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integrating m3sa changes with opendc
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,41 @@ | ||
| /* | ||
| * Copyright (c) 2020 AtLarge Research | ||
| * | ||
| * Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| * of this software and associated documentation files (the "Software"), to deal | ||
| * in the Software without restriction, including without limitation the rights | ||
| * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| * copies of the Software, and to permit persons to whom the Software is | ||
| * furnished to do so, subject to the following conditions: | ||
| * | ||
| * The above copyright notice and this permission notice shall be included in all | ||
| * copies or substantial portions of the Software. | ||
| * | ||
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| * SOFTWARE. | ||
| */ | ||
|
|
||
| description = "Multi-Meta-Model Simulation Analysis (M3SA) used across OpenDC modules" | ||
|
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||
| // Build configuration | ||
| plugins { | ||
| `kotlin-library-conventions` | ||
| } | ||
|
|
||
| dependencies { | ||
| api(libs.kotlinx.coroutines) | ||
|
|
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| testImplementation(projects.opendcSimulator.opendcSimulatorCore) | ||
| } | ||
|
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| sourceSets { | ||
| main { | ||
| kotlin.srcDirs("src/main/kotlin", "src/main/python") | ||
| resources.srcDir("src/main/resources") | ||
| } | ||
| } |
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56
opendc-experiments/opendc-m3sa/src/main/kotlin/M3saRunner.kt
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| /* | ||
| * Copyright (c) 2024 AtLarge Research | ||
| * | ||
| * Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| * of this software and associated documentation files (the "Software"), to deal | ||
| * in the Software without restriction, including without limitation the rights | ||
| * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| * copies of the Software, and to permit persons to whom the Software is | ||
| * furnished to do so, subject to the following conditions: | ||
| * | ||
| * The above copyright notice and this permission notice shall be included in all | ||
| * copies or substantial portions of the Software. | ||
| * | ||
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| * SOFTWARE. | ||
| */ | ||
|
|
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| import kotlin.io.path.Path | ||
|
|
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| /** | ||
| * This constant variable should be changed depending on the root folder that is being run. | ||
| * PATH_TO_PYTHON_MAIN should point to the main python file, ran when the analysis starts. | ||
| */ | ||
|
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| public val ANALYSIS_SCRIPTS_DIRECTORY: String = "./opendc-experiments/opendc-m3sa/src/main/python" | ||
| public val ABSOLUTE_SCRIPT_PATH: String = | ||
| Path("$ANALYSIS_SCRIPTS_DIRECTORY/main.py").toAbsolutePath().normalize().toString() | ||
| public val SCRIPT_LANGUAGE: String = "python3" | ||
|
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| public fun m3saRun( | ||
| outputFolderPath: String, | ||
| m3saSetupPath: String, | ||
| ) { | ||
| val process = | ||
| ProcessBuilder( | ||
| SCRIPT_LANGUAGE, | ||
| ABSOLUTE_SCRIPT_PATH, | ||
| outputFolderPath, | ||
| m3saSetupPath, | ||
| ) | ||
| .directory(Path(ANALYSIS_SCRIPTS_DIRECTORY).toFile()) | ||
| .start() | ||
|
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| val exitCode = process.waitFor() | ||
| if (exitCode == 0) { | ||
| println("[M3SA says] M3SA operation(s) completed successfully.") | ||
| } else { | ||
| val errors = process.errorStream.bufferedReader().readText() | ||
| println("[M3SA says] Exit code $exitCode; Error(s): $errors") | ||
| } | ||
| } |
114 changes: 114 additions & 0 deletions
114
opendc-experiments/opendc-m3sa/src/main/python/accuracy_evaluator.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| import numpy as np | ||
|
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| from models.MetaModel import MetaModel | ||
|
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|
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| def accuracy_evaluator( | ||
| real_data, | ||
| multi_model, | ||
| compute_mape=True, | ||
| compute_nad=True, | ||
| compute_rmsle=True, | ||
| rmsle_hyperparameter=0.5, | ||
| only_metamodel=False | ||
| ): | ||
| """ | ||
| :param real_data: the real-world data of the simulation | ||
| :param multi_model: the Multi-Model, containing individual models (possibly also a Meta-Model, with id=101) | ||
| :param MAPE: whether to calculate Mean Absolute Percentage Error (MAPE) | ||
| :param NAD: whether to calculate Normalized Absolute Differences (NAD) | ||
| :param RMSLE: whether to calculate Root Mean Square Logarithmic Error (RMSLE) | ||
| :param rmsle_hyperparameter: the hyperparameter that balances the ration underestimations:overestimations | ||
| - default is 0.5 (balanced penalty) | ||
| - < 0.5: more penalty for overestimations | ||
| - > 0.5: more penalty for underestimations | ||
| e.g., RMSLE_hyperparameter=0.3 -> 30% penalty for overestimations, 70% penalty for underestimations (3:7 ratio) | ||
| :return: None, but prints the accuracy metrics | ||
| """ | ||
|
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| meta_model = MetaModel(multimodel=multi_model) | ||
| multi_model.models.append(meta_model.meta_model) # metamodel | ||
| # multi_model.models.append(Model(raw_host_data=real_data, id=-1, path=None)) # real-world data | ||
|
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| with open(multi_model.output_folder_path + "/accuracy_report.txt", "a") as f: | ||
| f.write("====================================\n") | ||
| f.write("Accuracy Report, against ground truth\n") | ||
|
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| for model in multi_model.models: | ||
| if only_metamodel and model.id != 101: | ||
| continue | ||
|
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| if model.id == -1: | ||
| f.write("Real-World data") | ||
| elif model.id == 101: | ||
| f.write( | ||
| f"Meta-Model, meta-function: {multi_model.user_input['meta_function']}, window_size: {meta_model.multi_model.window_size}") | ||
| else: | ||
| f.write(f"Model {model.id}") | ||
|
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| simulation_data = model.raw_sim_data | ||
| min_len = min(len(real_data), len(simulation_data)) | ||
| real_data = real_data[:min_len] | ||
| simulation_data = simulation_data[:min_len] | ||
| if compute_mape: | ||
| accuracy_mape = mape( | ||
| real_data=real_data, | ||
| simulation_data=simulation_data | ||
| ) | ||
| f.write(f"\nMean Absolute Percentage Error (MAPE): {accuracy_mape}%") | ||
|
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| if compute_nad: | ||
| accuracy_nad = nad( | ||
| real_data=real_data, | ||
| simulation_data=simulation_data | ||
| ) | ||
| f.write(f"\nNormalized Absolute Differences (NAD): {accuracy_nad}%") | ||
|
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| if compute_rmsle: | ||
| accuracy_rmsle = rmsle( | ||
| real_data=real_data, | ||
| simulation_data=simulation_data, | ||
| alpha=rmsle_hyperparameter | ||
| ) | ||
| f.write( | ||
| f"\nRoot Mean Square Logarithmic Error (RMSLE), alpha={rmsle_hyperparameter}:{accuracy_rmsle}\n\n") | ||
|
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| f.write("====================================\n") | ||
|
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|
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| def mape(real_data, simulation_data): | ||
| """ | ||
| Calculate Mean Absolute Percentage Error (MAPE) | ||
| :param real_data: Array of real values | ||
| :param simulation_data: Array of simulated values | ||
| :return: MAPE value | ||
| """ | ||
| real_data = np.array(real_data) | ||
| simulation_data = np.array(simulation_data) | ||
| return round(np.mean(np.abs((real_data - simulation_data) / real_data)) * 100, 3) | ||
|
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|
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| def nad(real_data, simulation_data): | ||
| """ | ||
| Calculate Normalized Absolute Differences (NAD) | ||
| :param real_data: Array of real values | ||
| :param simulation_data: Array of simulated values | ||
| :return: NAD value | ||
| """ | ||
| real_data = np.array(real_data) | ||
| simulation_data = np.array(simulation_data) | ||
| return round(np.sum(np.abs(real_data - simulation_data)) / np.sum(real_data) * 100, 3) | ||
|
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|
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| def rmsle(real_data, simulation_data, alpha=0.5): | ||
| """ | ||
| Calculate Root Mean Square Logarithmic Error (RMSLE) with an adjustable alpha parameter | ||
| :param real_data: Array of real values | ||
| :param simulation_data: Array of simulated values | ||
| :param alpha: Hyperparameter that balances the penalty between underestimations and overestimations | ||
| :return: RMSLE value | ||
| """ | ||
| real_data = np.array(real_data) | ||
| simulation_data = np.array(simulation_data) | ||
| log_diff = alpha * np.log(real_data) - (1 - alpha) * np.log(simulation_data) | ||
| return round(np.sqrt(np.mean(log_diff ** 2)) * 100, 3) |
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