def test_groupby1(self):
df = load_iris()
random_group = []
for _ in range(len(df)):
random_group.append(random.randint(1, 2))
df['random_group'] = random_group
train_out = xgb_classification_train(table=df, feature_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], label_col='species', group_by=['random_group'])
predict_out = xgb_classification_predict(table=df, model=train_out['model'])
def groupby1(self):
df = get_iris()
random_group = []
for i in range(len(df)):
random_group.append(random.randint(1, 2))
df['random_group'] = random_group
train_out = xgb_classification_train(df,
feature_cols=[
'sepal_length', 'sepal_width',
'petal_length', 'petal_width'
],
label_col='species',
group_by=['random_group'])
predict_out = xgb_classification_predict(df, train_out['model'])
print(predict_out['out_table'][['species', 'prediction']])