def cross_validate_by_trial(model: ModelBridge, trial: int = -1) -> List[CVResult]:
"""Cross validation for model predictions on a particular trial.
Uses all of the data up until the specified trial to predict each of the
arms that was launched in that trial. Defaults to the last trial.
Args:
model: Fitted model (ModelBridge) to cross validate.
trial: Trial for which predictions are evaluated.
Returns:
A CVResult for each observation in the training data.
"""
# Get in-design training points
training_data = [
obs
for i, obs in enumerate(model.get_training_data())
if model.training_in_design[i]
]
all_trials = {
# pyre-fixme[6]: Expected `Union[bytes, str, typing.SupportsInt]` for 1st
# param but got `Optional[np.int64]`.
int(d.features.trial_index)
for d in training_data
if d.features.trial_index is not None
}
if len(all_trials) < 2:
raise ValueError(f"Training data has fewer than 2 trials ({all_trials})")
if trial < 0:
trial = max(all_trials)
elif trial not in all_trials:
raise ValueError(f"Trial {trial} not found in training data")
# Construct train/test data
cv_training_data = []
cv_test_data = []
cv_test_points = []
for obs in training_data:
if obs.features.trial_index is None:
continue
# pyre-fixme[58]: `<` is not supported for operand types
# `Optional[np.int64]` and `int`.
elif obs.features.trial_index < trial:
cv_training_data.append(obs)
elif obs.features.trial_index == trial:
cv_test_points.append(obs.features)
cv_test_data.append(obs)
# Make the prediction
cv_test_predictions = model.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points
)
# Form CVResult objects
result = [
CVResult(observed=obs, predicted=cv_test_predictions[i])
for i, obs in enumerate(cv_test_data)
]
return result
def testModelBridge(self, mock_fit, mock_gen_arms,
mock_observations_from_data):
# Test that on init transforms are stored and applied in the correct order
transforms = [transform_1, transform_2]
exp = get_experiment_for_value()
ss = get_search_space_for_value()
modelbridge = ModelBridge(ss, 0, transforms, exp, 0)
self.assertEqual(list(modelbridge.transforms.keys()),
["transform_1", "transform_2"])
fit_args = mock_fit.mock_calls[0][2]
self.assertTrue(
fit_args["search_space"] == get_search_space_for_value(8.0))
self.assertTrue(fit_args["observation_features"] == [])
self.assertTrue(fit_args["observation_data"] == [])
self.assertTrue(mock_observations_from_data.called)
# Test prediction on out of design features.
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
side_effect=ValueError("Out of Design"),
)
# This point is in design, and thus failures in predict are legitimate.
with mock.patch.object(ModelBridge,
"model_space",
return_value=get_search_space_for_range_values):
with self.assertRaises(ValueError):
modelbridge.predict([get_observation2().features])
# This point is out of design, and not in training data.
with self.assertRaises(ValueError):
modelbridge.predict([get_observation_status_quo0().features])
# Now it's in the training data.
with mock.patch.object(
ModelBridge,
"get_training_data",
return_value=[get_observation_status_quo0()],
):
# Return raw training value.
self.assertEqual(
modelbridge.predict([get_observation_status_quo0().features]),
unwrap_observation_data([get_observation_status_quo0().data]),
)
# Test that transforms are applied correctly on predict
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[get_observation2trans().data],
)
modelbridge.predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Check that _single_predict is equivalent here.
modelbridge._single_predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Test transforms applied on gen
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([get_observation1trans().features], [2], None, {}),
)
oc = OptimizationConfig(objective=Objective(metric=Metric(
name="test_metric")))
modelbridge._set_kwargs_to_save(model_key="TestModel",
model_kwargs={},
bridge_kwargs={})
gr = modelbridge.gen(
n=1,
search_space=get_search_space_for_value(),
optimization_config=oc,
pending_observations={"a": [get_observation2().features]},
fixed_features=ObservationFeatures({"x": 5}),
)
self.assertEqual(gr._model_key, "TestModel")
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc,
pending_observations={"a": [get_observation2trans().features]},
fixed_features=ObservationFeatures({"x": 36}),
model_gen_options=None,
)
mock_gen_arms.assert_called_with(
arms_by_signature={},
observation_features=[get_observation1().features])
# Gen with no pending observations and no fixed features
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=None)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=None,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Gen with multi-objective optimization config.
oc2 = OptimizationConfig(objective=ScalarizedObjective(
metrics=[Metric(name="test_metric"),
Metric(name="test_metric_2")]))
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=oc2)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc2,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Test transforms applied on cross_validate
modelbridge._cross_validate = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._cross_validate",
autospec=True,
return_value=[get_observation1trans().data],
)
cv_training_data = [get_observation2()]
cv_test_points = [get_observation1().features]
cv_predictions = modelbridge.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points)
modelbridge._cross_validate.assert_called_with(
obs_feats=[get_observation2trans().features],
obs_data=[get_observation2trans().data],
cv_test_points=[get_observation1().features
], # untransformed after
)
self.assertTrue(cv_predictions == [get_observation1().data])
# Test stored training data
obs = modelbridge.get_training_data()
self.assertTrue(obs == [get_observation1(), get_observation2()])
self.assertEqual(modelbridge.metric_names, {"a", "b"})
self.assertIsNone(modelbridge.status_quo)
self.assertTrue(
modelbridge.model_space == get_search_space_for_value())
self.assertEqual(modelbridge.training_in_design, [False, False])
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
# Test feature_importances
with self.assertRaises(NotImplementedError):
modelbridge.feature_importances("a")
def cross_validate(model: ModelBridge,
folds: int = -1,
test_selector: Optional[Callable] = None) -> List[CVResult]:
"""Cross validation for model predictions.
Splits the model's training data into train/test folds and makes
out-of-sample predictions on the test folds.
Train/test splits are made based on arm names, so that repeated
observations of a arm will always be in the train or test set
together.
The test set can be limited to a specific set of observations by passing in
a test_selector callable. This function should take in an Observation
and return a boolean indiciating if it should be used in the test set or
not. For example, we can limit the test set to arms with trial 0 with
test_selector = lambda obs: obs.features.trial_index == 0
If not provided, all observations will be available for the test set.
Args:
model: Fitted model (ModelBridge) to cross validate.
folds: Number of folds. Use -1 for leave-one-out, otherwise will be
k-fold.
test_selector: Function for selecting observations for the test set.
Returns:
A CVResult for each observation in the training data.
"""
# Get in-design training points
training_data = [
obs for i, obs in enumerate(model.get_training_data())
if model.training_in_design[i]
]
arm_names = {obs.arm_name for obs in training_data}
n = len(arm_names)
if folds > n:
raise ValueError(
f"Training data only has {n} arms, which is less than folds")
elif folds < 2 and folds != -1:
raise ValueError("Folds must be -1 for LOO, or > 1.")
elif folds == -1:
folds = n
arm_names_rnd = np.array(list(arm_names))
np.random.shuffle(arm_names_rnd)
result = []
for train_names, test_names in _gen_train_test_split(
folds=folds, arm_names=arm_names_rnd):
# Construct train/test data
cv_training_data = []
cv_test_data = []
cv_test_points = []
for obs in training_data:
if obs.arm_name in train_names:
cv_training_data.append(obs)
elif obs.arm_name in test_names and (test_selector is None
or test_selector(obs)):
cv_test_points.append(obs.features)
cv_test_data.append(obs)
if len(cv_test_points) == 0:
continue
# Make the prediction
cv_test_predictions = model.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points)
# Form CVResult objects
for i, obs in enumerate(cv_test_data):
result.append(
CVResult(observed=obs, predicted=cv_test_predictions[i]))
return result
def testModelBridge(self, mock_fit, mock_gen_arms, mock_observations_from_data):
# Test that on init transforms are stored and applied in the correct order
transforms = [t1, t2]
exp = get_experiment()
modelbridge = ModelBridge(search_space_for_value(), 0, transforms, exp, 0)
self.assertEqual(list(modelbridge.transforms.keys()), ["t1", "t2"])
fit_args = mock_fit.mock_calls[0][2]
self.assertTrue(fit_args["search_space"] == search_space_for_value(8.0))
self.assertTrue(
fit_args["observation_features"]
== [observation1trans().features, observation2trans().features]
)
self.assertTrue(
fit_args["observation_data"]
== [observation1trans().data, observation2trans().data]
)
self.assertTrue(mock_observations_from_data.called)
# Test that transforms are applied correctly on predict
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[observation2trans().data],
)
modelbridge.predict([observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([observation2().features])
# Test transforms applied on gen
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([observation1trans().features], [2], None),
)
oc = OptimizationConfig(objective=Objective(metric=Metric(name="test_metric")))
modelbridge._set_kwargs_to_save(
model_key="TestModel", model_kwargs={}, bridge_kwargs={}
)
gr = modelbridge.gen(
n=1,
search_space=search_space_for_value(),
optimization_config=oc,
pending_observations={"a": [observation2().features]},
fixed_features=ObservationFeatures({"x": 5}),
)
self.assertEqual(gr._model_key, "TestModel")
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc,
pending_observations={"a": [observation2trans().features]},
fixed_features=ObservationFeatures({"x": 36}),
model_gen_options=None,
)
mock_gen_arms.assert_called_with(
arms_by_signature={}, observation_features=[observation1().features]
)
# Gen with no pending observations and no fixed features
modelbridge.gen(
n=1, search_space=search_space_for_value(), optimization_config=None
)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=None,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Gen with multi-objective optimization config.
oc2 = OptimizationConfig(
objective=ScalarizedObjective(
metrics=[Metric(name="test_metric"), Metric(name="test_metric_2")]
)
)
modelbridge.gen(
n=1, search_space=search_space_for_value(), optimization_config=oc2
)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc2,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Test transforms applied on cross_validate
modelbridge._cross_validate = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._cross_validate",
autospec=True,
return_value=[observation1trans().data],
)
cv_training_data = [observation2()]
cv_test_points = [observation1().features]
cv_predictions = modelbridge.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points
)
modelbridge._cross_validate.assert_called_with(
obs_feats=[observation2trans().features],
obs_data=[observation2trans().data],
cv_test_points=[observation1().features], # untransformed after
)
self.assertTrue(cv_predictions == [observation1().data])
# Test stored training data
obs = modelbridge.get_training_data()
self.assertTrue(obs == [observation1(), observation2()])
self.assertEqual(modelbridge.metric_names, {"a", "b"})
self.assertIsNone(modelbridge.status_quo)
self.assertTrue(modelbridge.model_space == search_space_for_value())
self.assertEqual(modelbridge.training_in_design, [True, True])
modelbridge.training_in_design = [True, False]
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
ood_obs = modelbridge.out_of_design_data()
self.assertTrue(ood_obs == unwrap_observation_data([observation2().data]))
