Metadata-Version: 2.1
Name: coci
Version: 0.1.6
Summary: Collective Observation on Causal Inference
Home-page: UNKNOWN
Author: Koki Fujiwara
Author-email: koki.fujiwara@exwzd.com
License: UNKNOWN
Description: # COCI
        
        <strong>C</strong>ollective <strong>O</strong>bservation on <strong>C</strong>ausal <strong>I</strong>nferences
        
        Coci makes it easy to observe the changes in predictions from machine learning models based on the alterations of 
        feature values.
        
        ## Why Coci?
        
        Machine learning has always been understood as a black box algorithm, which makes the decision makers hesitant to trust 
        the predictions from this approach.
        
        <a href="https://github.com/slundberg/shap">Shap</a> and <a href="https://github.com/marcotcr/lime">Lime</a> has 
        unveiled a lot of mysteries around the effects of the presence of each feature on outcomes. However, these methods 
        cannot show the change in outcomes when features are tweaked.
        
        Coci takes it a step further, and reveals the effects on outcomes when changing feature values. 
        
        
        # Installation
        
        `pip install coci==0.1.5`
        
        # Summary Plot
        
        ## Sample code
        
        ```
        import coci
        
        explainer = coci.TreeExplainer(model)
        
        explainer.sensitivity(X_test, 
                            feature_names=feature_names,
                            split_num=2,
                            sample_size=300)
        
        explainer.summary_plot(max_display=10)
        
        ```
        
        ## Reading the summary plot
        
        ![Summary Plot](images/summary_plot.png)
        
        # Trend Plot
        ## Sample code
        ```
        import coci 
        
        explainer = coci.TreeExplainer(model)
        
        explainer.sensitivity(X_test, 
                            feature_names=feature_names,
                            split_num=2,
                            sample_size=300)
        
        explainer.trend_plot(feature_name='要介護認定等基準時間（食事）')
        
        ## or
        explainer.trend_plot(feature_index=1276)
        
        ```
        
        ## Reading the trend plot
        
        
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
