ai-content-maker/.venv/Lib/site-packages/sklearn/semi_supervised/_self_training.py

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2024-05-03 04:18:51 +03:00
import warnings
from numbers import Integral, Real
import numpy as np
from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone
from ..utils import safe_mask
from ..utils._param_validation import HasMethods, Interval, StrOptions
from ..utils.metadata_routing import _RoutingNotSupportedMixin
from ..utils.metaestimators import available_if
from ..utils.validation import check_is_fitted
__all__ = ["SelfTrainingClassifier"]
# Authors: Oliver Rausch <rauscho@ethz.ch>
# Patrice Becker <beckerp@ethz.ch>
# License: BSD 3 clause
def _estimator_has(attr):
"""Check if we can delegate a method to the underlying estimator.
First, we check the fitted `base_estimator_` if available, otherwise we check
the unfitted `base_estimator`. We raise the original `AttributeError` if
`attr` does not exist. This function is used together with `available_if`.
"""
def check(self):
if hasattr(self, "base_estimator_"):
getattr(self.base_estimator_, attr)
else:
getattr(self.base_estimator, attr)
return True
return check
class SelfTrainingClassifier(
_RoutingNotSupportedMixin, MetaEstimatorMixin, BaseEstimator
):
"""Self-training classifier.
This :term:`metaestimator` allows a given supervised classifier to function as a
semi-supervised classifier, allowing it to learn from unlabeled data. It
does this by iteratively predicting pseudo-labels for the unlabeled data
and adding them to the training set.
The classifier will continue iterating until either max_iter is reached, or
no pseudo-labels were added to the training set in the previous iteration.
Read more in the :ref:`User Guide <self_training>`.
Parameters
----------
base_estimator : estimator object
An estimator object implementing `fit` and `predict_proba`.
Invoking the `fit` method will fit a clone of the passed estimator,
which will be stored in the `base_estimator_` attribute.
threshold : float, default=0.75
The decision threshold for use with `criterion='threshold'`.
Should be in [0, 1). When using the `'threshold'` criterion, a
:ref:`well calibrated classifier <calibration>` should be used.
criterion : {'threshold', 'k_best'}, default='threshold'
The selection criterion used to select which labels to add to the
training set. If `'threshold'`, pseudo-labels with prediction
probabilities above `threshold` are added to the dataset. If `'k_best'`,
the `k_best` pseudo-labels with highest prediction probabilities are
added to the dataset. When using the 'threshold' criterion, a
:ref:`well calibrated classifier <calibration>` should be used.
k_best : int, default=10
The amount of samples to add in each iteration. Only used when
`criterion='k_best'`.
max_iter : int or None, default=10
Maximum number of iterations allowed. Should be greater than or equal
to 0. If it is `None`, the classifier will continue to predict labels
until no new pseudo-labels are added, or all unlabeled samples have
been labeled.
verbose : bool, default=False
Enable verbose output.
Attributes
----------
base_estimator_ : estimator object
The fitted estimator.
classes_ : ndarray or list of ndarray of shape (n_classes,)
Class labels for each output. (Taken from the trained
`base_estimator_`).
transduction_ : ndarray of shape (n_samples,)
The labels used for the final fit of the classifier, including
pseudo-labels added during fit.
labeled_iter_ : ndarray of shape (n_samples,)
The iteration in which each sample was labeled. When a sample has
iteration 0, the sample was already labeled in the original dataset.
When a sample has iteration -1, the sample was not labeled in any
iteration.
n_features_in_ : int
Number of features seen during :term:`fit`.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0
n_iter_ : int
The number of rounds of self-training, that is the number of times the
base estimator is fitted on relabeled variants of the training set.
termination_condition_ : {'max_iter', 'no_change', 'all_labeled'}
The reason that fitting was stopped.
- `'max_iter'`: `n_iter_` reached `max_iter`.
- `'no_change'`: no new labels were predicted.
- `'all_labeled'`: all unlabeled samples were labeled before `max_iter`
was reached.
See Also
--------
LabelPropagation : Label propagation classifier.
LabelSpreading : Label spreading model for semi-supervised learning.
References
----------
:doi:`David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling
supervised methods. In Proceedings of the 33rd annual meeting on
Association for Computational Linguistics (ACL '95). Association for
Computational Linguistics, Stroudsburg, PA, USA, 189-196.
<10.3115/981658.981684>`
Examples
--------
>>> import numpy as np
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import SelfTrainingClassifier
>>> from sklearn.svm import SVC
>>> rng = np.random.RandomState(42)
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
>>> iris.target[random_unlabeled_points] = -1
>>> svc = SVC(probability=True, gamma="auto")
>>> self_training_model = SelfTrainingClassifier(svc)
>>> self_training_model.fit(iris.data, iris.target)
SelfTrainingClassifier(...)
"""
_estimator_type = "classifier"
_parameter_constraints: dict = {
# We don't require `predic_proba` here to allow passing a meta-estimator
# that only exposes `predict_proba` after fitting.
"base_estimator": [HasMethods(["fit"])],
"threshold": [Interval(Real, 0.0, 1.0, closed="left")],
"criterion": [StrOptions({"threshold", "k_best"})],
"k_best": [Interval(Integral, 1, None, closed="left")],
"max_iter": [Interval(Integral, 0, None, closed="left"), None],
"verbose": ["verbose"],
}
def __init__(
self,
base_estimator,
threshold=0.75,
criterion="threshold",
k_best=10,
max_iter=10,
verbose=False,
):
self.base_estimator = base_estimator
self.threshold = threshold
self.criterion = criterion
self.k_best = k_best
self.max_iter = max_iter
self.verbose = verbose
@_fit_context(
# SelfTrainingClassifier.base_estimator is not validated yet
prefer_skip_nested_validation=False
)
def fit(self, X, y):
"""
Fit self-training classifier using `X`, `y` as training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
y : {array-like, sparse matrix} of shape (n_samples,)
Array representing the labels. Unlabeled samples should have the
label -1.
Returns
-------
self : object
Fitted estimator.
"""
# we need row slicing support for sparse matrices, but costly finiteness check
# can be delegated to the base estimator.
X, y = self._validate_data(
X, y, accept_sparse=["csr", "csc", "lil", "dok"], force_all_finite=False
)
self.base_estimator_ = clone(self.base_estimator)
if y.dtype.kind in ["U", "S"]:
raise ValueError(
"y has dtype string. If you wish to predict on "
"string targets, use dtype object, and use -1"
" as the label for unlabeled samples."
)
has_label = y != -1
if np.all(has_label):
warnings.warn("y contains no unlabeled samples", UserWarning)
if self.criterion == "k_best" and (
self.k_best > X.shape[0] - np.sum(has_label)
):
warnings.warn(
(
"k_best is larger than the amount of unlabeled "
"samples. All unlabeled samples will be labeled in "
"the first iteration"
),
UserWarning,
)
self.transduction_ = np.copy(y)
self.labeled_iter_ = np.full_like(y, -1)
self.labeled_iter_[has_label] = 0
self.n_iter_ = 0
while not np.all(has_label) and (
self.max_iter is None or self.n_iter_ < self.max_iter
):
self.n_iter_ += 1
self.base_estimator_.fit(
X[safe_mask(X, has_label)], self.transduction_[has_label]
)
# Predict on the unlabeled samples
prob = self.base_estimator_.predict_proba(X[safe_mask(X, ~has_label)])
pred = self.base_estimator_.classes_[np.argmax(prob, axis=1)]
max_proba = np.max(prob, axis=1)
# Select new labeled samples
if self.criterion == "threshold":
selected = max_proba > self.threshold
else:
n_to_select = min(self.k_best, max_proba.shape[0])
if n_to_select == max_proba.shape[0]:
selected = np.ones_like(max_proba, dtype=bool)
else:
# NB these are indices, not a mask
selected = np.argpartition(-max_proba, n_to_select)[:n_to_select]
# Map selected indices into original array
selected_full = np.nonzero(~has_label)[0][selected]
# Add newly labeled confident predictions to the dataset
self.transduction_[selected_full] = pred[selected]
has_label[selected_full] = True
self.labeled_iter_[selected_full] = self.n_iter_
if selected_full.shape[0] == 0:
# no changed labels
self.termination_condition_ = "no_change"
break
if self.verbose:
print(
f"End of iteration {self.n_iter_},"
f" added {selected_full.shape[0]} new labels."
)
if self.n_iter_ == self.max_iter:
self.termination_condition_ = "max_iter"
if np.all(has_label):
self.termination_condition_ = "all_labeled"
self.base_estimator_.fit(
X[safe_mask(X, has_label)], self.transduction_[has_label]
)
self.classes_ = self.base_estimator_.classes_
return self
@available_if(_estimator_has("predict"))
def predict(self, X):
"""Predict the classes of `X`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
Returns
-------
y : ndarray of shape (n_samples,)
Array with predicted labels.
"""
check_is_fitted(self)
X = self._validate_data(
X,
accept_sparse=True,
force_all_finite=False,
reset=False,
)
return self.base_estimator_.predict(X)
@available_if(_estimator_has("predict_proba"))
def predict_proba(self, X):
"""Predict probability for each possible outcome.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
Returns
-------
y : ndarray of shape (n_samples, n_features)
Array with prediction probabilities.
"""
check_is_fitted(self)
X = self._validate_data(
X,
accept_sparse=True,
force_all_finite=False,
reset=False,
)
return self.base_estimator_.predict_proba(X)
@available_if(_estimator_has("decision_function"))
def decision_function(self, X):
"""Call decision function of the `base_estimator`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
Returns
-------
y : ndarray of shape (n_samples, n_features)
Result of the decision function of the `base_estimator`.
"""
check_is_fitted(self)
X = self._validate_data(
X,
accept_sparse=True,
force_all_finite=False,
reset=False,
)
return self.base_estimator_.decision_function(X)
@available_if(_estimator_has("predict_log_proba"))
def predict_log_proba(self, X):
"""Predict log probability for each possible outcome.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
Returns
-------
y : ndarray of shape (n_samples, n_features)
Array with log prediction probabilities.
"""
check_is_fitted(self)
X = self._validate_data(
X,
accept_sparse=True,
force_all_finite=False,
reset=False,
)
return self.base_estimator_.predict_log_proba(X)
@available_if(_estimator_has("score"))
def score(self, X, y):
"""Call score on the `base_estimator`.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
y : array-like of shape (n_samples,)
Array representing the labels.
Returns
-------
score : float
Result of calling score on the `base_estimator`.
"""
check_is_fitted(self)
X = self._validate_data(
X,
accept_sparse=True,
force_all_finite=False,
reset=False,
)
return self.base_estimator_.score(X, y)