from collections import namedtuple
import pandas as pd
from sklearn.model_selection import KFold, StratifiedKFold
from evalml.objectives import get_objective
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.
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_seed=0):
"""Given the training data and ML problem parameters, compute a data splitting method to use during AutoML search.
Arguments:
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.
"""
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)
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.
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)
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.")
AutoMLConfig = namedtuple("AutoMLConfig", ["data_splitter", "problem_type",
"objective", "additional_objectives", "optimize_thresholds",
"error_callback", "random_seed",
"X_schema", "y_schema"])
def get_best_sampler_for_data(X, y, sampler_method, sampler_balanced_ratio):
"""Returns the name of the sampler component to use for AutoMLSearch.
Arguments:
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")
cat_cols = X.ww.select('Categorical').columns
# Use different samplers depending on the number of categorical columns
if len(cat_cols) == X.shape[1]:
return 'SMOTEN Oversampler'
elif not len(cat_cols):
return 'SMOTE Oversampler'
else:
return 'SMOTENC Oversampler'
except ImportError:
return 'Undersampler'