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']])