make_multilabel_classification的教学举例

“”"

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.
“”"

from future import print_function
import numpy as np
import matplotlib
matplotlib.use(“TkAgg”)
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, #s用来标记marker“*”的大小
           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))
#sharex=‘row’,每一行的子图共享行
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©’, ‘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]))