Evaluation¶
The coclust.evaluation
module provides functions to evaluate the
results of clustering or co-clustering algorithms.
Internal measures¶
The coclust.evaluation.internal
module provides functions to evaluate
clustering or co-clustering given internal criteria.
-
coclust.evaluation.internal.
best_modularity_partition
(in_data, nbr_clusters_range, n_rand_init=1)[source]¶ Evaluate the best partition over a range of number of cluster using co-clustering by direct maximization of graph modularity.
Parameters: - in_data (numpy array or scipy sparse matrix, shape=(n_samples, n_features)) – Matrix to be analyzed
- nbr_clusters_range – Number of clusters to be evaluated
- n_rand_init – Number of time the algorithm will be run with different initializations
Returns: - tmp_best_model (
coclust.coclustering.CoclustMod
) – model with highest final modularity - tmp_max_modularities (list) – final modularities for all evaluated partitions
External measures¶
The coclust.evaluation.external
module provides functions
to evaluate clustering or co-clustering results with external information
such as the true labeling of the clusters.
-
coclust.evaluation.external.
accuracy
(true_row_labels, predicted_row_labels)[source]¶ Get the best accuracy.
Parameters: - true_row_labels (array-like) – The true row labels, given as external information
- predicted_row_labels (array-like) – The row labels predicted by the model
Returns: Best value of accuracy
Return type: float