# completeness_score

### cuml.metrics.cluster.completeness_score(labels_true, labels_pred) → [float](https://docs.python.org/3/builtins/functions.html#float)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/cluster/completeness_score.pyx#L19)

cython_completeness_score(labels_true, labels_pred) -> float

Completeness metric of a cluster labeling given a ground truth.

A clustering result satisfies completeness if all the data points that are
members of a given class are elements of the same cluster.

This metric is independent of the absolute values of the labels:
a permutation of the class or cluster label values won’t change the score
value in any way.

This metric is not symmetric: switching label_true with label_pred will
return the homogeneity_score which will be different in general.

The labels in labels_pred and labels_true are assumed to be drawn from a
contiguous set (Ex: drawn from {2, 3, 4}, but not from {2, 4}). If your
set of labels looks like {2, 4}, convert them to something like {0, 1}.

* **Parameters:**
  **labels_pred**
  : The labels predicted by the model for the test dataset.
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy

  **labels_true**
  : The ground truth labels (ints) of the test dataset.
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy
* **Returns:**
  float
  : The completeness of the predicted labeling given the ground truth.
    Score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling.

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