def test_invalid_fit_type1():
fd = DropOutliers(features=col)
fd.fit(data)
with pytest.raises(TypeError):
fd.transform(data.values)
def test_tranf_equal_null_display1():
fd = DropOutliers(features=col, display=True)
fd.fit(data)
assert_frame_equal(fd.transform(data), data_transf)
def test_tranf_equal():
fd = DropOutliers(features=col)
fd.fit(data)
assert_frame_equal(fd.transform(data), data_transf)
def test_tranf_equal_null():
fd = DropOutliers()
fd.fit(data)
assert_frame_equal(fd.transform(data), data_transf)
def test_coldrop_fail_tranf_data_type():
fdo = DropOutliers(col)
fdo.fit(data)
with pytest.raises(TypeError):
fdo.transform(np.asarray(data))
def test_fitting_error():
"""DropOutliers has not been fitted, yet."""
fd = DropOutliers(features=col)
with pytest.raises(AttributeError):
fd.transform(data)
def test_features_col_object():
fdo = DropOutliers(col_object)
with pytest.raises(ValueError):
fdo.fit(data)
def test_coldrop_fail_fit_data_type():
fdo = DropOutliers(col_object)
with pytest.raises(TypeError):
fdo.fit(np.asarray(data))
def test_coldrop_fail_type():
with pytest.raises(TypeError):
DropOutliers(features=np.array(col))
def test_display_fail_type():
with pytest.raises(TypeError):
DropOutliers(display="a")
def test_all():
filename = "mlearner/data/data/titanic3.csv"
dataset = DataLoad.load_data(filename, sep=",")
fd = DropOutliers(display=True)
fd.fit(dataset.data)
fd.transform(dataset.data)