scikit-p4 0.1.1

P4 metric calculation library compatible with scikit-learn

Scikit-P4: P4 Metric Calculation for Python

Scikit-P4 is a Python library for calculating the P4 metric [1][2] across binary, multiclass, and multilabel classification tasks. Its API closely follows that of Scikit-learn’s metrics (e.g., f1_score).

Installation

pip install scikit-p4

Usage

Binary Case

Example for a binary classification problem.

from skp4 import p4_score
y_true = [0, 0, 0, 0, 1, 1, 1, 1, 0, 0]
y_pred = [0, 1, 0, 0, 1, 1, 1, 0, 1, 0]
p4_score(y_true, y_pred)

Output

np.float64(0.6956521739130435)

Multiclass Case

Example with multiple classes.

from skp4 import p4_score
y_true = ['versicolor', 'versicolor', 'setosa', 'setosa', 'virginica', 'virginica', 'setosa']
y_pred = ['versicolor', 'setosa', 'setosa', 'virginica', 'setosa', 'virginica', 'setosa']
p4_score(y_true, y_pred)

Output

Returns a MultiResult object:

   micro avg: 0.6617
   macro avg: 0.6520
weighted avg: 0.6405
 samples avg: 0.5714

Multilabel Case

Example with multilabel classification.

from skp4 import p4_score
y_true = [['A', 'B'], ['A', 'C'], ['B'], ['A', 'B', 'C']]
y_pred = [['A'], ['A', 'B'], [], ['A', 'B', 'C']]
p4_score(y_true, y_pred)

Output

Returns a MultiResult object:

   micro avg: 0.6522
   macro avg: 0.5758
weighted avg: 0.5568
 samples avg: 0.1667

Function signature

def p4_score(y_true, y_pred, *, zero_division='warn') -> np.number | MultiResult:
    ...

Calculates the P4 metric for binary, multiclass, and multilabel classification tasks. The classifier type is detected automatically.

Parameters:

• y_true – Ground-truth labels.
• y_pred – Predicted labels.
• zero_division – Defines the behavior when division by zero occurs (compatible with Scikit-learn):
  • 0.0 – return 0 in case of division by zero
  • 1.0 – return 1 in case of division by zero
  • np.nan – return NaN in case of division by zero
  • warn (default) – return 0 and issue a warning

Remarks

1. In the multiclass case, the samples average for the P4 metric equals accuracy, just like for the F1 metric.

2. In the multilabel case, it is possible to obtain a P4 result below 1.0 even when y_true is perfectly matched. This occurs due to division-by-zero issues, which are common to many true-negative–dependent metrics. To avoid this:

   • Use the micro average, which does not exhibit this issue.
   • Set the parameter zero_division=1.0 during calculation.

References

[1] Wikipedia – P4 metric

[2] Sitarz, Mikołaj (2023). Extending F1 Metric: A Probabilistic Approach. Advances in Artificial Intelligence and Machine Learning, 03(2), 1025–1038. arXiv:2210.11997

Contributing / License

Contributions are welcome! If you would like to report a bug, suggest an improvement, or contribute code, please open an issue or submit a pull request on GitHub.

This project is licensed under the BSD 3-Clause License.
See the LICENSE file for details.