.. _sphx_glr_auto_examples_datasets_plot_random_multilabel_dataset.py: ============================================== Plot randomly generated multilabel dataset ============================================== This illustrates the `datasets.make_multilabel_classification` dataset generator. Each sample consists of counts of two features (up to 50 in total), which are differently distributed in each of two classes. Points are labeled as follows, where Y means the class is present: ===== ===== ===== ====== 1 2 3 Color ===== ===== ===== ====== Y N N Red N Y N Blue N N Y Yellow Y Y N Purple Y N Y Orange Y Y N Green Y Y Y Brown ===== ===== ===== ====== A star marks the expected sample for each class; its size reflects the probability of selecting that class label. The left and right examples highlight the ``n_labels`` parameter: more of the samples in the right plot have 2 or 3 labels. Note that this two-dimensional example is very degenerate: generally the number of features would be much greater than the "document length", while here we have much larger documents than vocabulary. Similarly, with ``n_classes > n_features``, it is much less likely that a feature distinguishes a particular class. .. image:: /auto_examples/datasets/images/sphx_glr_plot_random_multilabel_dataset_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: The data was generated from (random_state=54): Class P(C) P(w0|C) P(w1|C) red 0.43 0.39 0.61 blue 0.38 0.01 0.99 yellow 0.19 0.59 0.41 | .. code-block:: python from __future__ import print_function import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_multilabel_classification as make_ml_clf print(__doc__) COLORS = np.array(['!', '#FF3333', # red '#0198E1', # blue '#BF5FFF', # purple '#FCD116', # yellow '#FF7216', # orange '#4DBD33', # green '#87421F' # brown ]) # Use same random seed for multiple calls to make_multilabel_classification to # ensure same distributions RANDOM_SEED = np.random.randint(2 ** 10) def plot_2d(ax, n_labels=1, n_classes=3, length=50): X, Y, p_c, p_w_c = make_ml_clf(n_samples=150, n_features=2, n_classes=n_classes, n_labels=n_labels, length=length, allow_unlabeled=False, return_distributions=True, random_state=RANDOM_SEED) ax.scatter(X[:, 0], X[:, 1], color=COLORS.take((Y * [1, 2, 4] ).sum(axis=1)), marker='.') ax.scatter(p_w_c[0] * length, p_w_c[1] * length, marker='*', linewidth=.5, edgecolor='black', s=20 + 1500 * p_c ** 2, color=COLORS.take([1, 2, 4])) ax.set_xlabel('Feature 0 count') return p_c, p_w_c _, (ax1, ax2) = plt.subplots(1, 2, sharex='row', sharey='row', figsize=(8, 4)) plt.subplots_adjust(bottom=.15) p_c, p_w_c = plot_2d(ax1, n_labels=1) ax1.set_title('n_labels=1, length=50') ax1.set_ylabel('Feature 1 count') plot_2d(ax2, n_labels=3) ax2.set_title('n_labels=3, length=50') ax2.set_xlim(left=0, auto=True) ax2.set_ylim(bottom=0, auto=True) plt.show() print('The data was generated from (random_state=%d):' % RANDOM_SEED) print('Class', 'P(C)', 'P(w0|C)', 'P(w1|C)', sep='\t') for k, p, p_w in zip(['red', 'blue', 'yellow'], p_c, p_w_c.T): print('%s\t%0.2f\t%0.2f\t%0.2f' % (k, p, p_w[0], p_w[1])) **Total running time of the script:** (0 minutes 0.157 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_random_multilabel_dataset.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_random_multilabel_dataset.ipynb `