基于 Iris 数据集的 Python 模糊聚类

Posted 2023-03-13

【中文标题】基于 Iris 数据集的 Python 模糊聚类

【英文标题】：Fuzzy clustering on Python with Iris dataset

【发布时间】：2017-07-31 04:48:18

【问题描述】：

我正在研究 iris 数据集的模糊 c-means 聚类，但是由于一些错误而无法可视化。Using this tutorial 我为 iris 编写了以下内容，但是它显示了名为“AttributeError: shape”的错误。这是我的代码：

from sklearn import datasets
from sklearn.cluster import KMeans
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics as sm
import skfuzzy as fuzz

iris = datasets.load_iris()

x = pd.DataFrame(iris.data, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])
y = pd.DataFrame(iris.target, columns=['Target'])
plt.figure(figsize=(6, 3))

model =fuzz.cluster.cmeans(iris,3,2,error=0.005,maxiter=1000,init=None,seed=None)
model.fit(x)
plt.show()

我认为在变量模型中传递参数就足够了，但是它显示了上述错误。如果可能的话，你能告诉我哪里出错了吗？如何解决这个问题？非常感谢您的帮助！

【参考方案1】：

我尝试先对数据进行预处理，我创建了一个很好的绘图，我只是按照教程进行，然后我执行 SVD 将维度减少到两个，然后我开始绘图，似乎对于教程你只需要二维（x，y）。不需要做model.fit()我在documentation没有找到这种命令，代码如下：

import numpy as np, pandas as pd, os
import matplotlib
import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import confusion_matrix
import statsmodels.api as sm
import statsmodels.formula.api as smf
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import [![TruncatedSVD
from skle][1]][1]arn.preprocessing import Normalizer
import skfuzzy as fuzz
from sklearn import datasets
################################################################################
iris = datasets.load_iris()

x = pd.DataFrame(iris.data, columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])
y = pd.DataFrame(iris.target, columns=['Target'])
scaler = StandardScaler()
X_std = scaler.fit_transform(x)
lsa = TruncatedSVD(2, algorithm = 'arpack')
dtm_lsa = lsa.fit_transform(X_std)
dtm_lsa = Normalizer(copy=False).fit_transform(dtm_lsa)
a= pd.DataFrame(dtm_lsa, columns = ["component_1","component_2"])
a['targets']=y
fig1, axes1 = plt.subplots(3, 3, figsize=(8, 8))
alldata = np.vstack((a['component_1'], a['component_2']))
fpcs = []

colors = ['b', 'orange', 'g', 'r', 'c', 'm', 'y', 'k', 'Brown', 'ForestGreen'] 

for ncenters, ax in enumerate(axes1.reshape(-1), 2):
    cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
        alldata, ncenters, 2, error=0.005, maxiter=1000, init=None)

    # Store fpc values for later plots
    fpcs.append(fpc)

    # Plot assigned clusters, for each data point in training set
    cluster_membership = np.argmax(u, axis=0)
    for j in range(ncenters):
        ax.plot(a['component_1'][cluster_membership == j],
                a['component_2'][cluster_membership == j], '.', color=colors[j])

    # Mark the center of each fuzzy cluster
    for pt in cntr:
        ax.plot(pt[0], pt[1], 'rs')

    ax.set_title('Centers = 0; FPC = 1:.2f'.format(ncenters, fpc))
    ax.axis('off')

fig1.tight_layout()
fig1.savefig('iris_dataset.png')

【讨论】：

from sklearn.decomposition import TruncatedSVD from sklearn.preprocessing import Normalizer 修复导入