python - 值错误: could not convert string to float: 'lisans' in Python

Question

我正在尝试计算 k-prototypes 算法输出的轮廓索引以对混合特征数据集进行聚类。我收到 ValueError: Could not Convert string to float: 'lisans' 作为错误，即使我的代码在仅执行 k-prototypes 算法时工作正常。我的输入是 Excel 文件，单元格中没有空格或缩进。错误如下:

  File "C:\Users\...\Continuum\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 433, in check_array
array = np.array(array, dtype=dtype, order=order, copy=copy)

ValueError: could not convert string to float: 'lisans'

这里validation.py给出错误:

此外，每当我更改 Excel 文件中列的位置时，用先前给出错误的旧列位置替换的新列也会在同一位置给出错误，无论在 Excel 文件中写入的文本是什么细胞。

我还尝试创建一个新的 Excel 文件并使用它，但我再次失败。下面是代码:

#silhouette score index calculation
import matplotlib.cm as cm
from sklearn.metrics import silhouette_samples, silhouette_score
range_n_clusters = [2, 3, 4, 5, 6, 7]

for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)

# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KPrototypes(n_clusters=n_clusters, init = 'Cao', verbose = 2)
cluster_labels = clusterer.fit_predict(X, categorical=[0, 8, 9])

# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
      "The average silhouette_score is :", silhouette_avg)

# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)

y_lower = 10
for i in range(n_clusters):
    # Aggregate the silhouette scores for samples belonging to
    # cluster i, and sort them
    ith_cluster_silhouette_values = \
        sample_silhouette_values[cluster_labels == i]

    ith_cluster_silhouette_values.sort()

    size_cluster_i = ith_cluster_silhouette_values.shape[0]
    y_upper = y_lower + size_cluster_i

    color = cm.spectral(float(i) / n_clusters)
    ax1.fill_betweenx(np.arange(y_lower, y_upper),
                      0, ith_cluster_silhouette_values,
                      facecolor=color, edgecolor=color, alpha=0.7)

    # Label the silhouette plots with their cluster numbers at the middle
    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

    # Compute the new y_lower for next plot
    y_lower = y_upper + 10  # 10 for the 0 samples

ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")

# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

ax1.set_yticks([])  # Clear the yaxis labels / ticks
ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])

# 2nd Plot showing the actual clusters formed
colors = cm.spectral(cluster_labels.astype(float) / n_clusters)
ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
            c=colors, edgecolor='k')

# Labeling the clusters
centers = clusterer.cluster_centers_
# Draw white circles at cluster centers
ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
            c="white", alpha=1, s=200, edgecolor='k')

for i, c in enumerate(centers):
    ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
                s=50, edgecolor='k')

ax2.set_title("The visualization of the clustered data.")
ax2.set_xlabel("Feature space for the 1st feature")
ax2.set_ylabel("Feature space for the 2nd feature")

plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
              "with n_clusters = %d" % n_clusters),
             fontsize=14, fontweight='bold')

plt.show()

此轮廓得分代码也可以与其他数据集一起正常工作，不会出现错误。有谁可以修复它吗？ (我在复制代码时遇到了一些问题，所以通常源代码中的缩进是正确的)

Answer 1

在 silhouette_score 调用中，您计算​​所有成对的欧几里德距离。

如果您有一个包含字符串值“lisans”的单元格，则这是不可能的。

您可能需要首先计算成对距离矩阵，然后使用 metric="precompulated"。