def test_save_and_load(self):
L = np.array([[0, -1, 0], [0, 1, 1]])
label_model = LabelModel(cardinality=2, verbose=False)
label_model.fit(L, n_epochs=1)
original_preds = label_model.predict(L)
dir_path = tempfile.mkdtemp()
save_path = dir_path + "label_model.pkl"
label_model.save(save_path)
label_model_new = LabelModel(cardinality=2, verbose=False)
label_model_new.load(save_path)
loaded_preds = label_model_new.predict(L)
shutil.rmtree(dir_path)
np.testing.assert_array_equal(loaded_preds, original_preds)
def label_dataset(
task: Task,
dataset: Dataset,
path_config: Optional[PathConfig] = None,
debug: bool = False,
):
path_config = path_config or PathConfig.load()
applied_heuristics_df = pd.read_pickle(
str(path_config.generated / task.name /
f"heuristic_matrix_{dataset.name}.pkl"))
label_model = LabelModel()
label_model.load(str(path_config.generated / task.name /
"label_model.pkl"))
df = dataset.load()
df_labeled = do_labeling(label_model, applied_heuristics_df.to_numpy(), df,
task.labels)
if debug:
for (
heuristic_name,
applied_heuristic_series,
) in applied_heuristics_df.iteritems():
applied_heuristics_df[
heuristic_name] = applied_heuristic_series.map({
0: heuristic_name,
1: heuristic_name,
-1: ""
})
col_lfs = applied_heuristics_df.apply(
lambda row: ";".join([elm for elm in row if elm]), axis=1)
df_labeled["lfs"] = col_lfs
labeled_data_path = path_config.labeled_data / task.name
if not labeled_data_path.exists():
labeled_data_path.mkdir(parents=True)
target_file = labeled_data_path / f"{dataset.name}.labeled.csv"
df_labeled.to_csv(target_file, index=False)
print(f"Labeled dataset has been written to {target_file}.")
