import pandas as pd
import numpy as np
import random
import operator
import math
df_full = pd.read_csv("SPECTF_New.csv")
columns = list(df_full.columns)
features = columns[:len(columns)-1]
class_labels = list(df_full[columns[-1]])
df = df_full[features]
# Number of Attributes
num_attr = len(df.columns) - 1
# Number of Clusters
k = 2
# Maximum number of iterations
MAX_ITER = 100
# Number of data points
n = len(df)
# Fuzzy parameter
m = 2.00
def accuracy(cluster_labels, class_labels):
county = [0,0]
countn = [0,0]
tp = [0, 0]
tn = [0, 0]
fp = [0, 0]
fn = [0, 0]
for i in range(len(df)):
# Yes = 1, No = 0
if cluster_labels[i] == 1 and class_labels[i] == 'Yes':
tp[0] = tp[0] + 1
if cluster_labels[i] == 0 and class_labels[i] == 'No':
tn[0] = tn[0] + 1
if cluster_labels[i] == 1 and class_labels[i] == 'No':
fp[0] = fp[0] + 1
if cluster_labels[i] == 0 and class_labels[i] == 'Yes':
fn[0] = fn[0] + 1
for i in range(len(df)):
# Yes = 0, No = 1
if cluster_labels[i] == 0 and class_labels[i] == 'Yes':
tp[1] = tp[1] + 1
if cluster_labels[i] == 1 and class_labels[i] == 'No':
tn[1] = tn[1] + 1
if cluster_labels[i] == 0 and class_labels[i] == 'No':
fp[1] = fp[1] + 1
if cluster_labels[i] == 1 and class_labels[i] == 'Yes':
fn[1] = fn[1] + 1
a0 = float((tp[0] + tn[0]))/(tp[0] + tn[0] + fn[0] + fp[0])
a1 = float((tp[1] + tn[1]))/(tp[1] + tn[1] + fn[1] + fp[1])
p0 = float(tp[0])/(tp[0] + fp[0])
p1 = float(tp[1])/(tp[1] + fp[1])
r0 = float(tp[0])/(tp[0] + fn[0])
r1 = float(tp[1])/(tp[1] + fn[1])
accuracy = [a0*100,a1*100]
precision = [p0*100,p1*100]
recall = [r0*100,r1*100]
return accuracy, precision, recall
def initializeMembershipMatrix():
membership_mat = list()
for i in range(n):
random_num_list = [random.random() for i in range(k)]
summation = sum(random_num_list)
temp_list = [x/summation for x in random_num_list]
membership_mat.append(temp_list)
return membership_mat
def calculateClusterCenter(membership_mat):
cluster_mem_val = zip(*membership_mat)
cluster_mem_val = list(cluster_mem_val)
cluster_centers = list()
for j in range(k):
x = list(cluster_mem_val[j])
xraised = [e ** m for e in x]
denominator = sum(xraised)
temp_num = list()
for i in range(n):
data_point = list(df.iloc[i])
prod = [xraised[i] * val for val in data_point]
temp_num.append(prod)
numerator = map(sum, zip(*temp_num))
center = [z/denominator for z in numerator]
cluster_centers.append(center)
return cluster_centers
def updateMembershipValue(membership_mat, cluster_centers):
p = float(2/(m-1))
for i in range(n):
x = list(df.iloc[i])
distances = [np.linalg.norm(list(map(operator.sub, x, cluster_centers[j]))) for j in range(k)]
for j in range(k):
den = sum([math.pow(float(distances[j]/distances[c]), p) for c in range(k)])
membership_mat[i][j] = float(1/den)
return membership_mat
def getClusters(membership_mat):
cluster_labels = list()
# print(membership_mat)
for i in range(n):
max_val, idx = max((val, idx) for (idx, val) in enumerate(membership_mat[i]))
cluster_labels.append(idx)
# print(max_val)
return cluster_labels
def fuzzyCMeansClustering():
# Membership Matrix
membership_mat = initializeMembershipMatrix()
curr = 0
while curr <= MAX_ITER:
cluster_centers = calculateClusterCenter(membership_mat)
membership_mat = updateMembershipValue(membership_mat, cluster_centers)
cluster_labels = getClusters(membership_mat)
curr += 1
# print(membership_mat)
return cluster_labels, cluster_centers
labels, centers = fuzzyCMeansClustering()
print(labels)
print(class_labels)
a,p,r = accuracy(labels, class_labels)
print("Accuracy = " + str(a))
print("Precision = " + str(p))
print("Recall = " + str(r))