Metadata-Version: 2.1
Name: SeqMetrics
Version: 1.3.2
Summary: SeqMetrics: Various errors for sequential data
Home-page: https://github.com/AtrCheema/SeqMetrics
Author: Ather Abbas
Author-email: ather_abbas786@yahoo.com
License: UNKNOWN
Description: 
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        The purpose of this repository to collect various classification and regression 
        performance metrics or errors which can be calculated for time-series/sequential/tabular data, 
        at one place. Currently only 1d data is supported.
        
        ## How to Install
        
        using `pip`
        
            pip install SeqMetrics
        
        using github link for the latest code
        
        	python -m pip install git+https://github.com/AtrCheema/SeqMetrics.git
        
        using setup file, go to folder where repo is downloaded
        
            python setup.py install
        
        
        ## How to Use
        
        ```python
        import numpy as np
        from SeqMetrics import RegressionMetrics
        
        true = np.random.random((20, 1))
        pred = np.random.random((20, 1))
        
        er = RegressionMetrics(true, pred)
        
        for m in er.all_methods: print("{:20}".format(m)) # get names of all availabe methods
        
        er.nse()   # calculate Nash Sutcliff efficiency
        
        er.calculate_all(verbose=True)  # or calculate errors using all available methods 
        ```
        
        The API is same for classification performance metrics.
        ```python
        import numpy as np
        from SeqMetrics import ClassificationMetrics
        
        # boolean array
        
        t = np.array([True, False, False, False])
        p = np.array([True, True, True, True])
        metrics = ClassificationMetrics(t, p)
        accuracy = metrics.accuracy()
        
        # binary classification with numerical labels
        
        true = np.array([1, 0, 0, 0])
        pred = np.array([1, 1, 1, 1])
        metrics = ClassificationMetrics(true, pred)
        accuracy = metrics.accuracy()
        
        # multiclass classification with numerical labels
        
        true = np.random.randint(1, 4, 100)
        pred = np.random.randint(1, 4, 100)
        metrics = ClassificationMetrics(true, pred)
        accuracy = metrics.accuracy()
        ```
        
        
        ## RegressionMetrics
        
        Currently following regression performance metrics are being calculated.
        
        | Name                          | Name in this repository  |
        | -------------------------- | ------------- |
        | Absolute Percent Bias | `abs_pbias` |
        | Agreement Index | `agreement_index` |
        | Aitchison Distance | `aitchison` |
        | Alpha decomposition of the NSE | `nse_alpha` |
        | Anomaly correction coefficient | `acc` |
        | Bias | `bias` |
        | Beta decomposition of NSE | `nse_beta` |
        | Bounded NSE | `nse_bound` |
        | Bounded KGE | `kge_bound` |
        | Brier Score | `brier_score` |
        | Correlation Coefficient | `corr_coeff` |
        | Coefficient of Determination | `r2` |
        | Centered Root Mean Square Deviation | `centered_rms_dev` |
        | Covariances | `covariance` |
        | Decomposed Mean Square Error | `decomposed_mse` |
        | Explained variance score | `exp_var_score` |
        | Euclid Distance | `euclid_distance` |
        | Geometric Mean Difference | `gmaen_diff` |
        | Geometric Mean Absolute Error | `gmae` |
        | Geometric Mean Relative Absolute Error | `gmrae` |
        | Inertial Root Squared Error | `irmse` |
        | Integral Normalized Root Squared Error | `inrse` |
        | Inter-percentile Normalized Root Mean Squared Error | `nrmse_ipercentile` |
        | Jensen-shannon divergence | `JS` |
        | Kling-Gupta Efficiency | `kge` |
        | Legate-McCabe Efficiency Index | `lm_index` |
        | Logrithmic Nash Sutcliff Efficiency | `log_nse` |
        | Logrithmic probability distribution | `log_prob` |
        | maximum error | `max_error` |
        | Mean Absolute Error | `mae` |
        | Mean Absolute Percentage Deviation | `mapd` |
        | Mean Absolute Percentage Error | `mape` |
        | Mean Absolute Relative Error | `mare` |
        | Mean Absolute Scaled Error | `mase` |
        | Mean Arctangle Absolute Percentage Error | `maape` |
        | Mean Bias Error | `mean_bias_error` |
        | Mean Bounded relative Absolute Error | `mbrae` |
        | Mean Errors | `me` |
        | Mean Gamma Deviances | `mean_gamma_deviance` |
        | Mean Log Error | `mle` |
        | Mean Normalized Root Mean Square Error | `nrmse_mean` |
        | Mean Percentage Error | `mpe` |
        | Mean Poisson Deviance | `mean_poisson_deviance` |
        | Mean Relative Absolute Error | `mrae` |
        | Mean Square Error | `mse` |
        | Mean Square Logrithmic Errors | `mean_square_log_error` |
        | Mean Variance | `mean_var` |
        | Median Absolute Error | `median_abs_error` |
        | Median Absolute Percentage Error | `mdape` |
        | Median Dictionary Accuracy | |
        | Median Error | `mde` |
        | Median Relative Absolute Error | `mdrae` |
        | Median Squared Error | `med_seq_error` |
        | Mielke-Berry R | `mb_r` |
        | Modified Agreement of Index | `mod_agreement_index` |
        | Modified Kling-Gupta Efficiency | `kge_mod` |
        | Modified Nash-Sutcliff Efficiency | `nse_mod` |
        | Nash-Sutcliff Efficiency | `nse` |
        | Non parametric Kling-Gupta Efficiency | `kge_np` |
        | Normalized Absolute Error | `norm_ae` |
        | Normalized Absolute Percentage Error | `norm_ape` |
        | Normalized Euclid Distance | `norm_euclid_distance` |
        | Normalized Root Mean Square Error | `nrmse` |
        | Peak flow bias of the flow duration curve | `fdc_fhv` |
        | Pearson correlation coefficient | `person_r` |
        | Percent Bias | `pbias` |
        | Range Normalized root mean square | `nrmse_range` |
        | Refined Agreement of Index | `ref_agreement_index` |
        | Relative Agreement of Index | `rel_agreement_index` |
        | Relative Absolute Error | `rae` |
        | Relative Root Mean Squared Error | `relative_rmse` |
        | Relative Nash-Sutcliff Efficiency | `nse_rel` |
        | Root Mean Square Errors | `rmse` |
        | Root Mean Square Log Error | `rmsle` |
        | Root Mean Square Percentage Error | `rmspe` |
        | Root Mean Squared Scaled Error | `rmsse` |
        | Root Median Squared Scaled Error | `rmsse` |
        | Root Relative Squared Error | `rrse` |
        | RSR | `rsr` |
        | Separmann correlation coefficient | `spearmann_corr` |
        | Skill Score of Murphy | `skill_score_murphy` |
        | Spectral Angle | `sa` |
        | Spectral Correlation | `sc` |
        | Spectral Gradient Angle | `sga` |
        | Spectral Information Divergence | `sid` |
        | Symmetric kullback-leibler divergence | `KLsym` |
        | Symmetric Mean Absolute Percentage Error | `smape` |
        | Symmetric Median Absolute Percentage Error | `smdape` |
        | sum of squared errors | `sse` | 
        | Volume Errors | `volume_error` |
        | Volumetric Efficiency | `ve` |
        | Unscaled Mean Bounded Relative Absolute Error | `umbrae` |
        | Watterson's M | `watt_m` |
        | Weighted Mean Absolute Percent Errors | `wmape` |
        | Weighted Absolute Percentage Error | `wape` |
        
        ## Related
        
        [forecasting_metrics](https://gist.github.com/bshishov/5dc237f59f019b26145648e2124ca1c9)
        
        [hydroeval](https://github.com/ThibHlln/hydroeval)
        
        [SkillMetrics](https://github.com/PeterRochford/SkillMetrics)
        
        [HydroErr](https://github.com/BYU-Hydroinformatics/HydroErr)
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: all
