def test_csv_decoder_with_schema(self): input_lines = ['1,1,2.0,hello', '5,5,12.34,world'] column_names = [ 'int_feature_parsed_as_float', 'int_feature', 'float_feature', 'str_feature' ] schema = text_format.Parse( """ feature { name: "int_feature_parsed_as_float" type: FLOAT } feature { name: "int_feature" type: INT } feature { name: "float_feature" type: FLOAT } feature { name: "str_feature" type: BYTES } """, schema_pb2.Schema()) expected_result = [ pa.Table.from_arrays([ pa.array([[1], [5]], pa.list_(pa.float32())), pa.array([[1], [5]], pa.list_(pa.int64())), pa.array([[2.0], [12.34]], pa.list_(pa.float32())), pa.array([[b'hello'], [b'world']], pa.list_(pa.binary())), ], [ 'int_feature_parsed_as_float', 'int_feature', 'float_feature', 'str_feature' ]) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(column_names=column_names, schema=schema, infer_type_from_schema=True)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_batch_examples(self): examples = [{ 'a': np.array([1.0, 2.0], dtype=np.float32), 'b': np.array(['a', 'b', 'c', 'e']) }, { 'a': np.array([3.0, 4.0, 5.0], dtype=np.float32), }, { 'b': np.array(['d', 'e', 'f']), 'd': np.array([10, 20, 30], dtype=np.int64), }, { 'b': np.array(['a', 'b', 'c']) }, { 'c': np.array(['d', 'e', 'f']) }] expected_tables = [ pa.Table.from_arrays([ pa.array([[1.0, 2.0], [3.0, 4.0, 5.0]], type=pa.list_(pa.float32())), pa.array([['a', 'b', 'c', 'e'], None]) ], ['a', 'b']), pa.Table.from_arrays([ pa.array([['d', 'e', 'f'], ['a', 'b', 'c']]), pa.array([[10, 20, 30], None], type=pa.list_(pa.int64())) ], ['b', 'd']), pa.Table.from_arrays([pa.array([['d', 'e', 'f']])], ['c']), ] with beam.Pipeline() as p: result = ( p | beam.Create(examples) | batch_util.BatchExamplesToArrowTables(desired_batch_size=2)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_tables))
def test_csv_decoder_with_int_and_float_in_same_column(self): input_lines = ['2,1.5', '1.5,2'] column_names = ['float_feature1', 'float_feature2'] expected_result = [ pa.Table.from_arrays([ pa.array([[2.0], [1.5]], pa.list_(pa.float32())), pa.array([[1.5], [2.0]], pa.list_(pa.float32())), ], ['float_feature1', 'float_feature2']) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(column_names=column_names)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_csv_decoder_consider_blank_line(self): input_lines = ['', '1,2'] column_names = ['float_feature1', 'float_feature2'] expected_result = [ pa.Table.from_arrays([ pa.array([None, [1.0]], pa.list_(pa.float32())), pa.array([None, [2.0]], pa.list_(pa.float32())), ], ['float_feature1', 'float_feature2']) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV( column_names=column_names, skip_blank_lines=False)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def testNumberArrayShouldShareBuffer(self): float_array = pa.array([1, 2, np.NaN], pa.float32()) np_array = arrow_util.primitive_array_to_numpy(float_array) self.assertEqual(np_array.dtype, np.float32) self.assertEqual(np_array.shape, (3, )) # Check that they share the same buffer. self.assertEqual(np_array.ctypes.data, float_array.buffers()[1].address)
def test_csv_decoder_missing_values(self): input_lines = ['1,,hello', ',12.34,'] column_names = ['int_feature', 'float_feature', 'str_feature'] expected_result = [ pa.Table.from_arrays([ pa.array([[1.0], None], pa.list_(pa.float32())), pa.array([None, [12.34]], pa.list_(pa.float32())), pa.array([[b'hello'], None], pa.list_(pa.binary())), ], ['int_feature', 'float_feature', 'str_feature']) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(column_names=column_names)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_basic_stats_generator_only_nan(self): b1 = pa.Table.from_arrays( [pa.array([[np.NaN]], type=pa.list_(pa.float32()))], ['a']) batches = [b1] expected_result = { types.FeaturePath(['a']): text_format.Parse( """ path { step: 'a' } type: FLOAT num_stats { common_stats { num_non_missing: 1 min_num_values: 1 max_num_values: 1 avg_num_values: 1.0 tot_num_values: 1 num_values_histogram { buckets { low_value: 1.0 high_value: 1.0 sample_count: 0.5 } buckets { low_value: 1.0 high_value: 1.0 sample_count: 0.5 } type: QUANTILES } } histograms { num_nan: 1 type: STANDARD } histograms { num_nan: 1 type: QUANTILES } } """, statistics_pb2.FeatureNameStatistics()) } generator = basic_stats_generator.BasicStatsGenerator( num_values_histogram_buckets=2, num_histogram_buckets=3, num_quantiles_histogram_buckets=4) self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_csv_decoder_empty_row(self): input_lines = [',,', '1,2.0,hello'] column_names = ['int_feature', 'float_feature', 'str_feature'] expected_result = [ pa.Table.from_arrays([ pa.array([None, [1]], pa.list_(pa.int64())), pa.array([None, [2.0]], pa.list_(pa.float32())), pa.array([None, [b'hello']], pa.list_(pa.binary())), ], ['int_feature', 'float_feature', 'str_feature']) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(column_names=column_names)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def test_csv_decoder(self): input_lines = ['1,2.0,hello', '5,12.34,world'] column_names = ['int_feature', 'float_feature', 'str_feature'] expected_result = [ pa.Table.from_arrays([ pa.array([[1], [5]], pa.list_(pa.int64())), pa.array([[2.0], [12.34]], pa.list_(pa.float32())), pa.array([[b'hello'], [b'world']], pa.list_(pa.binary())), ], ['int_feature', 'float_feature', 'str_feature']) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines, reshuffle=False) | csv_decoder.DecodeCSV(column_names=column_names)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
def _process_column_infos(self, column_infos: List[csv_decoder.ColumnInfo]): column_handlers = [] column_arrow_types = [] for c in column_infos: if c.type == statistics_pb2.FeatureNameStatistics.INT: column_handlers.append(lambda v: (int(v),)) column_arrow_types.append(pa.list_(pa.int64())) elif c.type == statistics_pb2.FeatureNameStatistics.FLOAT: column_handlers.append(lambda v: (float(v),)) column_arrow_types.append(pa.list_(pa.float32())) elif c.type == statistics_pb2.FeatureNameStatistics.STRING: column_handlers.append(lambda v: (v,)) column_arrow_types.append(pa.list_(pa.binary())) else: column_handlers.append(lambda _: None) column_arrow_types.append(pa.null()) self._column_handlers = column_handlers self._column_arrow_types = column_arrow_types self._column_names = [c.name for c in column_infos]
def test_csv_decoder_with_unicode(self): input_lines = [u'1,שקרכלשהו,22.34,text field'] column_names = [ 'int_feature', 'unicode_feature', 'float_feature', 'str_feature' ] expected_result = [ pa.Table.from_arrays([ pa.array([[1]], pa.list_(pa.int64())), pa.array([[22.34]], pa.list_(pa.float32())), pa.array([[u'שקרכלשהו'.encode('utf-8')]], pa.list_( pa.binary())), pa.array([[b'text field']], pa.list_(pa.binary())), ], [ 'int_feature', 'float_feature', 'unicode_feature', 'str_feature' ]) ] with beam.Pipeline() as p: result = (p | beam.Create(input_lines) | csv_decoder.DecodeCSV(column_names=column_names)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_result))
value { float_list { value: [ 4.0 ] } } } feature { key: "float_feature_2" value { float_list { value: [ 5.0, 6.0 ] } } } feature { key: "str_feature_1" value { bytes_list { value: [ 'female' ] } } } feature { key: "str_feature_2" value { bytes_list { value: [ 'string', 'list' ] } } } } ''', 'decoded_table': pa.Table.from_arrays([ pa.array([[0]], pa.list_(pa.int64())), pa.array([[1, 2, 3]], pa.list_(pa.int64())), pa.array([[4.0]], pa.list_(pa.float32())), pa.array([[5.0, 6.0]], pa.list_(pa.float32())), pa.array([[b'female']], pa.list_(pa.binary())), pa.array([[b'string', b'list']], pa.list_(pa.binary())) ], [ 'int_feature_1', 'int_feature_2', 'float_feature_1', 'float_feature_2', 'str_feature_1', 'str_feature_2' ]) }, ]
def test_topk_uniques_combiner_with_numeric_feature(self): # fa: 4 'a', 2 'b', 3 'c', 2 'd', 1 'e' batches = [ pa.Table.from_arrays([ pa.array([['a', 'b', 'c', 'e'], None, ['a', 'c', 'd']]), pa.array([[1.0, 2.0, 3.0], [4.0, 5.0], None]), ], ['fa', 'fb']), pa.Table.from_arrays([ pa.array([['a', 'a', 'b', 'c', 'd']]), pa.array([None], type=pa.list_(pa.float32())), ], ['fa', 'fb']), ] expected_result = { types.FeaturePath(['fa']): text_format.Parse( """ path { step: 'fa' } type: STRING string_stats { unique: 5 top_values { value: 'a' frequency: 4 } top_values { value: 'c' frequency: 3 } top_values { value: 'd' frequency: 2 } top_values { value: 'b' frequency: 2 } rank_histogram { buckets { low_rank: 0 high_rank: 0 label: "a" sample_count: 4.0 } buckets { low_rank: 1 high_rank: 1 label: "c" sample_count: 3.0 } buckets { low_rank: 2 high_rank: 2 label: "d" sample_count: 2.0 } } }""", statistics_pb2.FeatureNameStatistics()) } generator = (top_k_uniques_combiner_stats_generator. TopKUniquesCombinerStatsGenerator( num_top_values=4, num_rank_histogram_buckets=3)) self.assertCombinerOutputEqual(batches, generator, expected_result)
def test_batch_serialized_examples(self): examples = [ """ features { feature { key: "a" value { float_list { value: [ 1.0, 2.0 ] } } } feature { key: "b" value { bytes_list { value: [ 'a', 'b', 'c', 'e' ] } } } }""", """ features { feature { key: "a" value { float_list { value: [ 3.0, 4.0, 5.0 ] } } } }""", """ features { feature { key: "b" value { bytes_list { value: [ 'd', 'e', 'f' ] } } } feature { key: "d" value { int64_list { value: [ 10, 20, 30 ] } } } }""", """ features { feature { key: "b" value { bytes_list { value: [ 'a', 'b', 'c' ] } } } }""", """ features { feature { key: "c" value { bytes_list { value: [ 'd', 'e', 'f' ] } } } }""", ] serialized_examples = [ text_format.Merge(example_pbtxt, tf.train.Example()).SerializeToString() for example_pbtxt in examples ] expected_tables = [ pa.Table.from_arrays([ pa.array([[1.0, 2.0], [3.0, 4.0, 5.0]], type=pa.list_(pa.float32())), pa.array([['a', 'b', 'c', 'e'], None], type=pa.list_(pa.binary())) ], ['a', 'b']), pa.Table.from_arrays([ pa.array([['d', 'e', 'f'], ['a', 'b', 'c']], type=pa.list_(pa.binary())), pa.array([[10, 20, 30], None], type=pa.list_(pa.int64())) ], ['b', 'd']), pa.Table.from_arrays( [pa.array([['d', 'e', 'f']], type=pa.list_(pa.binary()))], ['c']), ] with beam.Pipeline() as p: result = (p | beam.Create(serialized_examples) | batch_util.BatchSerializedExamplesToArrowTables( desired_batch_size=2)) util.assert_that( result, test_util.make_arrow_tables_equal_fn(self, expected_tables))
"double_feature": np.array([2., 3., 4.], dtype=np.float64), "bytes_feature": np.array([b"ghi"], dtype=np.object), "unicode_feature": np.array([u"ghi"], dtype=np.object), }, ], expected_output={ "int64_feature": pa.array([[1, 2, 3], [4]], type=pa.list_(pa.int64())), "uint64_feature": pa.array([[1, 2, 3], None], type=pa.list_(pa.uint64())), "int32_feature": pa.array([[1, 2, 3], [4]], type=pa.list_(pa.int32())), "uint32_feature": pa.array([[1, 2, 3], None], type=pa.list_(pa.uint32())), "float_feature": pa.array([[1.], [2., 3., 4.]], type=pa.list_(pa.float32())), "double_feature": pa.array([[1.], [2., 3., 4.]], type=pa.list_(pa.float64())), "bytes_feature": pa.array([[b"abc", b"def"], [b"ghi"]], type=pa.list_(pa.binary())), "unicode_feature": pa.array([[b"abc", b"def"], [b"ghi"]], type=pa.list_(pa.string())), }), dict(testcase_name="mixed_unicode_and_bytes", input_examples=[ { "a": np.array([b"abc"], dtype=np.object), }, {
def test_stats_pipeline_with_examples_with_no_values(self): tables = [ pa.Table.from_arrays([ pa.array([[]], type=pa.list_(pa.float32())), pa.array([[]], type=pa.list_(pa.binary())), pa.array([[]], type=pa.list_(pa.int32())), pa.array([[2]]), ], ['a', 'b', 'c', 'w']), pa.Table.from_arrays([ pa.array([[]], type=pa.list_(pa.float32())), pa.array([[]], type=pa.list_(pa.binary())), pa.array([[]], type=pa.list_(pa.int32())), pa.array([[2]]), ], ['a', 'b', 'c', 'w']), pa.Table.from_arrays([ pa.array([[]], type=pa.list_(pa.float32())), pa.array([[]], type=pa.list_(pa.binary())), pa.array([[]], type=pa.list_(pa.int32())), pa.array([[2]]), ], ['a', 'b', 'c', 'w']) ] expected_result = text_format.Parse( """ datasets{ num_examples: 3 features { path { step: 'a' } type: FLOAT num_stats { common_stats { num_non_missing: 3 num_values_histogram { buckets { sample_count: 1.5 } buckets { sample_count: 1.5 } type: QUANTILES } weighted_common_stats { num_non_missing: 6 } } } } features { path { step: 'b' } type: STRING string_stats { common_stats { num_non_missing: 3 num_values_histogram { buckets { sample_count: 1.5 } buckets { sample_count: 1.5 } type: QUANTILES } weighted_common_stats { num_non_missing: 6 } } } } features { path { step: 'c' } type: INT num_stats { common_stats { num_non_missing: 3 num_values_histogram { buckets { sample_count: 1.5 } buckets { sample_count: 1.5 } type: QUANTILES } weighted_common_stats { num_non_missing: 6 } } } } } """, statistics_pb2.DatasetFeatureStatisticsList()) with beam.Pipeline() as p: options = stats_options.StatsOptions( weight_feature='w', num_top_values=1, num_rank_histogram_buckets=1, num_values_histogram_buckets=2, num_histogram_buckets=1, num_quantiles_histogram_buckets=1, epsilon=0.001) result = (p | beam.Create(tables) | stats_api.GenerateStatistics(options)) util.assert_that( result, test_util.make_dataset_feature_stats_list_proto_equal_fn( self, expected_result))