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"""Tests for the the database concept."""
from pathlib import Path
from typing import Generator, Iterable, Optional, Type, cast
import numpy as np
import pytest
from pytest_mock import MockerFixture
from typing_extensions import override
from ..config import CONFIG
from ..data.dataset_duckdb import DatasetDuckDB
from ..data.dataset_utils import lilac_embedding
from ..db_manager import set_default_dataset_cls
from ..schema import Item, RichData, SignalInputType
from ..signals.signal import TextEmbeddingSignal, clear_signal_registry, register_signal
from .concept import (
DRAFT_MAIN,
Concept,
ConceptModel,
DraftId,
Example,
ExampleIn,
LogisticEmbeddingModel,
)
from .db_concept import (
ConceptDB,
ConceptInfo,
ConceptModelDB,
ConceptUpdate,
DiskConceptDB,
DiskConceptModelDB,
)
ALL_CONCEPT_DBS = [DiskConceptDB]
ALL_CONCEPT_MODEL_DBS = [DiskConceptModelDB]
@pytest.fixture(autouse=True)
def set_data_path(tmp_path: Path, mocker: MockerFixture) -> None:
mocker.patch.dict(CONFIG, {'LILAC_DATA_PATH': str(tmp_path)})
EMBEDDING_MAP: dict[str, list[float]] = {
'not in concept': [1.0, 0.0, 0.0],
'in concept': [0.9, 0.1, 0.0],
'a new data point': [0.1, 0.2, 0.3],
'a true draft point': [0.4, 0.5, 0.6],
'a false draft point': [0.7, 0.8, 0.9],
}
class TestEmbedding(TextEmbeddingSignal):
"""A test embed function."""
name = 'test_embedding'
@override
def compute(self, data: Iterable[RichData]) -> Iterable[Item]:
"""Embed the examples, use a hashmap to the vector for simplicity."""
for example in data:
if example not in EMBEDDING_MAP:
raise ValueError(f'Example "{str(example)}" not in embedding map')
yield [lilac_embedding(0, len(example), np.array(EMBEDDING_MAP[cast(str, example)]))]
@pytest.fixture(scope='module', autouse=True)
def setup_teardown() -> Generator:
set_default_dataset_cls(DatasetDuckDB)
register_signal(TestEmbedding)
# Unit test runs.
yield
# Teardown.
clear_signal_registry()
@pytest.mark.parametrize('db_cls', ALL_CONCEPT_DBS)
class ConceptDBSuite:
def test_create_concept(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
db.create(namespace='test', name='test_concept', type=SignalInputType.TEXT)
assert db.list() == [
ConceptInfo(
namespace='test', name='test_concept', type=SignalInputType.TEXT, drafts=[DRAFT_MAIN])
]
# Make sure list with drafts relects the drafts.
train_data = [
ExampleIn(label=False, text='not in concept', draft='test_draft'),
ExampleIn(label=True, text='in concept', draft='test_draft')
]
db.edit('test', 'test_concept', ConceptUpdate(insert=train_data))
assert db.list() == [
ConceptInfo(
namespace='test',
name='test_concept',
type=SignalInputType.TEXT,
drafts=[DRAFT_MAIN, 'test_draft'])
]
def test_add_example(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept')
]
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
keys[0]: Example(id=keys[0], label=False, text='not in concept'),
keys[1]: Example(id=keys[1], label=True, text='in concept')
},
version=1)
# Add a draft labels.
db.edit(
namespace, concept_name,
ConceptUpdate(insert=[
ExampleIn(label=False, text='really not in concept', draft='test_draft'),
ExampleIn(label=True, text='really in concept', draft='test_draft')
]))
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
keys[0]: Example(id=keys[0], label=False, text='not in concept'),
keys[1]: Example(id=keys[1], label=True, text='in concept'),
keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
},
version=2)
def test_update_concept(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept'),
ExampleIn(label=False, text='really not in concept', draft='test_draft'),
ExampleIn(label=True, text='really in concept', draft='test_draft')
]
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
# Edit the first example.
db.edit(
namespace, concept_name,
ConceptUpdate(update=[Example(id=keys[0], label=False, text='not in concept, updated')]))
concept = db.get(namespace, concept_name)
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
# The first example should be updated alone.
keys[0]: Example(id=keys[0], label=False, text='not in concept, updated'),
keys[1]: Example(id=keys[1], label=True, text='in concept'),
# Drafts are untouched.
keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
},
version=2)
# Edit the second example on the draft.
db.edit(
namespace, concept_name,
ConceptUpdate(update=[
Example(id=keys[3], label=True, text='really in concept, updated', draft='test_draft')
]))
concept = db.get(namespace, concept_name)
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
# Main remains the same.
keys[0]: Example(id=keys[0], label=False, text='not in concept, updated'),
keys[1]: Example(id=keys[1], label=True, text='in concept'),
keys[2]: Example(id=keys[2], label=False, text='really not in concept', draft='test_draft'),
keys[3]: Example(
id=keys[3], label=True, text='really in concept, updated', draft='test_draft'),
},
version=3)
def test_remove_concept(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept')
]
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
concept = db.get(namespace, concept_name)
db.remove(namespace, concept_name)
concept = db.get(namespace, concept_name)
assert concept is None
def test_remove_concept_examples(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept')
]
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
db.edit(namespace, concept_name, ConceptUpdate(remove=[keys[0]]))
concept = db.get(namespace, concept_name)
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
# key_0 was removed.
keys[1]: Example(id=keys[1], label=True, text='in concept')
},
version=2)
def test_remove_concept_examples_draft(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept'),
ExampleIn(label=False, text='really not in concept', draft='test_draft'),
ExampleIn(label=True, text='really in concept', draft='test_draft')
]
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
db.edit(namespace, concept_name, ConceptUpdate(remove=[keys[2]]))
concept = db.get(namespace, concept_name)
assert concept == Concept(
namespace=namespace,
concept_name=concept_name,
type=SignalInputType.TEXT,
data={
keys[0]: Example(id=keys[0], label=False, text='not in concept'),
keys[1]: Example(id=keys[1], label=True, text='in concept'),
# The first draft example is removed.
keys[3]: Example(id=keys[3], label=True, text='really in concept', draft='test_draft'),
},
version=2)
def test_remove_invalid_id(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept'),
ExampleIn(label=False, text='really not in concept', draft='test_draft'),
ExampleIn(label=True, text='really in concept', draft='test_draft')
]
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
with pytest.raises(ValueError, match='Example with id "invalid_id" does not exist'):
db.edit(namespace, concept_name, ConceptUpdate(remove=['invalid_id']))
def test_edit_before_creation(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
with pytest.raises(
ValueError, match='Concept with namespace "test" and name "test_concept" does not exist'):
db.edit(namespace, concept_name,
ConceptUpdate(insert=[
ExampleIn(label=False, text='not in concept'),
]))
def test_edit_invalid_id(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept')
]
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
with pytest.raises(ValueError, match='Example with id "invalid_id" does not exist'):
db.edit(namespace, concept_name,
ConceptUpdate(update=[Example(id='invalid_id', label=False, text='not in concept')]))
def test_merge_draft(self, db_cls: Type[ConceptDB]) -> None:
db = db_cls()
namespace = 'test'
concept_name = 'test_concept'
db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=True, text='hello'),
ExampleIn(label=False, text='world'),
ExampleIn(label=True, text='hello draft 1', draft='draft1'),
ExampleIn(label=False, text='world draft 1', draft='draft1'),
# Duplicate of main.
ExampleIn(label=False, text='hello', draft='draft2'),
ExampleIn(label=True, text='world draft 2', draft='draft2'),
]
db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
db.merge_draft(namespace, concept_name, 'draft1')
concept = db.get(namespace, concept_name)
assert concept is not None
keys = list(concept.data.keys())
assert concept.dict() == Concept(
namespace='test',
concept_name='test_concept',
type=SignalInputType.TEXT,
data={
keys[0]: Example(id=keys[0], label=True, text='hello'),
keys[1]: Example(id=keys[1], label=False, text='world'),
# Draft examples are merged.
keys[2]: Example(id=keys[2], label=True, text='hello draft 1'),
keys[3]: Example(id=keys[3], label=False, text='world draft 1'),
# Draft 2 is untouched.
keys[4]: Example(id=keys[4], label=False, text='hello', draft='draft2'),
keys[5]: Example(id=keys[5], label=True, text='world draft 2', draft='draft2'),
},
version=2).dict()
db.merge_draft(namespace, concept_name, 'draft2')
concept = db.get(namespace, concept_name)
assert concept is not None
assert concept == Concept(
namespace='test',
concept_name='test_concept',
type=SignalInputType.TEXT,
data={
# The first example is a duplicate of the label from the draft, so it is removed.
keys[1]: Example(id=keys[1], label=False, text='world'),
# Draft examples are merged.
keys[2]: Example(id=keys[2], label=True, text='hello draft 1'),
keys[3]: Example(id=keys[3], label=False, text='world draft 1'),
# Draft examples are merged.
keys[4]: Example(id=keys[4], label=False, text='hello'),
keys[5]: Example(id=keys[5], label=True, text='world draft 2'),
},
version=3)
def _make_test_concept_model(
concept_db: ConceptDB,
logistic_models: dict[DraftId, LogisticEmbeddingModel] = {}) -> ConceptModel:
namespace = 'test'
concept_name = 'test_concept'
concept_db.create(namespace=namespace, name=concept_name, type=SignalInputType.TEXT)
train_data = [
ExampleIn(label=False, text='not in concept'),
ExampleIn(label=True, text='in concept')
]
concept_db.edit(namespace, concept_name, ConceptUpdate(insert=train_data))
model = ConceptModel(
namespace='test', concept_name='test_concept', embedding_name='test_embedding')
model._logistic_models = logistic_models
return model
class TestLogisticModel(LogisticEmbeddingModel):
@override
def score_embeddings(self, embeddings: np.ndarray) -> np.ndarray:
"""Get the scores for the provided embeddings."""
return np.array([.1])
@override
def fit(self, embeddings: np.ndarray, labels: list[bool],
implicit_negatives: Optional[np.ndarray]) -> None:
pass
@pytest.mark.parametrize('concept_db_cls', ALL_CONCEPT_DBS)
@pytest.mark.parametrize('model_db_cls', ALL_CONCEPT_MODEL_DBS)
class ConceptModelDBSuite:
def test_save_and_get_model(self, concept_db_cls: Type[ConceptDB],
model_db_cls: Type[ConceptModelDB]) -> None:
concept_db = concept_db_cls()
model_db = model_db_cls(concept_db)
model = _make_test_concept_model(concept_db)
model_db.sync(model)
retrieved_model = model_db.get(
namespace='test', concept_name='test_concept', embedding_name='test_embedding')
if not retrieved_model:
retrieved_model = model_db.create(
namespace='test', concept_name='test_concept', embedding_name='test_embedding')
assert retrieved_model.namespace == model.namespace
assert retrieved_model.concept_name == model.concept_name
assert retrieved_model.embedding_name == model.embedding_name
assert retrieved_model.version == model.version
assert retrieved_model.column_info == model.column_info
def test_sync_model(self, concept_db_cls: Type[ConceptDB], model_db_cls: Type[ConceptModelDB],
mocker: MockerFixture) -> None:
concept_db = concept_db_cls()
model_db = model_db_cls(concept_db)
logistic_model = TestLogisticModel()
score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
fit_mock = mocker.spy(TestLogisticModel, 'fit')
model = _make_test_concept_model(concept_db, logistic_models={DRAFT_MAIN: logistic_model})
assert model_db.in_sync(model) is False
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 0
model_db.sync(model)
assert model_db.in_sync(model) is True
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 1
def test_out_of_sync_model(self, concept_db_cls: Type[ConceptDB],
model_db_cls: Type[ConceptModelDB], mocker: MockerFixture) -> None:
concept_db = concept_db_cls()
model_db = model_db_cls(concept_db)
score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
fit_mock = mocker.spy(TestLogisticModel, 'fit')
logistic_model = TestLogisticModel()
model = _make_test_concept_model(concept_db, logistic_models={DRAFT_MAIN: logistic_model})
model_db.sync(model)
assert model_db.in_sync(model) is True
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 1
(called_model, called_embeddings, called_labels,
called_implicit_negatives) = fit_mock.call_args_list[-1].args
assert called_model == logistic_model
np.testing.assert_array_equal(
called_embeddings, np.array([EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept']]))
assert called_labels == [False, True]
assert called_implicit_negatives is None
# Edit the concept.
concept_db.edit('test', 'test_concept',
ConceptUpdate(insert=[ExampleIn(label=False, text='a new data point')]))
# Make sure the model is out of sync.
assert model_db.in_sync(model) is False
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 1
model_db.sync(model)
assert model_db.in_sync(model) is True
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 2
# Fit is called again with new points on main only.
(called_model, called_embeddings, called_labels,
called_implicit_negatives) = fit_mock.call_args_list[-1].args
assert called_model == logistic_model
np.testing.assert_array_equal(
called_embeddings,
np.array([
EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept'],
EMBEDDING_MAP['a new data point']
]))
assert called_labels == [False, True, False]
assert called_implicit_negatives is None
def test_out_of_sync_draft_model(self, concept_db_cls: Type[ConceptDB],
model_db_cls: Type[ConceptModelDB],
mocker: MockerFixture) -> None:
concept_db = concept_db_cls()
model_db = model_db_cls(concept_db)
score_embeddings_mock = mocker.spy(TestLogisticModel, 'score_embeddings')
fit_mock = mocker.spy(TestLogisticModel, 'fit')
main_model = TestLogisticModel()
draft_model = TestLogisticModel()
model = _make_test_concept_model(
concept_db, logistic_models={
DRAFT_MAIN: main_model,
'test_draft': draft_model
})
model_db.sync(model)
assert model_db.in_sync(model) is True
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 1
# Make sure drafts cause the model to be out of sync.
concept_db.edit(
'test',
'test_concept',
ConceptUpdate(insert=[
ExampleIn(label=True, text='a true draft point', draft='test_draft'),
ExampleIn(label=False, text='a false draft point', draft='test_draft'),
# This point exists in main, but we switched the label.
ExampleIn(label=False, text='in concept', draft='test_draft'),
]))
# Make sure the model is out of sync.
assert model_db.in_sync(model) is False
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 1
model_db.sync(model)
assert model_db.in_sync(model) is True
assert score_embeddings_mock.call_count == 0
assert fit_mock.call_count == 3 # Fit is called on both the draft, and main.
# Fit is called again with the same points.
((called_model, called_embeddings, called_labels, called_implicit_negatives),
(called_draft_model, called_draft_embeddings, called_draft_labels,
called_draft_implicit_negatives)) = (
c.args for c in fit_mock.call_args_list[-2:])
# The draft model is called with the data from main, and the data from draft.
assert called_draft_model == draft_model
np.testing.assert_array_equal(
called_draft_embeddings,
np.array([
EMBEDDING_MAP['a true draft point'], EMBEDDING_MAP['a false draft point'],
EMBEDDING_MAP['in concept'], EMBEDDING_MAP['not in concept']
]))
assert called_draft_labels == [
True,
False,
# This was overriden by the draft.
False,
False
]
assert called_draft_implicit_negatives is None
# The main model was fit without the data from the draft.
assert called_model == main_model
np.testing.assert_array_equal(
called_embeddings, np.array([EMBEDDING_MAP['not in concept'], EMBEDDING_MAP['in concept']]))
assert called_labels == [False, True]
assert called_implicit_negatives is None
def test_embedding_not_found_in_map(self, concept_db_cls: Type[ConceptDB],
model_db_cls: Type[ConceptModelDB]) -> None:
concept_db = concept_db_cls()
model_db = model_db_cls(concept_db)
model = _make_test_concept_model(concept_db)
model_db.sync(model)
# Edit the concept.
concept_db.edit('test', 'test_concept',
ConceptUpdate(insert=[ExampleIn(label=False, text='unknown text')]))
# Make sure the model is out of sync.
assert model_db.in_sync(model) is False
with pytest.raises(ValueError, match='Example "unknown text" not in embedding map'):
model_db.sync(model)
model_db.sync(model)
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