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 input_fn(data_set):
    feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES}
    labels = tf.constant(data_set[LABEL].values)
    return feature_cols, labels


def main(unused_argv):
    # Load datasets
    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)

    # Set of 6 examples for which to predict median house values
    prediction_set = pd.read_csv("boston_predict.csv",    skipinitialspace=True,
                           skiprows=1, names=COLUMNS)

    # Feature cols
    feature_cols = [tf.contrib.layers.real_valued_column(k)
              for k in FEATURES]

    # Build 2 layer fully connected DNN with 10, 10 units respectively.
    regressor = tf.contrib.learn.DNNRegressor(
  feature_columns=feature_cols, hidden_units=[10, 10])

    # Fit
    regressor.fit(input_fn=lambda: input_fn(training_set), steps=1000)

    # Score accuracy
    ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
    loss_score = ev["loss"]
    print("Loss: {0:f}".format(loss_score))

    # Print out predictions
    y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
    print("Predictions: {}".format(str(y)))

if __name__ == "__main__":
    tf.app.run()