examples/others/recommender_wide_and_deep.py

'''
Pedagogical example realization of wide & deep networks, using TensorFlow and TFLearn.

This is a re-implementation of http://arxiv.org/abs/1606.07792, using the combination
of a wide linear model, and a deep feed-forward neural network, for binary classification  
This example realization is based on Tensorflow's TF.Learn tutorial 
(https://www.tensorflow.org/versions/r0.10/tutorials/wide_and_deep/index.html),
but implemented in TFLearn.  Note that despite the closeness of names, TFLearn is distinct
from TF.Learn (previously known as scikit flow).

This implementation explicitly presents the construction of layers in the deep part of the
network, and allows direct access to changing the layer architecture, and customization
of methods used for regression and optimization.

In contrast, the TF.Learn tutorial offers more sophistication, but hides the layer
architecture behind a black box function, tf.contrib.learn.DNNLinearCombinedClassifier.

See https://github.com/ichuang/tflearn_wide_and_deep for more about this example.
'''

from __future__ import division, print_function

import os
import sys
import argparse
import tflearn
import tempfile
import urllib

import numpy as np
import pandas as pd
import tensorflow.compat.v1 as tf

#-----------------------------------------------------------------------------

COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
           "marital_status", "occupation", "relationship", "race", "gender",
           "capital_gain", "capital_loss", "hours_per_week", "native_country",
           "income_bracket"]
LABEL_COLUMN = "label"
CATEGORICAL_COLUMNS = {"workclass": 10, "education": 17, "marital_status":8, 
                       "occupation": 16, "relationship": 7, "race": 6, 
                       "gender": 3, "native_country": 43, "age_binned": 14}
CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
                      "hours_per_week"]

#-----------------------------------------------------------------------------

class TFLearnWideAndDeep(object):
    '''
    Wide and deep model, implemented using TFLearn
    '''
    AVAILABLE_MODELS = ["wide", "deep", "wide+deep"]
    def __init__(self, model_type="wide+deep", verbose=None, name=None, tensorboard_verbose=3, 
                 wide_learning_rate=0.001, deep_learning_rate=0.001, checkpoints_dir=None):
        '''
        model_type = `str`: wide or deep or wide+deep
        verbose = `bool`
        name = `str` used for run_id (defaults to model_type)
        tensorboard_verbose = `int`: logging level for tensorboard (0, 1, 2, or 3)
        wide_learning_rate = `float`: defaults to 0.001
        deep_learning_rate = `float`: defaults to 0.001
        checkpoints_dir = `str`: where checkpoint files will be stored (defaults to "CHECKPOINTS")
        '''
        self.model_type = model_type or "wide+deep"
        assert self.model_type in self.AVAILABLE_MODELS
        self.verbose = verbose or 0
        self.tensorboard_verbose = tensorboard_verbose
        self.name = name or self.model_type	# name is used for the run_id
        self.data_columns = COLUMNS
        self.continuous_columns = CONTINUOUS_COLUMNS
        self.categorical_columns = CATEGORICAL_COLUMNS	# dict with category_name: category_size
        self.label_column = LABEL_COLUMN
        self.checkpoints_dir = checkpoints_dir or "CHECKPOINTS"
        if not os.path.exists(self.checkpoints_dir):
            os.mkdir(self.checkpoints_dir)
            print("Created checkpoints directory %s" % self.checkpoints_dir)
        self.build_model([wide_learning_rate, deep_learning_rate])

    def load_data(self, train_dfn="adult.data", test_dfn="adult.test"):
        '''
        Load data (use files offered in the Tensorflow wide_n_deep_tutorial)
        '''
        if not os.path.exists(train_dfn):
            urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_dfn)
            print("Training data is downloaded to %s" % train_dfn)

        if not os.path.exists(test_dfn):
            urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_dfn)
            print("Test data is downloaded to %s" % test_dfn)

        self.train_data = pd.read_csv(train_dfn, names=COLUMNS, skipinitialspace=True)
        self.test_data = pd.read_csv(test_dfn, names=COLUMNS, skipinitialspace=True, skiprows=1)

        self.train_data[self.label_column] = (self.train_data["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
        self.test_data[self.label_column] = (self.test_data["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)


    def build_model(self, learning_rate=[0.001, 0.01]):
        '''
        Model - wide and deep - built using tflearn
        '''
        n_cc = len(self.continuous_columns)
        n_categories = 1			# two categories: is_idv and is_not_idv
        input_shape = [None, n_cc]
        if self.verbose:
            print ("="*77 + " Model %s (type=%s)" % (self.name, self.model_type))
            print ("  Input placeholder shape=%s" % str(input_shape))
        wide_inputs = tflearn.input_data(shape=input_shape, name="wide_X")
        if not isinstance(learning_rate, list):
            learning_rate = [learning_rate, learning_rate]	# wide, deep
        if self.verbose:
            print ("  Learning rates (wide, deep)=%s" % learning_rate)

        with tf.name_scope("Y"):			# placeholder for target variable (i.e. trainY input)
            Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y")

        with tf.variable_scope(None, "cb_unit", [wide_inputs]) as scope:
            central_bias = tflearn.variables.variable('central_bias', shape=[1],
                                                      initializer=tf.constant_initializer(np.random.randn()),
                                                      trainable=True, restore=True)
            tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit', central_bias)

        if 'wide' in self.model_type:
            wide_network = self.wide_model(wide_inputs, n_cc)
            network = wide_network
            wide_network_with_bias = tf.add(wide_network, central_bias, name="wide_with_bias")

        if 'deep' in self.model_type:
            deep_network = self.deep_model(wide_inputs, n_cc)
            deep_network_with_bias = tf.add(deep_network, central_bias, name="deep_with_bias")
            if 'wide' in self.model_type:
                network = tf.add(wide_network, deep_network)
                if self.verbose:
                    print ("Wide + deep model network %s" % network)
            else:
                network = deep_network

        network = tf.add(network, central_bias, name="add_central_bias")

        # add validation monitor summaries giving confusion matrix entries
        with tf.name_scope('Monitors'):
            predictions = tf.cast(tf.greater(network, 0), tf.int64)
            print ("predictions=%s" % predictions)
            Ybool = tf.cast(Y_in, tf.bool)
            print ("Ybool=%s" % Ybool)
            pos = tf.boolean_mask(predictions, Ybool)
            neg = tf.boolean_mask(predictions, ~Ybool)
            psize = tf.cast(tf.shape(pos)[0], tf.int64)
            nsize = tf.cast(tf.shape(neg)[0], tf.int64)
            true_positive = tf.reduce_sum(pos, name="true_positive")
            false_negative = tf.subtract(psize, true_positive, name="false_negative")
            false_positive = tf.reduce_sum(neg, name="false_positive")
            true_negative = tf.subtract(nsize, false_positive, name="true_negative")
            overall_accuracy = tf.truediv(tf.add(true_positive, true_negative), tf.add(nsize, psize), name="overall_accuracy")
        vmset = [true_positive, true_negative, false_positive, false_negative, overall_accuracy]

        trainable_vars = tf.trainable_variables()
        tv_deep = [v for v in trainable_vars if v.name.startswith('deep_')]
        tv_wide = [v for v in trainable_vars if v.name.startswith('wide_')]

        if self.verbose:
            print ("DEEP trainable_vars")
            for v in tv_deep:
                print ("  Variable %s: %s" % (v.name, v))
            print ("WIDE trainable_vars")
            for v in tv_wide:
                print ("  Variable %s: %s" % (v.name, v))

        if 'wide' in self.model_type:
            if not 'deep' in self.model_type:
                tv_wide.append(central_bias)
            tflearn.regression(wide_network_with_bias, 
                               placeholder=Y_in,
                               optimizer='sgd', 
                               #loss='roc_auc_score',
                               loss='binary_crossentropy',
                               metric="accuracy",
                               learning_rate=learning_rate[0],
                               validation_monitors=vmset,
                               trainable_vars=tv_wide,
                               op_name="wide_regression",
                               name="Y")

        if 'deep' in self.model_type:
            if not 'wide' in self.model_type:
                tv_wide.append(central_bias)
            tflearn.regression(deep_network_with_bias, 
                               placeholder=Y_in,
                               optimizer='adam', 
                               #loss='roc_auc_score',
                               loss='binary_crossentropy',
                               metric="accuracy",
                               learning_rate=learning_rate[1],
                               validation_monitors=vmset if not 'wide' in self.model_type else None,
                               trainable_vars=tv_deep,
                               op_name="deep_regression",
                               name="Y")

        if self.model_type=='wide+deep':	# learn central bias separately for wide+deep
            tflearn.regression(network, 
                               placeholder=Y_in,
                               optimizer='adam', 
                               loss='binary_crossentropy',
                               metric="accuracy",
                               learning_rate=learning_rate[0],	# use wide learning rate
                               trainable_vars=[central_bias],
                               op_name="central_bias_regression",
                               name="Y")

        self.model = tflearn.DNN(network,
                                 tensorboard_verbose=self.tensorboard_verbose,
                                 max_checkpoints=5,
                                 checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name),
        )

        if self.verbose:
            print ("Target variables:")
            for v in tf.get_collection(tf.GraphKeys.TARGETS):
                print ("  variable %s: %s" % (v.name, v))

            print ("="*77)


    def deep_model(self, wide_inputs, n_inputs, n_nodes=[100, 50], use_dropout=False):
        '''
        Model - deep, i.e. two-layer fully connected network model
        '''
        cc_input_var = {}
        cc_embed_var = {}
        flat_vars = []
        if self.verbose:
            print ("--> deep model: %s categories, %d continuous" % (len(self.categorical_columns), n_inputs))
        for cc, cc_size in self.categorical_columns.items():
            cc_input_var[cc] = tflearn.input_data(shape=[None, 1], name="%s_in" % cc,  dtype=tf.int32)
            # embedding layers only work on CPU!  No GPU implementation in tensorflow, yet!
            cc_embed_var[cc] = tflearn.layers.embedding_ops.embedding(cc_input_var[cc],    cc_size,  8, name="deep_%s_embed" % cc)
            if self.verbose:
                print ("    %s_embed = %s" % (cc, cc_embed_var[cc]))
            flat_vars.append(tf.squeeze(cc_embed_var[cc], squeeze_dims=[1], name="%s_squeeze" % cc))

        network = tf.concat([wide_inputs] + flat_vars, 1, name="deep_concat")
        for k in range(len(n_nodes)):
            network = tflearn.fully_connected(network, n_nodes[k], activation="relu", name="deep_fc%d" % (k+1))
            if use_dropout:
                network = tflearn.dropout(network, 0.5, name="deep_dropout%d" % (k+1))
        if self.verbose:
            print ("Deep model network before output %s" % network)
        network = tflearn.fully_connected(network, 1, activation="linear", name="deep_fc_output", bias=False)
        network = tf.reshape(network, [-1, 1])	# so that accuracy is binary_accuracy
        if self.verbose:
            print ("Deep model network %s" % network)
        return network

    def wide_model(self, inputs, n_inputs):
        '''
        Model - wide, i.e. normal linear model (for logistic regression)
        '''
        network = inputs
        # use fully_connected (instad of single_unit) because fc works properly with batches, whereas single_unit is 1D only
        network = tflearn.fully_connected(network, n_inputs, activation="linear", name="wide_linear", bias=False)	# x*W (no bias)
        network = tf.reduce_sum(network, 1, name="reduce_sum")	# batched sum, to produce logits
        network = tf.reshape(network, [-1, 1])	# so that accuracy is binary_accuracy
        if self.verbose:
            print ("Wide model network %s" % network)
        return network

    def prepare_input_data(self, input_data, name="", category_map=None):
        '''
        Prepare input data dicts
        '''
        print ("-"*40 + " Preparing %s" % name)
        X = input_data[self.continuous_columns].values.astype(np.float32)
        Y = input_data[self.label_column].values.astype(np.float32)
        Y = Y.reshape([-1, 1])
        if self.verbose:
            print ("  Y shape=%s, X shape=%s" % (Y.shape, X.shape))

        X_dict = {"wide_X": X}

        if 'deep' in self.model_type:
            # map categorical value strings to integers
            td = input_data
            if category_map is None:
                category_map = {}
                for cc in self.categorical_columns:
                    if not cc in td.columns:
                        continue
                    cc_values = sorted(td[cc].unique())
                    cc_max = 1+len(cc_values)
                    cc_map = dict(zip(cc_values, range(1, cc_max)))	# start from 1 to avoid 0:0 mapping (save 0 for missing)
                    if self.verbose:
                        print ("  category %s max=%s,  map=%s" % (cc, cc_max, cc_map))
                    category_map[cc] = cc_map
                
            td = td.replace(category_map)
    
            # bin ages (cuts off extreme values)
            age_bins = [ 0, 12, 18, 25, 30, 35, 40, 45, 50, 55, 60, 65, 80, 65535 ]
            td['age_binned'] = pd.cut(td['age'], age_bins, labels=False)
            td = td.replace({'age_binned': {np.nan: 0}})
            print ("  %d age bins: age bins = %s" % (len(age_bins), age_bins))

            X_dict.update({ ("%s_in" % cc): td[cc].values.astype(np.int32).reshape([-1, 1]) for cc in self.categorical_columns})

        Y_dict = {"Y": Y}
        if self.verbose:
            print ("-"*40)
        return X_dict, Y_dict, category_map


    def train(self, n_epoch=1000, snapshot_step=10, batch_size=None):

        self.X_dict, self.Y_dict, category_map = self.prepare_input_data(self.train_data, "train data")
        self.testX_dict, self.testY_dict, _ = self.prepare_input_data(self.test_data, "test data", category_map)
        validation_batch_size = batch_size or self.testY_dict['Y'].shape[0]
        batch_size = batch_size or self.Y_dict['Y'].shape[0]

        print ("Input data shape = %s; output data shape=%s, batch_size=%s" % (str(self.X_dict['wide_X'].shape), 
                                                                               str(self.Y_dict['Y'].shape), 
                                                                               batch_size))
        print ("Test data shape = %s; output data shape=%s, validation_batch_size=%s" % (str(self.testX_dict['wide_X'].shape), 
                                                                                         str(self.testY_dict['Y'].shape), 
                                                                                         validation_batch_size))
        print ("="*60 + "  Training")
        self.model.fit(self.X_dict, 
                       self.Y_dict,
                       n_epoch=n_epoch,
                       validation_set=(self.testX_dict, self.testY_dict),
                       snapshot_step=snapshot_step,
                       batch_size=batch_size,
                       validation_batch_size=validation_batch_size,
                       show_metric=True, 
                       snapshot_epoch=False,
                       shuffle=True,
                       run_id=self.name,
        )
        
    def evaluate(self):
        logits = np.array(self.model.predict(self.testX_dict)).reshape([-1])
        print ("="*60 + "  Evaluation")
        print ("  logits: %s, min=%s, max=%s" % (logits.shape, logits.min(), logits.max()))
        probs =  1.0 / (1.0 + np.exp(-logits))
        y_pred = pd.Series((probs > 0.5).astype(np.int32))
        Y = pd.Series(self.testY_dict['Y'].astype(np.int32).reshape([-1]))
        self.confusion_matrix = self.output_confusion_matrix(Y, y_pred)
        print ("="*60)

    def output_confusion_matrix(self, y, y_pred):
        assert y.size == y_pred.size
        print("Actual IDV")
        print(y.value_counts())
        print("Predicted IDV")
        print(y_pred.value_counts())
        print()
        print("Confusion matrix:")
        cmat = pd.crosstab(y_pred, y, rownames=['predictions'], colnames=['actual'])
        print(cmat)
        sys.stdout.flush()
        return cmat
    
#-----------------------------------------------------------------------------

def CommandLine(args=None):
    '''
    Main command line.  Accepts args, to allow for simple unit testing.
    '''
    flags = tf.app.flags
    FLAGS = flags.FLAGS
    if args:
        FLAGS.__init__()
        FLAGS.__dict__.update(args)

    try:
        flags.DEFINE_string("model_type", "wide+deep","Valid model types: {'wide', 'deep', 'wide+deep'}.")
        flags.DEFINE_string("run_name", None, "name for this run (defaults to model type)")
        flags.DEFINE_string("load_weights", None, "filename with initial weights to load")
        flags.DEFINE_string("checkpoints_dir", None, "name of directory where checkpoints should be saved")
        flags.DEFINE_integer("n_epoch", 200, "Number of training epoch steps")
        flags.DEFINE_integer("snapshot_step", 100, "Step number when snapshot (and validation testing) is done")
        flags.DEFINE_float("wide_learning_rate", 0.001, "learning rate for the wide part of the model")
        flags.DEFINE_float("deep_learning_rate", 0.001, "learning rate for the deep part of the model")
        flags.DEFINE_boolean("verbose", False, "Verbose output")
    except argparse.ArgumentError:
        pass	# so that CommandLine can be run more than once, for testing

    twad = TFLearnWideAndDeep(model_type=FLAGS.model_type, verbose=FLAGS.verbose, 
                              name=FLAGS.run_name, wide_learning_rate=FLAGS.wide_learning_rate,
                              deep_learning_rate=FLAGS.deep_learning_rate,
                              checkpoints_dir=FLAGS.checkpoints_dir)
    twad.load_data()
    if FLAGS.load_weights:
        print ("Loading initial weights from %s" % FLAGS.load_weights)
        twad.model.load(FLAGS.load_weights)
    twad.train(n_epoch=FLAGS.n_epoch, snapshot_step=FLAGS.snapshot_step)
    twad.evaluate()
    return twad

#-----------------------------------------------------------------------------
# unit tests

def test_wide_and_deep():
    import glob
    tf.reset_default_graph()
    cdir = "test_checkpoints"
    if os.path.exists(cdir):
        os.system("rm -rf %s" % cdir)
    twad = CommandLine(args=dict(verbose=True, n_epoch=5, model_type="wide+deep", snapshot_step=5, 
                                 wide_learning_rate=0.0001, checkpoints_dir=cdir))
    cfiles = glob.glob("%s/*.tfl-*" % cdir)
    print ("cfiles=%s" % cfiles)
    assert(len(cfiles))
    cm = twad.confusion_matrix.values.astype(np.float32)
    assert(cm[1][1])

def test_deep():
    import glob
    tf.reset_default_graph()
    cdir = "test_checkpoints"
    if os.path.exists(cdir):
        os.system("rm -rf %s" % cdir)
    twad = CommandLine(args=dict(verbose=True, n_epoch=5, model_type="deep", snapshot_step=5, 
                                 wide_learning_rate=0.0001, checkpoints_dir=cdir))
    cfiles = glob.glob("%s/*.tfl-*" % cdir)
    print ("cfiles=%s" % cfiles)
    assert(len(cfiles))
    cm = twad.confusion_matrix.values.astype(np.float32)
    assert(cm[1][1])

def test_wide():
    import glob
    tf.reset_default_graph()
    cdir = "test_checkpoints"
    if os.path.exists(cdir):
        os.system("rm -rf %s" % cdir)
    twad = CommandLine(args=dict(verbose=True, n_epoch=5, model_type="wide", snapshot_step=5, 
                                 wide_learning_rate=0.0001, checkpoints_dir=cdir))
    cfiles = glob.glob("%s/*.tfl-*" % cdir)
    print ("cfiles=%s" % cfiles)
    assert(len(cfiles))
    cm = twad.confusion_matrix.values.astype(np.float32)
    assert(cm[1][1])

#-----------------------------------------------------------------------------

if __name__=="__main__":
    CommandLine()
    None