Metadata-Version: 2.1
Name: TakeConfusionMatrix
Version: 0.0.1
Summary: TakeConfusionMatrix is a tool for batched metrics calculations
Home-page: https://curupira.visualstudio.com/DefaultCollection/Data%20Analytics%20%28DA%29/_git/TakeConfusionMatrix
Author: Cecília Assis
Author-email: cecilia.assis@take.net
Maintainer: D&A Team
Maintainer-email: analytics.ped@take.net
License: UNKNOWN
Description: ![](https://img.shields.io/badge/python-3.8-blue)
        
        # TakeConfunsionMatrix 
        TakeConfusionMatrix is a Python package for batched Machine Learning metrics calculation and is distributed under MIT License.
        
        ## Goal
        The main goal of this package is to enable calculation of Machine Learning metrics for hundreds of milions of results through batch computation.
        
        ## Features
        The current package features are:
        - Confusion Matrix 
            - Image output
            - Normalized output
            - Custom labelled output
        - Precision Score
        - Recall Score
        - F1-Score
        - Accuracy Score (Exact Match Ratio)
        
        ## Installation
        ### Dependencies
        TakeConfusionMatrix requires:
        - Python (>= 3.8)
        - Pandas (>= 1.0.4)
        - scikit-learn (>= 0.23.1)
        
        ### User installation
        Install the 64bit version of Python, for instance from https://www.python.org/. Then run:
        
        ```bash
        pip install -U TakeConfusionMatrix
        ```
        
        ### VirutalEnv installation
        In order to avoid potential conflicts with other packages it is strongly recommended to use a virtual environment, e.g. python3 `virtualenv` (see [python3 virtualenv documentation](https://docs.python.org/3/tutorial/venv.html)) or conda environments. 
        
        To do so, install the 64bit version of Python3 if you doesn't have it yet, then run:
        
        ```bash
        python3 -m venv venv
        source venv/bin/activate
        pip install -U TakeConfusionMatrix
        ```
        
        **NOTE**: Please note that the above instructions assume a Linux-based SO. If you are using another environment, see [scikit-learn installation documentation](https://scikit-learn.org/stable/install.html).
        
        ## Usage
        Here the package's features are briefly presented. For more advanced examples, please refer to the methods documentation.
        
        ### Matrix computation
        ```python
        # Import MetricsComputation class
        from take_confusion_matrix import MetricsCalculation
        
        # Initialize class
        labels = [0, 1, 2]
        mc = MetricsCalculator(labels)
        
        # Compute matrix
        y_true = [0, 1, 0, 1]
        y_pred = [0, 0, 0, 0]
        mc.compute_matrix(y_true, y_pred)
        
        y_true = [0, 2, 0, 2]
        y_pred = [0, 0, 0, 0]
        mc.compute_matrix(y_true, y_pred)
        
        # Generate matrix
        confusion_matrix = mc.generate_confusion_matrix()
        print(confusion_matrix)
        ```
        
        #### Normalized matrix
        ```python
        confusion_matrix = mc.generate_confusion_matrix(normalize=True)
        ```
        
        #### Custom labelled matrix
        ```python
        labels = ['class_0', 'class_1', 'class_2']
        confusion_matrix = mc.generate_confusion_matrix(labels=labels)
        ```
        
        #### Label free matrix
        ```python
        confusion_matrix = mc.generate_confusion_matrix(with_labels=False)
        ```
        
        ### Metrics computation
        ```python
        # Import MetricsComputation class
        from take_confusion_matrix import MetricsCalculation
        
        # Initialize class
        labels = [0, 1, 2]
        mc = MetricsCalculator(labels)
        
        # Compute matrix
        y_true = [0, 1, 0, 1]
        y_pred = [0, 0, 0, 0]
        mc.compute_matrix(y_true, y_pred)
        
        y_true = [0, 2, 0, 2]
        y_pred = [0, 0, 0, 0]
        mc.compute_matrix(y_true, y_pred)
        
        # Generate metics
        metrics = mc.generate_metrics()
        print(metrics)
        ```
        
        ## Testing
        In order to test package's features, you must download the code and change you current directory (cd) to the package's one. After that, open a terminal inside package's folder and type:
        
        ```bash
        pytest
        ```
        
        All tests are stored inside `tests` folders, meaning that any test folder named `tests` contains a test set.
        
        ## Maintainer
        Take's D&A Team | [analytics.ped@take.net](mailto:analytics.ped@take.net)
        
        ## Author
        Cecília Regina Oliveira de Assis | [@ceciliassis](https://github.com/ceciliassis)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
