Ex 4: Plot randomly generated multilabel dataset 多標籤數據集

https://scikit-learn.org/stable/auto_examples/datasets/plot_random_multilabel_dataset.html

這個範例示範了如何使用make_multilabel_classification函數,每個樣本都包含兩個特徵的計數(總共最多50個), 這兩個特徵在兩個類別的每個類別中的分佈不同。

點的標記如下,其中Y表示類別是否存在:

設定分類的顏色

COLORS = np.array(['!',
'#FF3333', # red
'#0198E1', # blue
'#BF5FFF', # purple
'#FCD116', # yellow
'#FF7216', # orange
'#4DBD33', # green
'#87421F' # brown
])

從0~1024中隨機設定種子,使用相同的隨機種子多次調用make_ml_clf,確保相同的分佈

RANDOM_SEED = np.random.randint(2 ** 10)

(一)Make multilabel classification

使用make_ml_clf生成隨機的多標籤分類,其中回傳四個變數: X 表示產生的樣本 Y 表示標籤的集合 p_c 表示每個分類被選中的機率 p_w_c 表示給定每一個分類,特徵被選中的機率

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='.'

星號標記每個類別的預期樣本;它的大小反映了選擇該類別標籤的可能性。

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]))

(二)顯示圖形與結果

請注意,由於此範例過於簡化:特徵的數量通常會比“文檔長度”大得多,而此範例的文檔長度比特徵量大得多。也就是說n_classes> n_features,特徵要分辨特定分類的機率相對小得很多。

(三)完整程式碼

Python source code:plot_random_multilabel_dataset.py

https://scikit-learn.org/stable/_downloads/e35860bbf32dbc6fb903781f623874e3/plot_random_multilabel_dataset.py

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]))