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# Simple program that creates a ElementwiseProduct instance and uses it for feature
# engineering.
#
# Before executing this program, please make sure you have followed Flink ML's
# quick start guideline to set up Flink ML and Flink environment. The guideline
# can be found at
#
# https://nightlies.apache.org/flink/flink-ml-docs-master/docs/try-flink-ml/quick-start/

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.lib.feature.elementwiseproduct import ElementwiseProduct
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)),
        (2, Vectors.dense(1.1, 3.3))
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'vec'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create an elementwise product object and initialize its parameters
elementwise_product = ElementwiseProduct() \
    .set_input_col('vec') \
    .set_output_col('output_vec') \
    .set_scaling_vec(Vectors.dense(1.1, 1.1))

# use the elementwise product object for feature engineering
output = elementwise_product.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(elementwise_product.get_input_col())]
    output_value = result[field_names.index(elementwise_product.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))