def test__get_target_info_regressor():
np.random.seed(123)
y = np.random.randn(100)
result = OutSamplerTransformer._get_target_info(y, False)
assert result["multi_output"] == False
result = OutSamplerTransformer._get_target_info(y.reshape((50, 2)), False)
assert result["multi_output"] == True
assert result["y_names"] == ["output0", "output1"]
result = OutSamplerTransformer._get_target_info(
pd.DataFrame(y.reshape((50, 2)), columns=["tA", "tB"]), False)
assert result["multi_output"] == True
assert result["y_names"] == ["tA", "tB"]
def test__get_target_info_classifier():
np.random.seed(123)
y = 1 * (np.random.randn(100) > 0)
result = OutSamplerTransformer._get_target_info(y, True)
assert result["multi_output"] == False
assert result["nby"] == 2
result = OutSamplerTransformer._get_target_info(y.reshape((50, 2)), True)
assert result["multi_output"] == True
assert result["y_names"] == ["output0", "output1"]
assert result["nby"] == [2, 2]
result = OutSamplerTransformer._get_target_info(
pd.DataFrame(y.reshape((50, 2)), columns=["tA", "tB"]), True)
assert result["multi_output"] == True
assert result["y_names"] == ["tA", "tB"]
assert result["nby"] == [2, 2]
y = np.array(["a", "b", "c"])[np.random.randint(0, 3, 100)]
result = OutSamplerTransformer._get_target_info(y, True)
assert result["multi_output"] == False
assert result["nby"] == 3
result = OutSamplerTransformer._get_target_info(y.reshape((50, 2)), True)
assert result["multi_output"] == True
assert result["y_names"] == ["output0", "output1"]
assert result["nby"] == [3, 3]
result = OutSamplerTransformer._get_target_info(
pd.DataFrame(y.reshape((50, 2)), columns=["tA", "tB"]), True)
assert result["multi_output"] == True
assert result["y_names"] == ["tA", "tB"]
assert result["nby"] == [3, 3]