# from evalml.pipelines import get_pipelines_by_model_type
from sklearn.model_selection import StratifiedKFold
from .auto_base import AutoBase
from evalml.objectives import get_objective
from evalml.problem_types import ProblemTypes
[docs]class AutoClassifier(AutoBase):
"""Automatic pipeline search for classification problems"""
[docs] def __init__(self,
objective=None,
multiclass=False,
max_pipelines=None,
max_time=None,
model_types=None,
cv=None,
tuner=None,
detect_label_leakage=True,
start_iteration_callback=None,
add_result_callback=None,
additional_objectives=None,
random_state=0,
verbose=True):
"""Automated classifier pipeline search
Arguments:
objective (Object): the objective to optimize
multiclass (bool): If True, expecting multiclass data. By default: False.
max_pipelines (int): Maximum number of pipelines to search. If max_pipelines and
max_time is not set, then max_pipelines will default to max_pipelines 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.
model_types (list): The model types to search. By default searches over all
model_types. Run evalml.list_model_types("classification") to see options.
cv: cross validation method to use. By default StratifiedKFold
tuner: the tuner class to use. Defaults to scikit-optimize tuner
detect_label_leakage (bool): If True, check input features for label leakage and
warn if found. Defaults to true.
start_iteration_callback (callable): function called before each pipeline training iteration.
Passed two parameters: pipeline_class, parameters.
add_result_callback (callable): function called after each pipeline training iteration.
Passed two parameters: results, trained_pipeline.
additional_objectives (list): Custom set of objectives to score on.
Will override default objectives for problem type if not empty.
random_state (int): the random_state
verbose (boolean): If True, turn verbosity on. Defaults to True
"""
if cv is None:
cv = StratifiedKFold(n_splits=3, random_state=random_state)
# set default objective if none provided
if objective is None and not multiclass:
objective = "precision"
problem_type = ProblemTypes.BINARY
elif objective is None and multiclass:
objective = "precision_micro"
problem_type = ProblemTypes.MULTICLASS
else:
problem_type = self.set_problem_type(objective, multiclass)
super().__init__(
tuner=tuner,
objective=objective,
cv=cv,
max_pipelines=max_pipelines,
max_time=max_time,
model_types=model_types,
problem_type=problem_type,
detect_label_leakage=detect_label_leakage,
start_iteration_callback=start_iteration_callback,
add_result_callback=add_result_callback,
additional_objectives=additional_objectives,
random_state=random_state,
verbose=verbose
)
[docs] def set_problem_type(self, objective, multiclass):
"""
If there is an objective either:
a. Set problem_type to MULTICLASS if objective is only multiclass and multiclass is false
b. Set problem_type to MUTLICLASS if multiclass is true
c. Default to BINARY
"""
problem_type = ProblemTypes.BINARY
# if exclusively multiclass: infer
if [ProblemTypes.MULTICLASS] == get_objective(objective).problem_types:
problem_type = ProblemTypes.MULTICLASS
elif multiclass:
problem_type = ProblemTypes.MULTICLASS
return problem_type