# https://tensorflow.google.cn/beta/tutorials/keras/basic_regression

from __future__ import absolute_import, division, print_function, unicode_literals

import pathlib

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers

print(tf.__version__)

dataset_path = keras.utils.get_file(
    "auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")

column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']
raw_dataset = pd.read_csv(dataset_path, names=column_names,
                          na_values="?", comment='\t',
                          sep=" ", skipinitialspace=True)

dataset = raw_dataset.copy()
dataset.tail()

dataset.isna().sum()

dataset = dataset.dropna()

origin = dataset.pop('Origin')

dataset['USA'] = (origin == 1)*1.0
dataset['Europe'] = (origin == 2)*1.0
dataset['Japan'] = (origin == 3)*1.0
dataset.tail()

train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

sns.pairplot(
    train_dataset[["MPG", "Cylinders", "Displacement", "Weight"]], diag_kind="kde")


train_stats = train_dataset.describe()
train_stats.pop("MPG")
train_stats = train_stats.transpose()
train_stats

train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')


def norm(x):
  return (x - train_stats['mean']) / train_stats['std']


normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)


def build_model():
  model = keras.Sequential([
      layers.Dense(64, activation='relu', input_shape=[
                   len(train_dataset.keys())]),
      layers.Dense(64, activation='relu'),
      layers.Dense(1)
  ])

  optimizer = tf.keras.optimizers.RMSprop(0.001)

  model.compile(loss='mse',
                optimizer=optimizer,
                metrics=['mae', 'mse'])
  return model


model = build_model()

model.summary()

example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)


# Display training progress by printing a single dot for each completed epoch
class PrintDot(keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs):
    if epoch % 100 == 0:
      print('')
    print('.', end='')


EPOCHS = 1000

history = model.fit(
    normed_train_data, train_labels,
    epochs=EPOCHS, validation_split=0.2, verbose=0,
    callbacks=[PrintDot()])

hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail()


def plot_history(history):
  hist = pd.DataFrame(history.history)
  hist['epoch'] = history.epoch

  plt.figure()
  plt.xlabel('Epoch')
  plt.ylabel('Mean Abs Error [MPG]')
  plt.plot(hist['epoch'], hist['mae'],
           label='Train Error')
  plt.plot(hist['epoch'], hist['val_mae'],
           label='Val Error')
  plt.ylim([0, 5])
  plt.legend()

  plt.figure()
  plt.xlabel('Epoch')
  plt.ylabel('Mean Square Error [$MPG^2$]')
  plt.plot(hist['epoch'], hist['mse'],
           label='Train Error')
  plt.plot(hist['epoch'], hist['val_mse'],
           label='Val Error')
  plt.ylim([0, 20])
  plt.legend()
  plt.show()


plot_history(history)

model = build_model()

# The patience parameter is the amount of epochs to check for improvement
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0, callbacks=[early_stop, PrintDot()])

plot_history(history)

loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)

print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))

test_predictions = model.predict(normed_test_data).flatten()

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [MPG]')
plt.ylabel('Predictions [MPG]')
plt.axis('equal')
plt.axis('square')
plt.xlim([0, plt.xlim()[1]])
plt.ylim([0, plt.ylim()[1]])
_ = plt.plot([-100, 100], [-100, 100])

error = test_predictions - test_labels
plt.hist(error, bins=25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")
