import numpy as np
from ..base import BaseEstimator, ClassifierMixin
from .validation import _check_sample_weight, _num_samples, check_array
from .validation import check_is_fitted
class ArraySlicingWrapper:
"""
Parameters
----------
array
"""
def __init__(self, array):
self.array = array
def __getitem__(self, aslice):
return MockDataFrame(self.array[aslice])
class MockDataFrame:
"""
Parameters
----------
array
"""
# have shape and length but don't support indexing.
def __init__(self, array):
self.array = array
self.values = array
self.shape = array.shape
self.ndim = array.ndim
# ugly hack to make iloc work.
self.iloc = ArraySlicingWrapper(array)
def __len__(self):
return len(self.array)
def __array__(self, dtype=None):
# Pandas data frames also are array-like: we want to make sure that
# input validation in cross-validation does not try to call that
# method.
return self.array
def __eq__(self, other):
return MockDataFrame(self.array == other.array)
def __ne__(self, other):
return not self == other
def take(self, indices, axis=0):
return MockDataFrame(self.array.take(indices, axis=axis))
class CheckingClassifier(ClassifierMixin, BaseEstimator):
"""Dummy classifier to test pipelining and meta-estimators.
Checks some property of `X` and `y`in fit / predict.
This allows testing whether pipelines / cross-validation or metaestimators
changed the input.
Can also be used to check if `fit_params` are passed correctly, and
to force a certain score to be returned.
Parameters
----------
check_y, check_X : callable, default=None
The callable used to validate `X` and `y`. These callable should return
a bool where `False` will trigger an `AssertionError`.
check_y_params, check_X_params : dict, default=None
The optional parameters to pass to `check_X` and `check_y`.
methods_to_check : "all" or list of str, default="all"
The methods in which the checks should be applied. By default,
all checks will be done on all methods (`fit`, `predict`,
`predict_proba`, `decision_function` and `score`).
foo_param : int, default=0
A `foo` param. When `foo > 1`, the output of :meth:`score` will be 1
otherwise it is 0.
expected_sample_weight : bool, default=False
Whether to check if a valid `sample_weight` was passed to `fit`.
expected_fit_params : list of str, default=None
A list of the expected parameters given when calling `fit`.
Attributes
----------
classes_ : int
The classes seen during `fit`.
n_features_in_ : int
The number of features seen during `fit`.
Examples
--------
>>> from sklearn.utils._mocking import CheckingClassifier
This helper allow to assert to specificities regarding `X` or `y`. In this
case we expect `check_X` or `check_y` to return a boolean.
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> clf = CheckingClassifier(check_X=lambda x: x.shape == (150, 4))
>>> clf.fit(X, y)
CheckingClassifier(...)
We can also provide a check which might raise an error. In this case, we
expect `check_X` to return `X` and `check_y` to return `y`.
>>> from sklearn.utils import check_array
>>> clf = CheckingClassifier(check_X=check_array)
>>> clf.fit(X, y)
CheckingClassifier(...)
"""
def __init__(
self,
*,
check_y=None,
check_y_params=None,
check_X=None,
check_X_params=None,
methods_to_check="all",
foo_param=0,
expected_sample_weight=None,
expected_fit_params=None,
):
self.check_y = check_y
self.check_y_params = check_y_params
self.check_X = check_X
self.check_X_params = check_X_params
self.methods_to_check = methods_to_check
self.foo_param = foo_param
self.expected_sample_weight = expected_sample_weight
self.expected_fit_params = expected_fit_params
def _check_X_y(self, X, y=None, should_be_fitted=True):
"""Validate X and y and make extra check.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The data set.
y : array-like of shape (n_samples), default=None
The corresponding target, by default None.
should_be_fitted : bool, default=True
Whether or not the classifier should be already fitted.
By default True.
Returns
-------
X, y
"""
if should_be_fitted:
check_is_fitted(self)
if self.check_X is not None:
params = {} if self.check_X_params is None else self.check_X_params
checked_X = self.check_X(X, **params)
if isinstance(checked_X, (bool, np.bool_)):
assert checked_X
else:
X = checked_X
if y is not None and self.check_y is not None:
params = {} if self.check_y_params is None else self.check_y_params
checked_y = self.check_y(y, **params)
if isinstance(checked_y, (bool, np.bool_)):
assert checked_y
else:
y = checked_y
return X, y
def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples, n_outputs) or (n_samples,), \
default=None
Target relative to X for classification or regression;
None for unsupervised learning.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict of string -> object
Parameters passed to the ``fit`` method of the estimator
Returns
-------
self
"""
assert _num_samples(X) == _num_samples(y)
if self.methods_to_check == "all" or "fit" in self.methods_to_check:
X, y = self._check_X_y(X, y, should_be_fitted=False)
self.n_features_in_ = np.shape(X)[1]
self.classes_ = np.unique(check_array(y, ensure_2d=False, allow_nd=True))
if self.expected_fit_params:
missing = set(self.expected_fit_params) - set(fit_params)
if missing:
raise AssertionError(
f"Expected fit parameter(s) {list(missing)} not seen."
)
for key, value in fit_params.items():
if _num_samples(value) != _num_samples(X):
raise AssertionError(
f"Fit parameter {key} has length {_num_samples(value)}"
f"; expected {_num_samples(X)}."
)
if self.expected_sample_weight:
if sample_weight is None:
raise AssertionError("Expected sample_weight to be passed")
_check_sample_weight(sample_weight, X)
return self
def predict(self, X):
"""Predict the first class seen in `classes_`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
preds : ndarray of shape (n_samples,)
Predictions of the first class seens in `classes_`.
"""
if self.methods_to_check == "all" or "predict" in self.methods_to_check:
X, y = self._check_X_y(X)
return self.classes_[np.zeros(_num_samples(X), dtype=int)]
def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
proba : ndarray of shape (n_samples, n_classes)
The probabilities for each sample and class.
"""
if self.methods_to_check == "all" or "predict_proba" in self.methods_to_check:
X, y = self._check_X_y(X)
proba = np.zeros((_num_samples(X), len(self.classes_)))
proba[:, 0] = 1
return proba
def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples, n_classes)
Confidence score.
"""
if (
self.methods_to_check == "all"
or "decision_function" in self.methods_to_check
):
X, y = self._check_X_y(X)
if len(self.classes_) == 2:
# for binary classifier, the confidence score is related to
# classes_[1] and therefore should be null.
return np.zeros(_num_samples(X))
else:
decision = np.zeros((_num_samples(X), len(self.classes_)))
decision[:, 0] = 1
return decision
def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_samples, n_output) or (n_samples,)
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
Either 0 or 1 depending of `foo_param` (i.e. `foo_param > 1 =>
score=1` otherwise `score=0`).
"""
if self.methods_to_check == "all" or "score" in self.methods_to_check:
self._check_X_y(X, Y)
if self.foo_param > 1:
score = 1.0
else:
score = 0.0
return score
def _more_tags(self):
return {"_skip_test": True, "X_types": ["1dlabel"]}
class NoSampleWeightWrapper(BaseEstimator):
"""Wrap estimator which will not expose `sample_weight`.
Parameters
----------
est : estimator, default=None
The estimator to wrap.
"""
def __init__(self, est=None):
self.est = est
def fit(self, X, y):
return self.est.fit(X, y)
def predict(self, X):
return self.est.predict(X)
def predict_proba(self, X):
return self.est.predict_proba(X)
def _more_tags(self):
return {"_skip_test": True}