# 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.
# ==============================================================================
"""Linear regression using the LinearRegressor Estimator."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse

import numpy as np
import tensorflow as tf

import automobile_data

parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=1000, type=int,
                    help='number of training steps')
parser.add_argument('--price_norm_factor', default=1000., type=float,
                    help='price normalization factor')


def from_dataset(ds):
    return lambda: ds.make_one_shot_iterator().get_next()


def main(argv):
  """Builds, trains, and evaluates the model."""
  args = parser.parse_args(argv[1:])

  (train_x,train_y), (test_x, test_y) = automobile_data.load_data()

  train_y /= args.price_norm_factor
  test_y /= args.price_norm_factor

  # Build the training dataset.
  train = (
      automobile_data.make_dataset(train_x, train_y)
      # Shuffling with a buffer larger than the data set ensures
      # that the examples are well mixed.
      .shuffle(1000).batch(args.batch_size)
      # Repeat forever
      .repeat())

  # Build the validation dataset.
  test = automobile_data.make_dataset(test_x, test_y).batch(args.batch_size)

  feature_columns = [
      # "curb-weight" and "highway-mpg" are numeric columns.
      tf.feature_column.numeric_column(key="curb-weight"),
      tf.feature_column.numeric_column(key="highway-mpg"),
  ]

  # Build the Estimator.
  model = tf.estimator.LinearRegressor(feature_columns=feature_columns)

  # Train the model.
  # By default, the Estimators log output every 100 steps.
  model.train(input_fn=from_dataset(train), steps=args.train_steps)

  # Evaluate how the model performs on data it has not yet seen.
  eval_result = model.evaluate(input_fn=from_dataset(test))

  # The evaluation returns a Python dictionary. The "average_loss" key holds the
  # Mean Squared Error (MSE).
  average_loss = eval_result["average_loss"]

  # Convert MSE to Root Mean Square Error (RMSE).
  print("\n" + 80 * "*")
  print("\nRMS error for the test set: ${:.0f}"
        .format(args.price_norm_factor * average_loss**0.5))

  # Run the model in prediction mode.
  input_dict = {
      "curb-weight": np.array([2000, 3000]),
      "highway-mpg": np.array([30, 40])
  }
  predict = automobile_data.make_dataset(input_dict).batch(1)
  predict_results = model.predict(input_fn=from_dataset(predict))

  # Print the prediction results.
  print("\nPrediction results:")
  for i, prediction in enumerate(predict_results):
    msg = ("Curb weight: {: 4d}lbs, "
           "Highway: {: 0d}mpg, "
           "Prediction: ${: 9.2f}")
    msg = msg.format(input_dict["curb-weight"][i], input_dict["highway-mpg"][i],
                     args.price_norm_factor * prediction["predictions"][0])

    print("    " + msg)
  print()


if __name__ == "__main__":
  # The Estimator periodically generates "INFO" logs; make these logs visible.
  tf.logging.set_verbosity(tf.logging.INFO)
  tf.app.run(main=main)