)
if j % 10 == 0:
print "----BEST RESULT best_loss%.6f lr=%.3f,decay=%.3f,tol=%.4f ------ " % (
best_loss,
best_lr,
best_decay,
best_tol,
)
lr = best_lr
decay = best_decay
tol = best_tol
print "----BEST RESULT best_loss%.6f lr=%.3f,decay=%f,tol=%f ------ " % (best_loss, best_lr, best_decay, best_tol)
clf = StackingLinearRegression.StackingLinearRegression(lr, decay, max_epoch, tol)
# train with searched or defined parameters
clf.fit(preds_train, labels)
df = pd.DataFrame(index=sample.id.values)
i = 0
preds_test = []
for loss, ntree, param in param_list:
part_test = pd.read_csv(otto_utils.get_test_preds_from_param(param, train_name, ntree), index_col=0)
preds_test.append(np.asarray(part_test))
i += 1
# predict and output results
preds = clf.predict(preds_test)
preds = pd.DataFrame(preds, index=sample.id.values, columns=sample.columns[1:])
preds.to_csv("xgb_" + train_name + "_my_stacking_benchmark.csv", index_label="id")
cp.dump(clf, open("xgb_" + train_name + "_my_stacking.model", "wb"), protocol=1)
full_param = other.copy()
full_param.update(param)
plst = full_param.items()
# train on the trainning set
bst,loss,ntree = xgb.train(full_param, dtrain, num_round, watchlist)
# dump bst model
bst_dump_model = otto_utils.get_model_name_from_param(param, train_name, ntree)
bst.save_model(bst_dump_model)
# output train predictions
preds = bst.predict(dtrain)
preds = pd.DataFrame(preds, index=train_ids, columns=sample.columns[1:])
preds.to_csv(otto_utils.get_train_preds_from_param(param, train_name, ntree), index_label='id')
# output test predictions
preds = bst.predict(dtest)
preds = pd.DataFrame(preds, index=sample.id.values, columns=sample.columns[1:])
preds.to_csv(otto_utils.get_test_preds_from_param(param, train_name, ntree), index_label='id')
if avg_preds is None:
avg_preds = preds
else:
avg_preds += preds
avg_preds /= len(param_list)
# create submission file
avg_preds = pd.DataFrame(avg_preds, index=sample.id.values, columns=sample.columns[1:])
avg_preds.to_csv('xgb_avg_benchmark_'+train_name+'.csv', index_label='id')
