def run_classifier(out_folder, trend_probs, referrers, y, train, test):
F = referrers #static features
etree = create_grid_search('lr', n_jobs = 1)
y_pred = trend_probs[test].argmax(axis=1)
save_results(out_folder, 'tl-base-lr', y_pred, y[test])
aux = clone(etree)
aux.fit(F[train], y[train])
y_pred = aux.predict(F[test])
save_results(out_folder, 'tree-feats', y_pred, y[test])
aux = clone(etree)
aux.fit(trend_probs[train], y[train])
y_pred = aux.predict(trend_probs[test])
save_results(out_folder, 'tree-probs', y_pred, y[test])
C = np.hstack((F, trend_probs))
aux = clone(etree)
aux.fit(C[train], y[train])
y_pred = aux.predict(C[test])
save_results(out_folder, 'meta-combine', y_pred, y[test])
#stack_clf = stacking.Stacking(3, [etree], 'tree')
#stack_clf.fit(F[train], y[train], trend_probs[train])
#y_pred = stack_clf.predict(F[test], trend_probs[test])
#save_results(out_folder, 'meta-stack-tree', y_pred)
stack_clf = stacking.Stacking(3, [etree], 'linear')
stack_clf.fit(F[train], y[train], trend_probs[train])
y_pred = stack_clf.predict(F[test], trend_probs[test])
save_results(out_folder, 'meta-stack-linear', y_pred, y[test])
def run_classifier(out_folder, trend_probs, referrers, y, train, test):
F = referrers #static features
etree = create_grid_search('lr', n_jobs = 1)
y_pred = trend_probs[test].argmax(axis=1)
save_results(out_folder, 'tl-base-lr', y_pred, y[test])
aux = clone(etree)
aux.fit(F[train], y[train])
y_pred = aux.predict(F[test])
save_results(out_folder, 'tree-feats', y_pred, y[test])
aux = clone(etree)
aux.fit(trend_probs[train], y[train])
y_pred = aux.predict(trend_probs[test])
save_results(out_folder, 'tree-probs', y_pred, y[test])
C = np.hstack((F, trend_probs))
aux = clone(etree)
aux.fit(C[train], y[train])
y_pred = aux.predict(C[test])
save_results(out_folder, 'meta-combine', y_pred, y[test])
#stack_clf = stacking.Stacking(3, [etree], 'tree')
#stack_clf.fit(F[train], y[train], trend_probs[train])
#y_pred = stack_clf.predict(F[test], trend_probs[test])
#save_results(out_folder, 'meta-stack-tree', y_pred)
stack_clf = stacking.Stacking(3, [etree], 'linear')
stack_clf.fit(F[train], y[train], trend_probs[train])
y_pred = stack_clf.predict(F[test], trend_probs[test])
save_results(out_folder, 'meta-stack-linear', y_pred, y[test])
def __init__(self):
self.model = etree = create_grid_search('extra_trees', n_jobs = 1)
def __init__(self):
self.model = etree = create_grid_search('extra_trees', n_jobs = 1)