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 modulesimport pandas as pd from sklearn import datasetsfrom 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.targetX = 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
# Importing the modulefrom sklearn.metrics.cluster import completeness_score# Evaluating the scoreCscore = 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 modulefrom sklearn.metrics.cluster import completeness_score# Evaluating the scoreCscore = 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 modulefrom sklearn.metrics.cluster import completeness_score# Evaluating the scoreCscore = completeness_score([0, 0, 0, 0], [0, 1, 2, 3])print(Cscore) |
Output:
0.0
