r = pd.concat([data, pd.Series(model.labels_, index = data.index)], axis = 1)是什么意思

这行代码的作用是将 `data` 和 `model.labels_` 这两个对象合并为一个数据表，并将它们按行拼接在一起。具体来说，`pd.Series(model.labels_, index = data.index)` 创建了一个 Series 对象，其中 `model.labels_` 是一个聚类模型对数据 `data` 进行聚类后的标签列表，`data.index` 是 `data` 的行索引，两者合并后得到的 Series 对象就是将 `model.labels_` 列表转化为了一个带有相同行索引的 Series。而 `pd.concat` 函数则将 `data` 和这个新的 Series 对象按照行方向（即 `axis = 1`）进行拼接，最终得到的结果是一个数据表 `r`，其中包含了原始数据 `data` 和聚类模型的标签信息。

相关问题

ValueError Traceback (most recent call last) Cell In[39], line 3 1 from sklearn.cluster import KMeans 2 model_kmean = KMeans(n_clusters=3) ----> 3 cluster_labels_1= model_kmean.fit_predict(df1) 4 cluster_labels1=pd.DataFrame(cluster_labels_1, columns=['clusters']) 5 merge_data1=pd.concat([a, pd.Series(cluster_labels_1, index=df1.index)], axis=1) File ~\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1033, in _BaseKMeans.fit_predict(self, X, y, sample_weight) 1010 def fit_predict(self, X, y=None, sample_weight=None): 1011 """Compute cluster centers and predict cluster index for each sample. 1012 1013 Convenience method; equivalent to calling fit(X) followed by (...) 1031 Index of the cluster each sample belongs to. 1032 """ -> 1033 return self.fit(X, sample_weight=sample_weight).labels_

这段代码出现了一个 ValueError，可能是由于数据类型不匹配或参数设置错误导致的。建议检查一下 df1 的数据类型和值是否符合 KMeans 模型的要求。另外，也可以尝试调整一下 n_clusters 参数的值看看是否能够解决问题。

修改下面代码，另画一张可视化图展示出t_sne里面的数据每15行数据个用一种颜色画出。 import pandas as pd from sklearn import cluster from sklearn import metrics import matplotlib.pyplot as plt from sklearn.manifold import TSNE from sklearn.decomposition import PCA def k_means(data_set, output_file, png_file, t_labels, score_file, set_name): model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++") model.fit(data_set) # print(list(model.labels_)) p_labels = list(model.labels_) r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1) r.columns = list(data_set.columns) + [u'聚类类别'] print(r) # r.to_excel(output_file) with open(score_file, "a") as sf: sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n") sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n") '''pca = PCA(n_components=2) pca.fit(data_set) pca_result = pca.transform(data_set) t_sne = pd.DataFrame(pca_result, index=data_set.index)''' t_sne = TSNE() t_sne.fit(data_set) t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False dd = t_sne[r[u'聚类类别'] == 0] plt.plot(dd[0], dd[1], 'r.') dd = t_sne[r[u'聚类类别'] == 1] plt.plot(dd[0], dd[1], 'go') dd = t_sne[r[u'聚类类别'] == 2] plt.plot(dd[0], dd[1], 'b*') dd = t_sne[r[u'聚类类别'] == 3] plt.plot(dd[0], dd[1], 'o') dd = t_sne[r[u'聚类类别'] == 4] plt.plot(dd[0], dd[1], 'm.') dd = t_sne[r[u'聚类类别'] == 5] plt.plot(dd[0], dd[1], 'co') dd = t_sne[r[u'聚类类别'] == 6] plt.plot(dd[0], dd[1], 'y*') plt.savefig(png_file) plt.clf() '''plt.scatter(data_set.iloc[:, 0], data_set.iloc[:, 1], c=model.labels_) plt.savefig(png_file) plt.clf()''' frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv") tLabel = [] for family in frog_data['name']: if family == "A": tLabel.append(0) elif family == "B": tLabel.append(1) elif family == "C": tLabel.append(2) elif family == "D": tLabel.append(3) elif family == "E": tLabel.append(4) elif family == "F": tLabel.append(5) elif family == "G": tLabel.append(6) scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt" first_set = frog_data.iloc[:, 1:1327] k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", tLabel, scoreFile, "Set_1")

修改后的代码如下所示：

import pandas as pd
from sklearn import cluster
from sklearn import metrics
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE

from sklearn.decomposition import PCA

def k_means(data_set, output_file, png_file, t_labels, score_file, set_name):
    model = cluster.KMeans(n_clusters=7, max_iter=1000, init="k-means++")
    model.fit(data_set)
    p_labels = list(model.labels_)
    r = pd.concat([data_set, pd.Series(model.labels_, index=data_set.index)], axis=1)
    r.columns = list(data_set.columns) + [u'聚类类别']
    print(r)
    with open(score_file, "a") as sf:
        sf.write("By k-means, the f-m_score of " + set_name + " is: " + str(metrics.fowlkes_mallows_score(t_labels, p_labels))+"\n")
        sf.write("By k-means, the rand_score of " + set_name + " is: " + str(metrics.adjusted_rand_score(t_labels, p_labels))+"\n")

    t_sne = TSNE()
    t_sne.fit(data_set)
    t_sne = pd.DataFrame(t_sne.embedding_, index=data_set.index)
    plt.rcParams['font.sans-serif'] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    
    colors = ['r', 'g', 'b', 'o', 'm', 'c', 'y']
    
    for i in range(0, len(t_sne), 15):
        dd = t_sne[r[u'聚类类别'] == i]
        plt.plot(dd[0], dd[1], colors[i % len(colors)]+'.')
    
    plt.savefig(png_file)
    plt.clf()

frog_data = pd.read_csv("D:/PyCharmPython/pythonProject/mfcc3.csv")
tLabel = []
for family in frog_data['name']:
    if family == "A":
        tLabel.append(0)
    elif family == "B":
        tLabel.append(1)
    elif family == "C":
        tLabel.append(2)
    elif family == "D":
        tLabel.append(3)
    elif family == "E":
        tLabel.append(4)
    elif family == "F":
        tLabel.append(5)
    elif family == "G":
        tLabel.append(6)

scoreFile = "D:/PyCharmPython/pythonProject/scoreOfClustering.txt"
first_set = frog_data.iloc[:, 1:1327]
k_means(first_set, "D:/PyCharmPython/pythonProject/kMeansSet_1.xlsx", "D:/PyCharmPython/pythonProject/kMeansSet_2.png", tLabel, scoreFile, "Set_1")

这段代码对原始代码进行了修改，使用了循环来绘制 t_sne 中每15行数据的散点图，并且使用不同的颜色来表示每个类别。修改后的代码会生成一个名为 "kMeansSet_2.png" 的可视化图。请确保已经安装了相关的依赖库，并将相关路径替换为正确的文件路径。