Metadata-Version: 2.1
Name: roc_utils
Version: 0.2.2
Summary: Tools to compute and visualize ROC curves.
Home-page: https://github.com/hirsch-lab/roc-utils
Author: Norman Juchler
License: MIT
Description: # roc-utils
        
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        This Python package provides tools to compute and visualize [ROC curves](https://en.wikipedia.org/wiki/Receiver_operating_characteristic), which are used to graphically assess the diagnostic ability of binary classifiers. 
        
        
        Use [`roc_utils`](https://github.com/hirsch-lab/roc-utils) to perform ROC analyses, including the calculation of the ROC-AUC (the area under the ROC curve) and the identification of optimal classification thresholds for different objective functions. In addition, it is possible to compute mean, tolerance interval (TI) and confidence interval (CI) for a set of (related) ROC curves. Finally, error bounds can be estimated and visualized by means of boostrap sampling.
        
        ![Exemplary plots generated with `roc_utils`](data/plots-small.png)
        
        
        ### Installation:
        
            pip install roc-utils
            
        Use the following commands for a quick verification of the installation.
        
            python -c "import roc_utils; print(roc_utils.__version__)"
            python -c "import roc_utils; roc_utils.demo_bootstrap()"
        
        
        ### Usage:
        
        See [examples/tutorial.ipynb](https://github.com/hirsch-lab/roc-utils/blob/main/examples/tutorial.ipynb) for step-by-step introduction.
        
        ```python
        import numpy as np
        import matplotlib.pyplot as plt
        import roc_utils as ru
        
        # Construct a binary classification problem
        x, y = ru.demo_sample_data(n1=300, mu1=0.0, std1=0.5,
                                   n2=300, mu2=1.0, std2=0.7)
        
        # Compute the ROC curve...
        pos_label = True
        roc = ru.compute_roc(X=x, y=y, pos_label=pos_label)
        
        # ...and visualize it
        ru.plot_roc(roc, label="Sample data", color="red")
        plt.show()
        
        # To perform a ROC analysis using bootstrapping
        n_samples = 20
        ru.plot_roc_bootstrap(X=x, y=y, pos_label=pos_label,
                              n_bootstrap=n_samples,
                              title="Bootstrap demo");
        plt.show()
        ```
        
        
        ### Build from source:
        
        To fetch the project and run the tests or examples:
        
        ```bash
        git clone https://github.com/hirsch-lab/roc-utils.git
        cd roc-utils
        python tests/test_all.py
        python examples/examples.py
        ```
        
        To create distribution packages (a source archive and a wheel):
        
        ```bash 
        python setup.py sdist bdist_wheel
        ```
        
        To install the newly created Python package from the source archive:
        
        ```bash
        pip uninstall roc-utils
        pip cache remove roc_utils
        pip install dist/roc_utils*.tar.gz
        
        # Verify installation
        python -c "import roc_utils; print(roc_utils.__version__)"
        python -c "import roc_utils; roc_utils.demo_bootstrap()"
        ```
        
        
Keywords: ROC AUC receiver operating characteristic
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
