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1.3.2
sklearn/dummy.py
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Loïc Estève
MNT Adjust code after NEP 51 numpy scalar formatting changes (#27042)
20 сен 2023, 15:40
20 сен 2023, 15:40
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# Author: Mathieu Blondel <mathieu@mblondel.org> # Arnaud Joly <a.joly@ulg.ac.be> # Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk> # License: BSD 3 clause import warnings from numbers import Integral, Real import numpy as np import scipy.sparse as sp from .base import ( BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin, _fit_context, ) from .utils import check_random_state from .utils._param_validation import Interval, StrOptions from .utils.multiclass import class_distribution from .utils.random import _random_choice_csc from .utils.stats import _weighted_percentile from .utils.validation import ( _check_sample_weight, _num_samples, check_array, check_consistent_length, check_is_fitted, ) class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator): """DummyClassifier makes predictions that ignore the input features. This classifier serves as a simple baseline to compare against other more complex classifiers. The specific behavior of the baseline is selected with the `strategy` parameter. All strategies make predictions that ignore the input feature values passed as the `X` argument to `fit` and `predict`. The predictions, however, typically depend on values observed in the `y` parameter passed to `fit`. Note that the "stratified" and "uniform" strategies lead to non-deterministic predictions that can be rendered deterministic by setting the `random_state` parameter if needed. The other strategies are naturally deterministic and, once fit, always return the same constant prediction for any value of `X`. Read more in the :ref:`User Guide <dummy_estimators>`. .. versionadded:: 0.13 Parameters ---------- strategy : {"most_frequent", "prior", "stratified", "uniform", \ "constant"}, default="prior" Strategy to use to generate predictions. * "most_frequent": the `predict` method always returns the most frequent class label in the observed `y` argument passed to `fit`. The `predict_proba` method returns the matching one-hot encoded vector. * "prior": the `predict` method always returns the most frequent class label in the observed `y` argument passed to `fit` (like "most_frequent"). ``predict_proba`` always returns the empirical class distribution of `y` also known as the empirical class prior distribution. * "stratified": the `predict_proba` method randomly samples one-hot vectors from a multinomial distribution parametrized by the empirical class prior probabilities. The `predict` method returns the class label which got probability one in the one-hot vector of `predict_proba`. Each sampled row of both methods is therefore independent and identically distributed. * "uniform": generates predictions uniformly at random from the list of unique classes observed in `y`, i.e. each class has equal probability. * "constant": always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class. .. versionchanged:: 0.24 The default value of `strategy` has changed to "prior" in version 0.24. random_state : int, RandomState instance or None, default=None Controls the randomness to generate the predictions when ``strategy='stratified'`` or ``strategy='uniform'``. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. constant : int or str or array-like of shape (n_outputs,), default=None The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. Attributes ---------- classes_ : ndarray of shape (n_classes,) or list of such arrays Unique class labels observed in `y`. For multi-output classification problems, this attribute is a list of arrays as each output has an independent set of possible classes. n_classes_ : int or list of int Number of label for each output. class_prior_ : ndarray of shape (n_classes,) or list of such arrays Frequency of each class observed in `y`. For multioutput classification problems, this is computed independently for each output. n_outputs_ : int Number of outputs. sparse_output_ : bool True if the array returned from predict is to be in sparse CSC format. Is automatically set to True if the input `y` is passed in sparse format. See Also -------- DummyRegressor : Regressor that makes predictions using simple rules. Examples -------- >>> import numpy as np >>> from sklearn.dummy import DummyClassifier >>> X = np.array([-1, 1, 1, 1]) >>> y = np.array([0, 1, 1, 1]) >>> dummy_clf = DummyClassifier(strategy="most_frequent") >>> dummy_clf.fit(X, y) DummyClassifier(strategy='most_frequent') >>> dummy_clf.predict(X) array([1, 1, 1, 1]) >>> dummy_clf.score(X, y) 0.75 """ _parameter_constraints: dict = { "strategy": [ StrOptions({"most_frequent", "prior", "stratified", "uniform", "constant"}) ], "random_state": ["random_state"], "constant": [Integral, str, "array-like", None], } def __init__(self, *, strategy="prior", random_state=None, constant=None): self.strategy = strategy self.random_state = random_state self.constant = constant @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit the baseline classifier. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Returns the instance itself. """ self._strategy = self.strategy if self._strategy == "uniform" and sp.issparse(y): y = y.toarray() warnings.warn( ( "A local copy of the target data has been converted " "to a numpy array. Predicting on sparse target data " "with the uniform strategy would not save memory " "and would be slower." ), UserWarning, ) self.sparse_output_ = sp.issparse(y) if not self.sparse_output_: y = np.asarray(y) y = np.atleast_1d(y) if y.ndim == 1: y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] check_consistent_length(X, y) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) if self._strategy == "constant": if self.constant is None: raise ValueError( "Constant target value has to be specified " "when the constant strategy is used." ) else: constant = np.reshape(np.atleast_1d(self.constant), (-1, 1)) if constant.shape[0] != self.n_outputs_: raise ValueError( "Constant target value should have shape (%d, 1)." % self.n_outputs_ ) (self.classes_, self.n_classes_, self.class_prior_) = class_distribution( y, sample_weight ) if self._strategy == "constant": for k in range(self.n_outputs_): if not any(constant[k][0] == c for c in self.classes_[k]): # Checking in case of constant strategy if the constant # provided by the user is in y. err_msg = ( "The constant target value must be present in " "the training data. You provided constant={}. " "Possible values are: {}.".format( self.constant, self.classes_[k].tolist() ) ) raise ValueError(err_msg) if self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] self.class_prior_ = self.class_prior_[0] return self def predict(self, X): """Perform classification on test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) Test data. Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) Predicted target values for X. """ check_is_fitted(self) # numpy random_state expects Python int and not long as size argument # under Windows n_samples = _num_samples(X) rs = check_random_state(self.random_state) n_classes_ = self.n_classes_ classes_ = self.classes_ class_prior_ = self.class_prior_ constant = self.constant if self.n_outputs_ == 1: # Get same type even for self.n_outputs_ == 1 n_classes_ = [n_classes_] classes_ = [classes_] class_prior_ = [class_prior_] constant = [constant] # Compute probability only once if self._strategy == "stratified": proba = self.predict_proba(X) if self.n_outputs_ == 1: proba = [proba] if self.sparse_output_: class_prob = None if self._strategy in ("most_frequent", "prior"): classes_ = [np.array([cp.argmax()]) for cp in class_prior_] elif self._strategy == "stratified": class_prob = class_prior_ elif self._strategy == "uniform": raise ValueError( "Sparse target prediction is not " "supported with the uniform strategy" ) elif self._strategy == "constant": classes_ = [np.array([c]) for c in constant] y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state) else: if self._strategy in ("most_frequent", "prior"): y = np.tile( [ classes_[k][class_prior_[k].argmax()] for k in range(self.n_outputs_) ], [n_samples, 1], ) elif self._strategy == "stratified": y = np.vstack( [ classes_[k][proba[k].argmax(axis=1)] for k in range(self.n_outputs_) ] ).T elif self._strategy == "uniform": ret = [ classes_[k][rs.randint(n_classes_[k], size=n_samples)] for k in range(self.n_outputs_) ] y = np.vstack(ret).T elif self._strategy == "constant": y = np.tile(self.constant, (n_samples, 1)) if self.n_outputs_ == 1: y = np.ravel(y) return y def predict_proba(self, X): """ Return probability estimates for the test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) Test data. Returns ------- P : ndarray of shape (n_samples, n_classes) or list of such arrays Returns the probability of the sample for each class in the model, where classes are ordered arithmetically, for each output. """ check_is_fitted(self) # numpy random_state expects Python int and not long as size argument # under Windows n_samples = _num_samples(X) rs = check_random_state(self.random_state) n_classes_ = self.n_classes_ classes_ = self.classes_ class_prior_ = self.class_prior_ constant = self.constant if self.n_outputs_ == 1: # Get same type even for self.n_outputs_ == 1 n_classes_ = [n_classes_] classes_ = [classes_] class_prior_ = [class_prior_] constant = [constant] P = [] for k in range(self.n_outputs_): if self._strategy == "most_frequent": ind = class_prior_[k].argmax() out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64) out[:, ind] = 1.0 elif self._strategy == "prior": out = np.ones((n_samples, 1)) * class_prior_[k] elif self._strategy == "stratified": out = rs.multinomial(1, class_prior_[k], size=n_samples) out = out.astype(np.float64) elif self._strategy == "uniform": out = np.ones((n_samples, n_classes_[k]), dtype=np.float64) out /= n_classes_[k] elif self._strategy == "constant": ind = np.where(classes_[k] == constant[k]) out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64) out[:, ind] = 1.0 P.append(out) if self.n_outputs_ == 1: P = P[0] return P def predict_log_proba(self, X): """ Return log probability estimates for the test vectors X. Parameters ---------- X : {array-like, object with finite length or shape} Training data. Returns ------- P : ndarray of shape (n_samples, n_classes) or list of such arrays Returns the log probability of the sample for each class in the model, where classes are ordered arithmetically for each output. """ proba = self.predict_proba(X) if self.n_outputs_ == 1: return np.log(proba) else: return [np.log(p) for p in proba] def _more_tags(self): return { "poor_score": True, "no_validation": True, "_xfail_checks": { "check_methods_subset_invariance": "fails for the predict method", "check_methods_sample_order_invariance": "fails for the predict method", }, } def score(self, X, y, sample_weight=None): """Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : None or array-like of shape (n_samples, n_features) Test samples. Passing None as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float Mean accuracy of self.predict(X) w.r.t. y. """ if X is None: X = np.zeros(shape=(len(y), 1)) return super().score(X, y, sample_weight) class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator): """Regressor that makes predictions using simple rules. This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems. Read more in the :ref:`User Guide <dummy_estimators>`. .. versionadded:: 0.13 Parameters ---------- strategy : {"mean", "median", "quantile", "constant"}, default="mean" Strategy to use to generate predictions. * "mean": always predicts the mean of the training set * "median": always predicts the median of the training set * "quantile": always predicts a specified quantile of the training set, provided with the quantile parameter. * "constant": always predicts a constant value that is provided by the user. constant : int or float or array-like of shape (n_outputs,), default=None The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. quantile : float in [0.0, 1.0], default=None The quantile to predict using the "quantile" strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum. Attributes ---------- constant_ : ndarray of shape (1, n_outputs) Mean or median or quantile of the training targets or constant value given by the user. n_outputs_ : int Number of outputs. See Also -------- DummyClassifier: Classifier that makes predictions using simple rules. Examples -------- >>> import numpy as np >>> from sklearn.dummy import DummyRegressor >>> X = np.array([1.0, 2.0, 3.0, 4.0]) >>> y = np.array([2.0, 3.0, 5.0, 10.0]) >>> dummy_regr = DummyRegressor(strategy="mean") >>> dummy_regr.fit(X, y) DummyRegressor() >>> dummy_regr.predict(X) array([5., 5., 5., 5.]) >>> dummy_regr.score(X, y) 0.0 """ _parameter_constraints: dict = { "strategy": [StrOptions({"mean", "median", "quantile", "constant"})], "quantile": [Interval(Real, 0.0, 1.0, closed="both"), None], "constant": [ Interval(Real, None, None, closed="neither"), "array-like", None, ], } def __init__(self, *, strategy="mean", constant=None, quantile=None): self.strategy = strategy self.constant = constant self.quantile = quantile @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None): """Fit the random regressor. Parameters ---------- X : array-like of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_outputs) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Fitted estimator. """ y = check_array(y, ensure_2d=False, input_name="y") if len(y) == 0: raise ValueError("y must not be empty.") if y.ndim == 1: y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] check_consistent_length(X, y, sample_weight) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) if self.strategy == "mean": self.constant_ = np.average(y, axis=0, weights=sample_weight) elif self.strategy == "median": if sample_weight is None: self.constant_ = np.median(y, axis=0) else: self.constant_ = [ _weighted_percentile(y[:, k], sample_weight, percentile=50.0) for k in range(self.n_outputs_) ] elif self.strategy == "quantile": if self.quantile is None: raise ValueError( "When using `strategy='quantile', you have to specify the desired " "quantile in the range [0, 1]." ) percentile = self.quantile * 100.0 if sample_weight is None: self.constant_ = np.percentile(y, axis=0, q=percentile) else: self.constant_ = [ _weighted_percentile(y[:, k], sample_weight, percentile=percentile) for k in range(self.n_outputs_) ] elif self.strategy == "constant": if self.constant is None: raise TypeError( "Constant target value has to be specified " "when the constant strategy is used." ) self.constant_ = check_array( self.constant, accept_sparse=["csr", "csc", "coo"], ensure_2d=False, ensure_min_samples=0, ) if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]: raise ValueError( "Constant target value should have shape (%d, 1)." % y.shape[1] ) self.constant_ = np.reshape(self.constant_, (1, -1)) return self def predict(self, X, return_std=False): """Perform classification on test vectors X. Parameters ---------- X : array-like of shape (n_samples, n_features) Test data. return_std : bool, default=False Whether to return the standard deviation of posterior prediction. All zeros in this case. .. versionadded:: 0.20 Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) Predicted target values for X. y_std : array-like of shape (n_samples,) or (n_samples, n_outputs) Standard deviation of predictive distribution of query points. """ check_is_fitted(self) n_samples = _num_samples(X) y = np.full( (n_samples, self.n_outputs_), self.constant_, dtype=np.array(self.constant_).dtype, ) y_std = np.zeros((n_samples, self.n_outputs_)) if self.n_outputs_ == 1: y = np.ravel(y) y_std = np.ravel(y_std) return (y, y_std) if return_std else y def _more_tags(self): return {"poor_score": True, "no_validation": True} def score(self, X, y, sample_weight=None): """Return the coefficient of determination R^2 of the prediction. The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the residual sum of squares `((y_true - y_pred) ** 2).sum()` and `v` is the total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Parameters ---------- X : None or array-like of shape (n_samples, n_features) Test samples. Passing None as test samples gives the same result as passing real test samples, since `DummyRegressor` operates independently of the sampled observations. y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float R^2 of `self.predict(X)` w.r.t. y. """ if X is None: X = np.zeros(shape=(len(y), 1)) return super().score(X, y, sample_weight)