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# Simple program that creates an NGram instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.ngram import NGram
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([
        ([],),
        (['a', 'b', 'c'],),
        (['a', 'b', 'c', 'd'],),
    ],
        type_info=Types.ROW_NAMED(
            ["input", ],
            [Types.OBJECT_ARRAY(Types.STRING())])))

# Creates an NGram object and initializes its parameters.
n_gram = NGram() \
    .set_input_col('input') \
    .set_n(2) \
    .set_output_col('output')

# Uses the NGram object for feature transformations.
output = n_gram.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(n_gram.get_input_col())]
    output_value = result[field_names.index(n_gram.get_output_col())]
    print('Input Value: ' + ' '.join(input_value) + '\tOutput Value: ' + str(output_value))