Fuzzy clustering on Python with Iris dataset

Question

I am working on fuzzy c-means clustering of iris dataset, however can not visualize due to some errors.Using this tutorial I wrote the following for the iris, however it shows error called "AttributeError: shape". This is my code:

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()

I assumed that passing the parameter in the variable model would be enough, however it shows above error. If possible, could you show where I make a mistake ? How to fix this ? I really appreciate your help!

Answer 1

I tried pre-processing the data first, I created a good plot, I simply followed the tutorials, and I perform SVD to reduce the dimension into two, then I started to plot, it seems that for the tutorials you only need two dimensions (x,y). You do not need to do the model.fit() I have not found this kind of command in the documentation, here is the code:

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')