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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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