def test_write_dataframe_to_ts_success(tmp_path, dataset):
"""Tests whether a dataset can be written by the .ts writer then read in."""
# load an example dataset
path = os.path.join(
os.path.dirname(sktime.__file__),
f"datasets/data/{dataset}/{dataset}_TEST.ts",
)
test_X, test_y = load_from_tsfile_to_dataframe(path)
# output the dataframe in a ts file
write_dataframe_to_tsfile(
data=test_X,
path=tmp_path,
problem_name=dataset,
class_label=np.unique(test_y),
class_value_list=test_y,
comment="""
The data was derived from twelve monthly electrical power demand
time series from Italy and first used in the paper "Intelligent
Icons: Integrating Lite-Weight Data Mining and Visualization into
GUI Operating Systems". The classification task is to distinguish
days from Oct to March (inclusive) from April to September.
""",
fold="_transform",
)
# load data back from the ts file
result = f"{tmp_path}/{dataset}/{dataset}_transform.ts"
res_X, res_y = load_from_tsfile_to_dataframe(result)
# check if the dataframes are the same
assert_frame_equal(res_X, test_X)
def test_write_dataframe_to_ts_fail(tmp_path):
"""Tests if non-dataframes are handled correctly."""
with pytest.raises(ValueError, match="Data provided must be a DataFrame"):
write_dataframe_to_tsfile(
data=np.random.rand(3, 2),
path=str(tmp_path),
problem_name="GunPoint",
)