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