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# Simple program that creates a PolynomialExpansion instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.feature.polynomialexpansion import PolynomialExpansion
from pyflink.table import StreamTableEnvironment

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

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

# generate input data
input_data_table = t_env.from_data_stream(
    env.from_collection([
        (1, Vectors.dense(2.1, 3.1, 1.2, 2.1)),
        (2, Vectors.dense(2.3, 2.1, 1.3, 1.2)),
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'input_vec'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create a polynomial expansion object and initialize its parameters
polynomialExpansion = PolynomialExpansion() \
    .set_input_col('input_vec') \
    .set_degree(2) \
    .set_output_col('output_vec')

# use the polynomial expansion model for feature engineering
output = polynomialExpansion.transform(input_data_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(polynomialExpansion.get_input_col())]
    output_value = result[field_names.index(polynomialExpansion.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))