"""This module implements commons for machine learning."""
# Carl is free software; you can redistribute it and/or modify it
# under the terms of the Revised BSD License; see LICENSE file for
# more details.
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.base import clone
from sklearn.model_selection import check_cv as sklearn_check_cv
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import check_array
from sklearn.utils import check_X_y
def as_classifier(regressor):
"""Wrap a Scikit-Learn regressor into a binary classifier.
This function can be used to solve a binary classification problem as a
regression problem, where output labels {0,1} are treated as real values.
The wrapped regressor exhibits the classifier API, with the corresponding
`predict`, `predict_proba` and `score` methods.
Parameters
----------
* `regressor` [`RegressorMixin`]:
The regressor object.
Returns
-------
* `clf` [`ClassifierMixin`]:
The wrapped regressor, but with a classifier API.
"""
class Wrapper(BaseEstimator, ClassifierMixin):
def __init__(self, base_estimator):
self.base_estimator = base_estimator
def fit(self, X, y, **kwargs):
# Check inputs
X, y = check_X_y(X, y)
# Convert y
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y).astype(np.float)
if len(label_encoder.classes_) != 2:
raise ValueError
self.classes_ = label_encoder.classes_
# Fit regressor
self.regressor_ = clone(self.base_estimator).fit(X, y, **kwargs)
return self
def predict(self, X):
return np.where(self.predict_proba(X)[:, 1] >= 0.5,
self.classes_[1],
self.classes_[0])
def predict_proba(self, X):
X = check_array(X)
df = self.regressor_.predict(X)
df = np.clip(df, 0., 1.)
probas = np.zeros((len(X), 2))
probas[:, 0] = 1. - df
probas[:, 1] = df
return probas
def score(self, X, y):
return self.regressor_.score(X, y)
return Wrapper(regressor)
def check_cv(cv=3, X=None, y=None, classifier=False):
"""Input checker utility for building a cross-validator.
Parameters
----------
* `cv` [integer, cross-validation generator or an iterable, default=`3`]:
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if classifier is True and `y` is either
binary or multiclass, `StratifiedKFold` used. In all other
cases, `KFold` is used.
* `y` [array-like, optional]:
The target variable for supervised learning problems.
* `classifier` [boolean, default=`False`]:
Whether the task is a classification task, in which case
stratified `KFold` will be used.
Returns
-------
* `checked_cv` [a cross-validator instance]:
The return value is a cross-validator which generates the train/test
splits via the `split` method.
Note
----
This method is backported from scikit-learn 0.18.
"""
return sklearn_check_cv(cv, y=y, classifier=classifier)
