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| author | Daniel Friesel <daniel.friesel@uos.de> | 2022-03-31 18:39:25 +0200 |
|---|---|---|
| committer | Daniel Friesel <daniel.friesel@uos.de> | 2022-03-31 18:39:25 +0200 |
| commit | 58616f7177ffc6a1db7689f727688518ce07ad5c (patch) | |
| tree | 0cc50ac7a6a2d16bb16a103575d4aa890abd1e49 /lib | |
| parent | 406c138847fd8cf14c0b0f5a6a111e17250c4848 (diff) | |
--export-raw-predictions: add parameter values
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/model.py | 28 |
1 files changed, 22 insertions, 6 deletions
diff --git a/lib/model.py b/lib/model.py index 1c19f14..34bb564 100644 --- a/lib/model.py +++ b/lib/model.py @@ -362,7 +362,11 @@ class AnalyticModel: ref = self.by_name for name, elem in sorted(ref.items()): detailed_results[name] = dict() - raw_results[name] = dict() + raw_results[name] = { + "paramValues": elem["param"], + "paramNames": self.parameters, + "attribute": dict(), + } for attribute in elem["attributes"]: predicted_data = np.array( list( @@ -377,9 +381,9 @@ class AnalyticModel: measures = regression_measures(predicted_data, elem[attribute]) detailed_results[name][attribute] = measures if return_raw: - raw_results[name][attribute] = { - "ground_truth": list(elem[attribute]), - "model_output": list(predicted_data), + raw_results[name]["attribute"][attribute] = { + "groundTruth": list(elem[attribute]), + "modelOutput": list(predicted_data), } if return_raw: @@ -1055,7 +1059,7 @@ class PTAModel(AnalyticModel): pairs.add((trace[i - 1]["name"], trace[i + 1]["name"])) return list(pairs) - def assess(self, model_function, ref=None): + def assess(self, model_function, ref=None, return_raw=False): """ Calculate MAE, SMAPE, etc. of model_function for each by_name entry. @@ -1070,7 +1074,12 @@ class PTAModel(AnalyticModel): """ if ref is None: ref = self.by_name - detailed_results = super().assess(model_function, ref=ref) + if return_raw: + detailed_results, raw_results = super().assess( + model_function, ref=ref, return_raw=return_raw + ) + else: + detailed_results = super().assess(model_function, ref=ref) for name, elem in sorted(ref.items()): if elem["isa"] == "transition": predicted_data = np.array( @@ -1088,7 +1097,14 @@ class PTAModel(AnalyticModel): predicted_data, elem["power"] * elem["duration"] ) detailed_results[name]["energy_Pt"] = measures + if return_raw: + raw_results[name]["attribute"]["energy_Pt"] = { + "groundTruth": list(elem["power"] * elem["duration"]), + "modelOutput": list(predicted_data), + } + if return_raw: + return detailed_results, raw_results return detailed_results def assess_states( |