    "                     name='combined')(final_branch)\n",
    "\n",
    "model = Model(inputs=[input_crossed,input_cat,input_num], \n",
    "              outputs=final_branch)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# you will need to install pydot properly on your machine to get this running\n",
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "model.compile(optimizer='adagrad',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# lets also add the history variable to see how we are doing\n",
    "# and lets add a validation set to keep track of our progress\n",
    "history = model.fit([X_train_crossed,X_train_cat,X_train_num],\n",
    "                    y_train, \n",
    "                    epochs=15, \n",
    "                    batch_size=32, \n",
    "                    verbose=1, \n",
    "                    validation_data = ([X_test_crossed,X_test_cat,X_test_num],y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = np.round(model.predict([X_test_crossed,X_test_cat,X_test_num]))\n",
    "print(mt.confusion_matrix(y_test,yhat))\n",
    "print(mt.classification_report(y_test,yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "\n",
    "plt.ylabel('Accuracy %')\n",
    "plt.title('Training')\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Validation')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.ylabel('Training Loss')\n",
    "plt.xlabel('epochs')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.xlabel('epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Generalization Better\n",
    "In what ways might we try to make the model found generalize more concretely?\n",
    "\n",
    "**Self Test:** What is the best method to make the results better?  \n",
    "- A. Add Dropout to deep network\n",
    "- B. Add Dropout to wide network\n",
    "- C. Perturb the Categorical Data during Training (augmentation)\n",
    "- D. Add L2 regularization to different layer's weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}