Demonstrating the different strategies of KBinsDiscretizer#

This example presents the different strategies implemented in KBinsDiscretizer:

  • ‘uniform’: The discretization is uniform in each feature, which means that the bin widths are constant in each dimension.

  • quantile’: The discretization is done on the quantiled values, which means that each bin has approximately the same number of samples.

  • ‘kmeans’: The discretization is based on the centroids of a KMeans clustering procedure.

The plot shows the regions where the discretized encoding is constant.

Input data, strategy='uniform', strategy='quantile', strategy='kmeans'
# Author: Tom Dupré la Tour
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import make_blobs
from sklearn.preprocessing import KBinsDiscretizer

strategies = ["uniform", "quantile", "kmeans"]

n_samples = 200
centers_0 = np.array([[0, 0], [0, 5], [2, 4], [8, 8]])
centers_1 = np.array([[0, 0], [3, 1]])

# construct the datasets
random_state = 42
X_list = [
    np.random.RandomState(random_state).uniform(-3, 3, size=(n_samples, 2)),
    make_blobs(
        n_samples=[
            n_samples // 10,
            n_samples * 4 // 10,
            n_samples // 10,
            n_samples * 4 // 10,
        ],
        cluster_std=0.5,
        centers=centers_0,
        random_state=random_state,
    )[0],
    make_blobs(
        n_samples=[n_samples // 5, n_samples * 4 // 5],
        cluster_std=0.5,
        centers=centers_1,
        random_state=random_state,
    )[0],
]

figure = plt.figure(figsize=(14, 9))
i = 1
for ds_cnt, X in enumerate(X_list):
    ax = plt.subplot(len(X_list), len(strategies) + 1, i)
    ax.scatter(X[:, 0], X[:, 1], edgecolors="k")
    if ds_cnt == 0:
        ax.set_title("Input data", size=14)

    xx, yy = np.meshgrid(
        np.linspace(X[:, 0].min(), X[:, 0].max(), 300),
        np.linspace(X[:, 1].min(), X[:, 1].max(), 300),
    )
    grid = np.c_[xx.ravel(), yy.ravel()]

    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xticks(())
    ax.set_yticks(())

    i += 1
    # transform the dataset with KBinsDiscretizer
    for strategy in strategies:
        enc = KBinsDiscretizer(n_bins=4, encode="ordinal", strategy=strategy)
        enc.fit(X)
        grid_encoded = enc.transform(grid)

        ax = plt.subplot(len(X_list), len(strategies) + 1, i)

        # horizontal stripes
        horizontal = grid_encoded[:, 0].reshape(xx.shape)
        ax.contourf(xx, yy, horizontal, alpha=0.5)
        # vertical stripes
        vertical = grid_encoded[:, 1].reshape(xx.shape)
        ax.contourf(xx, yy, vertical, alpha=0.5)

        ax.scatter(X[:, 0], X[:, 1], edgecolors="k")
        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        if ds_cnt == 0:
            ax.set_title("strategy='%s'" % (strategy,), size=14)

        i += 1

plt.tight_layout()
plt.show()

Total running time of the script: (0 minutes 0.650 seconds)

Related examples

Feature discretization

Feature discretization

Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron

Illustration of Gaussian process classification (GPC) on the XOR dataset

Illustration of Gaussian process classification (GPC) on the XOR dataset

SVM Margins Example

SVM Margins Example

Gallery generated by Sphinx-Gallery