import pandas as pd
from sklearn.model_selection import KFold
from evalml.objectives import get_objective
from evalml.preprocessing.data_splitters import (
BalancedClassificationDataCVSplit,
BalancedClassificationDataTVSplit,
TimeSeriesSplit,
TrainingValidationSplit
)
from evalml.problem_types import (
ProblemTypes,
handle_problem_types,
is_binary,
is_time_series
)
from evalml.utils import deprecate_arg
_LARGE_DATA_ROW_THRESHOLD = int(1e5)
_LARGE_DATA_PERCENT_VALIDATION = 0.75
[docs]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)
[docs]def make_data_splitter(X, y, problem_type, problem_configuration=None, n_splits=3, shuffle=True,
random_state=None, random_seed=0):
"""Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.
Arguments:
X (ww.DataTable, pd.DataFrame): The input training data of shape [n_samples, n_features].
y (ww.DataColumn, pd.Series): The target training data of length [n_samples].
problem_type (ProblemType): The type of machine learning problem.
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. Defaults to None.
n_splits (int, None): The number of CV splits, if applicable. Defaults to 3.
shuffle (bool): Whether or not to shuffle the data before splitting, if applicable. Defaults to True.
random_state (None, int): Deprecated - use random_seed instead.
random_seed (int): Seed for the random number generator. Defaults to 0.
Returns:
sklearn.model_selection.BaseCrossValidator: Data splitting method.
"""
random_seed = deprecate_arg("random_state", "random_seed", random_state, random_seed)
problem_type = handle_problem_types(problem_type)
if is_time_series(problem_type):
if not problem_configuration:
raise ValueError("problem_configuration is required for time series problem types")
return TimeSeriesSplit(n_splits=n_splits, gap=problem_configuration.get('gap'),
max_delay=problem_configuration.get('max_delay'))
if X.shape[0] > _LARGE_DATA_ROW_THRESHOLD:
if problem_type == ProblemTypes.REGRESSION:
return TrainingValidationSplit(test_size=_LARGE_DATA_PERCENT_VALIDATION, shuffle=shuffle)
elif problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
return BalancedClassificationDataTVSplit(test_size=_LARGE_DATA_PERCENT_VALIDATION, shuffle=shuffle, random_seed=random_seed)
if problem_type == ProblemTypes.REGRESSION:
return KFold(n_splits=n_splits, random_state=random_seed, shuffle=shuffle)
elif problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
return BalancedClassificationDataCVSplit(n_splits=n_splits, random_seed=random_seed, shuffle=shuffle)
def tune_binary_threshold(pipeline, objective, problem_type, X_threshold_tuning, y_threshold_tuning):
"""Tunes the threshold of a binary pipeline to the X and y thresholding data
Arguments:
pipeline (Pipeline): Pipeline instance to threshold
X_threshold_tuning (ww.DataTable): Features to tune pipeline to
y_threshold_tuning (ww.DataColumn): Target data to tune pipeline to
"""
if is_binary(problem_type) and objective.is_defined_for_problem_type(problem_type) and objective.can_optimize_threshold:
pipeline.threshold = 0.5
if X_threshold_tuning:
y_predict_proba = pipeline.predict_proba(X_threshold_tuning)
y_predict_proba = y_predict_proba.iloc[:, 1]
pipeline.threshold = objective.optimize_threshold(y_predict_proba, y_threshold_tuning, X=X_threshold_tuning)
def check_all_pipeline_names_unique(pipelines):
"""Checks whether all the pipeline names are unique.
Arguments:
pipelines (list(PipelineBase)): List of pipelines to check if all names are unique.
Returns:
None
Raises:
ValueError: if any pipeline names are duplicated.
"""
name_count = pd.Series([p.name for p in pipelines]).value_counts()
duplicate_names = name_count[name_count > 1].index.tolist()
if duplicate_names:
plural, tense = ("s", "were") if len(duplicate_names) > 1 else ("", "was")
duplicates = ", ".join([f"'{name}'" for name in sorted(duplicate_names)])
raise ValueError(f"All pipeline names must be unique. The name{plural} {duplicates} {tense} repeated.")