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Calculating the completeness score using sklearn in Python
  • Last Updated : 01 Oct, 2020

An entirely complete clustering is one where each cluster has information that directs a place toward a similar class cluster. Completeness portrays the closeness of the clustering algorithm to this (completeness_score) perfection. 

This metric is autonomous of the outright values of the labels. A permutation of the cluster label values won’t change the score value in any way.

sklearn.metrics.completeness_score()

Syntax: sklearn.metrics.completeness_score(labels_true, labels_pred)

Parameters:

  • labels_true:<int array, shape = [n_samples]>: It accepts the ground truth class labels to be used as a reference.
  • labels_pred: <array-like of shape (n_samples,)>: It accepts the cluster labels to evaluate.

Returns: completeness score between 0.0 and 1.0. 1.0 stands for perfectly completeness labeling.



Switching label_true with label_pred will return the homogeneity_score.

Example 1:

Python3




# Importing the modules
import pandas as pd  
from sklearn import datasets
from sklearn.cluster import KMeans  
from sklearn.metrics import completeness_score
  
# Loading the data  
digits = datasets.load_digits()
  
# Separating the dependent and independent variables  
Y = digits.target
X = digits.data
  
# Building the clustering model  
kmeans = KMeans(n_clusters = 2)  
  
# Training the clustering model  
kmeans.fit(X)  
  
# Storing the predicted Clustering labels  
labels = kmeans.predict(X)  
  
# Evaluating the performance  
print(completeness_score(Y, labels))

Output:

0.8471148027985769

Example 2: Perfectly completeness:

Python3




# Impoting the module
from sklearn.metrics.cluster import completeness_score
  
# Evaluating the score
Cscore = completeness_score([0, 1, 0, 1], 
                            [1, 0, 1, 0])
print(Cscore)

Output:

1.0

Example 3: Non-perfect labeling that further split classes into more clusters can be perfectly completeness:



Python3




# Importing the module
from sklearn.metrics.cluster import completeness_score
  
# Evaluating the score
Cscore = completeness_score([0, 1, 2, 3], 
                            [0, 0, 1, 1])
print(Cscore)

Output:

0.9999999999999999

Example 4: Include samples from different classes don’t make for completeness labeling:

Python3




# Importing the module
from sklearn.metrics.cluster import completeness_score
  
# Evaluating the score
Cscore = completeness_score([0, 0, 0, 0], 
                            [0, 1, 2, 3])
print(Cscore)

Output:

0.0

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