def test_infer_schema_with_infer_shape(self):
statistics = text_format.Parse(
"""
datasets {
num_examples: 7
features: {
name: 'feature1'
type: STRING
string_stats: {
common_stats: {
num_missing: 0
num_non_missing: 7
min_num_values: 1
max_num_values: 1
}
unique: 3
}
}
features: {
name: 'feature2'
type: STRING
string_stats: {
common_stats: {
num_missing: 0
num_non_missing: 7
min_num_values: 3
max_num_values: 3
}
unique: 5
}
}
}
""", statistics_pb2.DatasetFeatureStatisticsList())
expected_schema = text_format.Parse(
"""
feature {
name: "feature1"
shape { dim { size: 1 } }
presence: {
min_fraction: 1.0
min_count: 1
}
type: BYTES
}
feature {
name: "feature2"
value_count: { min: 1 }
presence: {
min_fraction: 1.0
min_count: 1
}
type: BYTES
}
""", schema_pb2.Schema())
# Infer the schema from the stats.
actual_schema = validation_api.infer_schema(statistics,
infer_feature_shape=True)
self.assertEqual(actual_schema, expected_schema)
def test_infer_schema_without_string_domain(self):
statistics = text_format.Parse(
"""
datasets {
num_examples: 7
features: {
name: 'feature1'
type: STRING
string_stats: {
common_stats: {
num_missing: 3
num_non_missing: 4
min_num_values: 1
max_num_values: 1
}
unique: 3
rank_histogram {
buckets {
low_rank: 0
high_rank: 0
label: "a"
sample_count: 2.0
}
buckets {
low_rank: 1
high_rank: 1
label: "b"
sample_count: 1.0
}
buckets {
low_rank: 2
high_rank: 2
label: "c"
sample_count: 1.0
}
}
}
}
}
""", statistics_pb2.DatasetFeatureStatisticsList())
expected_schema = text_format.Parse(
"""
feature {
name: "feature1"
value_count: {
min: 1
max: 1
}
presence: {
min_count: 1
}
type: BYTES
}
""", schema_pb2.Schema())
# Infer the schema from the stats.
actual_schema = validation_api.infer_schema(statistics,
max_string_domain_size=2)
self.assertEqual(actual_schema, expected_schema)
def test_infer_schema_invalid_multiple_datasets(self):
statistics = statistics_pb2.DatasetFeatureStatisticsList()
statistics.datasets.extend([
statistics_pb2.DatasetFeatureStatistics(),
statistics_pb2.DatasetFeatureStatistics()
])
with self.assertRaisesRegexp(ValueError,
'.*statistics proto with one dataset.*'):
_ = validation_api.infer_schema(statistics)
def test_infer_schema(self):
statistics = text_format.Parse(
"""
datasets {
num_examples: 7
features: {
name: 'feature1'
type: STRING
string_stats: {
common_stats: {
num_non_missing: 7
min_num_values: 1
max_num_values: 1
}
unique: 3
}
}
}
""", statistics_pb2.DatasetFeatureStatisticsList())
expected_schema = text_format.Parse(
"""
feature {
name: "feature1"
value_count: {
min: 1
max: 1
}
presence: {
min_fraction: 1.0
min_count: 1
}
type: BYTES
}
""", schema_pb2.Schema())
validation_api._may_be_set_legacy_flag(expected_schema)
# Infer the schema from the stats.
actual_schema = validation_api.infer_schema(statistics,
infer_feature_shape=False)
self.assertEqual(actual_schema, expected_schema)
def test_infer_schema_invalid_statistics_input(self):
with self.assertRaisesRegexp(
TypeError,
'.*should be a DatasetFeatureStatisticsList proto.*'):
_ = validation_api.infer_schema({})
