def test_builder_metadata(raw_model_config):
"""
Ensure the builder works with various model configs and that each has
expected/valid metadata results.
"""
model_config = yaml.load(raw_model_config, Loader=yaml.FullLoader)
data_config = get_random_data()
machine = Machine(
name="model-name", dataset=data_config, model=model_config, project_name="test"
)
model, machine_out = ModelBuilder(machine).build()
# Check metadata, and only verify 'history' if it's a *Keras* type model
machine_check(machine_out, "Keras" in raw_model_config)
def test_provide_saved_model_simple_happy_path(tmpdir):
"""
Test provide_saved_model with no caching
"""
model_config = {"sklearn.decomposition.PCA": {"svd_solver": "auto"}}
data_config = get_random_data()
output_dir = os.path.join(tmpdir, "model")
machine = Machine(
name="model-name", dataset=data_config, model=model_config, project_name="test"
)
ModelBuilder(machine).build(output_dir=output_dir)
# Assert the model was saved at the location
# Should be model file, and the metadata
assert len(os.listdir(output_dir)) == 2
def test_output_scores_metadata():
data_config = get_random_data()
raw_model_config = f"""
gordo.machine.model.anomaly.diff.DiffBasedAnomalyDetector:
scaler: sklearn.preprocessing.MinMaxScaler
base_estimator:
sklearn.compose.TransformedTargetRegressor:
transformer: sklearn.preprocessing.MinMaxScaler
regressor:
sklearn.pipeline.Pipeline:
steps:
- sklearn.preprocessing.MinMaxScaler
- gordo.machine.model.models.KerasAutoEncoder:
kind: feedforward_hourglass
batch_size: 3
compression_factor: 0.5
encoding_layers: 1
func: tanh
out_func: linear
epochs: 1
"""
model_config = yaml.load(raw_model_config, Loader=yaml.FullLoader)
machine = Machine(name="model-name",
dataset=data_config,
model=model_config,
project_name="test")
model, machine_out = ModelBuilder(machine).build()
scores_metadata = machine_out.metadata.build_metadata.model.cross_validation.scores
assert (scores_metadata["explained-variance-score-Tag-1"]["fold-mean"] +
scores_metadata["explained-variance-score-Tag-2"]["fold-mean"]
) / 2 == pytest.approx(
scores_metadata["explained-variance-score"]["fold-mean"])
assert (
scores_metadata["r2-score-Tag-1"]["fold-mean"] +
scores_metadata["r2-score-Tag-2"]["fold-mean"]) / 2 == pytest.approx(
scores_metadata["r2-score"]["fold-mean"])
assert (scores_metadata["mean-squared-error-Tag-1"]["fold-mean"] +
scores_metadata["mean-squared-error-Tag-2"]["fold-mean"]
) / 2 == pytest.approx(
scores_metadata["mean-squared-error"]["fold-mean"])
assert (scores_metadata["mean-absolute-error-Tag-1"]["fold-mean"] +
scores_metadata["mean-absolute-error-Tag-2"]["fold-mean"]
) / 2 == pytest.approx(
scores_metadata["mean-absolute-error"]["fold-mean"])
def test_get_metadata_helper(model: BaseEstimator, expect_empty_dict: bool):
"""
Ensure the builder works with various model configs and that each has
expected/valid metadata results.
"""
X, y = np.random.random((1000, 4)), np.random.random((1000, ))
model.fit(X, y)
metadata = ModelBuilder._extract_metadata_from_model(model)
# All the metadata we've implemented so far is 'history', so we'll check that
if not expect_empty_dict:
assert "history" in metadata
assert all(name in metadata["history"]
for name in ("params", "loss", "accuracy"))
else:
assert dict() == metadata
def test_provide_saved_model_caching_handle_existing_different_register(tmpdir):
"""If the model exists in the model register, but the output_dir is not where
the model is, the model is copied to the new location, unless the new location
already exists. If it does then return it"""
model_config = {"sklearn.decomposition.PCA": {"svd_solver": "auto"}}
data_config = get_random_data()
output_dir1 = os.path.join(tmpdir, "model1")
output_dir2 = os.path.join(tmpdir, "model2")
registry_dir = os.path.join(tmpdir, "registry")
machine = Machine(
name="model-name", dataset=data_config, model=model_config, project_name="test"
)
builder = ModelBuilder(machine)
builder.build(output_dir=output_dir1, model_register_dir=registry_dir)
builder.build(output_dir=output_dir2, model_register_dir=registry_dir)
assert builder.cached_model_path == output_dir2
builder.build(output_dir=output_dir2, model_register_dir=registry_dir)
assert builder.cached_model_path == output_dir2
def test_get_metrics_dict_scaler(scaler, mock):
mock_model = mock
metrics_list = [sklearn.metrics.mean_squared_error]
# make the features in y be in different scales
y = pd.DataFrame(
np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100],
columns=["Tag 1", "Tag 2"],
)
metrics_dict = ModelBuilder.build_metrics_dict(metrics_list, y, scaler=scaler)
metric_func = metrics_dict["mean-squared-error"]
mock_model.predict = lambda _y: _y * [0.8, 1]
mse_feature_one_wrong = metric_func(mock_model, y, y)
mock_model.predict = lambda _y: _y * [1, 0.8]
mse_feature_two_wrong = metric_func(mock_model, y, y)
if scaler:
assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)
else:
assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)
def test_provide_saved_model_caching_handle_existing_same_dir(tmpdir):
"""If the model exists in the model register, and the path there is the
same as output_dir, output_dir is returned"""
model_config = {"sklearn.decomposition.PCA": {"svd_solver": "auto"}}
data_config = get_random_data()
output_dir = os.path.join(tmpdir, "model")
registry_dir = os.path.join(tmpdir, "registry")
machine = Machine(
name="model-name", dataset=data_config, model=model_config, project_name="test"
)
builder = ModelBuilder(machine)
builder.build(output_dir=output_dir, model_register_dir=registry_dir)
assert builder.cached_model_path == output_dir
# Saving to same output_dir as the one saved in the registry just returns the output_dir
builder.build(output_dir=output_dir, model_register_dir=registry_dir)
assert builder.cached_model_path == output_dir
def test_provide_saved_model_caching(
should_be_equal: bool,
metadata: Optional[Metadata],
tag_list: Optional[List[SensorTag]],
replace_cache,
tmpdir,
):
"""
Test provide_saved_model with caching and possible cache busting if tag_list, or replace_cache is set.
Builds two models and checks if their model-creation-date's are the same,
which will be if and only if there is caching.
Parameters
----------
should_be_equal : bool
Do we expect the two generated models to be at the same location or not? I.e. do
we expect caching.
metadata: Metadata
Optional metadata which will be used as metadata for the second model.
tag_list
Optional list of strings which be used as the taglist in the dataset for the
second model.
replace_cache: bool
Should we force a model cache replacement?
"""
if tag_list is None:
tag_list = []
if metadata is None:
metadata = Metadata()
model_config = {"sklearn.decomposition.pca.PCA": {"svd_solver": "auto"}}
data_config = get_random_data()
output_dir = os.path.join(tmpdir, "model")
registry_dir = os.path.join(tmpdir, "registry")
machine = Machine(name="model-name",
dataset=data_config,
model=model_config,
project_name="test")
_, first_machine = ModelBuilder(machine).build(
output_dir=output_dir, model_register_dir=registry_dir)
if tag_list:
data_config["tag_list"] = tag_list
new_output_dir = os.path.join(tmpdir, "model2")
_, second_machine = ModelBuilder(machine=Machine(
name="model-name",
dataset=data_config,
model=model_config,
metadata=metadata,
project_name="test",
runtime={"something": True},
)).build(
output_dir=new_output_dir,
model_register_dir=registry_dir,
replace_cache=replace_cache,
)
model1_creation_date = (
first_machine.metadata.build_metadata.model.model_creation_date)
model2_creation_date = (
second_machine.metadata.build_metadata.model.model_creation_date)
assert "something" in second_machine.runtime
if should_be_equal:
assert model1_creation_date == model2_creation_date
else:
assert model1_creation_date != model2_creation_date
if metadata is not None:
assert metadata.user_defined == second_machine.metadata.user_defined
