B-Cubed Metrics

What are B-Cubed Metrics

$$ Precision = \frac{1}{\sum {elements}}\sum_{i=1}^n {\frac{(count ; of ; element)^2}{count ; of ; all ; elements ; in ; cluster}} $$

$$ Recall = \frac{1}{\sum {elements}}\sum_{i=1}^n {\frac{(count ; of ; element)^2}{count ; of ; total ; elements ; from ; this ; category}} $$

$$ F-score = \frac{1}{k}\sum_{i=1}^n {\frac{2\times Precision(C)_k \times Recall(C)_k}{Precision(C)_k + Recall(C)_k}} $$

Installation and Use

pip install bcubed-metrics

from bcubed_metrics.bcubed import Bcubed

predicted_clustering = [
            {'blue': 4, 'red': 2, 'green': 1},
            {'blue': 2, 'red': 2, 'green': 3},
            {'blue': 1, 'red': 5},
            {'blue': 1, 'red': 2, 'green': 3}
        ]

ground_truth_clustering = {'blue': 8, 'red': 11, 'green': 7}

bcubed = Bcubed(predicted_clustering=predicted_clustering, ground_truth_clustering=ground_truth_clustering)

metrics = bcubed.get_metrics() # returns all metrics as dictionary

bcubed.print_metrics() # prints all metrics