ir-metric 0.1.3

ir_metrics is a Python package designed to facilitate information retrieval (IR) metric calculations. It provides a simple and convenient way to compute various metrics commonly used in IR evaluation.

ir_metrics

Overview

ir_metrics is a Python package designed to facilitate information retrieval (IR) metric calculations. It provides a simple and convenient way to compute various metrics commonly used in IR evaluation.

Installation

Install the package using pip:

pip install ir_metric

Quick Start

To use ir_metrics, import the metric module and call the desired metric function. Here's a quick example of computing the average precision:

from ir_metric import metric

# Example data
actual_ranking = [1, 2, 3]
predicted_ranking = [5, 3, 1]

# Compute average precision at k=2
result = metric.average_precision(actual_ranking, predicted_ranking, k=2)

print(result)  # Output: 0.25

Available Metrics

Currently, ir_metrics supports the following metrics:

• Average Precision (average_precision)
• Precision at k (precision_at_k)
• Recall at k (recall_at_k)
• F1 Score at k (f1_score_at_k)

Contributions

Contributions and bug reports are welcome! Feel free to open issues or submit pull requests on the GitHub repository: [https://github.com/harshkumarchourasia/ir_metric]

License

This project is licensed under the MIT License.