for estimator in n_estimators:
print('n_estimators = {0}'.format(estimator))
#Create model
sklmodel = RandomForestRegressor(n_estimators=estimator,
criterion="mse",
max_features=max_features,
bootstrap=True,
oob_score=False,
n_jobs=int(cpus / 2))
model = SklearnModel(sklmodel, modeldir)
model.fit(train_dataset)
#Append trains cores and results
train_scores = model.evaluate(
train_dataset,
[metric, dc.metrics.Metric(dc.metrics.mae_score)])
train_results = np.concatenate(
(train_results, list(train_scores.values())))
valid_scores = model.evaluate(
valid_dataset,
[metric, dc.metrics.Metric(dc.metrics.mae_score)])
test_results = np.concatenate((test_results, list(valid_scores.values())))
#Append trains cores and results
predict_train = pd.DataFrame(
model.predict(train_dataset),
columns=['prediction']).to_csv(modeldir + "predict_train_" +
str(estimator) + '.csv')
predict_valid = pd.DataFrame(
model.predict(valid_dataset),
