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# Simple program that trains a OneHotEncoder model and uses it for feature
# engineering.

from pyflink.common import Row
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.onehotencoder import OneHotEncoder
from pyflink.table import StreamTableEnvironment, DataTypes

# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)

# generate input training and prediction data
train_table = t_env.from_elements(
    [Row(0.0), Row(1.0), Row(2.0), Row(0.0)],
    DataTypes.ROW([
        DataTypes.FIELD('input', DataTypes.DOUBLE())
    ]))

predict_table = t_env.from_elements(
    [Row(0.0), Row(1.0), Row(2.0)],
    DataTypes.ROW([
        DataTypes.FIELD('input', DataTypes.DOUBLE())
    ]))

# create a one-hot-encoder object and initialize its parameters
one_hot_encoder = OneHotEncoder().set_input_cols('input').set_output_cols('output')

# train the one-hot-encoder model
model = one_hot_encoder.fit(train_table)

# use the one-hot-encoder model for predictions
output = model.transform(predict_table)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_value = result[field_names.index(one_hot_encoder.get_input_cols()[0])]
    output_value = result[field_names.index(one_hot_encoder.get_output_cols()[0])]
    print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))