07_linear_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/Users/i550012/.pyenv/versions/3.7.17/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.7\n",
      "  return f(*args, **kwds)\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "import pandas as pd\n",
    "from six.moves import urllib\n",
    "import shutil\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('census/adult.test', <http.client.HTTPMessage at 0x1253905d0>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRAIN_FILE_NAME = \"census/adult.data\"\n",
    "TEST_FILE_NAME = \"census/adult.test\"\n",
    "urllib.request.urlretrieve(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", TRAIN_FILE_NAME)\n",
    "urllib.request.urlretrieve(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", TEST_FILE_NAME)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_COLUMNS = [\n",
    "    \"age\", \"workclass\", \"fnlwgt\",  \"education\", \"education_num\", \"marital_status\",\n",
    "    \"occupation\", \"relationship\",  \"race\", \"gender\", \"capital_gain\", \"capital_loss\",\n",
    "    \"hours_per_week\", \"native_country\", \"income_bracket\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(TRAIN_FILE_NAME, names= CSV_COLUMNS, skipinitialspace=True, skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</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>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</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  education_num  \\\n",
       "0   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "1   38           Private  215646    HS-grad              9   \n",
       "2   53           Private  234721       11th              7   \n",
       "3   28           Private  338409  Bachelors             13   \n",
       "4   37           Private  284582    Masters             14   \n",
       "\n",
       "       marital_status         occupation   relationship   race  gender  \\\n",
       "0  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "1            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "2  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "3  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "4  Married-civ-spouse    Exec-managerial           Wife  White  Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week native_country income_bracket  \n",
       "0             0             0              13  United-States          <=50K  \n",
       "1             0             0              40  United-States          <=50K  \n",
       "2             0             0              40  United-States          <=50K  \n",
       "3             0             0              40           Cuba          <=50K  \n",
       "4             0             0              40  United-States          <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "TRIMMED_REORDERED_COLUMNS = [\n",
    "    \"age\", \"workclass\", \"education\", \"education_num\", \"marital_status\",\n",
    "    \"relationship\",  \"race\", \"gender\", \"occupation\",\n",
    "    \"hours_per_week\", \"native_country\", \"income_bracket\"\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>40</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>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  education  education_num      marital_status  \\\n",
       "0   50  Self-emp-not-inc  Bachelors             13  Married-civ-spouse   \n",
       "1   38           Private    HS-grad              9            Divorced   \n",
       "2   53           Private       11th              7  Married-civ-spouse   \n",
       "3   28           Private  Bachelors             13  Married-civ-spouse   \n",
       "4   37           Private    Masters             14  Married-civ-spouse   \n",
       "\n",
       "    relationship   race  gender         occupation  hours_per_week  \\\n",
       "0        Husband  White    Male    Exec-managerial              13   \n",
       "1  Not-in-family  White    Male  Handlers-cleaners              40   \n",
       "2        Husband  Black    Male  Handlers-cleaners              40   \n",
       "3           Wife  Black  Female     Prof-specialty              40   \n",
       "4           Wife  White  Female    Exec-managerial              40   \n",
       "\n",
       "  native_country income_bracket  \n",
       "0  United-States          <=50K  \n",
       "1  United-States          <=50K  \n",
       "2  United-States          <=50K  \n",
       "3           Cuba          <=50K  \n",
       "4  United-States          <=50K  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[TRIMMED_REORDERED_COLUMNS]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = tf.feature_column.categorical_column_with_vocabulary_list(\"gender\", df[\"gender\"].unique())\n",
    "race = tf.feature_column.categorical_column_with_vocabulary_list(\"race\", df[\"race\"].unique())\n",
    "education = tf.feature_column.categorical_column_with_vocabulary_list(\"education\", df[\"education\"].unique())\n",
    "marital_status = tf.feature_column.categorical_column_with_vocabulary_list(\"marital_status\", df[\"marital_status\"].unique())\n",
    "relationship = tf.feature_column.categorical_column_with_vocabulary_list(\"relationship\", df[\"relationship\"].unique())\n",
    "workclass= tf.feature_column.categorical_column_with_vocabulary_list(\"workclass\", df[\"workclass\"].unique())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = tf.feature_column.numeric_column(\"age\")\n",
    "education_num = tf.feature_column.numeric_column(\"education_num\")\n",
    "hours_per_week = tf.feature_column.numeric_column(\"hours_per_week\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_buckets = tf.feature_column.bucketized_column(age, boundaries=[18,25,30,35,40,45,50,55,60,65])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupation = tf.feature_column.categorical_column_with_hash_bucket(\"occupation\", hash_bucket_size=1000)\n",
    "native_country = tf.feature_column.categorical_column_with_hash_bucket(\"native_country\", hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_columns = [\n",
    "    gender, race, marital_status, workclass, occupation, native_country, age_buckets, education\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossed_columns = [\n",
    "    tf.feature_column.crossed_column([\"education\", \"occupation\"], hash_bucket_size=1000),\n",
    "    tf.feature_column.crossed_column([age_buckets, \"education\", \"occupation\"], hash_bucket_size=1000),\n",
    "    tf.feature_column.crossed_column([\"native_country\", \"occupation\"], hash_bucket_size=1000)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "deep_columns = [ education_num, hours_per_week]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_fn(file_name, num_epochs, shuffle): \n",
    "    df = pd.read_csv(file_name, names=CSV_COLUMNS, skipinitialspace=True, skiprows=1)\n",
    "    df = df[TRIMMED_REORDERED_COLUMNS]\n",
    "    df = df.dropna(how=\"any\", axis=0)\n",
    "    labels = df[\"income_bracket\"].apply(lambda x: \">50K\" in x).astype(int)\n",
    "    return tf.estimator.inputs.pandas_input_fn(x=df, y= labels, batch_size=100, num_epochs=num_epochs, shuffle=shuffle, num_threads=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': './linear_classifier', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x125f79150>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "MODEL_DIR = \"./linear_classifier\"\n",
    "import os\n",
    "os.makedirs(MODEL_DIR)\n",
    "shutil.rmtree(MODEL_DIR)\n",
    "linear_estimator = tf.estimator.LinearClassifier(model_dir=MODEL_DIR, feature_columns=base_columns+crossed_columns+deep_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /var/folders/nl/yn0d4tg1107g2jk38nj1hc740000gn/T/ipykernel_34280/2361288793.py:6: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.\n",
      "\n",
      "WARNING:tensorflow:From /var/folders/nl/yn0d4tg1107g2jk38nj1hc740000gn/T/ipykernel_34280/2361288793.py:6: The name tf.estimator.inputs.pandas_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.pandas_input_fn instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Entity <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1260dad50>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1260dad50>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING: Entity <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1260dad50>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1260dad50>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING:tensorflow:Entity <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1260daed0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1260daed0>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING: Entity <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1260daed0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1260daed0>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/ops/sparse_ops.py:1719: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:308: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-04 09:38:23.701386: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/training/monitored_session.py:875: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Saving checkpoints for 0 into ./linear_classifier/model.ckpt.\n",
      "INFO:tensorflow:loss = 69.31472, step = 1\n",
      "INFO:tensorflow:global_step/sec: 141.494\n",
      "INFO:tensorflow:loss = 33.72049, step = 101 (0.707 sec)\n",
      "INFO:tensorflow:global_step/sec: 314.513\n",
      "INFO:tensorflow:loss = 39.91265, step = 201 (0.318 sec)\n",
      "INFO:tensorflow:global_step/sec: 295.939\n",
      "INFO:tensorflow:loss = 32.669117, step = 301 (0.338 sec)\n",
      "INFO:tensorflow:global_step/sec: 301.039\n",
      "INFO:tensorflow:loss = 29.18893, step = 401 (0.332 sec)\n",
      "INFO:tensorflow:global_step/sec: 309.004\n",
      "INFO:tensorflow:loss = 36.321507, step = 501 (0.324 sec)\n",
      "INFO:tensorflow:global_step/sec: 278.156\n",
      "INFO:tensorflow:loss = 34.26846, step = 601 (0.359 sec)\n",
      "INFO:tensorflow:global_step/sec: 288.825\n",
      "INFO:tensorflow:loss = 25.266666, step = 701 (0.347 sec)\n",
      "INFO:tensorflow:global_step/sec: 262.176\n",
      "INFO:tensorflow:loss = 35.13993, step = 801 (0.380 sec)\n",
      "INFO:tensorflow:global_step/sec: 274.188\n",
      "INFO:tensorflow:loss = 27.917595, step = 901 (0.365 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into ./linear_classifier/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 27.475878.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.canned.linear.LinearClassifier at 0x109889f10>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_estimator.train(input_fn=input_fn(TRAIN_FILE_NAME, num_epochs=None, shuffle=True), steps=1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:enqueue_data was called with num_epochs and num_threads > 1. num_epochs is applied per thread, so this will produce more epochs than you probably intend. If you want to limit epochs, use one thread.\n",
      "WARNING:tensorflow:enqueue_data was called with shuffle=False and num_threads > 1. This will create multiple threads, all reading the array/dataframe in order. If you want examples read in order, use one thread; if you want multiple threads, enable shuffling.\n",
      "INFO:tensorflow:Calling model_fn.\n",
      "WARNING:tensorflow:Entity <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1430a7890>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1430a7890>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING: Entity <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1430a7890>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method LinearModel.call of <tensorflow.python.feature_column.feature_column_v2.LinearModel object at 0x1430a7890>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING:tensorflow:Entity <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1430a79d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1430a79d0>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING: Entity <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1430a79d0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method _LinearModelLayer.call of <tensorflow.python.feature_column.feature_column_v2._LinearModelLayer object at 0x1430a79d0>>: AttributeError: module 'gast' has no attribute 'Str'\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/ops/metrics_impl.py:2027: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2024-04-04T09:39:48Z\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "WARNING:tensorflow:From /Users/i550012/virtualenv/tensorflow1/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./linear_classifier/model.ckpt-1000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2024-04-04-09:39:55\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.8386671, accuracy_baseline = 0.75918305, auc = 0.8960385, auc_precision_recall = 0.725801, average_loss = 0.3428352, global_step = 1000, label/mean = 0.24081695, loss = 34.283516, precision = 0.7615198, prediction/mean = 0.20802665, recall = 0.48055094\n",
      "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: ./linear_classifier/model.ckpt-1000\n"
     ]
    }
   ],
   "source": [
    "results = linear_estimator.evaluate(input_fn=input_fn(TEST_FILE_NAME, num_epochs=1, shuffle=False), steps=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.8386671\n",
      "accuracy_baseline: 0.75918305\n",
      "auc: 0.8960385\n",
      "auc_precision_recall: 0.725801\n",
      "average_loss: 0.3428352\n",
      "global_step: 1000\n",
      "label/mean: 0.24081695\n",
      "loss: 34.283516\n",
      "precision: 0.7615198\n",
      "prediction/mean: 0.20802665\n",
      "recall: 0.48055094\n"
     ]
    }
   ],
   "source": [
    "for key in sorted(results): \n",
    "    print (\"%s: %s\"%(key, results[key]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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