"""Utilities useful in AutoML."""
from collections import namedtuple
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold
from evalml.objectives import get_objective
from evalml.pipelines import (
BinaryClassificationPipeline,
MulticlassClassificationPipeline,
RegressionPipeline,
TimeSeriesBinaryClassificationPipeline,
TimeSeriesMulticlassClassificationPipeline,
TimeSeriesRegressionPipeline,
)
from evalml.preprocessing.data_splitters import (
TimeSeriesSplit,
TrainingValidationSplit,
)
from evalml.problem_types import (
ProblemTypes,
handle_problem_types,
is_binary,
is_time_series,
)
from evalml.utils import import_or_raise
_LARGE_DATA_ROW_THRESHOLD = int(1e5)
_SAMPLER_THRESHOLD = 20000
_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.
Args:
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_seed=0,
):
"""Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.
Args:
X (pd.DataFrame): The input training data of shape [n_samples, n_features].
y (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 date_index, 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_seed (int): Seed for the random number generator. Defaults to 0.
Returns:
sklearn.model_selection.BaseCrossValidator: Data splitting method.
Raises:
ValueError: If problem_configuration is not given for a time-series problem.
"""
random_seed = 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"),
date_index=problem_configuration.get("date_index"),
)
if X.shape[0] > _LARGE_DATA_ROW_THRESHOLD:
return TrainingValidationSplit(
test_size=_LARGE_DATA_PERCENT_VALIDATION, shuffle=shuffle
)
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 StratifiedKFold(
n_splits=n_splits, random_state=random_seed, shuffle=shuffle
)
[docs]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.
Args:
pipeline (Pipeline): Pipeline instance to threshold.
objective (ObjectiveBase): The objective we want to tune with. If not tuneable and best_pipeline is True, will use F1.
problem_type (ProblemType): The problem type of the pipeline.
X_threshold_tuning (pd.DataFrame): Features to tune pipeline to.
y_threshold_tuning (pd.Series): 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 is not None:
y_predict_proba = pipeline.predict_proba(X_threshold_tuning)
y_predict_proba = y_predict_proba.iloc[:, 1]
pipeline.optimize_threshold(
X_threshold_tuning, y_threshold_tuning, y_predict_proba, objective
)
[docs]def check_all_pipeline_names_unique(pipelines):
"""Checks whether all the pipeline names are unique.
Args:
pipelines (list[PipelineBase]): List of pipelines to check if all names are unique.
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."
)
AutoMLConfig = namedtuple(
"AutoMLConfig",
[
"data_splitter",
"problem_type",
"objective",
"additional_objectives",
"alternate_thresholding_objective",
"optimize_thresholds",
"error_callback",
"random_seed",
"X_schema",
"y_schema",
],
)
[docs]def get_best_sampler_for_data(X, y, sampler_method, sampler_balanced_ratio):
"""Returns the name of the sampler component to use for AutoMLSearch.
Args:
X (pd.DataFrame): The input feature data
y (pd.Series): The input target data
sampler_method (str): The sampler_type argument passed to AutoMLSearch
sampler_balanced_ratio (float): The ratio of min:majority targets that we would consider balanced,
or should balance the classes to.
Returns:
str, None: The string name of the sampling component to use, or None if no sampler is necessary
"""
# we check for the class balances
counts = y.value_counts()
minority_class = min(counts)
class_ratios = minority_class / counts
# if all class ratios are larger than the ratio provided, we don't need to sample
if all(class_ratios >= sampler_balanced_ratio):
return None
# We set a threshold to use the Undersampler in order to avoid long runtimes
elif len(y) >= _SAMPLER_THRESHOLD and sampler_method != "Oversampler":
return "Undersampler"
else:
try:
import_or_raise(
"imblearn.over_sampling", error_msg="imbalanced-learn is not installed"
)
return "Oversampler"
except ImportError:
return "Undersampler"
[docs]def get_pipelines_from_component_graphs(
component_graphs_dict, problem_type, parameters=None, random_seed=0
):
"""Returns created pipelines from passed component graphs based on the specified problem type.
Args:
component_graphs_dict (dict): The dict of component graphs.
problem_type (str or ProblemType): The problem type for which pipelines will be created.
parameters (dict): Pipeline-level parameters that should be passed to the proposed pipelines. Defaults to None.
random_seed (int): Random seed. Defaults to 0.
Returns:
list: List of pipelines made from the passed component graphs.
"""
pipeline_class = {
ProblemTypes.BINARY: BinaryClassificationPipeline,
ProblemTypes.MULTICLASS: MulticlassClassificationPipeline,
ProblemTypes.REGRESSION: RegressionPipeline,
ProblemTypes.TIME_SERIES_BINARY: TimeSeriesBinaryClassificationPipeline,
ProblemTypes.TIME_SERIES_MULTICLASS: TimeSeriesMulticlassClassificationPipeline,
ProblemTypes.TIME_SERIES_REGRESSION: TimeSeriesRegressionPipeline,
}[handle_problem_types(problem_type)]
created_pipelines = []
for graph_name, component_graph in component_graphs_dict.items():
created_pipelines.append(
pipeline_class(
component_graph=component_graph,
parameters=parameters,
custom_name=graph_name,
random_seed=random_seed,
)
)
return created_pipelines