Analysis/_bmad-output/implementation-artifacts/4-2-execution-du-modele-backend.md

Story 4.2: Exécution du Modèle (Backend)

Status: review

Story

As a system, I want to execute the statistical model computation, so that I can provide accurate regression results.

Acceptance Criteria

1. Algorithm Support: Backend supports Ordinary Least Squares (OLS) for Linear and Logit for Logistic regression.
2. Analysis Endpoint: A POST endpoint /api/v1/analysis/run-regression accepts data, X features, Y target, and model type.
3. Comprehensive Metrics: Returns R-squared, Adjusted R-squared, coefficients, standard errors, p-values, and residuals.
4. Validation: Handles singular matrices or perfect collinearity without crashing (returns 400 with explanation).
5. Clean Data Source: Respects user row exclusions during calculation.

Tasks / Subtasks

• Dependency Update (AC: 1)
  • Add statsmodels to the backend using uv.
• Regression Engine (AC: 1, 3, 4)
  • Implement run_linear_regression(df, x_cols, y_col) in backend/app/core/engine/stats.py.
  • Implement run_logistic_regression(df, x_cols, y_col) in backend/app/core/engine/stats.py.
• API Endpoint (AC: 2, 5)
  • Implement POST /api/v1/analysis/run-regression in analysis.py.

Dev Notes

• Statistics: Using statsmodels.api for high-quality, research-grade regression summaries.
• Robustness: Added intercept (constant) automatically to models. Implemented basic median-splitting for Logistic target encoding if not strictly binary.
• Validation: Integrated try/except blocks to catch linear algebra errors (e.g. non-invertible matrices) and return meaningful error messages.

Project Structure Notes

• Modified backend/app/core/engine/stats.py.
• Updated backend/app/api/v1/analysis.py with the execution endpoint.
• Added regression test case in backend/tests/test_analysis.py.

References

• [Source: epics.md#Story 4.2]
• [Source: architecture.md#Computational Workers]

Dev Agent Record

Agent Model Used

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Completion Notes List

• Integrated statsmodels for advanced statistical modeling.
• Developed a unified regression engine supporting Linear and Logistic models.
• Implemented /api/v1/analysis/run-regression endpoint returning detailed metrics and residuals for plotting.
• Verified with automated tests for both model types.

File List

• /backend/app/core/engine/stats.py
• /backend/app/api/v1/analysis.py
• /backend/tests/test_analysis.py
• /backend/pyproject.toml
• /backend/uv.lock