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Handled errors during validation via error_score parameter #82

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Handled errors during validation via error_score parameter #82

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20bd47e
errors in validation are now handled via error_score parameter
jsosulski Feb 6, 2020
5d1c3ac
errors in validation are now handled via error_score parameter
jsosulski Feb 6, 2020
226fc91
Merge branch 'nan_on_validation_error' of github.com:jsosulski/moabb …
jsosulski Jun 4, 2020
0f534f9
add error_score to other two evaluations
jsosulski Jun 4, 2020
42fdaf8
Merge branch 'master' into nan_on_validation_error
Jun 24, 2020
1ff7d9f
Merge branch 'master' into nan_on_validation_error
Jul 13, 2020
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36 changes: 27 additions & 9 deletions moabb/evaluations/evaluations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,9 +61,15 @@ def score(self, clf, X, y, scoring):

le = LabelEncoder()
y = le.fit_transform(y)
acc = cross_val_score(clf, X, y, cv=cv, scoring=scoring,
n_jobs=self.n_jobs, error_score=self.error_score)
return acc.mean()
try:
acc = cross_val_score(clf, X, y, cv=cv, scoring=scoring,
n_jobs=self.n_jobs, error_score=self.error_score).mean()
except ValueError as e:
if self.error_score == 'raise':
raise e
elif self.error_score is np.nan:
acc = np.nan
return acc

def is_valid(self, dataset):
return True
Expand Down Expand Up @@ -103,11 +109,17 @@ def evaluate(self, dataset, pipelines):
cv = LeaveOneGroupOut()
for train, test in cv.split(X, y, groups):
t_start = time()
score = _fit_and_score(clone(clf), X, y, scorer, train,
test, verbose=False,
parameters=None,
fit_params=None,
error_score=self.error_score)[0]
try:
score = _fit_and_score(clone(clf), X, y, scorer, train,
test, verbose=False,
parameters=None,
fit_params=None,
error_score=self.error_score)[0]
except ValueError as e:
if self.error_score == 'raise':
raise e
elif self.error_score is np.nan:
score = np.nan
duration = time() - t_start
res = {'time': duration,
'dataset': dataset,
Expand Down Expand Up @@ -176,7 +188,13 @@ def evaluate(self, dataset, pipelines):
# we eval on each session
for session in np.unique(sessions[test]):
ix = sessions[test] == session
score = _score(model, X[test[ix]], y[test[ix]], scorer)
try:
score = _score(model, X[test[ix]], y[test[ix]], scorer)
except ValueError as e:
if self.error_score == 'raise':
raise e
elif self.error_score is np.nan:
score = np.nan

res = {'time': duration,
'dataset': dataset,
Expand Down