I am using snorkel to create labels for my training data. I currently have five labeling functions for the task which I have stored in a list. I am using the following code to apply the labeling function:
lfs = [lf_a, lf_b, lf_c, lf_d, lf_e]
applier = PandasLFApplier(lfs)
L_train = applier.apply(df_data_sample)
# Train the label model and compute the training labels
label_model = LabelModel(cardinality=2, verbose=True)
label_model.fit(L_train, n_epochs=500, log_freq=50, seed=123)
going into the task I want to give a higher weight to lf_e labeling function as I my testing shows it has a higher accuracy than others. Not being able to do that leads to outputs from other lfs dominating output from lf_e. And I also don't want to remove any of the functions as I reduce my coverage if I do that.
Is there a way to do that in Snorkel?