Created
October 24, 2019 16:21
-
-
Save ijpulidos/45702a52645af8a0dc4136dc5acc958f to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from sklearn import datasets | |
from sklearn.metrics import accuracy_score | |
from sklearn.cluster import KMeans | |
import matplotlib.pyplot as plt | |
import numpy as np | |
# Use iris dataset included in sklearn | |
iris = datasets.load_iris | |
k = 3 # Number of clusters | |
km = KMeans(k, random_state=0) # make the randomness deterministic for centroid (reproducibility) | |
X = iris()["data"] | |
Y = iris()["target"] | |
y_kmeans = km.fit(X) | |
print(Y) | |
print(km.labels_) | |
acc = accuracy_score(Y, km.labels_) | |
print("accuracy before transforming:", acc) | |
# Transforming data, where Y is 1 put 0 and viceversa. | |
# This always works because of determinism in KMeans by random_state parameter. | |
ind1 = np.where(Y==1) | |
ind0 = np.where(Y==0) | |
Y[ind1] = 0 | |
Y[ind0] = 1 | |
acc = accuracy_score(Y, km.labels_) | |
print("accuracy after transforming:", acc) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment