104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
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"""
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==========================================================
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Demonstrating the different strategies of KBinsDiscretizer
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==========================================================
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This example presents the different strategies implemented in KBinsDiscretizer:
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- 'uniform': The discretization is uniform in each feature, which means that
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the bin widths are constant in each dimension.
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- quantile': The discretization is done on the quantiled values, which means
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that each bin has approximately the same number of samples.
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- 'kmeans': The discretization is based on the centroids of a KMeans clustering
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procedure.
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The plot shows the regions where the discretized encoding is constant.
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"""
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# Author: Tom Dupré la Tour
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_blobs
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from sklearn.preprocessing import KBinsDiscretizer
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strategies = ["uniform", "quantile", "kmeans"]
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n_samples = 200
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centers_0 = np.array([[0, 0], [0, 5], [2, 4], [8, 8]])
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centers_1 = np.array([[0, 0], [3, 1]])
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# construct the datasets
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random_state = 42
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X_list = [
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np.random.RandomState(random_state).uniform(-3, 3, size=(n_samples, 2)),
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make_blobs(
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n_samples=[
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n_samples // 10,
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n_samples * 4 // 10,
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n_samples // 10,
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n_samples * 4 // 10,
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],
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cluster_std=0.5,
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centers=centers_0,
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random_state=random_state,
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)[0],
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make_blobs(
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n_samples=[n_samples // 5, n_samples * 4 // 5],
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cluster_std=0.5,
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centers=centers_1,
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random_state=random_state,
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)[0],
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]
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figure = plt.figure(figsize=(14, 9))
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i = 1
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for ds_cnt, X in enumerate(X_list):
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ax = plt.subplot(len(X_list), len(strategies) + 1, i)
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ax.scatter(X[:, 0], X[:, 1], edgecolors="k")
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if ds_cnt == 0:
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ax.set_title("Input data", size=14)
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xx, yy = np.meshgrid(
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np.linspace(X[:, 0].min(), X[:, 0].max(), 300),
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np.linspace(X[:, 1].min(), X[:, 1].max(), 300),
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)
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grid = np.c_[xx.ravel(), yy.ravel()]
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ax.set_xlim(xx.min(), xx.max())
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ax.set_ylim(yy.min(), yy.max())
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ax.set_xticks(())
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ax.set_yticks(())
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i += 1
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# transform the dataset with KBinsDiscretizer
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for strategy in strategies:
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enc = KBinsDiscretizer(n_bins=4, encode="ordinal", strategy=strategy)
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enc.fit(X)
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grid_encoded = enc.transform(grid)
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ax = plt.subplot(len(X_list), len(strategies) + 1, i)
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# horizontal stripes
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horizontal = grid_encoded[:, 0].reshape(xx.shape)
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ax.contourf(xx, yy, horizontal, alpha=0.5)
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# vertical stripes
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vertical = grid_encoded[:, 1].reshape(xx.shape)
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ax.contourf(xx, yy, vertical, alpha=0.5)
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ax.scatter(X[:, 0], X[:, 1], edgecolors="k")
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ax.set_xlim(xx.min(), xx.max())
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ax.set_ylim(yy.min(), yy.max())
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ax.set_xticks(())
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ax.set_yticks(())
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if ds_cnt == 0:
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ax.set_title("strategy='%s'" % (strategy,), size=14)
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i += 1
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plt.tight_layout()
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plt.show()
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