Metadata-Version: 2.1
Name: cfnow
Version: 0.0.5
Summary: Generate counterfactuals with ease. This package takes a model and point (with a certain class) and minimally changes it to flip the classification result.
Home-page: https://github.com/rmazzine/CFNOW
Author: Raphael Mazzine Barbosa de Oliveira
Author-email: mazzine.r@gmail.com
License: MIT
Download-URL: https://pypi.org/project/cfnow/
Description: <div style="text-align:center">
            <img src="/imgs/cfnow_hq_logo.gif" alt="image" width="100%"/>
        </div>
        
        
        ## CFNOW - CounterFactual Nearest Optimal Wololo
        ![unittests](https://github.com/rmazzine/CFNOW/actions/workflows/unittests.yaml/badge.svg)
        [![codecov](https://codecov.io/gh/rmazzine/CFNOW/graph/badge.svg?token=4NHY0V9CN9)](https://codecov.io/gh/rmazzine/CFNOW)
        
        
        ### Description
        
        > TL;DR: You just need a `dataset point` and a `model prediction function`. CFNOW will find the closest point with a different class.
        
        The simplest way to generate counterfactuals for any tabular dataset and model.
        
        This package finds an optimal point (closer to the  input dataset point), which the classification is different from the original classification (i.e. "flips" the classification of the original input by minimally changin it).
        
        ## Table of Contents
        
        - [Minimal example](#minimal-example)
        - [Counterfactual Charts](#showing-the-counterfactuals-graphically)
        - [I have: binary categorical features!](#i-have-binary-categorical-features)
        - [I have: one-hot encoded features!](#i-have-one-hot-encoded-features)
        - [I have: binary and OHE features!](#i-have-a-mix-of-binary-categorical-and-one-hot-encoded-features)
        - [How to cite](#how-to-cite)
        
        ## Requirements
        
        - Python >= 3.8
        
        ### Minimal example:
        ```python
        from cfnow import find_tabular
        import sklearn.datasets
        import sklearn.ensemble
        import pandas as pd
        
        # Generating a sample model
        X, y = sklearn.datasets.load_iris(return_X_y=True)
        model = sklearn.ensemble.RandomForestClassifier()
        model.fit(X, y)
        
        # Selecting a random point
        x = X[0]
        
        # Here we can see the original class
        print(f"Factual: {x}\nFactual class: {model.predict([x])}")
        
        # Then, we use CFNOW to generate the minimum modification to change the classification
        cf_obj = find_tabular(
            factual=pd.Series(x),
            model_predict_proba=model.predict_proba,
            limit_seconds=10)
        
        # Here we can see the new class
        print(f"CF: {cf_obj.cfs[0]}\nCF class: {model.predict([cf_obj.cfs[0]])}")
        ```
        
        ### Showing the Counterfactuals graphically
        This package is integrated with [CounterPlots](https://github.com/ADMAntwerp/CounterPlots), that allows you to graphically represent your counterfactual explanations!
        
        You can simply generate Greedy, CounterShapley, and Constellation charts for a given CF with:
        #### Greedy
        ```python
        # Get the counterplots for the first CF and save as greedy.png
        cf_obj.generate_counterplots(0).greedy('greedy.png')
        ```
        #### Output Example
        ![image](/imgs/greedy_ex.png)
        #### CounterShapley
        ```python
        # Get the counterplots for the first CF and save as greedy.png
        cf_obj.generate_counterplots(0).countershapley('countershapley.png')
        ```
        ![image](/imgs/countershapley_ex.png)
        #### Constellation
        ```python
        # Get the counterplots for the first CF and save as greedy.png
        cf_obj.generate_counterplots(0).constellation('constellation.png')
        ```
        ![image](/imgs/const_ex.png)
        
        ### Improving your results
        The minimal example above considers all features as numerical continuous, however, some datasets can have categorical (binary or one-hot encoded) features. CFNOW can handle these data types in a simple way as demonstrated below:
        
        ### I have binary categorical features!
        #### 1 - Prepare the dataset
        ```python
        import pandas as pd
        import numpy as np
        import sklearn.ensemble
        
        # Generate data with 5 binary categorical features and 3 continuous numerical features
        X = np.hstack((
            np.random.randint(0, 2, size=(1000, 5)),
            np.random.rand(1000, 3) * 100
        ))
        
        # Random binary target variable
        y = np.random.randint(0, 2, 1000)
        
        # Train RandomForestClassifier
        model = sklearn.ensemble.RandomForestClassifier().fit(X, y)
        
        # Display the original class for a random test sample
        x = X[0]
        print(f"Factual: {x}\nFactual class: {model.predict([x])}")
        ```
        
        #### 2 - Find the CF
        ```python
        from cfnow import find_tabular
        # Then, we use CFNOW to generate the minimum modification to change the classification
        cf_obj = find_tabular(
            factual=pd.Series(x),
            feat_types={i: 'cat' if i < 5 else 'cont' for i in range(8)},
            model_predict_proba=model.predict_proba,
            limit_seconds=10)
        
        # Here we can see the new class
        print(f"CF: {cf_obj.cfs[0]}\nCF class: {model.predict([cf_obj.cfs[0]])}")
        
        
        
        ```
        
        ### I have one-hot encoded features!
        #### 1 - Prepare the dataset
        ```python
        import warnings
        import pandas as pd
        import numpy as np
        from sklearn.preprocessing import OneHotEncoder
        import sklearn.ensemble
        warnings.filterwarnings("ignore", message="X does not have valid feature names, but RandomForestClassifier was fitted with feature names")
        
        
        # Generate data
        X = np.hstack((np.random.randint(0, 10, size=(1000, 5)), np.random.rand(1000, 3) * 100))
        
        # One-hot encode the first 5 categorical columns
        
        # !!!IMPORTANT!!! The naming of OHE encoding features columns MUST follow feature_value format.
        # Therefore, for a feature called color with value equal to red or blue, the OHE encoding columns
        # must be named color_red and color_blue. Otherwise, the CF will not be able to find the correct
        # columns to modify.
        encoder = OneHotEncoder(sparse=False)
        X_cat_encoded = encoder.fit_transform(X[:, :5])
        names = encoder.get_feature_names_out(['cat1', 'cat2', 'cat3', 'cat4', 'cat5'])
        
        # Combine and convert to DataFrame
        df = pd.DataFrame(np.hstack((X_cat_encoded, X[:, 5:])), columns=list(names) + ['num1', 'num2', 'num3'])
        
        # Random binary target variable
        y = np.random.randint(0, 2, 1000)
        
        # Train RandomForestClassifier
        model = sklearn.ensemble.RandomForestClassifier().fit(df, y)
        
        # Display the original class for a random test sample
        x = df.iloc[0]
        print(f"Factual: {x.tolist()}\nFactual class: {model.predict([x])}")
        ```
        
        #### 2 - Find the CF
        ```python
        from cfnow import find_tabular
        # Then, we use CFNOW to generate the minimum modification to change the classification
        cf_obj = find_tabular(
            factual=x,
            feat_types={c: 'cat' if 'cat' in c else 'cont' for c in df.columns},
            has_ohe=True,
            model_predict_proba=model.predict_proba,
            limit_seconds=10)
        
        # Here we can see the new class
        print(f"CF: {cf_obj.cfs[0]}\nCF class: {model.predict([cf_obj.cfs[0]])}")
        ```
        
        ### I have one-hot and binary categorical features!
        #### 1 - Prepare the dataset
        ```python
        import warnings
        import pandas as pd
        import numpy as np
        from sklearn.preprocessing import OneHotEncoder
        import sklearn.ensemble
        warnings.filterwarnings("ignore", message="X does not have valid feature names, but RandomForestClassifier was fitted with feature names")
        
        
        # Generate data
        X = np.hstack((np.random.randint(0, 10, size=(1000, 5)), np.random.rand(1000, 3) * 100))
        
        # One-hot encode the first 5 categorical columns
        
        # !!!IMPORTANT!!! The naming of OHE encoding features columns MUST follow feature_value format.
        # Therefore, for a feature called color with value equal to red or blue, the OHE encoding columns
        # must be named color_red and color_blue. Otherwise, the CF will not be able to find the correct
        # columns to modify. For binary, it is just sufficient to name refer the column as cat.
        encoder = OneHotEncoder(sparse=False)
        X_cat_encoded = encoder.fit_transform(X[:, :5])
        names = encoder.get_feature_names_out(['cat1', 'cat2', 'cat3', 'cat4', 'cat5'])
        
        # Combine and convert to DataFrame
        df = pd.DataFrame(np.hstack((X_cat_encoded, X[:, 5:])), columns=list(names) + ['num1', 'num2', 'num3'])
        
        # Random binary target variable
        y = np.random.randint(0, 2, 1000)
        
        # Train RandomForestClassifier
        model = sklearn.ensemble.RandomForestClassifier().fit(df, y)
        
        # Display the original class for a random test sample
        x = df.iloc[0]
        print(f"Factual: {x.tolist()}\nFactual class: {model.predict([x])}")
        ```
        
        #### 2 - Find the CF
        ```python
        from cfnow import find_tabular
        # Then, we use CFNOW to generate the minimum modification to change the classification
        cf_obj = find_tabular(
            factual=x,
            feat_types={c: 'cat' if 'cat' in c else 'cont' for c in df.columns},
            has_ohe=True,
            model_predict_proba=model.predict_proba,
            limit_seconds=10)
        
        # Here we can see the new class
        print(f"CF: {cf_obj.cfs[0]}\nCF class: {model.predict([cf_obj.cfs[0]])}")
        ```
        
        ## How to cite
        If you use CFNOW in your research, please cite the following paper:
        ```
        @article{DEOLIVEIRA2023,
        title = {A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data},
        journal = {European Journal of Operational Research},
        year = {2023},
        issn = {0377-2217},
        doi = {https://doi.org/10.1016/j.ejor.2023.08.031},
        url = {https://www.sciencedirect.com/science/article/pii/S0377221723006598},
        author = {Raphael Mazzine Barbosa {de Oliveira} and Kenneth Sörensen and David Martens},
        }
        ```
        ### Version 0.0.0
        Initial package version
Keywords: counterfactuals,counterfactual explanations,flipping original class,explainable artificial intelligence
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
