def test2(self):
useless = ['RegionID', 'resulttime', 'CellID', 'PRB_DL_Used_Rate']
analyzer.drop_unnecessary_columns(useless)
arguments = {"C": 0.3}
analyzer.create_model(ModelType.logregression, arguments,
analyzer.data,
analyzer.data['cnt_averload_cell'])
print("Cross variation score:\n" + str(analyzer.models[0].cv_score))
# print("Feature importances:\n" + str(analyzer.models[0].model.feature_importances_))
print(analyzer.models[0].feature_names)
Visualizer.draw_class_scatter(analyzer.models[0])
def test3(self):
classifier = "DL_MCS_64QAM"
# TODO: redo it, so it doesn't delete the classifier from the dataset, what if you want to reuse it?
classifier_values = analyzer.data[classifier]
arguments = {"C": 0.8, "kernel": 'linear'}
useless = [
'RegionID', 'resulttime', 'CellID', 'cnt_averload_cell', classifier
]
analyzer.drop_unnecessary_columns(useless)
analyzer.create_model(ModelType.svc, arguments, analyzer.data,
classifier_values)
print("Cross variation score:\n" + str(analyzer.models[0].cv_score))
# print("Feature importances:\n" + str(analyzer.models[0].model.feature_importances_))
print(analyzer.models[0].feature_names)
Visualizer.draw_class_scatter(analyzer.models[0])
