def train_predict(clf, X_train, y_train, X_test):
preds_br = np.zeros((X_test.shape[0], 9))
for i in range(0, 9):
clf.fit(X_train, y_train[:, i])
preds_br[:, i] = clf.predict_proba(X_test)[:, 1]
nn_preds = np.array([])
n_iter = 20
for i in range(n_iter):
nn_clf.fit(X_train, y_train)
s_preds = nn_clf.predict_proba(X_test)
nn_preds = nn_preds + s_preds if nn_preds.size else s_preds
nn_preds = (nn_preds / n_iter)
n_chains = 20
preds_cc = np.zeros((X_test.shape[0], 9))
for i in range(n_chains):
cc = ClassifierChain(clf)
cc.fit(X_train, y_train)
preds_cc = preds_cc + cc.predict(X_test)
preds_cc = preds_cc / n_chains
preds_br = (preds_br + 3*nn_preds + 2*preds_cc) / 6
return preds_br
def train_predict(fold, feature, clf, X_train, y_train, X_test, y_test):
preds_br = np.zeros((X_test.shape[0], 9))
for i in range(0, 9):
clf.fit(X_train, y_train[:, i])
preds_br[:, i] = clf.predict_proba(X_test)[:, 1]
np.save('val3/' + str(fold) + '_' + feature + '_br', preds_br)
nn_preds = np.array([])
n_iter = 10
for i in range(n_iter):
nn_clf.fit(X_train, y_train)
s_preds = nn_clf.predict_proba(X_test)
nn_preds = nn_preds + s_preds if nn_preds.size else s_preds
nn_preds = (nn_preds / n_iter)
np.save('val3/' + str(fold) + '_' + feature + '_nn', nn_preds)
n_chains = 10
preds_cc = np.zeros((X_test.shape[0], 9))
for i in range(n_chains):
cc = ClassifierChain(clf)
cc.fit(X_train, y_train)
preds_cc = preds_cc + cc.predict(X_test)
preds_cc = preds_cc / n_chains
np.save('val3/' + str(fold) + '_' + feature + '_cc', preds_cc)
np.save('val3/' + str(fold) + '_' + feature + '_test', y_test)
preds_br = (preds_br + 3*nn_preds + 2*preds_cc) / 6
return preds_br
def train_predict(clf, X_train, y_train, X_test):
preds_br = np.zeros((X_test.shape[0], 9))
for i in range(0, 9):
clf.fit(X_train, y_train[:, i])
preds_br[:, i] = clf.predict_proba(X_test)[:, 1]
nn_preds = np.array([])
n_iter = 20
for i in range(n_iter):
nn_clf.fit(X_train, y_train)
s_preds = nn_clf.predict_proba(X_test)
nn_preds = nn_preds + s_preds if nn_preds.size else s_preds
nn_preds = (nn_preds / n_iter)
n_chains = 20
preds_cc = np.zeros((X_test.shape[0], 9))
for i in range(n_chains):
cc = ClassifierChain(clf)
cc.fit(X_train, y_train)
preds_cc = preds_cc + cc.predict(X_test)
preds_cc = preds_cc / n_chains
preds_br = (preds_br + 3 * nn_preds + 2 * preds_cc) / 6
return preds_br
def train_predict(feature, clf, X_train, y_train, X_test):
preds_br = np.zeros((X_test.shape[0], 9))
for i in range(0, 9):
clf.fit(X_train, y_train[:, i])
preds_br[:, i] = clf.predict_proba(X_test)[:, 1]
np.save('test/' + feature + '_br', preds_br)
nn_preds = np.array([])
n_iter = 40
for i in range(n_iter):
nn_clf.fit(X_train, y_train)
s_preds = nn_clf.predict_proba(X_test)
nn_preds = nn_preds + s_preds if nn_preds.size else s_preds
nn_preds = (nn_preds / n_iter)
np.save('test/' + feature + '_nn', nn_preds)
n_chains = 40
preds_cc = np.zeros((X_test.shape[0], 9))
for i in range(n_chains):
cc = ClassifierChain(clf)
cc.fit(X_train, y_train)
preds_cc = preds_cc + cc.predict(X_test)
preds_cc = preds_cc / n_chains
np.save('test/' + feature + '_cc', preds_cc)
preds_br = (preds_br + 3 * nn_preds + 2 * preds_cc) / 6
return preds_br
