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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.tokenizer import Tokenizer
from pyflink.table import StreamTableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

t_env = StreamTableEnvironment.create(env)

# Generates input data.
input_data_table = t_env.from_data_stream(
    env.from_collection([
        ('Test for tokenization.',),
        ('Te,st. punct',),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [Types.STRING()])))

# Creates a Tokenizer object and initializes its parameters.
tokenizer = Tokenizer() \
    .set_input_col("input") \
    .set_output_col("output")

# Uses the Tokenizer object for feature transformations.
output = tokenizer.transform(input_data_table)[0]

# Extracts and displays 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(tokenizer.get_input_col())]
    output_value = result[field_names.index(tokenizer.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Values: ' + str(output_value))