views_evaluation 0.3.1

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VIEWS Evaluation 📊

Part of the VIEWS Platform ecosystem for large-scale conflict forecasting.

📚 Table of Contents

1. Overview
2. Role in the VIEWS Pipeline
3. Features
4. Installation
5. Architecture
6. Project Structure
7. Contributing
8. License
9. Acknowledgements

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🧠 Overview

The VIEWS Evaluation repository provides a standardized framework for assessing time-series forecasting models used in the VIEWS conflict prediction pipeline. It ensures consistent, robust, and interpretable evaluations through metrics tailored to conflict-related data, which often exhibit right-skewness and zero-inflation.

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🌍 Role in the VIEWS Pipeline

VIEWS Evaluation ensures forecasting accuracy and model robustness as the official evaluation component of the VIEWS ecosystem.

Pipeline Integration:

1. Model Predictions →
2. Evaluation Metrics Processing →
3. Metrics Computation (via EvaluationManager) →
4. Final Performance Reports

Integration with Other Repositories:

• views-pipeline-core: Supplies preprocessed data for evaluation.
• views-models: Provides trained models to be assessed.
• views-stepshifter: Evaluates time-shifted forecasting models.
• views-hydranet: Supports spatiotemporal deep learning model evaluations.

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✨ Features

• Comprehensive Evaluation Framework: The EvaluationManager class provides structured methods to evaluate time series predictions based on point and uncertainty metrics.
• Multiple Evaluation Schemas:
  • Step-wise evaluation: groups and evaluates predictions by the respective steps from all models.
  • Time-series-wise evaluation: evaluates predictions for each time-series.
  • Month-wise evaluation: groups and evaluates predictions at a monthly level.
• Support for Mulyiple Metrics
  • Point Evaluation Metrics: RMSLE, CRPS, Average Precision (Brier Score, Jeffreys Divergence, Pearson Correlation, Sinkhorn/Earth-mover Distance & pEMDiv and Variogram to be added).
  • Uncertainty Evaluation Metrics: CRPS (and more to be added in the future).
• Data Integrity Checks: Ensures that input DataFrames conform to expected structures before evaluation based on point and uncertainty evaluation.
• Automatic Index Matching: Aligns actual and predicted values based on MultiIndex structures.
• Planned Enhancements:
  • Expanding metric calculations beyond RMSLE, CRPS, and AP.
  • New visualization tools for better interpretability of evaluation reports.

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⚙️ Installation

Prerequisites

• Python >= 3.11

From PyPI

pip install views_evaluation

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🏗 Architecture

1. Evaluation Metrics Framework

• Handles forecasting evaluation across multiple models, levels of analysis, and forecasting windows.
• Converts model outputs into standardized evaluation reports.

2. Metrics Computation Pipeline

1. Input: Predictions from models in standardized DataFrames.
2. Processing: Calculation of relevant evaluation metrics.
3. Output: Performance scores for comparison across models.

3. Error Handling & Standardization

• Ensures conformity to VIEWS evaluation standards.
• Warns about unrecognized or incorrectly formatted metrics.

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🗂 Project Structure

views-evaluation/
├── README.md                   # Documentation
├── .github/workflows/           # CI/CD pipelines
├── tests/                       # Unit tests
├── views_evaluation/            # Main source code
│   ├── evaluation/
│   │   ├── metrics.py
│   ├── __init__.py              # Package initialization
├── .gitignore                   # Git ignore rules
├── pyproject.toml               # Poetry project file
├── poetry.lock                  # Dependency lock file

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🤝 Contributing

We welcome contributions! Please follow the VIEWS Contribution Guidelines.

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📜 License

This project is licensed under the LICENSE file.

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💬 Acknowledgements

Special thanks to the VIEWS MD&D Team for their collaboration and support.