def _calculate(self, X, y, categorical):
import sklearn.lda
if len(y.shape) == 1 or y.shape[1] == 1:
kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10)
else:
kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10)
accuracy = 0.
try:
for train, test in kf:
lda = sklearn.lda.LDA()
if len(y.shape) == 1 or y.shape[1] == 1:
lda.fit(X[train], y[train])
else:
lda = OneVsRestClassifier(lda)
lda.fit(X[train], y[train])
predictions = lda.predict(X[test])
accuracy += sklearn.metrics.accuracy_score(predictions, y[test])
return accuracy / 10
except scipy.linalg.LinAlgError as e:
self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
return np.NaN
except ValueError as e:
self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
return np.NaN
def _calculate(self, X, y, categorical):
import sklearn.lda
if len(y.shape) == 1 or y.shape[1] == 1:
kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10)
else:
kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10)
accuracy = 0.
try:
for train, test in kf:
lda = sklearn.lda.LDA()
if len(y.shape) == 1 or y.shape[1] == 1:
lda.fit(X[train], y[train])
else:
lda = OneVsRestClassifier(lda)
lda.fit(X[train], y[train])
predictions = lda.predict(X[test])
accuracy += sklearn.metrics.accuracy_score(
predictions, y[test])
return accuracy / 10
except scipy.linalg.LinAlgError as e:
self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
return np.NaN
except ValueError as e:
self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
return np.NaN
def _calculate(self, X, y, categorical):
import sklearn.neighbors
kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10)
accuracy = 0.
for train, test in kf:
lda = sklearn.neighbors.KNeighborsClassifier(1)
lda.fit(X[train], y[train])
predictions = lda.predict(X[test])
accuracy += sklearn.metrics.accuracy_score(predictions, y[test])
return accuracy / 10
def _calculate(self, X, y, categorical):
import sklearn.lda
kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10)
accuracy = 0.
try:
for train, test in kf:
lda = sklearn.lda.LDA()
lda.fit(X[train], y[train])
predictions = lda.predict(X[test])
accuracy += sklearn.metrics.accuracy_score(predictions, y[test])
return accuracy / 10
except scipy.linalg.LinAlgError as e:
logging.warning("LDA failed: %s Returned 0 instead!" % e)
return 0
except ValueError as e:
logging.warning("LDA failed: %s Returned 0 instead!" % e)
return 0