print('Train subject %d, class %s' % (subject, cols[i]))
# Fit models
lda.fit(X_train, y_train)
rf.fit(X_train, y_train)
lr2.fit(X_train, y_train)
# Grab predictions
pred1[:, i] = lda.predict_proba(X_test)[:, 1]
pred2[:, i] = rf.predict_proba(X_test)[:, 1]
pred3[:, i] = lr2.predict_proba(X_test)[:, 1]
# Ensemble!
pred[:, i] = (pred1[:, i] + pred2[:, i] + pred3[:, i]) / 3
pred_tot.append(pred)
# submission file
#lda_file = 'lda.csv'
lda_file = 'lda_rf.csv'
# create pandas object for sbmission
lda = pd.DataFrame(index=np.concatenate(ids_tot),
columns=cols,
data=np.concatenate(pred_tot))
# write file
lda.to_csv(lda_file, index_label='id', float_format='%.3f')
print('Done!')
print('Train subject %d, class %s' % (subject, cols[i]))
# Fit models
lda.fit(X_train,y_train)
rf.fit(X_train, y_train)
lr2.fit(X_train,y_train)
# Grab predictions
pred1[:,i] = lda.predict_proba(X_test)[:,1]
pred2[:,i] = rf.predict_proba(X_test)[:,1]
pred3[:,i] = lr2.predict_proba(X_test)[:,1]
# Ensemble!
pred[:,i]=(pred1[:,i] + pred2[:,i] + pred3[:,i])/3
pred_tot.append(pred)
# submission file
#lda_file = 'lda.csv'
lda_file = 'lda_rf.csv'
# create pandas object for sbmission
lda = pd.DataFrame(index=np.concatenate(ids_tot),
columns=cols,
data=np.concatenate(pred_tot))
# write file
lda.to_csv(lda_file,index_label='id',float_format='%.3f')
print('Done!')
