def update_metrics(metric_to_predictions_all,
metric_to_predictions_forecast, location, name,
predictions, ground_truth, num_forecast_steps,
use_quantiles):
gt_data = ground_truth[location] if ground_truth is not None else None
metric_to_predictions_all = model_utils.update_metric_to_predictions(
metric=name,
values=(predictions[location]
[eval_train_start_idx:train_window_end_index +
num_forecast_steps]),
metric_to_predictions=metric_to_predictions_all,
train_end_of_window=eval_train_start_idx,
gt_data=gt_data,
time_horizon_offset=time_horizon_offset,
quantiles=quantiles if use_quantiles else None,
quantiles_output=quantiles_output if use_quantiles else None,
metric_string_format=metric_string_format)
metric_to_predictions_forecast = model_utils.update_metric_to_predictions(
metric=name,
values=(predictions[location]
[train_window_end_index:train_window_end_index +
num_forecast_steps]),
metric_to_predictions=metric_to_predictions_forecast,
train_end_of_window=train_window_end_index,
gt_data=gt_data,
quantiles=quantiles if use_quantiles else None,
quantiles_output=quantiles_output if use_quantiles else None,
metric_string_format=metric_string_format)
return metric_to_predictions_all, metric_to_predictions_forecast
def test_update_metric_to_predictions_offset(self):
metric_to_predictions = {
"Infected": [
model.Prediction(time_horizon=1,
predicted_value=20,
ground_truth=12)
]
}
gt_data = [7, 2, 5, 7]
got = model_utils.update_metric_to_predictions(
metric="recovered_cases",
values=np.array([[12], [23]], np.float32),
metric_to_predictions=metric_to_predictions,
train_end_of_window=2,
gt_data=gt_data,
time_horizon_offset=2)
wanted = {
"Infected": [
model.Prediction(time_horizon=1,
predicted_value=20,
ground_truth=12)
],
"recovered_cases": [
model.Prediction(time_horizon=-1,
predicted_value=12,
ground_truth=5),
model.Prediction(time_horizon=0,
predicted_value=23,
ground_truth=7)
],
}
self.assertEqual(wanted, got)
def test_update_metric_to_predictions_with_quantiles(self):
metric_to_predictions = {}
gt_data = [7, 2, 5, 7]
got = model_utils.update_metric_to_predictions(
metric="recovered_cases",
values=np.array([[12, 14], [23, 25]], np.float32),
metric_to_predictions=metric_to_predictions,
train_end_of_window=2,
gt_data=gt_data,
quantiles=[0.1, 0.9],
metric_string_format="{metric}_{quantile}_quantile")
wanted = {
"recovered_cases_0.1_quantile": [
model.Prediction(time_horizon=1,
predicted_value=12,
ground_truth=5),
model.Prediction(time_horizon=2,
predicted_value=23,
ground_truth=7)
],
"recovered_cases_0.9_quantile": [
model.Prediction(time_horizon=1,
predicted_value=14,
ground_truth=5),
model.Prediction(time_horizon=2,
predicted_value=25,
ground_truth=7)
],
}
self.assertEqual(wanted, got)