for (filter,
keep), shortcut in zip(zip(["CTCF_|contact_dist|RNA"], [True]),
["CTCF_|contact_dist|RNA"]):
# filter2, keep2 = zip(["OrientBlock|plus_orientation|minus_orientation"], [False])
if contact_type == "oe":
# You can choose weight function from the Weight_funcs_modul
weightFuncs = [ones_like]
else:
weightFuncs = [ones_like]
for h in range(
2, 3
): # h is a coefficient for matrix smoothing. Used for SCC estimation
for weightFunc in weightFuncs:
predictor = Predictor()
predictor.read_data_predictors(training_file)
predictor.filter_predictors(filter, keep)
# for (filter2, keep2), shortcut in zip(
# zip(["CTCF_ConvergentPair"], [False]),
# ["CTCF|contact_dist|RNA"]):
# predictor.filter_predictors(filter2, keep2)
trained_predictor = predictor.train(shortcut=shortcut,
apply_log=apply_log,
weightsFunc=weightFunc,
show_plot=False)
trained_predictor.out_dir = out_dir
trained_predictor.draw_Feature_importances(show_plot=False)
for validation_file in validation_files:
if apply_log:
trained_predictor.validate(
validation_file,
show_plot=False,
