107 lines
3.1 KiB
Python
107 lines
3.1 KiB
Python
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"""
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==============================================
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Plot randomly generated multilabel dataset
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==============================================
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This illustrates the :func:`~sklearn.datasets.make_multilabel_classification`
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dataset generator. Each sample consists of counts of two features (up to 50 in
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total), which are differently distributed in each of two classes.
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Points are labeled as follows, where Y means the class is present:
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===== ===== ===== ======
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1 2 3 Color
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===== ===== ===== ======
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Y N N Red
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N Y N Blue
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N N Y Yellow
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Y Y N Purple
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Y N Y Orange
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Y Y N Green
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Y Y Y Brown
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===== ===== ===== ======
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A star marks the expected sample for each class; its size reflects the
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probability of selecting that class label.
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The left and right examples highlight the ``n_labels`` parameter:
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more of the samples in the right plot have 2 or 3 labels.
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Note that this two-dimensional example is very degenerate:
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generally the number of features would be much greater than the
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"document length", while here we have much larger documents than vocabulary.
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Similarly, with ``n_classes > n_features``, it is much less likely that a
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feature distinguishes a particular class.
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"""
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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_multilabel_classification as make_ml_clf
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COLORS = np.array(
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[
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"!",
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"#FF3333", # red
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"#0198E1", # blue
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"#BF5FFF", # purple
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"#FCD116", # yellow
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"#FF7216", # orange
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"#4DBD33", # green
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"#87421F", # brown
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]
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)
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# Use same random seed for multiple calls to make_multilabel_classification to
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# ensure same distributions
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RANDOM_SEED = np.random.randint(2**10)
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def plot_2d(ax, n_labels=1, n_classes=3, length=50):
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X, Y, p_c, p_w_c = make_ml_clf(
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n_samples=150,
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n_features=2,
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n_classes=n_classes,
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n_labels=n_labels,
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length=length,
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allow_unlabeled=False,
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return_distributions=True,
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random_state=RANDOM_SEED,
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)
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ax.scatter(
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X[:, 0], X[:, 1], color=COLORS.take((Y * [1, 2, 4]).sum(axis=1)), marker="."
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)
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ax.scatter(
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p_w_c[0] * length,
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p_w_c[1] * length,
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marker="*",
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linewidth=0.5,
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edgecolor="black",
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s=20 + 1500 * p_c**2,
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color=COLORS.take([1, 2, 4]),
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)
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ax.set_xlabel("Feature 0 count")
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return p_c, p_w_c
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_, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
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plt.subplots_adjust(bottom=0.15)
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p_c, p_w_c = plot_2d(ax1, n_labels=1)
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ax1.set_title("n_labels=1, length=50")
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ax1.set_ylabel("Feature 1 count")
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plot_2d(ax2, n_labels=3)
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ax2.set_title("n_labels=3, length=50")
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ax2.set_xlim(left=0, auto=True)
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ax2.set_ylim(bottom=0, auto=True)
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plt.show()
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print("The data was generated from (random_state=%d):" % RANDOM_SEED)
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print("Class", "P(C)", "P(w0|C)", "P(w1|C)", sep="\t")
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for k, p, p_w in zip(["red", "blue", "yellow"], p_c, p_w_c.T):
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print("%s\t%0.2f\t%0.2f\t%0.2f" % (k, p, p_w[0], p_w[1]))
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