forked from moreo/QuaPy
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
from sklearn.decomposition import TruncatedSVD
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from sklearn.linear_model import LogisticRegression
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class PCALR:
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"""
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An example of a classification method that also generates embedded inputs, as those required for QuaNet.
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This example simply combines a Principal Component Analysis (PCA) with Logistic Regression (LR).
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"""
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def __init__(self, n_components=300, **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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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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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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self.pca = TruncatedSVD(self.n_components)
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embedded = self.pca.fit_transform(X, y)
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self.learner.fit(embedded, 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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embedded = self.transform(X)
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return self.learner.predict(embedded)
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def predict_proba(self, X):
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embedded = self.transform(X)
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return self.learner.predict_proba(embedded)
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def transform(self, X):
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return self.pca.transform(X)
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