interaction_plot.py

def interaction_plot(col1, col2, interaction_values, background_display, is_cat=True, width=700, height=500,opacity=0.7):
    """
    This function plots shap interaction values between two selected columns (col1 & col2), where x axis are values of column 1, y axis are shap interaction values, and hue (color) is the second column. 

    Args:
        col1 (str) : name of the first feature
        col2 (str) : name of the second feature
        interaction_values (numpy.array) : matrix obtained with shap shap_interaction_values
        background_display (pandas.DataFrame) : the original values for background data used in shap explainer 
        is_cat (bool) : is the col2 a categorical value, True by default
        
    Returns:
        an interactive version of shap dependence_plot
        
    """
    interact_val_array=np.empty(background_display.iloc[:,0].size)
    for idx, __ in enumerate(background_display.iloc[:,0]):
        i = background_display.columns.get_loc(col1)
        j= background_display.columns.get_loc(col2)
        res = interaction_values[idx,i,j]
        interact_val_array[idx]=res

        a = background_display.loc[:,col1].values
        b = background_display.loc[:,col2].values
    if is_cat:
        b = b.astype(str)

    assert len(a) == len(b)
    y_name = "Shapley interaction values for "+col1+ " & "+col2
    x_name = col1
    color_name = col2
    df = pd.DataFrame({y_name:interact_val_array,x_name:a,color_name:b })
    fig = px.scatter(df,  x=x_name, y=y_name, color=color_name,width=width, height=height,opacity=opacity)

    fig.show()