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

import itertools

import pandas as pd
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)

COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
           "dis", "tax", "ptratio", "medv"]
FEATURES = ["crim", "zn", "indus", "nox", "rm",
            "age", "dis", "tax", "ptratio"]
LABEL = "medv"



def get_input_fn(data_set, num_epochs=None, shuffle=True):
  return tf.estimator.inputs.pandas_input_fn(
      x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),
      y=pd.Series(data_set[LABEL].values),
      num_epochs=num_epochs,
      shuffle=shuffle)



training_set = pd.read_csv("boston_train.csv", skipinitialspace=True, skiprows=1, names=COLUMNS)
test_set = pd.read_csv("boston_test.csv", skipinitialspace=True, skiprows=1, names=COLUMNS)


prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True, skiprows=1, names=COLUMNS)

feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]

regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols, hidden_units=[10, 10], model_dir="/tmp/boston_model")

regressor.train(input_fn=get_input_fn(training_set), steps=5000)

ev = regressor.evaluate(input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))
loss_score = ev["loss"]
print("Loss: {0:f}".format(loss_score))

y = regressor.predict(input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))
predictions = list(p["predictions"] for p in itertools.islice(y, 6))
print("Predictions: {}".format(str(predictions)))