forked from moreo/QuaPy
98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
from sklearn.base import BaseEstimator
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from sklearn.decomposition import TruncatedSVD
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from sklearn.linear_model import LogisticRegression
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class LowRankLogisticRegression(BaseEstimator):
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"""
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An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)
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that also generates embedded inputs (i.e., that implements `transform`), as those required for
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:class:`quapy.method.neural.QuaNet`. This is a mock method to allow for easily instantiating
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:class:`quapy.method.neural.QuaNet` on array-like real-valued instances.
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The transformation consists of applying :class:`sklearn.decomposition.TruncatedSVD`
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while classification is performed using :class:`sklearn.linear_model.LogisticRegression` on the low-rank space.
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:param n_components: the number of principal components to retain
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:param kwargs: parameters for the
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`Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ classifier
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"""
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def __init__(self, n_components=100, **kwargs):
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self.n_components = n_components
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self.learner = LogisticRegression(**kwargs)
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def get_params(self):
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"""
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Get hyper-parameters for this estimator.
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:return: a dictionary with parameter names mapped to their values
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"""
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params = {'n_components': self.n_components}
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params.update(self.learner.get_params())
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return params
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def set_params(self, **params):
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"""
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Set the parameters of this estimator.
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:param parameters: a `**kwargs` dictionary with the estimator parameters for
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`Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__
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and eventually also `n_components` for `TruncatedSVD`
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"""
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params_ = dict(params)
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if 'n_components' in params_:
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self.n_components = params_['n_components']
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del params_['n_components']
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self.learner.set_params(**params_)
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def fit(self, X, y):
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"""
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Fit the model according to the given training data. The fit consists of
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fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.
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:param X: array-like of shape `(n_samples, n_features)` with the instances
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:param y: array-like of shape `(n_samples, n_classes)` with the class labels
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:return: `self`
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"""
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nF = X.shape[1]
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self.pca = None
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if nF > self.n_components:
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self.pca = TruncatedSVD(self.n_components).fit(X)
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X = self.transform(X)
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self.learner.fit(X, y)
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self.classes_ = self.learner.classes_
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return self
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def predict(self, X):
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"""
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Predicts labels for the instances `X` embedded into the low-rank space.
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:param X: array-like of shape `(n_samples, n_features)` instances to classify
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:return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of
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instances in `X`
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"""
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X = self.transform(X)
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return self.learner.predict(X)
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def predict_proba(self, X):
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"""
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Predicts posterior probabilities for the instances `X` embedded into the low-rank space.
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:param X: array-like of shape `(n_samples, n_features)` instances to classify
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:return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities
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"""
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X = self.transform(X)
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return self.learner.predict_proba(X)
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def transform(self, X):
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"""
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Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if
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`n_components` >= `X.shape[1]`.
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:param X: array-like of shape `(n_samples, n_features)` instances to embed
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:return: array-like of shape `(n_samples, n_components)` with the embedded instances
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"""
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if self.pca is None:
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return X
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return self.pca.transform(X)
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