def test_multiple_class_failure():
config = {
"input_features": [
{"name": "Index", "proc_column": "Index", "type": "number"},
{"name": "random_1", "proc_column": "random_1", "type": "number"},
{"name": "random_2", "proc_column": "random_2", "type": "number"},
],
"output_features": [
{"name": "Label", "proc_column": "Label", "type": "binary"},
{"name": "Label2", "proc_column": "Label2", "type": "binary"},
],
"preprocessing": {},
}
input_df = pd.DataFrame(
{
"Index": np.arange(0, 200, 1),
"random_1": np.random.randint(0, 50, 200),
"random_2": np.random.choice(["Type A", "Type B", "Type C", "Type D"], 200),
"Label": np.concatenate((np.zeros(180), np.ones(20))),
"Label2": np.concatenate((np.zeros(180), np.ones(20))),
"split": np.zeros(200),
}
)
backend = LocalBackend()
with pytest.raises(ValueError):
balance_data(input_df, config["output_features"], config["preprocessing"], backend)
def test_balance(method, balance):
config = {
"input_features": [
{"name": "Index", "proc_column": "Index", "type": "number"},
{"name": "random_1", "proc_column": "random_1", "type": "number"},
{"name": "random_2", "proc_column": "random_2", "type": "number"},
],
"output_features": [{"name": "Label", "proc_column": "Label", "type": "binary"}],
"preprocessing": {"oversample_minority": None, "undersample_majority": None},
}
input_df = pd.DataFrame(
{
"Index": np.arange(0, 200, 1),
"random_1": np.random.randint(0, 50, 200),
"random_2": np.random.choice(["Type A", "Type B", "Type C", "Type D"], 200),
"Label": np.concatenate((np.zeros(180), np.ones(20))),
"split": np.zeros(200),
}
)
config["preprocessing"][method] = balance
backend = LocalBackend()
test_df = balance_data(input_df, config["output_features"], config["preprocessing"], backend)
target = config["output_features"][0][NAME]
majority_class = test_df[target].value_counts()[test_df[target].value_counts().idxmax()]
minority_class = test_df[target].value_counts()[test_df[target].value_counts().idxmin()]
new_class_balance = round(minority_class / majority_class, 2)
assert abs(balance - new_class_balance) < BALANCE_PERCENTAGE_TOLERANCE
def test_balance_ray(method, balance):
config = {
"input_features": [
{"name": "Index", "proc_column": "Index", "type": "number"},
{"name": "random_1", "proc_column": "random_1", "type": "number"},
{"name": "random_2", "proc_column": "random_2", "type": "number"},
],
"output_features": [{"name": "Label", "proc_column": "Label", "type": "binary"}],
"preprocessing": {"oversample_minority": None, "undersample_majority": None},
}
input_df = pd.DataFrame(
{
"Index": np.arange(0, 200, 1),
"random_1": np.random.randint(0, 50, 200),
"random_2": np.random.choice(["Type A", "Type B", "Type C", "Type D"], 200),
"Label": np.concatenate((np.zeros(180), np.ones(20))),
"split": np.zeros(200),
}
)
config["preprocessing"][method] = balance
target = config["output_features"][0][NAME]
with ray_start(num_cpus=2, num_gpus=None):
backend = create_ray_backend()
input_df = backend.df_engine.from_pandas(input_df)
test_df = balance_data(input_df, config["output_features"], config["preprocessing"], backend)
majority_class = test_df[target].value_counts().compute()[test_df[target].value_counts().compute().idxmax()]
minority_class = test_df[target].value_counts().compute()[test_df[target].value_counts().compute().idxmin()]
new_class_balance = round(minority_class / majority_class, 2)
assert (balance - BALANCE_PERCENTAGE_TOLERANCE) <= new_class_balance
assert (balance + BALANCE_PERCENTAGE_TOLERANCE) >= new_class_balance