pred density

pred_density.py

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

def plot_prediction_density(target, probs, figsize=(8,5),
                            title='Prediction Density Plot'):

    class_set = sorted(set(target))

    x_grid = np.linspace(0, 1, 1000)

    fig, ax = plt.subplots(figsize=figsize)
    for value in class_set:
        arr = probs[target == value]
        kernel = gaussian_kde(arr, bw_method='scott')
        kde = kernel.evaluate(x_grid)
        ax.plot(x_grid, kde, linewidth=2.5, label='Target = {}'.format(value))
        ax.fill_between(x_grid, kde, alpha=0.6)
    plt.title(title)
    plt.xlabel('Model Score')
    plt.ylabel('Kernel Density')
    plt.legend()
    plt.close(fig)
    return fig
    
# target will usually be y_test and probs will be your predicted scores.
# Example of how to run this - 


plot_prediction_density(y_test, scores)