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| # A Novel Workload Prediction Model Using ELM with Opposition-based Tug of War Optimization | ||
| []() | ||
| [](https://doi.org/10.5281/zenodo.3626114) | ||
| []() | ||
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| ## Cite us | ||
| * If you see my code and data useful and use it, please cite us as follows | ||
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| * Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. | ||
| ## Dear friends and followers | ||
| * I updated to repository to the newest version, which is very easy to read and reproduce. | ||
| * All of our optimizer (meta-heuristics) now deleted and taken the new one from my newest library: | ||
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| https://pypi.org/project/mealpy/ | ||
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| * If you use my code or library in your project, I would appreciate the cites: | ||
| * Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. | ||
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| * Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham. | ||
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| * Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE. | ||
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| * If you want to know more about code, or want a pdf of both above paper, contact me: [email protected] | ||
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| * Take a look at this repos, the simplify code using python (numpy) for all algorithms above. (without neural networks) | ||
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| * https://github.com/thieunguyen5991/metaheuristics | ||
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| ## How to read my repository | ||
| 1. data: include raw and formatted data | ||
| 2. envs: include conda environment and how to install conda environment | ||
| 3. utils: Helped functions such as IO, Draw, Math, Settings (for all model and parameters), Preprocessing... | ||
| 4. paper: include 2 main folders: | ||
| * results: forecasting results of all models (3 folders inside) | ||
| * final: final forecasting results (runs on server) | ||
| * test: testing forecasting results (runs on personal computer, just for test) | ||
| * temp: nothing (just for copy of test folder) | ||
| * scaling: scaling results | ||
| 5. model: (4 folders) | ||
| 4. model: (2 folders) | ||
| * root: (want to understand the code, read this classes first) | ||
| * root_base.py: root for all models (traditional, hybrid and variants...) | ||
| * root_algo.py: root for all optimization algorithms | ||
| * traditional: root for all traditional models (inherit: root_base) | ||
| * hybrid: root for all hybrid models (inherit: root_base) | ||
| * optimizer: (this classes inherit: root_algo.py) | ||
| * evolutionary: include algorithms related to evolution algorithm such as GA, DE,.. | ||
| * swarm: include algorithms related to swarm optimization such as PSO, BFO, ... | ||
| * physics: include algorithms related to physics optimization such as QSO, TWO, ... | ||
| * main: (final models) | ||
| * this classes will use those optimizer above and those root (traditional, hybrid) above | ||
| * the running files (outside with the downloaded folder) will call this classes | ||
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@@ -80,16 +72,7 @@ For example: | |
| 8. OTWO-ELM => otwo_elm_script.py | ||
| ``` | ||
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| 3. In paper/results/final model includes folder's name represent the data such as | ||
| ```code | ||
| cpu: input model would be cpu, output model would be cpu | ||
| ram: same as cpu | ||
| multi_cpu : input model would be cpu and ram, output model would be cpu | ||
| multi_ram : input model would be cpu and ram, output model would be ram | ||
| multi : input model would be cpu and ram, output model would be cpu and ram | ||
| ``` | ||
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| 4. How to change model's parameters? | ||
| ### How to change model's parameters? | ||
| ```code | ||
| You can change the model's parameters in file: utils/SettingPaper.py | ||
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@@ -124,7 +107,7 @@ mlnn1hl_paras_final = { | |
| ga_elm_paras_final = { | ||
| "sliding": [2, 3, 5], | ||
| "hidden_size" : [(5, False)], | ||
| "activation": [0], # 0: elu, 1:relu, 2:tanh, 3:sigmoid | ||
| "activation": ["elu"], | ||
| "train_valid_rate": [(0.6, 0.4)], | ||
| "epoch": [100], | ||
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@@ -137,47 +120,28 @@ ga_elm_paras_final = { | |
| - Same as traditional models. | ||
| ``` | ||
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| 5. Where is the results folder? | ||
| ### Where is the results folder? | ||
| ```code | ||
| - Look at the running file, for example: ga_elm_script.py | ||
| +) For normal runs (Only run 1 time for each model). | ||
| +) For 1-time runs (Only run 1 time for each model). | ||
| _There are 3 type of results file include: model_name.csv file, model_name.png file and Error-model_name.csv file | ||
| _model_name.csv included: y_true and y_predict | ||
| _model_name.png is visualized of: y_true and y_predict (test dataset) | ||
| _Error-model_name.csv included errors of training dataset after epoch. 1st, 2nd column are: MSE, MAE errors | ||
| _Error-model_name.csv included errors of training dataset after epoch. 1st, 2nd column are: MSE errors | ||
| => All 3 type of files above is automatically generated in folder: paper/results/final/... | ||
| => All 3 type of files above is automatically generated in folder which you can set in SettingPaper | ||
| +) For stability runs (Run each model 15 times with same parameters). | ||
| +) For stability runs (Run each model n-time with same parameters). | ||
| Because in this test, we don't need to visualize the y_true and y_predict and also don't need to save y_true and y_predict | ||
| So I just save 15 times running in the same csv files in folder: paper/results/stability/... | ||
| So I just save n-time running in the same csv files in folder | ||
| - Noted: | ||
| + In the training set we can use MSE or MAE error. But for the testing set, we can use so much more error like: R2, MAPE, ... | ||
| + In the training set we use MSE. But for the testing set, we can use so much more error like: R2, MAPE, ... | ||
| You can find the function code described it in file: | ||
| model/root/root_base.py | ||
| + _save_results__: this is for normal runs | ||
| + _save_results_ntimes_run__: this is for stability runs. | ||
| + You want to change the directory to save results?. Simple. | ||
| For example: Look at running file: ga_elm_script.py | ||
| _Line 14: pathsave = "paper/results/final/" --> This is general path to save file. | ||
| But for each dataset like: wc, uk, eu,... I need to save it in different folder right. So now look at | ||
| _Line 34: "path_save_result": pathsave + requirement_variables[4]. | ||
| Here, the requirement_variables[4]: is inside file: utils/SettingPaper.py ("eu/", # path_save_result) | ||
| So the final path to save the results for normal runs is: paper/results/final/eu/ | ||
| + _save_results__: | ||
| ``` | ||
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| 6. Take a look at project_structure.md file. Describe how the project was built. | ||
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| ## License | ||
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| [](https://opensource.org/licenses/Apache-2.0) | ||
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| * Take a look at project_structure.md file. Describe how the project was built. | ||