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"""Tests for dataset.compute_signal()."""
from typing import Iterable, Optional, Union, cast
import numpy as np
import pytest
from typing_extensions import override
from ..concepts.concept import ExampleIn
from ..concepts.db_concept import ConceptUpdate, DiskConceptDB
from ..schema import UUID_COLUMN, VALUE_KEY, Field, Item, RichData, SignalInputType, field, schema
from ..signals.concept_scorer import ConceptScoreSignal
from ..signals.signal import (
TextEmbeddingSignal,
TextSignal,
TextSplitterSignal,
clear_signal_registry,
register_signal,
)
from .dataset import Column, DatasetManifest, GroupsSortBy, SortOrder, val
from .dataset_test_utils import (
TEST_DATASET_NAME,
TEST_NAMESPACE,
TestDataMaker,
enriched_embedding_span_field,
enriched_item,
)
from .dataset_utils import lilac_embedding, lilac_span
SIMPLE_ITEMS: list[Item] = [{
UUID_COLUMN: '1',
'str': 'a',
'int': 1,
'bool': False,
'float': 3.0
}, {
UUID_COLUMN: '2',
'str': 'b',
'int': 2,
'bool': True,
'float': 2.0
}, {
UUID_COLUMN: '3',
'str': 'b',
'int': 2,
'bool': True,
'float': 1.0
}]
class TestInvalidSignal(TextSignal):
name = 'test_invalid_signal'
@override
def fields(self) -> Field:
return field('int32')
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
# Return an invalid output that doesn't match the input length.
return []
class TestSparseSignal(TextSignal):
name = 'test_sparse_signal'
@override
def fields(self) -> Field:
return field('int32')
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
for text in data:
if text == 'hello':
# Skip this input.
yield None
else:
yield len(text)
class TestSparseRichSignal(TextSignal):
"""Find personally identifiable information (emails, phone numbers, etc)."""
name = 'test_sparse_rich_signal'
@override
def fields(self) -> Field:
return field(fields={'emails': ['string']})
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
for text in data:
if text == 'hello':
# Skip this input.
yield None
else:
yield {'emails': ['test1@hello.com', 'test2@hello.com']}
class TestParamSignal(TextSignal):
name = 'param_signal'
param: str
def fields(self) -> Field:
return field('string')
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
for text_content in data:
yield f'{str(text_content)}_{self.param}'
class TestSignal(TextSignal):
name = 'test_signal'
@override
def fields(self) -> Field:
return field(fields={'len': 'int32', 'flen': 'float32'})
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
return [{'len': len(text_content), 'flen': float(len(text_content))} for text_content in data]
class TestSplitSignal(TextSplitterSignal):
"""Split documents into sentence by splitting on period, generating entities."""
name = 'test_split'
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
for text in data:
if not isinstance(text, str):
raise ValueError(f'Expected text to be a string, got {type(text)} instead.')
sentences = [f'{sentence.strip()}.' for sentence in text.split('.') if sentence]
yield [
lilac_span(text.index(sentence),
text.index(sentence) + len(sentence)) for sentence in sentences
]
EMBEDDINGS: list[tuple[str, Union[list[float], list[list[float]]]]] = [
('hello.', [1.0, 0.0, 0.0]),
# This embedding has an outer dimension of 1.
('hello2.', [[1.0, 1.0, 0.0]]),
('hello3.', [[0, 0, 1.]])
]
STR_EMBEDDINGS: dict[str, Union[list[float], list[list[float]]]] = {
text: embedding for text, embedding in EMBEDDINGS
}
class TestEmbedding(TextEmbeddingSignal):
"""A test embed function."""
name = 'test_embedding'
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
"""Call the embedding function."""
for example in data:
example = cast(str, example)
yield [lilac_embedding(0, len(example), np.array(STR_EMBEDDINGS[example]))]
class ComputedKeySignal(TextSignal):
name = 'computed_key'
@override
def fields(self) -> Field:
return field('int64')
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Optional[Item]]:
for text in data:
yield 1
def key(self, is_computed_signal: Optional[bool] = False) -> str:
return f'key_{is_computed_signal}'
@pytest.fixture(scope='module', autouse=True)
def setup_teardown() -> Iterable[None]:
# Setup.
register_signal(TestSparseSignal)
register_signal(TestSparseRichSignal)
register_signal(TestParamSignal)
register_signal(TestSignal)
register_signal(TestSplitSignal)
register_signal(TestEmbedding)
register_signal(ComputedKeySignal)
register_signal(ConceptScoreSignal)
# Unit test runs.
yield
# Teardown.
clear_signal_registry()
def test_signal_output_validation(make_test_data: TestDataMaker) -> None:
signal = TestInvalidSignal()
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello',
}, {
UUID_COLUMN: '2',
'text': 'hello world',
}])
with pytest.raises(
ValueError, match='The signal generated 0 values but the input data had 2 values.'):
dataset.compute_signal(signal, 'text')
def test_sparse_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello',
}, {
UUID_COLUMN: '2',
'text': 'hello world',
}])
dataset.compute_signal(TestSparseSignal(), 'text')
result = dataset.select_rows(['text'])
assert list(result) == [{
UUID_COLUMN: '1',
'text': enriched_item('hello', {'test_sparse_signal': None})
}, {
UUID_COLUMN: '2',
'text': enriched_item('hello world', {'test_sparse_signal': 11})
}]
def test_sparse_rich_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello',
}, {
UUID_COLUMN: '2',
'text': 'hello world',
}])
dataset.compute_signal(TestSparseRichSignal(), 'text')
result = dataset.select_rows(['text'])
assert list(result) == [{
UUID_COLUMN: '1',
'text': enriched_item('hello', {'test_sparse_rich_signal': None})
}, {
UUID_COLUMN: '2',
'text': enriched_item(
'hello world',
{'test_sparse_rich_signal': {
'emails': ['test1@hello.com', 'test2@hello.com']
}})
}]
def test_source_joined_with_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data(SIMPLE_ITEMS)
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'str': 'string',
'int': 'int32',
'bool': 'boolean',
'float': 'float32',
}),
num_items=3)
test_signal = TestSignal()
dataset.compute_signal(test_signal, 'str')
# Check the enriched dataset manifest has 'text' enriched.
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'str': field(
'string',
fields={
'test_signal': field(
signal=test_signal.dict(), fields={
'len': 'int32',
'flen': 'float32'
}),
}),
'int': 'int32',
'bool': 'boolean',
'float': 'float32',
}),
num_items=3)
result = dataset.select_rows(['str'])
assert list(result) == [{
UUID_COLUMN: '1',
'str': enriched_item('a', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
}, {
UUID_COLUMN: '2',
'str': enriched_item('b', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
}, {
UUID_COLUMN: '3',
'str': enriched_item('b', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
}]
# Select a specific signal leaf test_signal.flen with val('str').
result = dataset.select_rows([val('str'), ('str', 'test_signal', 'flen')])
assert list(result) == [{
UUID_COLUMN: '1',
f'str.{VALUE_KEY}': 'a',
'str.test_signal.flen': 1.0
}, {
UUID_COLUMN: '2',
f'str.{VALUE_KEY}': 'b',
'str.test_signal.flen': 1.0
}, {
UUID_COLUMN: '3',
f'str.{VALUE_KEY}': 'b',
'str.test_signal.flen': 1.0
}]
# Select a specific signal leaf test_signal.flen and the whole 'str' subtree.
result = dataset.select_rows(['str', ('str', 'test_signal', 'flen')])
assert list(result) == [{
UUID_COLUMN: '1',
'str': enriched_item('a', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
'str.test_signal.flen': 1.0
}, {
UUID_COLUMN: '2',
'str': enriched_item('b', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
'str.test_signal.flen': 1.0
}, {
UUID_COLUMN: '3',
'str': enriched_item('b', {'test_signal': {
'len': 1,
'flen': 1.0
}}),
'str.test_signal.flen': 1.0
}]
# Select multiple signal leafs with aliasing.
result = dataset.select_rows([
val('str'),
Column(('str', 'test_signal', 'flen'), alias='flen'),
Column(('str', 'test_signal', 'len'), alias='len')
])
assert list(result) == [{
UUID_COLUMN: '1',
f'str.{VALUE_KEY}': 'a',
'flen': 1.0,
'len': 1
}, {
UUID_COLUMN: '2',
f'str.{VALUE_KEY}': 'b',
'flen': 1.0,
'len': 1
}, {
UUID_COLUMN: '3',
f'str.{VALUE_KEY}': 'b',
'flen': 1.0,
'len': 1
}]
def test_parameterized_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello'
}, {
UUID_COLUMN: '2',
'text': 'everybody'
}])
test_signal_a = TestParamSignal(param='a')
test_signal_b = TestParamSignal(param='b')
dataset.compute_signal(test_signal_a, 'text')
dataset.compute_signal(test_signal_b, 'text')
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'text': field(
'string',
fields={
'param_signal(param=a)': field('string', test_signal_a.dict()),
'param_signal(param=b)': field('string', test_signal_b.dict()),
}),
}),
num_items=2)
result = dataset.select_rows(['text'])
assert list(result) == [{
UUID_COLUMN: '1',
'text': enriched_item('hello', {
'param_signal(param=a)': 'hello_a',
'param_signal(param=b)': 'hello_b',
})
}, {
UUID_COLUMN: '2',
'text': enriched_item('everybody', {
'param_signal(param=a)': 'everybody_a',
'param_signal(param=b)': 'everybody_b',
})
}]
def test_split_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': '[1, 1] first sentence. [1, 1] second sentence.',
}, {
UUID_COLUMN: '2',
'text': 'b2 [2, 1] first sentence. [2, 1] second sentence.',
}])
signal = TestSplitSignal()
dataset.compute_signal(signal, 'text')
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'text': field(
'string', fields={'test_split': field(signal=signal.dict(), fields=[field('string_span')])})
}),
num_items=2)
result = dataset.select_rows(['text'])
expected_result = [{
UUID_COLUMN: '1',
'text': enriched_item('[1, 1] first sentence. [1, 1] second sentence.',
{'test_split': [lilac_span(0, 22), lilac_span(23, 46)]})
}, {
UUID_COLUMN: '2',
'text': enriched_item('b2 [2, 1] first sentence. [2, 1] second sentence.',
{'test_split': [
lilac_span(0, 25),
lilac_span(26, 49),
]})
}]
assert list(result) == expected_result
def test_signal_on_repeated_field(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': ['hello', 'everybody'],
}, {
UUID_COLUMN: '2',
'text': ['hello2', 'everybody2'],
}])
test_signal = TestSignal()
# Run the signal on the repeated field.
dataset.compute_signal(test_signal, ('text', '*'))
# Check the enriched dataset manifest has 'text' enriched.
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'text': field(fields=[
field(
'string',
fields={
'test_signal': field(
signal=test_signal.dict(), fields={
'len': 'int32',
'flen': 'float32'
})
})
])
}),
num_items=2)
result = dataset.select_rows([('text', '*')])
assert list(result) == [{
UUID_COLUMN: '1',
'text.*': [
enriched_item('hello', {'test_signal': {
'len': 5,
'flen': 5.0
}}),
enriched_item('everybody', {'test_signal': {
'len': 9,
'flen': 9.0
}})
]
}, {
UUID_COLUMN: '2',
'text.*': [
enriched_item('hello2', {'test_signal': {
'len': 6,
'flen': 6.0
}}),
enriched_item('everybody2', {'test_signal': {
'len': 10,
'flen': 10.0
}})
]
}]
def test_text_splitter(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': '[1, 1] first sentence. [1, 1] second sentence.',
}, {
UUID_COLUMN: '2',
'text': 'b2 [2, 1] first sentence. [2, 1] second sentence.',
}])
dataset.compute_signal(TestSplitSignal(), 'text')
result = dataset.select_rows(['text'])
expected_result = [{
UUID_COLUMN: '1',
'text': enriched_item('[1, 1] first sentence. [1, 1] second sentence.',
{'test_split': [
lilac_span(0, 22),
lilac_span(23, 46),
]}),
}, {
UUID_COLUMN: '2',
'text': enriched_item('b2 [2, 1] first sentence. [2, 1] second sentence.',
{'test_split': [
lilac_span(0, 25),
lilac_span(26, 49),
]}),
}]
assert list(result) == expected_result
def test_embedding_signal(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello.',
}, {
UUID_COLUMN: '2',
'text': 'hello2.',
}])
embedding_signal = TestEmbedding()
dataset.compute_signal(embedding_signal, 'text')
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'text': field(
'string',
fields={
'test_embedding': field(
signal=embedding_signal.dict(), fields=[enriched_embedding_span_field()])
}),
}),
num_items=2)
result = dataset.select_rows()
# Embeddings are replaced with "None".
expected_result = [{
UUID_COLUMN: '1',
'text': enriched_item('hello.', {'test_embedding': [lilac_embedding(0, 6, None)]})
}, {
UUID_COLUMN: '2',
'text': enriched_item('hello2.', {'test_embedding': [lilac_embedding(0, 7, None)]})
}]
assert list(result) == expected_result
def test_is_computed_signal_key(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello.',
}, {
UUID_COLUMN: '2',
'text': 'hello2.',
}])
signal = ComputedKeySignal()
dataset.compute_signal(signal, 'text')
assert dataset.manifest() == DatasetManifest(
namespace=TEST_NAMESPACE,
dataset_name=TEST_DATASET_NAME,
data_schema=schema({
UUID_COLUMN: 'string',
'text': field('string', fields={'key_True': field('int64', signal=signal.dict())}),
}),
num_items=2)
result = dataset.select_rows()
# Embeddings are replaced with "None".
expected_result = [{
UUID_COLUMN: '1',
'text': enriched_item('hello.', {'key_True': 1})
}, {
UUID_COLUMN: '2',
'text': enriched_item('hello2.', {'key_True': 1})
}]
assert list(result) == expected_result
def test_concept_signal_with_select_groups(make_test_data: TestDataMaker) -> None:
dataset = make_test_data([{
UUID_COLUMN: '1',
'text': 'hello.',
}, {
UUID_COLUMN: '2',
'text': 'hello2.',
}, {
UUID_COLUMN: '3',
'text': 'hello3.',
}])
embedding_signal = TestEmbedding()
dataset.compute_signal(embedding_signal, 'text')
concept_db = DiskConceptDB()
concept_db.create(namespace='test_namespace', name='test_concept', type=SignalInputType.TEXT)
concept_db.edit(
'test_namespace', 'test_concept',
ConceptUpdate(insert=[
ExampleIn(label=False, text='hello.'),
ExampleIn(label=True, text='hello2.'),
ExampleIn(label=False, text='hello3.')
]))
concept_signal = ConceptScoreSignal(
namespace='test_namespace', concept_name='test_concept', embedding='test_embedding')
dataset.compute_signal(concept_signal, 'text')
concept_key = concept_signal.key(is_computed_signal=True)
result = dataset.select_groups(f'text.test_embedding.*.embedding.{concept_key}')
assert result.counts == [('Not in concept', 2), ('In concept', 1)]
result = dataset.select_groups(
f'text.test_embedding.*.embedding.{concept_key}',
sort_by=GroupsSortBy.COUNT,
sort_order=SortOrder.ASC)
assert result.counts == [('In concept', 1), ('Not in concept', 2)]
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