.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/preprocessing/plot_discretization_strategies.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py: ========================================================== 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. .. GENERATED FROM PYTHON SOURCE LINES 18-96 .. image:: /auto_examples/preprocessing/images/sphx_glr_plot_discretization_strategies_001.png :alt: Input data, strategy='uniform', strategy='quantile', strategy='kmeans' :class: sphx-glr-single-img .. code-block:: default # Author: Tom Dupré la Tour # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import KBinsDiscretizer from sklearn.datasets import make_blobs print(__doc__) 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=.5) # vertical stripes vertical = grid_encoded[:, 1].reshape(xx.shape) ax.contourf(xx, yy, vertical, alpha=.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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.797 seconds) .. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_strategies.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_discretization_strategies.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_discretization_strategies.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_discretization_strategies.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_