Saving Feature Importances

saving_importances.py

path_to_save_fi="./test/"
def _checkpath(*paths):
    for path in paths:
        path = str(path)
        if not os.path.exists(path):
            os.makedirs(path)

def get_fe_lable(indices):
    fe_labels = [train_data.columns[index] for index in indices]
    return fe_labels

def _imo_to_file(importances, labels, filename):
    importances=np.sort(importances)[::-1]
    zipped = list(zip(labels,importances))
    df = pd.DataFrame(data=zipped,columns=['Feature Name', 'Importance Score'])
    df.to_csv(filename, index=False)

def save_importances(model, targets):
    for _, estimator, in enumerate(model.estimators_):
        importances = estimator.feature_importances_
        indices = np.argsort(importances)[::-1]
        fig = plt.gcf()
        fig.set_size_inches(16,10)
        plt.stem(importances[indices], use_line_collection=True)
        plt.ylim(0, max(importances)+0.005)
        title = targets[_]
        plt.title(f'Feature Importance Plot for prediction of {title}')
        plt.xlabel('Feature Name')
        plt.ylabel('Feature Importance Score')
        labels = get_fe_lable(indices)
        plt.xticks(range(train_data.shape[1]),labels, rotation='vertical',fontsize=12)
        for _,index in enumerate(indices):
            plt.annotate(round(importances[index],2),(_-0.05,importances[index]+0.001), fontsize=12)
        path = path_to_save_fi
        img_path = path+'images/'
        csv_path = path+'csv/'
        _checkpath(img_path, csv_path)
        img_name = img_path+title+".png"
        csv_name = csv_path+title+".csv"
        plt.savefig(img_name, bbox_inches='tight')
        plt.show()
        _imo_to_file(importances, labels, csv_name)

Usage.md

The above code can be used as follows

path_to_save_fi = 'your/path/'
save_importances(model,y_test.columns)