def score(self, X, y, sample_weight=None):
"""Returns the mean accuracy on the given test data and labels.
NOTE: In the condition of sklearn.svm.SVC with precomputed kernel
when the kernel matrix is computed portion by portion, the function
will ignore the first input argument X.
Parameters
----------
X: list of tuple (data1, data2)
data1 and data2 are numpy array in shape [num_TRs, num_voxels]
to be computed for correlation.
They are test samples.
They contain the activity data filtered by ROIs
and prepared for correlation computation.
Within list, all data1s must have the same num_voxels value,
all data2s must have the same num_voxels value.
len(X) is the number of test samples.
y: 1D numpy array
labels, len(X) equals len(y), which is num_samples
sample_weight: 1D array in shape [num_samples], optional
Sample weights.
Returns
-------
score : float
Mean accuracy of self.predict(X) wrt. y.
"""
from sklearn.metrics import accuracy_score
if isinstance(self.clf, sklearn.svm.SVC) \
and self.clf.kernel == 'precomputed' \
and self.training_data_ is None:
result = accuracy_score(y, self.predict(),
sample_weight=sample_weight)
else:
result = accuracy_score(y, self.predict(X),
sample_weight=sample_weight)
return result
评论列表
文章目录
