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QuaPy
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Merge branch 'master' of gitea-s2i2s.isti.cnr.it:moreo/QuaPy

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Alejandro Moreo Fernandez 2021-06-11 10:58:54 +02:00
parent 9c880c3d24 2586e6ed7d
commit 41fb8651b3
2 changed files with 7 additions and 2 deletions
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1
TODO.txt
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@ -13,6 +13,7 @@ Do we want to cover cross-lingual quantification natively in QuaPy, or does it m
Current issues: Current issues:
========================================== ==========================================
SVMperf-based learners do not remove temp files in __del__?
In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
an instance of single-label with 2 labels. Check an instance of single-label with 2 labels. Check

8
quapy/classification/methods.py
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@ -26,8 +26,10 @@ class PCALR(BaseEstimator):
def fit(self, X, y): def fit(self, X, y):
self.learner.fit(X, y) self.learner.fit(X, y)
self.pca = TruncatedSVD(self.n_components).fit(X, y) nF = X.shape[1]
# embedded = self.pca.transform(X) self.pca = None
if nF > self.n_components:
self.pca = TruncatedSVD(self.n_components).fit(X, y)
self.classes_ = self.learner.classes_ self.classes_ = self.learner.classes_
return self return self
@ -40,4 +42,6 @@ class PCALR(BaseEstimator):
return self.learner.predict_proba(X) return self.learner.predict_proba(X)
def transform(self, X): def transform(self, X):
if self.pca is None:
return X
return self.pca.transform(X) return self.pca.transform(X)