def get_modalities_base_clf(self, modalities_to_experemint, clf):
return Pipeline([
('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin(modalities_to_experemint),
'feature'])), ('pred', clf)
])
def get_mod_voting(self, given_modalities, clf):
estimators = []
for m in given_modalities:
text_pipe = Pipeline([('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin([m]),
'feature'])), ('pred', clf)])
estimators.append((m, text_pipe))
clf = VotingClassifier(estimators=estimators, voting='soft', n_jobs=-1)
return clf
def get_modalities_extra_trees_propagation(self, modalities_to_experemint,
X_unlabled):
return Pipeline([
('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin(modalities_to_experemint),
'feature'])),
('propagate',
propgate_lables_predictor_raw(X_unlabled,
ExtraTreesClassifier(n_jobs=-1))),
])
def get_mod_stacking(self, given_modalities, clf):
estimators = []
for m in given_modalities:
text_pipe = Pipeline([('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin([m]),
'feature'])), ('pred', clf)])
estimators.append((m, text_pipe))
clf = StackingClassifier(estimators=estimators,
final_estimator=LogisticRegression(),
n_jobs=-1)
return clf
def get_lasso_classifier(self, modalities_to_experemint, clf, r, X):
groups = pandas.DataFrame(X.columns, index=X.columns)
mods = self.modalities_df[self.modalities_df.modality.isin(
modalities_to_experemint)]
groups_ids = groups.join(mods.set_index('feature')).modality.astype(
'category').cat.codes.values
return Pipeline([
('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin(modalities_to_experemint),
'feature'])), ('scale', StandardScaler()),
('dummy_transform', lasso_transformer(r,
groups_ids)), ('pred', clf)
])
def get_orthogonal_stacking_voting(self, given_modalities, clf):
estimators = []
ort = pbdesign(len(given_modalities))
#print(ort)
for id, trial in enumerate(ort):
mods = []
for idx, val in enumerate(trial):
if val == 1:
mods.append(given_modalities[idx])
if len(mods) > 0:
text_pipe = Pipeline([
('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin(mods), 'feature'])),
('pred', clf)
])
estimators.append((str(id) + str(trial), text_pipe))
clf_stacking = StackingClassifier(estimators=estimators,
final_estimator=LogisticRegression(),
n_jobs=-1)
clf_voting = VotingClassifier(estimators=estimators, voting='soft')
return clf_voting, clf_stacking
def get_orthogonal_large_voting_stacking(self, given_modalities):
given_modalities = list(given_modalities)
estimators = []
for i in range(3):
random.shuffle(given_modalities)
ort = pbdesign(len(given_modalities))
#print(ort)
for id, trial in enumerate(ort):
mods = []
for idx, val in enumerate(trial):
if val == 1:
mods.append(given_modalities[idx])
if len(mods) > 0:
multinomal = make_pipeline( # improves
Normalizer(norm="max"),
MultinomialNB(alpha=0.01, fit_prior=True))
logistic = make_pipeline( # improve
MaxAbsScaler(),
LogisticRegression(C=10.0, dual=False, penalty="l2"))
for cl in [
ExtraTreesClassifier(n_jobs=-1), multinomal,
logistic
]:
text_pipe = Pipeline([
('select',
filter_cols(self.modalities_df.loc[
self.modalities_df.modality.isin(mods),
'feature'])), ('pred', cl)
])
estimators.append(
(str(id) + str(trial) + str(cl) + str(i),
text_pipe))
clf_stacking = StackingClassifier(estimators=estimators,
final_estimator=LogisticRegression(),
n_jobs=-1)
clf_voting = VotingClassifier(estimators=estimators, voting='soft')
return clf_voting, clf_stacking