flink-ml-python/pyflink/ml/lib/feature/tests/test_stringindexer.py

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from pyflink.common import Types, Row

from pyflink.ml.lib.feature.stringindexer import StringIndexer
from pyflink.ml.tests.test_utils import PyFlinkMLTestCase


class StringIndexerTest(PyFlinkMLTestCase):
    def setUp(self):
        super(StringIndexerTest, self).setUp()
        self.train_table = self.t_env.from_data_stream(
            self.env.from_collection([
                ('a', 1.0),
                ('b', 1.0),
                ('b', 2.0),
                ('c', 0.0),
                ('d', 2.0),
                ('a', 2.0),
                ('b', 2.0),
                ('b', -1.0),
                ('a', -1.0),
                ('c', -1.0),
            ],
                type_info=Types.ROW_NAMED(
                    ['input_col1', 'input_col2'],
                    [Types.STRING(), Types.DOUBLE()])))

        self.predict_table = self.t_env.from_data_stream(
            self.env.from_collection([
                ('a', 2.0),
                ('b', 1.0),
                ('e', 2.0),
            ],
                type_info=Types.ROW_NAMED(
                    ['input_col1', 'input_col2'],
                    [Types.STRING(), Types.DOUBLE()])))

        self.expected_alphabetic_asc_predict_data = [
            Row('a', 2.0, 0, 3),
            Row('b', 1.0, 1, 2),
            Row('e', 2.0, 4, 3)
        ]

    def test_param(self):
        string_indexer = StringIndexer()

        self.assertEqual('arbitrary', string_indexer.string_order_type)
        self.assertEqual('error', string_indexer.handle_invalid)

        string_indexer.set_input_cols('input_col1', 'input_col2') \
            .set_output_cols('output_col1', 'output_col2') \
            .set_string_order_type('alphabetAsc') \
            .set_handle_invalid('skip')

        self.assertEqual(('input_col1', 'input_col2'), string_indexer.input_cols)
        self.assertEqual(('output_col1', 'output_col2'), string_indexer.output_cols)
        self.assertEqual('alphabetAsc', string_indexer.string_order_type)
        self.assertEqual('skip', string_indexer.handle_invalid)

    def test_output_schema(self):
        string_indexer = StringIndexer() \
            .set_input_cols('input_col1', 'input_col2') \
            .set_output_cols('output_col1', 'output_col2') \
            .set_string_order_type('alphabetAsc') \
            .set_handle_invalid('skip')

        output = string_indexer.fit(self.train_table).transform(self.predict_table)[0]

        self.assertEqual(
            ['input_col1', 'input_col2', 'output_col1', 'output_col2'],
            output.get_schema().get_field_names())

    def test_string_order_type(self):
        string_indexer = StringIndexer() \
            .set_input_cols('input_col1', 'input_col2') \
            .set_output_cols('output_col1', 'output_col2') \
            .set_handle_invalid('keep')

        string_indexer.set_string_order_type('alphabetAsc')
        output = string_indexer.fit(self.train_table).transform(self.predict_table)[0]

        predicted_results = [result for result in
                             self.t_env.to_data_stream(output).execute_and_collect()]

        predicted_results.sort(key=lambda x: x[0])

        self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data)

    def test_fit_and_predict(self):
        string_indexer = StringIndexer() \
            .set_input_cols('input_col1', 'input_col2') \
            .set_output_cols('output_col1', 'output_col2') \
            .set_string_order_type('alphabetAsc') \
            .set_handle_invalid('keep')

        output = string_indexer.fit(self.train_table).transform(self.predict_table)[0]

        predicted_results = [result for result in
                             self.t_env.to_data_stream(output).execute_and_collect()]

        predicted_results.sort(key=lambda x: x[0])

        self.assertEqual(predicted_results, self.expected_alphabetic_asc_predict_data)