def test_get_feature_names_out_raises_when_input_features_is_string(df_time):
tr = ExpandingWindowFeatures(functions=["mean", "sum"])
tr.fit(df_time)
with pytest.raises(ValueError):
# get error when user does not pass a list
tr.get_feature_names_out(input_features="ambient_temp")
def test_get_feature_names_out_raises_when_input_features_not_transformed(
df_time):
tr = ExpandingWindowFeatures(functions=["mean", "sum"])
tr.fit(df_time)
with pytest.raises(ValueError):
# assert error when uses passes features that were not transformed
tr.get_feature_names_out(input_features=["color"])
def test_get_feature_names_out_single_variable_and_single_function(df_time):
# input features
original_features = ["ambient_temp", "module_temp", "irradiation", "color"]
tr = ExpandingWindowFeatures(variables="ambient_temp", functions="sum")
tr.fit(df_time)
# expected
output = [
"ambient_temp_expanding_sum",
]
assert tr.get_feature_names_out(
input_features=None) == original_features + output
assert tr.get_feature_names_out(input_features=["ambient_temp"]) == output
def test_get_feature_names_out_multiple_variables_and_functions(df_time):
# input features
input_features = ["ambient_temp", "module_temp", "irradiation"]
original_features = ["ambient_temp", "module_temp", "irradiation", "color"]
tr = ExpandingWindowFeatures(functions=["mean", "sum"])
tr.fit(df_time)
# expected
output = [
"ambient_temp_expanding_mean",
"ambient_temp_expanding_sum",
"module_temp_expanding_mean",
"module_temp_expanding_sum",
"irradiation_expanding_mean",
"irradiation_expanding_sum",
]
assert tr.get_feature_names_out(
input_features=None) == original_features + output
assert tr.get_feature_names_out(input_features=input_features) == output
assert tr.get_feature_names_out(
input_features=input_features[0:2]) == output[0:4]
assert tr.get_feature_names_out(
input_features=[input_features[0]]) == output[0:2]