def complete_analysis(dataset,
dataset_name,
name,
masklist,
processes=1,
features=None):
for t in [0.05]:
pipeline(OnevsallClassifier(dataset,
masklist,
thresh=t,
thresh_low=0,
classifier=GaussianNB(),
memsave=True),
"classification/" + name + "_GNB_t" + str(t) + "_" +
dataset_name,
features=features,
processes=processes,
scoring=roc_auc_score)
def complete_analysis(dataset, dataset_name, name, masklist, processes = 1, features=None):
# for i in [10]:
# pipeline(
# OnevsallClassifier(dataset, masklist,
# thresh=i, thresh_low = 0, memsave=False, classifier=RidgeClassifier()),
# name + "_OvA_RidgeClassifier_DM_hard0_roc_" + dataset_name + "_tn_" + str(i),
# features=features, processes=processes, post = False, scoring = roc_auc_score, dummy='most_frequent')
# pipeline(
# PairwiseClassifier(dataset, masklist,
# cv='4-Fold', thresh=i, memsave=True, remove_overlap = True, classifier=RidgeClassifier()),
# name + "_Pairwise_RidgeClassifier_roc_DM_" + dataset_name + "_tn_" + str(i),
# features=features, processes=processes, post = False, scoring = roc_auc_score, dummy='most_frequent')
pipeline(
OnevsallContinuous(dataset, masklist, classifier=Ridge(), memsave=True),
name + "_Ridge_" + dataset_name,
features=features, processes=processes, scoring = explained_variance_score)