zaganidis-scientific-ml-modules 0.1.7

Scientific, modular machine-learning workflow components for EDA, processing, feature selection, modeling, reporting, and explainability.

scientific-ml-modules v0.1.7

This version keeps the working module logic from the earlier package, while reorganizing the project into a clearer package layout.

v0.1.7 updates

• shap and lime are now part of the required installation dependencies.
• Pair PDP was fixed to use feature indices when calling scikit-learn partial dependence on pandas DataFrames.
• No extra install step is needed for SHAP and LIME support.

What changed

• File locations were moved into a package structure.
• File names were normalized around responsibility.
• Imports were made clearer inside the package.
• Old flat import names still work through compatibility shim modules.
• The modeling module was replaced with the provided implementation while preserving external inputs and outputs.
• Examples were patched so the explainability helper accepts out_dir and full_run.

Current layout

src/
├─ scientific_ml_modules/
│  ├─ core/
│  │  ├─ eda.py
│  │  ├─ processing.py
│  │  ├─ processing_core.py
│  │  ├─ feature_selection.py
│  │  ├─ modeling.py
│  │  ├─ reporting.py
│  │  └─ xai.py
│  ├─ config/
│  │  ├─ eda_builder.py
│  │  ├─ processing_builder.py
│  │  ├─ feature_selection_builder.py
│  │  └─ builders.py
│  ├─ workflow/
│  │  ├─ unified.py
│  │  ├─ deployment.py
│  │  └─ module_suite.py
│  ├─ utils/
│  │  └─ plot_style.py
│  └─ archive/
└─ compatibility shims for old flat imports

Main relationships

• core contains the working implementation modules.
• config contains builder entry points.
• workflow contains orchestration and scoring artifacts.
• utils contains shared styling helpers.
• archive is reserved for transitional material.
• Root-level shim files preserve imports like from modeling_only_module import ....

Public package usage

from scientific_ml_modules import UnifiedWorkflow
from scientific_ml_modules.core.modeling import ModelingOnly, ModelingConfigBuilder
from scientific_ml_modules.config.processing_builder import DataProcessingConfigBuilder

Compatibility usage

These still work:

from modeling_only_module import ModelingOnly, ModelingConfigBuilder
from results_reporting_module import ModelResultsReporter
from explainable_ai_module import ExplainableAIModule

Notes

This package version is a structure and naming cleanup. The EDA, processing, feature-selection, reporting, XAI, and workflow logic was kept in place. Only imports, locations, wrappers, and the modeling implementation were updated.

Detailed logging

Logging is enabled by default across the package. The logging layer is additive and does not change the core modeling, EDA, processing, reporting, or explainability logic. It records stage-level start/end messages and elapsed times for key operations such as EDA refreshes, processing steps, feature-selection runs, cross-validation, bootstrap summaries, graph generation, workflow orchestration, and model/artifact saving.

Typical logger entry points:

• scientific_ml_modules.configure_root_logger()
• builder-level .logging(True) where supported
• config objects with logging_enabled=True by default

The logs are intended to make long runs easier to audit without changing inputs or outputs.