Question

我想使用Scikit Learn的this DBSCAN clustering alogrithm exampl e进行一些数据探索。我想遍历eps的不同参数值。因此，我对以下代码进行了不同的修改，从第二个for循环中取出了最后一个图，格式设置为右下角。

print(__doc__)

import matplotlib.pyplot as plt
import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler

eps = [0.1, 0.2, 0.3, 0.4]

plt.figure(figsize=(15,8))
for i in eps:
    # #############################################################################
    # Generate sample data
    centers = [[1, 1], [-1, -1], [1, -1]]
    X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                                random_state=0)

    X = StandardScaler().fit_transform(X)

    # #############################################################################
    # Compute DBSCAN
    db = DBSCAN(eps=i, min_samples=10).fit(X)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    n_noise_ = list(labels).count(-1)

    print('Estimated number of clusters: %d' % n_clusters_)
    print('Estimated number of noise points: %d' % n_noise_)
    print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
    print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
    print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
    print("Adjusted Rand Index: %0.3f"
        % metrics.adjusted_rand_score(labels_true, labels))
    print("Adjusted Mutual Information: %0.3f"
        % metrics.adjusted_mutual_info_score(labels_true, labels))
    print("Silhouette Coefficient: %0.3f"
        % metrics.silhouette_score(X, labels))

    # #############################################################################
    # Plot result
    import matplotlib.pyplot as plt

    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = [plt.cm.Spectral(each)
            for each in np.linspace(0, 1, len(unique_labels))]

    plt.subplot(120 + i*1000 , title=i)

    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = [0, 0, 0, 1]

        class_member_mask = (labels == k)

        xy = X[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                markeredgecolor='k', markersize=14)

        xy = X[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
                markeredgecolor='k', markersize=6)

    plt.title('Estimated number of clusters: %d' % n_clusters_)

plt.show()

不过，我想将eps的不同结果看成是彼此相邻的子图。格式如下：

[ ] [ ] [ ] [ ]

其他问题：最后，我还要对min_samples进行10、12和15的迭代。如果我是对的，这将需要另一个for循环？

Answer 1

我解决了将plt.subplot(120 + i*1000 , title=i)的定义更改为

的问题

iterator = 0
plt.subplot(220 + iterator, title=i)
iterator += 1

解释必须是因为i迭代了一个float数组。乘以1000可能不会得到完美的1、2、3、4，但是会导致接近Stil的结果保持小数点为零。这破坏了子图迭代的逻辑。基本上很简单，但是昨晚我很努力。

DBSCAN参数迭代的子图

1 个答案: