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https://github.com/kplanisphere/metrics-calculation-precision-recall
Laboratory 7 - Retrieval Information
https://github.com/kplanisphere/metrics-calculation-precision-recall
data-preprocessing educational-project information-retrieval lowercase-conversion punctuation-removal python short-words-filter text-processing tokenization vocabulary-optimization
Last synced: about 9 hours ago
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Laboratory 7 - Retrieval Information
- Host: GitHub
- URL: https://github.com/kplanisphere/metrics-calculation-precision-recall
- Owner: KPlanisphere
- Created: 2025-01-16T03:42:06.000Z (6 days ago)
- Default Branch: main
- Last Pushed: 2025-01-16T03:50:15.000Z (6 days ago)
- Last Synced: 2025-01-16T04:37:00.324Z (6 days ago)
- Topics: data-preprocessing, educational-project, information-retrieval, lowercase-conversion, punctuation-removal, python, short-words-filter, text-processing, tokenization, vocabulary-optimization
- Language: Python
- Homepage: https://linktr.ee/planisphere.kgz
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Metrics Calculation: Precision, Recall, and F-Measure
This repository contains a Python script for calculating precision, recall, F-measure, and R-precision for information retrieval tasks. The script processes relevant and retrieved document data to compute these metrics for each query.
## Features
- **Read Relevant and Retrieved Data**: Parse files containing relevant and retrieved documents for each query.
- **Compute Metrics**:
- Precision
- Recall
- F-Measure
- R-Precision
- **Customizable Parameter**: Specify the cutoff point (`Z`) for evaluating the top documents retrieved.
- **Output**: Results are saved in a formatted table to a specified output file.
## Files
- `lab7.py`: Main Python script for processing input files and calculating metrics.
- `rlv-ass`: Input file containing relevant documents for each query.
- `NPL_tf_idf_rels.txt`: Input file containing retrieved documents and their scores.
- `metrics_results.txt`: Output file containing the computed metrics in tabular format.
## How It Works
1. **Load Relevant Documents**:
The script reads the `rlv-ass` file to create a mapping of relevant documents for each query.
2. **Load Retrieved Documents**:
The `NPL_tf_idf_rels.txt` file is parsed to retrieve the top `Z` documents for each query.
3. **Compute Metrics**:
For each query, the script calculates:
- Precision: The ratio of relevant documents among the top `Z` retrieved documents.
- Recall: The ratio of relevant documents retrieved to the total relevant documents.
- F-Measure: The harmonic mean of precision and recall.
- R-Precision: Precision when retrieving exactly `R` relevant documents.
4. **Output Results**:
Results are written to `metrics_results.txt` in a structured table:
```
| Consulta | Precision | Recuerdo | Medida F | Precision R |
|----------|-----------|----------|----------|-------------|
| 1 | 0.7500 | 0.6000 | 0.6667 | 0.7500 |
```
## Usage
1. Clone the repository:
```bash
git clone https://github.com/KPlanisphere/metrics-calculation-precision-recall.git
cd metrics-calculation-precision-recall
```
2. Place the `rlv-ass` and `NPL_tf_idf_rels.txt` files in the directory, or update the file paths in `lab7.py`.
3. Run the script:
```bash
python lab7.py
```
During execution, you will be prompted to enter the value of `Z` (cutoff point).
4. Check the output in `metrics_results.txt`.
## Requirements
- Python 3.10+
## Notes
- Ensure that the input files are correctly formatted. Each query's relevant and retrieved documents must follow the expected structure.
- The script includes error handling for empty queries or missing data.