Metadata-Version: 2.1
Name: model contrast
Version: 0.1.8
Summary: Compare two ML models.
Home-page: https://github.com/ArmandDS/model_contrast
Author: Armand Olivares
Author-email: armandds@users.noreply.github.com
License: MIT
Description: [![Test & Upload to TestPyPI](https://github.com/ArmandDS/model_contrast/actions/workflows/test_and_upload_to_TestPyPI.yml/badge.svg)](https://github.com/ArmandDS/model_contrast/actions/workflows/test_and_upload_to_TestPyPI.yml)
        
        ![License: MIT](https://img.shields.io/github/license/armandds/model_contrast)
        
        # Models Contrast
        
        A simple package for compare the performance of two ML models in sklearn, python.
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install model-contrast.
        
        ```bash
        pip install model-contrast
        ```
        
        # Usage
        
        ## Compare 2 Binary Classifiers
        
        ```python
        from sklearn.datasets import make_classification
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split
        
        # create two demo models
        X, y =  make_classification(n_samples=700, random_state=42)
        X_train, X_test, y_train, y_test = train_test_split(X, y,
                 test_size=0.2, random_state=42)
        
        model1 = RandomForestClassifier(n_estimators=10, random_state=42)
        model2 = LogisticRegression()
        
        #train the models
        model1.fit(X_train, y_train)
        model2.fit(X_train, y_train)
        
        ```
        
        Now let's compare them with our package:
        
        ```python
        from model_contrast import classificator_contrast
        
        classificator_contrast(model1, model2, X_test, y_test)
        
        ```
        and it return: 
        
        ![image](https://github.com/ArmandDS/model_contrast/blob/main/img/binary.PNG)
        
        
        ## Compare Multi-Class Classifiers
        
        ```python
        
        from sklearn.datasets import make_classification
        from sklearn.ensemble import RandomForestClassifier
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split
        
        # create two demo models
        X, y =  make_classification(n_samples=700, random_state=42, n_classes=4, n_informative=4)
        X_train, X_test, y_train, y_test = train_test_split(X, y,
                 test_size=0.2, random_state=42)
        
        model1 = RandomForestClassifier(n_estimators=10, random_state=42)
        model2 = LogisticRegression()
        
        #train the models
        model1.fit(X_train, y_train)
        model2.fit(X_train, y_train)
        
        
        ```
        Compare them:
        
        ```python
        from model_contrast import classificator_contrast
        
        classificator_contrast(model1, model2, X_test, y_test)
        
        ```
        and it returns:
        
        ![image](https://github.com/ArmandDS/model_contrast/blob/main/img/multiclass.PNG)
        
        
        ## Compare 2 Regressors
        
        ```python
        
        from sklearn.datasets import make_regression
        from sklearn.model_selection import train_test_split
        from sklearn.linear_model import LinearRegression
        from sklearn.ensemble import RandomForestRegressor
        
        #create the regressor
        X, y =  make_regression(n_samples=700, random_state=42)
        X_train, X_test, y_train, y_test = train_test_split(X, y,
                 test_size=0.2, random_state=42)
        
        model1 = RandomForestRegressor(n_estimators=10, random_state=42)
        model2 = LinearRegression()
        
        #train the regressors
        model1.fit(X_train, y_train)
        model2.fit(X_train, y_train)
        
        
        ```
        Compare them:
        
        ```python
        from model_contrast import  regressor_contrast
        
        regressor_contrast(model1, model2, X_test, y_test)
        
        ```
        and it returns:
        
        ![image](https://github.com/ArmandDS/model_contrast/blob/main/img/regressors.PNG)
        
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
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