content: # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Example code for TensorFlow Wide & Deep Tutorial using TF.Learn API."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  import tempfile
  
  from six.moves import urllib
  
  import pandas as pd
  import tensorflow 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", "education", "marital_status", "occupation",
                         "relationship", "race", "gender", "native_country"]
  CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
                        "hours_per_week"]
  
  
  def maybe_download(train_data, test_data):
    """Maybe downloads training data and returns train and test file names."""
    if train_data:
      train_file_name = train_data
    else:
      train_file = tempfile.NamedTemporaryFile(delete=False)
      urllib.request.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)  # pylint: disable=line-too-long
      train_file_name = train_file.name
      train_file.close()
      print("Training data is downloaded to %s" % train_file_name)
  
    if test_data:
      test_file_name = test_data
    else:
      test_file = tempfile.NamedTemporaryFile(delete=False)
      urllib.request.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test", test_file.name)  # pylint: disable=line-too-long
      test_file_name = test_file.name
      test_file.close()
      print("Test data is downloaded to %s" % test_file_name)
  
    return train_file_name, test_file_name
  
  
  def build_estimator(model_dir, model_type):
    """Build an estimator."""
    # Sparse base columns.
    gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender",
                                                       keys=["female", "male"])
    education = tf.contrib.layers.sparse_column_with_hash_bucket(
        "education", hash_bucket_size=1000)
    relationship = tf.contrib.layers.sparse_column_with_hash_bucket(
        "relationship", hash_bucket_size=100)
    workclass = tf.contrib.layers.sparse_column_with_hash_bucket(
        "workclass", hash_bucket_size=100)
    occupation = tf.contrib.layers.sparse_column_with_hash_bucket(
        "occupation", hash_bucket_size=1000)
    native_country = tf.contrib.layers.sparse_column_with_hash_bucket(
        "native_country", hash_bucket_size=1000)
  
    # Continuous base columns.
    age = tf.contrib.layers.real_valued_column("age")
    education_num = tf.contrib.layers.real_valued_column("education_num")
    capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
    capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
    hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
  
    # Transformations.
    age_buckets = tf.contrib.layers.bucketized_column(age,
                                                      boundaries=[
                                                          18, 25, 30, 35, 40, 45,
                                                          50, 55, 60, 65
                                                      ])
  
    # Wide columns and deep columns.
    wide_columns = [gender, native_country, education, occupation, workclass,
                    relationship, age_buckets,
                    tf.contrib.layers.crossed_column([education, occupation],
                                                     hash_bucket_size=int(1e4)),
                    tf.contrib.layers.crossed_column(
                        [age_buckets, education, occupation],
                        hash_bucket_size=int(1e6)),
                    tf.contrib.layers.crossed_column([native_country, occupation],
                                                     hash_bucket_size=int(1e4))]
    deep_columns = [
        tf.contrib.layers.embedding_column(workclass, dimension=8),
        tf.contrib.layers.embedding_column(education, dimension=8),
        tf.contrib.layers.embedding_column(gender, dimension=8),
        tf.contrib.layers.embedding_column(relationship, dimension=8),
        tf.contrib.layers.embedding_column(native_country,
                                           dimension=8),
        tf.contrib.layers.embedding_column(occupation, dimension=8),
        age,
        education_num,
        capital_gain,
        capital_loss,
        hours_per_week,
    ]
  
    if model_type == "wide":
      m = tf.contrib.learn.LinearClassifier(model_dir=model_dir,
                                            feature_columns=wide_columns)
    elif model_type == "deep":
      m = tf.contrib.learn.DNNClassifier(model_dir=model_dir,
                                         feature_columns=deep_columns,
                                         hidden_units=[100, 50])
    else:
      m = tf.contrib.learn.DNNLinearCombinedClassifier(
          model_dir=model_dir,
          linear_feature_columns=wide_columns,
          dnn_feature_columns=deep_columns,
          dnn_hidden_units=[100, 50])
    return m
  
  
  def input_fn(df):
    """Input builder function."""
    # Creates a dictionary mapping from each continuous feature column name (k) to
    # the values of that column stored in a constant Tensor.
    continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
    # Creates a dictionary mapping from each categorical feature column name (k)
    # to the values of that column stored in a tf.SparseTensor.
    categorical_cols = {
        k: tf.SparseTensor(
            indices=[[i, 0] for i in range(df[k].size)],
            values=df[k].values,
            dense_shape=[df[k].size, 1])
        for k in CATEGORICAL_COLUMNS}
    # Merges the two dictionaries into one.
    feature_cols = dict(continuous_cols)
    feature_cols.update(categorical_cols)
    # Converts the label column into a constant Tensor.
    label = tf.constant(df[LABEL_COLUMN].values)
    # Returns the feature columns and the label.
    return feature_cols, label
  
  
  def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
    """Train and evaluate the model."""
    train_file_name, test_file_name = maybe_download(train_data, test_data)
    df_train = pd.read_csv(
        tf.gfile.Open(train_file_name),
        names=COLUMNS,
        skipinitialspace=True,
        engine="python")
    df_test = pd.read_csv(
        tf.gfile.Open(test_file_name),
        names=COLUMNS,
        skipinitialspace=True,
        skiprows=1,
        engine="python")
  
    # remove NaN elements
    df_train = df_train.dropna(how='any', axis=0)
    df_test = df_test.dropna(how='any', axis=0)
  
    df_train[LABEL_COLUMN] = (
        df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
    df_test[LABEL_COLUMN] = (
        df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
  
    model_dir = tempfile.mkdtemp() if not model_dir else model_dir
    print("model directory = %s" % model_dir)
  
    m = build_estimator(model_dir, model_type)
    m.fit(input_fn=lambda: input_fn(df_train), steps=train_steps)
    results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)
    for key in sorted(results):
      print("%s: %s" % (key, results[key]))
  
  
  FLAGS = None
  
  
  def main(_):
    train_and_eval(FLAGS.model_dir, FLAGS.model_type, FLAGS.train_steps,
                   FLAGS.train_data, FLAGS.test_data)
  
  
  if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.register("type", "bool", lambda v: v.lower() == "true")
    parser.add_argument(
        "--model_dir",
        type=str,
        default="",
        help="Base directory for output models."
    )
    parser.add_argument(
        "--model_type",
        type=str,
        default="wide_n_deep",
        help="Valid model types: {'wide', 'deep', 'wide_n_deep'}."
    )
    parser.add_argument(
        "--train_steps",
        type=int,
        default=200,
        help="Number of training steps."
    )
    parser.add_argument(
        "--train_data",
        type=str,
        default="",
        help="Path to the training data."
    )
    parser.add_argument(
        "--test_data",
        type=str,
        default="",
        help="Path to the test data."
    )
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  