Model ensembles #231
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Model ensembles #231
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ofrancon
previously approved these changes
Oct 20, 2023
| @@ -69,6 +71,30 @@ def test_create_prediction_initial_context_and_action_vectors(self): | |||
| self.assertEqual(last_day_of_training_cases_data, prediction_context[-1]) | |||
| gc.collect() | |||
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| def test_convert_to_new_cases(self): | |||
| df = oxford_data.prepare_cases_dataframe(DATA_PATH) | |||
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+1 for the unit test 👍 . A docstring explaining what it does would make it even better. I had to read the code to see it was converting smooth cases per 100k back to the original new cases.
| @@ -348,8 +348,18 @@ def convert_smooth_cases_per_100K_to_new_cases(smooth_cases_per_100K, | |||
| window_size, | |||
| prev_new_cases_list, | |||
| pop_size): | |||
| return (((window_size * pop_size) / 100000.) * smooth_cases_per_100K \ | |||
| - np.sum(prev_new_cases_list[-(window_size-1):])).clip(min=0.0) | |||
| # Smooth cases per 100K -> Smooth cases | |||
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This function could use a docstring. It's pretty hard to understand what it does from the code...
I'm glad you have a unit test for it because I can't easily tell from the code if the logic is correct or off by 1...
…es value rather than the current day's value
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A couple of minor changes.