def test_get_objective_works_for_names_of_defined_objectives(obj):
assert get_objective(obj.name) == obj
assert get_objective(obj.name.lower()) == obj
args = []
if obj == CostBenefitMatrix:
args = [0] * 4
assert isinstance(get_objective(obj(*args)), obj)
def test_get_objective_return_instance_does_not_work_for_some_objectives():
with pytest.raises(
ObjectiveCreationError,
match=
"In get_objective, cannot pass in return_instance=True for Cost Benefit Matrix"
):
get_objective("Cost Benefit Matrix", return_instance=True)
cbm = CostBenefitMatrix(0, 0, 0, 0)
assert get_objective(cbm) == cbm
def test_get_objective_does_raises_error_for_incorrect_name_or_random_class():
class InvalidObjective:
pass
obj = InvalidObjective()
with pytest.raises(TypeError):
get_objective(obj)
with pytest.raises(ObjectiveNotFoundError):
get_objective("log loss")
def _predict(self, X, objective=None):
"""Make predictions using selected features.
Arguments:
X (pd.DataFrame): Data of shape [n_samples, n_features]
objective (Object or string): The objective to use to make predictions
Returns:
pd.Series: Estimated labels
"""
if objective is not None:
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError(
"You can only use a binary classification objective to make predictions for a binary classification pipeline."
)
if self.threshold is None:
return self._component_graph.predict(X)
ypred_proba = self.predict_proba(X)
ypred_proba = ypred_proba.iloc[:, 1]
if objective is None:
return ypred_proba > self.threshold
return objective.decision_function(ypred_proba,
threshold=self.threshold,
X=X)
def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
y (pd.Series, ww.DataColumn): True labels of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
# Only converting X for the call to _score_all_objectives
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y.to_series())
y_predicted = self.predict(X, y)
y_predicted = _convert_woodwork_types_wrapper(y_predicted.to_series())
y_shifted = y.shift(-self.gap)
objectives = [
get_objective(o, return_instance=True) for o in objectives
]
y_shifted, y_predicted = drop_rows_with_nans(y_shifted, y_predicted)
return self._score_all_objectives(X,
y_shifted,
y_predicted,
y_pred_proba=None,
objectives=objectives)
def _predict(self, X, y, objective=None, pad=False):
features = self.compute_estimator_features(X, y)
features = _convert_woodwork_types_wrapper(features.to_dataframe())
features_no_nan, y_no_nan = drop_rows_with_nans(features, y)
if objective is not None:
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError(
f"Objective {objective.name} is not defined for time series binary classification."
)
if self.threshold is None:
predictions = self._estimator_predict(features_no_nan,
y_no_nan).to_series()
else:
proba = self._estimator_predict_proba(features_no_nan,
y_no_nan).to_dataframe()
proba = proba.iloc[:, 1]
if objective is None:
predictions = proba > self.threshold
else:
predictions = objective.decision_function(
proba, threshold=self.threshold, X=features_no_nan)
if pad:
predictions = pad_with_nans(
predictions, max(0, features.shape[0] - predictions.shape[0]))
return infer_feature_types(predictions)
def score(self, X, y, objectives):
"""Evaluate model performance on objectives
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, or np.ndarray): True labels of length [n_samples]
objectives (list): List of objectives to score
Returns:
dict: Ordered dictionary of objective scores
"""
y = infer_feature_types(y)
y = _convert_woodwork_types_wrapper(y.to_series())
objectives = [
get_objective(o, return_instance=True) for o in objectives
]
y = self._encode_targets(y)
y_predicted, y_predicted_proba = self._compute_predictions(
X, y, objectives)
if y_predicted is not None:
y_predicted = _convert_woodwork_types_wrapper(
y_predicted.to_series())
if y_predicted_proba is not None:
y_predicted_proba = _convert_woodwork_types_wrapper(
y_predicted_proba.to_dataframe())
return self._score_all_objectives(X, y, y_predicted, y_predicted_proba,
objectives)
def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series): True labels of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
X, y = self._convert_to_woodwork(X, y)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
y = _convert_woodwork_types_wrapper(y.to_series())
objectives = [
get_objective(o, return_instance=True) for o in objectives
]
y_encoded = self._encode_targets(y)
y_shifted = y_encoded.shift(-self.gap)
y_predicted, y_predicted_proba = self._compute_predictions(
X, y, objectives, time_series=True)
if y_predicted is not None:
y_predicted = _convert_woodwork_types_wrapper(
y_predicted.to_series())
if y_predicted_proba is not None:
y_predicted_proba = _convert_woodwork_types_wrapper(
y_predicted_proba.to_dataframe())
y_shifted, y_predicted, y_predicted_proba = drop_rows_with_nans(
y_shifted, y_predicted, y_predicted_proba)
return self._score_all_objectives(X,
y_shifted,
y_predicted,
y_pred_proba=y_predicted_proba,
objectives=objectives)
def test_get_objective_kwargs():
obj = get_objective("cost benefit matrix",
return_instance=True,
true_positive=0,
true_negative=0,
false_positive=0,
false_negative=0)
assert isinstance(obj, CostBenefitMatrix)
def create_objectives(objectives):
objective_instances = []
for objective in objectives:
try:
objective_instances.append(
get_objective(objective, return_instance=True))
except ObjectiveCreationError as e:
msg = f"Cannot pass {objective} as a string in pipeline.score. Instantiate first and then add it to the list of objectives."
raise ObjectiveCreationError(msg) from e
return objective_instances
def test_describe_pipeline_objective_ordered(X_y_binary, caplog):
X, y = X_y_binary
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', objective='AUC', max_iterations=2, n_jobs=1)
automl.search()
automl.describe_pipeline(0)
out = caplog.text
out_stripped = " ".join(out.split())
objectives = [get_objective(obj) for obj in automl.additional_objectives]
objectives_names = [obj.name for obj in objectives]
expected_objective_order = " ".join(objectives_names)
assert expected_objective_order in out_stripped
def __init__(self, problem_type, objective, n_unique=100):
"""Check if the target is invalid for the specified problem type.
Arguments:
problem_type (str or ProblemTypes): The specific problem type to data check for.
e.g. 'binary', 'multiclass', 'regression, 'time series regression'
objective (str or ObjectiveBase): Name or instance of the objective class.
n_unique (int): Number of unique target values to store when problem type is binary and target
incorrectly has more than 2 unique values. Non-negative integer. Defaults to 100. If None, stores all unique values.
"""
self.problem_type = handle_problem_types(problem_type)
self.objective = get_objective(objective)
if n_unique is not None and n_unique <= 0:
raise ValueError("`n_unique` must be a non-negative integer value.")
self.n_unique = n_unique
def get_default_primary_search_objective(problem_type):
"""Get the default primary search objective for a problem type.
Arguments:
problem_type (str or ProblemType): problem type of interest.
Returns:
ObjectiveBase: primary objective instance for the problem type.
"""
problem_type = handle_problem_types(problem_type)
objective_name = {'binary': 'Log Loss Binary',
'multiclass': 'Log Loss Multiclass',
'regression': 'R2',
'time series regression': 'R2',
'time series binary': 'Log Loss Binary',
'time series multiclass': 'Log Loss Multiclass'}[problem_type.value]
return get_objective(objective_name, return_instance=True)
def test_pipeline_thresholding_errors(
mock_binary_pred_proba, mock_binary_score, mock_binary_fit,
make_data_type, logistic_regression_binary_pipeline_class, X_y_binary):
X, y = X_y_binary
X = make_data_type('ww', X)
y = make_data_type('ww', pd.Series([f"String value {i}" for i in y]))
objective = get_objective("Log Loss Binary", return_instance=True)
pipeline = logistic_regression_binary_pipeline_class(
parameters={"Logistic Regression Classifier": {
"n_jobs": 1
}})
pipeline.fit(X, y)
pred_proba = pipeline.predict_proba(X, y).iloc[:, 1]
with pytest.raises(
ValueError,
match=
"Problem type must be binary and objective must be optimizable"):
pipeline.optimize_threshold(X, y, pred_proba, objective)
def score_pipelines(self, pipelines, X_holdout, y_holdout, objectives):
"""Score a list of pipelines on the given holdout data.
Arguments:
pipelines (list(PipelineBase)): List of pipelines to train.
X_holdout (ww.DataTable, pd.DataFrame): Holdout features.
y_holdout (ww.DataTable, pd.DataFrame): Holdout targets for scoring.
objectives (list(str), list(ObjectiveBase)): Objectives used for scoring.
Returns:
Dict[str, Dict[str, float]]: Dictionary keyed by pipeline name that maps to a dictionary of scores.
Note that the any pipelines that error out during scoring will not be included in the dictionary
but the exception and stacktrace will be displayed in the log.
"""
check_all_pipeline_names_unique(pipelines)
scores = {}
objectives = [get_objective(o, return_instance=True) for o in objectives]
computations = []
for pipeline in pipelines:
computations.append(self._engine.submit_scoring_job(self.automl_config, pipeline, X_holdout, y_holdout, objectives))
while computations:
computation = computations.pop(0)
if computation.done():
pipeline_name = computation.meta_data["pipeline_name"]
try:
scores[pipeline_name] = computation.get_result()
except Exception as e:
logger.error(f"Score error for {pipeline_name}: {str(e)}")
if isinstance(e, PipelineScoreError):
nan_scores = {objective: np.nan for objective in e.exceptions}
scores[pipeline_name] = {**nan_scores, **e.scored_successfully}
else:
# Traceback already included in the PipelineScoreError so we only
# need to include it for all other errors
tb = traceback.format_tb(sys.exc_info()[2])
logger.error("Traceback:")
logger.error("\n".join(tb))
scores[pipeline_name] = {objective.name: np.nan for objective in objectives}
else:
computations.append(computation)
return scores
def score(self, X, y, objectives):
"""Evaluate model performance on current and additional objectives
Arguments:
X (ww.DataTable, pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, or np.ndarray): True values of length [n_samples]
objectives (list): Non-empty list of objectives to score on
Returns:
dict: Ordered dictionary of objective scores
"""
objectives = [
get_objective(o, return_instance=True) for o in objectives
]
y_predicted = self.predict(X)
return self._score_all_objectives(X,
y,
y_predicted,
y_pred_proba=None,
objectives=objectives)
def test_ts_binary_pipeline_target_thresholding(
make_data_type, time_series_binary_classification_pipeline_class,
X_y_binary):
X, y = X_y_binary
X = make_data_type('ww', X)
y = make_data_type('ww', y)
objective = get_objective("F1", return_instance=True)
binary_pipeline = time_series_binary_classification_pipeline_class(
parameters={
"Logistic Regression Classifier": {
"n_jobs": 1
},
"pipeline": {
"gap": 0,
"max_delay": 0
}
})
binary_pipeline.fit(X, y)
assert binary_pipeline.threshold is None
pred_proba = binary_pipeline.predict_proba(X, y).iloc[:, 1]
binary_pipeline.optimize_threshold(X, y, pred_proba, objective)
assert binary_pipeline.threshold is not None
def _predict(self, X, objective=None):
"""Make predictions using selected features.
Arguments:
X (ww.DataTable, pd.DataFrame): Data of shape [n_samples, n_features]
objective (Object or string): The objective to use to make predictions
Returns:
ww.DataColumn: Estimated labels
"""
if objective is not None:
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError(
"You can only use a binary classification objective to make predictions for a binary classification pipeline."
)
if self.threshold is None:
return self._component_graph.predict(X)
ypred_proba = self.predict_proba(X).to_dataframe()
predictions = self._predict_with_objective(X, ypred_proba, objective)
return infer_feature_types(predictions)
def __init__(self,
X_train=None,
y_train=None,
problem_type=None,
objective='auto',
max_iterations=None,
max_time=None,
patience=None,
tolerance=None,
data_splitter=None,
allowed_pipelines=None,
allowed_model_families=None,
start_iteration_callback=None,
add_result_callback=None,
error_callback=None,
additional_objectives=None,
random_state=None,
random_seed=0,
n_jobs=-1,
tuner_class=None,
optimize_thresholds=False,
ensembling=False,
max_batches=None,
problem_configuration=None,
train_best_pipeline=True,
pipeline_parameters=None,
_pipelines_per_batch=5):
"""Automated pipeline search
Arguments:
X_train (pd.DataFrame, ww.DataTable): The input training data of shape [n_samples, n_features]. Required.
y_train (pd.Series, ww.DataColumn): The target training data of length [n_samples]. Required for supervised learning tasks.
problem_type (str or ProblemTypes): type of supervised learning problem. See evalml.problem_types.ProblemType.all_problem_types for a full list.
objective (str, ObjectiveBase): The objective to optimize for. Used to propose and rank pipelines, but not for optimizing each pipeline during fit-time.
When set to 'auto', chooses:
- LogLossBinary for binary classification problems,
- LogLossMulticlass for multiclass classification problems, and
- R2 for regression problems.
max_iterations (int): Maximum number of iterations to search. If max_iterations and
max_time is not set, then max_iterations will default to max_iterations of 5.
max_time (int, str): Maximum time to search for pipelines.
This will not start a new pipeline search after the duration
has elapsed. If it is an integer, then the time will be in seconds.
For strings, time can be specified as seconds, minutes, or hours.
patience (int): Number of iterations without improvement to stop search early. Must be positive.
If None, early stopping is disabled. Defaults to None.
tolerance (float): Minimum percentage difference to qualify as score improvement for early stopping.
Only applicable if patience is not None. Defaults to None.
allowed_pipelines (list(class)): A list of PipelineBase subclasses indicating the pipelines allowed in the search.
The default of None indicates all pipelines for this problem type are allowed. Setting this field will cause
allowed_model_families to be ignored.
allowed_model_families (list(str, ModelFamily)): The model families to search. The default of None searches over all
model families. Run evalml.pipelines.components.utils.allowed_model_families("binary") to see options. Change `binary`
to `multiclass` or `regression` depending on the problem type. Note that if allowed_pipelines is provided,
this parameter will be ignored.
data_splitter (sklearn.model_selection.BaseCrossValidator): Data splitting method to use. Defaults to StratifiedKFold.
tuner_class: The tuner class to use. Defaults to SKOptTuner.
start_iteration_callback (callable): Function called before each pipeline training iteration.
Callback function takes three positional parameters: The pipeline class, the pipeline parameters, and the AutoMLSearch object.
add_result_callback (callable): Function called after each pipeline training iteration.
Callback function takes three positional parameters: A dictionary containing the training results for the new pipeline, an untrained_pipeline containing the parameters used during training, and the AutoMLSearch object.
error_callback (callable): Function called when `search()` errors and raises an Exception.
Callback function takes three positional parameters: the Exception raised, the traceback, and the AutoMLSearch object.
Must also accepts kwargs, so AutoMLSearch is able to pass along other appropriate parameters by default.
Defaults to None, which will call `log_error_callback`.
additional_objectives (list): Custom set of objectives to score on.
Will override default objectives for problem type if not empty.
random_state (int): Deprecated - use random_seed instead.
random_seed (int): Seed for the random number generator. Defaults to 0.
n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines.
None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
ensembling (boolean): If True, runs ensembling in a separate batch after every allowed pipeline class has been iterated over.
If the number of unique pipelines to search over per batch is one, ensembling will not run. Defaults to False.
max_batches (int): The maximum number of batches of pipelines to search. Parameters max_time, and
max_iterations have precedence over stopping the search.
problem_configuration (dict, None): Additional parameters needed to configure the search. For example,
in time series problems, values should be passed in for the gap and max_delay variables.
train_best_pipeline (boolean): Whether or not to train the best pipeline before returning it. Defaults to True
_pipelines_per_batch (int): The number of pipelines to train for every batch after the first one.
The first batch will train a baseline pipline + one of each pipeline family allowed in the search.
"""
if X_train is None:
raise ValueError(
'Must specify training data as a 2d array using the X_train argument'
)
if y_train is None:
raise ValueError(
'Must specify training data target values as a 1d vector using the y_train argument'
)
try:
self.problem_type = handle_problem_types(problem_type)
except ValueError:
raise ValueError(
'choose one of (binary, multiclass, regression) as problem_type'
)
self.tuner_class = tuner_class or SKOptTuner
self.start_iteration_callback = start_iteration_callback
self.add_result_callback = add_result_callback
self.error_callback = error_callback or log_error_callback
self.data_splitter = data_splitter
self.optimize_thresholds = optimize_thresholds
self.ensembling = ensembling
if objective == 'auto':
objective = get_default_primary_search_objective(
self.problem_type.value)
objective = get_objective(objective, return_instance=False)
self.objective = self._validate_objective(objective)
if self.data_splitter is not None and not issubclass(
self.data_splitter.__class__, BaseCrossValidator):
raise ValueError("Not a valid data splitter")
if not objective.is_defined_for_problem_type(self.problem_type):
raise ValueError(
"Given objective {} is not compatible with a {} problem.".
format(self.objective.name, self.problem_type.value))
if additional_objectives is None:
additional_objectives = get_core_objectives(self.problem_type)
# if our main objective is part of default set of objectives for problem_type, remove it
existing_main_objective = next(
(obj for obj in additional_objectives
if obj.name == self.objective.name), None)
if existing_main_objective is not None:
additional_objectives.remove(existing_main_objective)
else:
additional_objectives = [
get_objective(o) for o in additional_objectives
]
additional_objectives = [
self._validate_objective(obj) for obj in additional_objectives
]
self.additional_objectives = additional_objectives
self.objective_name_to_class = {
o.name: o
for o in [self.objective] + self.additional_objectives
}
if not isinstance(max_time, (int, float, str, type(None))):
raise TypeError(
f"Parameter max_time must be a float, int, string or None. Received {type(max_time)} with value {str(max_time)}.."
)
if isinstance(max_time, (int, float)) and max_time < 0:
raise ValueError(
f"Parameter max_time must be None or non-negative. Received {max_time}."
)
if max_batches is not None and max_batches < 0:
raise ValueError(
f"Parameter max_batches must be None or non-negative. Received {max_batches}."
)
if max_iterations is not None and max_iterations < 0:
raise ValueError(
f"Parameter max_iterations must be None or non-negative. Received {max_iterations}."
)
self.max_time = convert_to_seconds(max_time) if isinstance(
max_time, str) else max_time
self.max_iterations = max_iterations
self.max_batches = max_batches
self._pipelines_per_batch = _pipelines_per_batch
if not self.max_iterations and not self.max_time and not self.max_batches:
self.max_batches = 1
logger.info("Using default limit of max_batches=1.\n")
if patience and (not isinstance(patience, int) or patience < 0):
raise ValueError(
"patience value must be a positive integer. Received {} instead"
.format(patience))
if tolerance and (tolerance > 1.0 or tolerance < 0.0):
raise ValueError(
"tolerance value must be a float between 0.0 and 1.0 inclusive. Received {} instead"
.format(tolerance))
self.patience = patience
self.tolerance = tolerance or 0.0
self._results = {
'pipeline_results': {},
'search_order': [],
'errors': []
}
self.random_seed = deprecate_arg("random_state", "random_seed",
random_state, random_seed)
self.n_jobs = n_jobs
self.plot = None
try:
self.plot = PipelineSearchPlots(self)
except ImportError:
logger.warning(
"Unable to import plotly; skipping pipeline search plotting\n")
self._data_check_results = None
self.allowed_pipelines = allowed_pipelines
self.allowed_model_families = allowed_model_families
self._automl_algorithm = None
self._start = 0.0
self._baseline_cv_scores = {}
self.show_batch_output = False
self._validate_problem_type()
self.problem_configuration = self._validate_problem_configuration(
problem_configuration)
self._train_best_pipeline = train_best_pipeline
self._best_pipeline = None
self._searched = False
self.X_train = infer_feature_types(X_train)
self.y_train = infer_feature_types(y_train)
default_data_splitter = make_data_splitter(
self.X_train,
self.y_train,
self.problem_type,
self.problem_configuration,
n_splits=3,
shuffle=True,
random_seed=self.random_seed)
self.data_splitter = self.data_splitter or default_data_splitter
self.pipeline_parameters = pipeline_parameters if pipeline_parameters is not None else {}
self.search_iteration_plot = None
self._interrupted = False
self._engine = SequentialEngine(
self.X_train,
self.y_train,
self,
should_continue_callback=self._should_continue,
pre_evaluation_callback=self._pre_evaluation_callback,
post_evaluation_callback=self._post_evaluation_callback)
if self.allowed_pipelines is None:
logger.info("Generating pipelines to search over...")
allowed_estimators = get_estimators(self.problem_type,
self.allowed_model_families)
logger.debug(
f"allowed_estimators set to {[estimator.name for estimator in allowed_estimators]}"
)
self.allowed_pipelines = [
make_pipeline(self.X_train,
self.y_train,
estimator,
self.problem_type,
custom_hyperparameters=self.pipeline_parameters)
for estimator in allowed_estimators
]
if self.allowed_pipelines == []:
raise ValueError("No allowed pipelines to search")
run_ensembling = self.ensembling
if run_ensembling and len(self.allowed_pipelines) == 1:
logger.warning(
"Ensembling is set to True, but the number of unique pipelines is one, so ensembling will not run."
)
run_ensembling = False
if run_ensembling and self.max_iterations is not None:
# Baseline + first batch + each pipeline iteration + 1
first_ensembling_iteration = (
1 + len(self.allowed_pipelines) +
len(self.allowed_pipelines) * self._pipelines_per_batch + 1)
if self.max_iterations < first_ensembling_iteration:
run_ensembling = False
logger.warning(
f"Ensembling is set to True, but max_iterations is too small, so ensembling will not run. Set max_iterations >= {first_ensembling_iteration} to run ensembling."
)
else:
logger.info(
f"Ensembling will run at the {first_ensembling_iteration} iteration and every {len(self.allowed_pipelines) * self._pipelines_per_batch} iterations after that."
)
if self.max_batches and self.max_iterations is None:
self.show_batch_output = True
if run_ensembling:
ensemble_nth_batch = len(self.allowed_pipelines) + 1
num_ensemble_batches = (self.max_batches -
1) // ensemble_nth_batch
if num_ensemble_batches == 0:
logger.warning(
f"Ensembling is set to True, but max_batches is too small, so ensembling will not run. Set max_batches >= {ensemble_nth_batch + 1} to run ensembling."
)
else:
logger.info(
f"Ensembling will run every {ensemble_nth_batch} batches."
)
self.max_iterations = (
1 + len(self.allowed_pipelines) +
self._pipelines_per_batch *
(self.max_batches - 1 - num_ensemble_batches) +
num_ensemble_batches)
else:
self.max_iterations = 1 + len(
self.allowed_pipelines) + (self._pipelines_per_batch *
(self.max_batches - 1))
self.allowed_model_families = list(
set([p.model_family for p in (self.allowed_pipelines)]))
logger.debug(
f"allowed_pipelines set to {[pipeline.name for pipeline in self.allowed_pipelines]}"
)
logger.debug(
f"allowed_model_families set to {self.allowed_model_families}")
if len(self.problem_configuration):
pipeline_params = {
**{
'pipeline': self.problem_configuration
},
**self.pipeline_parameters
}
else:
pipeline_params = self.pipeline_parameters
self._automl_algorithm = IterativeAlgorithm(
max_iterations=self.max_iterations,
allowed_pipelines=self.allowed_pipelines,
tuner_class=self.tuner_class,
random_seed=self.random_seed,
n_jobs=self.n_jobs,
number_features=self.X_train.shape[1],
pipelines_per_batch=self._pipelines_per_batch,
ensembling=run_ensembling,
pipeline_params=pipeline_params)
def test_get_objective_does_not_work_for_none_type():
with pytest.raises(TypeError,
match="Objective parameter cannot be NoneType"):
get_objective(None)
