Metadata-Version: 2.1
Name: permetrics
Version: 1.0.1
Summary: A framework of PERformance METRICS (PerMetrics) for artificial intelligence models
Home-page: https://github.com/thieunguyen5991/permetrics
Author: Thieu Nguyen
Author-email: nguyenthieu2102@gmail.com
License: MIT
Download-URL: https://github.com/thieunguyen5991/permetrics/archive/v1.0.1.zip
Description: # A framework of PERformance METRICS (PerMetrics) for artificial intelligence models
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        ---
        > "Knowledge is power, sharing it is the premise of progress in life. It seems like a burden to someone, but it is the only way to achieve immortality."
        >  --- [Thieu Nguyen](https://www.researchgate.net/profile/Thieu_Nguyen6)
        ---
        
        ## Introduction
        * PerMetrics is a python library for performance metrics of artificial intelligence models.
        
        * The goals of this framework are:
            * Combine all metrics for regression, classification and clustering models
            * Helping users in all field access to metrics as fast as possible
            * Perform Qualitative Analysis of models.
            * Perform Quantitative Analysis of models.
        
        * Metrics
        
        | Problem        | STT | MetricÂ  | Metric Fullname                           | Characteristics                |
        |----------------|-----|---------|-------------------------------------------|--------------------------------|
        | Regression     | 1   | EVS     | Explained Variance Score                  | Larger is better \(Best = 1\)  |
        |                | 2   | ME      | Max Error                                 | Smaller is better \(Best = 0\) |
        |                | 3   | MAE     | Mean Absolute Error                       | Smaller is better \(Best = 0\) |
        |                | 4   | MSE     | Mean Squared Error                        | Smaller is better \(Best = 0\) |
        |                | 5   | RMSE    | Root Mean Squared Error                   | Smaller is better \(Best = 0\) |
        |                | 6   | MSLE    | Mean Squared Log Error                    | Smaller is better \(Best = 0\) |
        |                | 7   | MedAE   | Median Absolute Error                     | Smaller is better \(Best = 0\) |
        |                | 8   | MRE     | Mean Relative Error                       | Smaller is better \(Best = 0\) |
        |                | 9   | MAPE    | Mean Absolute Percentage Error            | Smaller is better \(Best = 0\) |
        |                | 10  | SMAPE   | Symmetric Mean Absolute Percentage Error  | Smaller is better \(Best = 0\) |
        |                | 11  | MAAPE   | Mean Arctangent Absolute Percentage Error | Smaller is better \(Best = 0\) |
        |                | 12  | MASE    | Mean Absolute Scaled Error                | Smaller is better \(Best = 0\) |
        |                | 13  | NSE     | Nash\-Sutcliffe Efficiency Coefficient    | Larger is better \(Best = 1\)  |
        |                | 14  | WI      | Willmott Index                            | Larger is better \(Best = 1\)  |
        |                | 15  | R       | Pearsonâ€™s Correlation Index               | Larger is better \(Best = 1\)  |
        |                | 16  | CI      | Confidence Index                          | Larger is better \(Best = 1\)  |
        |                | 17  | R2      | Coefficient of Determination              | Larger is better \(Best = 1\)  |
        | Classification | 1   |         |                                           |                                |
        | Clustering     | 1   |         |                                           |                                |
        
        
        
        ### Dependencies
        * Python (>= 3.7)
        * Numpy (>= 1.15.1)
        
        
        ### User installation
        Install the [current PyPI release](https://pypi.python.org/pypi/permetrics):
        
        ```bash
        pip install permetrics
        ```
        
        Or install the development version from GitHub:
        
        ```bash
        pip install git+https://github.com/thieunguyen5991/permetrics
        ```
        
        
        ### Example
        + All you need to do is: (Make sure your y_true and y_pred is a numpy array)
        
        ```code 
        * Simple example:
        
        ## For example with RMSE:
        
            from numpy import array
            from permetrics.regression import Metrics
            
            ## For 1-D array
            y_true = array([3, -0.5, 2, 7])
            y_pred = array([2.5, 0.0, 2, 8])
            
            obj1 = Metrics(y_true, y_pred)
            print(obj1.rmse_func(clean=True, decimal=5))
            
            ## For > 1-D array
            y_true = array([[0.5, 1], [-1, 1], [7, -6]])
            y_pred = array([[0, 2], [-1, 2], [8, -5]])
            
            multi_outputs = [None, "raw_values", [0.3, 1.2], array([0.5, 0.2]), (0.1, 0.9)]
            obj2 = Metrics(y_true, y_pred)
            for multi_output in multi_outputs:
                print(obj2.rmse_func(clean=False, multi_output=multi_output, decimal=5))
        
        * Or run the simple:
            python examples/RMSE.py
        
        * The more complicated tests in the folder: examples
        ```
        The [documentation](https://permetrics.readthedocs.io/) includes more detailed installation instructions and explanations.
        
        ### Changelog
        * See the [ChangeLog.md](https://github.com/thieunguyen5991/permetrics/blob/master/ChangeLog.md) for a history of notable changes to permetrics.
        
        
        ### Important links
        
        * Official source code repo: https://github.com/thieunguyen5991/permetrics
        * Official documentation: https://permetrics.readthedocs.io/
        * Download releases: https://pypi.org/project/permetrics/
        * Issue tracker: https://github.com/thieunguyen5991/permetrics/issues
        
        * This project also related to my another projects which are "meta-heuristics" and "neural-network", check it here
            * https://github.com/thieunguyen5991/opfunu
            * https://github.com/thieunguyen5991/metaheuristics
            * https://github.com/chasebk
            
           
        ## Contributions 
        
        ### Citation
        + If you use permetrics in your project, please cite my works: 
        ```code 
        @software{thieu_nguyen_2020_3951205,
          author       = {Thieu Nguyen},
          title        = {A framework of PERformance METRICS (PerMetrics) for artificial intelligence models},
          month        = jul,
          year         = 2020,
          publisher    = {Zenodo},
          doi          = {10.5281/zenodo.3951205},
          url          = {https://doi.org/10.5281/zenodo.3951205}
        }
        
        @article{nguyen2019efficient,
          title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
          author={Nguyen, Thieu and Nguyen, Tu and Nguyen, Binh Minh and Nguyen, Giang},
          journal={International Journal of Computational Intelligence Systems},
          volume={12},
          number={2},
          pages={1144--1161},
          year={2019},
          publisher={Atlantis Press}
        }
        ```
         
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: System :: Benchmark
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.7
Description-Content-Type: text/markdown
