Example of 1d KDE using scipy and sklearn

kde_example.py

import scipy.stats
import matplotlib.pyplot as plt
import sklearn.model_selection
import sklearn.neighbors

def kde_scipy(x, x_grid):
    """
    Using heuristic for bandwidth
    """
    kde = scipy.stats.gaussian_kde(x)
    return kde.evaluate(x_grid)

def kde_sklearn(x, x_grid):
    """
    Choosing bandwidth by CV
    """
    params = {'bandwidth': 10**np.linspace(-2,2,100)}
    cv = sklearn.model_selection.KFold(n_splits=5, shuffle=True)
    model = GridSearchCV(sklearn.neighbors.KernelDensity(), params)
    model.fit(x[:,None])
    
    # Check if the maximum or minimum of the bandwidth values were used
    if model.best_params_['bandwidth'] in params['bandwidth'][[0,-1]]:
        print("WARNING: Best bandwidth (%.3f) exceeds CV range (%.3f - %.3f)" % \
              (model.best_params_['bandwidth'], params['bandwidth'][0], params['bandwidth'][1]))
    
    log_pdf = model.score_samples(x_grid[:,None])
    return np.exp(log_pdf)


# Make fake data
x = np.random.random(100)

# Make grid to evaluate KDE
x_min = x.min()
x_max = x.max()
x_range = x_max - x_min
x_grid = np.linspace(x_min - x_range/10, x_max + x_range/10, 1000)

plt.plot(x_grid, kde_scipy(x, x_grid))