def test_get_flattened_array_parent_indices(self):
indices = arrow_util.GetFlattenedArrayParentIndices(
pa.array([], type=pa.list_(pa.int32())))
self.assertTrue(indices.equals(pa.array([], type=pa.int32())))
indices = arrow_util.GetFlattenedArrayParentIndices(
pa.array([[1.], [2.], [], [3.]]))
self.assertTrue(indices.equals(pa.array([0, 1, 3], type=pa.int32())))
def test_list_lengths(self):
list_lengths = arrow_util.ListLengthsFromListArray(
pa.array([], type=pa.list_(pa.int64())))
self.assertTrue(list_lengths.equals(pa.array([], type=pa.int32())))
list_lengths = arrow_util.ListLengthsFromListArray(
pa.array([[1., 2.], [], [3.]]))
self.assertTrue(list_lengths.equals(pa.array([2, 0, 1], type=pa.int32())))
list_lengths = arrow_util.ListLengthsFromListArray(
pa.array([[1., 2.], None, [3.]]))
self.assertTrue(list_lengths.equals(pa.array([2, 0, 1], type=pa.int32())))
def test_get_array_null_bitmap_as_byte_array(self):
array = pa.array([], type=pa.int32())
null_masks = arrow_util.GetArrayNullBitmapAsByteArray(array)
self.assertTrue(null_masks.equals(pa.array([], type=pa.uint8())))
array = pa.array([1, 2, None, 3, None], type=pa.int32())
null_masks = arrow_util.GetArrayNullBitmapAsByteArray(array)
self.assertTrue(
null_masks.equals(pa.array([0, 0, 1, 0, 1], type=pa.uint8())))
array = pa.array([1, 2, 3])
null_masks = arrow_util.GetArrayNullBitmapAsByteArray(array)
self.assertTrue(null_masks.equals(pa.array([0, 0, 0], type=pa.uint8())))
array = pa.array([None, None, None], type=pa.int32())
null_masks = arrow_util.GetArrayNullBitmapAsByteArray(array)
self.assertTrue(null_masks.equals(pa.array([1, 1, 1], type=pa.uint8())))
# Demonstrate that the returned array can be converted to a numpy boolean
# array w/o copying
np.testing.assert_equal(
np.array([True, True, True]), null_masks.to_numpy().view(np.bool))
{
"int64_feature": np.array([4], dtype=np.int64),
"int32_feature": np.array([4], dtype=np.int32),
"float_feature": np.array([2., 3., 4.], dtype=np.float32),
"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=[
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))
