SmartEco 0.1.1

SmartEco ecosystem: CPU-first machine learning benchmarking and tooling

SmartML

SmartML is a CPU-first machine learning benchmarking library focused on fair, leakage-free, and reproducible evaluation of tabular machine learning models.

SmartML is part of the SmartEco ecosystem.

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Core Principles

• CPU-only by default
• Deterministic and reproducible benchmarks
• Zero data leakage
• Minimal but safe preprocessing
• Honest model availability detection
• Fair comparison across models

SmartML only exposes models that actually run on the current system.

No fake availability. No “works on my GPU” nonsense.

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Intended Use & Scope

SmartML is not a commercial AutoML system.

It is an internal benchmarking and evaluation tool, designed to:

• Compare models fairly under identical conditions
• Measure real-world CPU performance
• Ensure leakage-free and reproducible results

All models are evaluated using:

• Fixed default hyperparameters
• Identical preprocessing
• Identical train/test splits

No model is tuned, favored, or given any special advantage.

SmartML prioritizes fairness, transparency, and repeatability over leaderboard-style optimization.

For full details on:

• Default hyperparameters
• Preprocessing rules
• Benchmark methodology

Please refer to the official SmartEco documentation and website.

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Installation

SmartML is used as part of the SmartEco package.

Install SmartEco in editable mode from the directory that contains the SmartEco folder.

Required Dependencies

• numpy
• pandas
• scikit-learn

Optional Dependencies

Installing these unlocks additional models:

• lightgbm
• xgboost
• catboost
• interpret (for NAM / Explainable Boosting)
• pytorch-tabnet
• torch (CPU)
• smart-knn (SmartEco-native)

Some research libraries are platform-dependent and may not be available on Windows CPU.
SmartML automatically hides unavailable models.

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Data Encoding & Preprocessing

SmartML uses minimal, transparent, and safe preprocessing.

Feature Encoding

• Numerical features
  Passed directly without modification.

• Categorical features

  • Low-cardinality → One-Hot Encoding (OHE)
  • High-cardinality → Target Encoding

Target Encoding Safety

• Computed only on the training split
• Validation and test data never influence encoding
• Guarantees zero target leakage

Additional guarantees:

• Classification targets → label-encoded
• Regression targets → remain continuous
• Encoding logic is task-aware
• Test targets are never used during preprocessing
• Linear models are feature-scaled to ensure fair and stable benchmarking

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Train / Test Split

• Fixed random seed is always used
• Default split is deterministic
• Stratification is applied automatically for classification
• Regression splits are random but reproducible

This ensures:

• Identical splits across runs
• Fair comparison between models
• Benchmark repeatability

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Available Models

SmartML dynamically detects and exposes models that are usable on the current machine.

Classification Models

Depending on installed dependencies:

• Logistic Regression
• Support Vector Classifier (SVC)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• Random Forest
• Extra Trees
• LightGBM
• XGBoost
• CatBoost
• NAM (Explainable Boosting)
• TabNet
• SmartKNN
• Optional research models (platform-dependent)

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Regression Models

Depending on installed dependencies:

• Linear Regression
• Ridge Regression
• Lasso
• ElasticNet
• Support Vector Regressor (SVR)
• KNN Regressor
• Random Forest
• Extra Trees
• LightGBM
• XGBoost
• CatBoost
• NAM (Explainable Boosting)
• TabNet
• SmartKNN
• Optional research models (platform-dependent)

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Model Availability Policy

If a model cannot run on the current system, it does not appear.

SmartML:

• Does not fake availability
• Does not crash on missing dependencies
• Does not assume Linux or GPU environments

Model availability is determined at runtime.

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Inspection Utility

SmartML provides a runtime inspection utility called SmartML_Inspect.

It generates a JSON file reporting:

• Available classification models
• Available regression models
• Metrics used by SmartML

The output reflects actual runtime capability, not theoretical support.
No terminal output is produced.

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Evaluation Metrics

Classification Metrics

• Accuracy
• Macro F1 Score

Regression Metrics

• R² Score
• Mean Squared Error (MSE)

Inference & Performance Metrics

• Training time
• Batch inference time
• Batch throughput
• Single-sample mean latency
• Single-sample P95 latency

These metrics evaluate both model quality and real-world performance.

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Benchmarking Behavior

For each model, SmartML:

• Uses the same train/test split
• Applies identical preprocessing
• Trains the model
• Measures training time
• Measures batch inference time
• Measures single-sample latency distribution
• Records results in a unified format

Result: fair, comparable, honest benchmarks.

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Platform Notes

• Windows + CPU → fully supported
• Linux / WSL → additional research models may become available
• GPU → not required, not assumed, not enforced

SmartML remains CPU-safe by default.

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Experimental & Research Models

Some models exist in the codebase but may be hidden at runtime due to missing dependencies:

• Torch-Tabular models (MLP, FTTransformer, SAINT, TabTransformer)
• DeepGBM
• GrowNet
• ModernNCA

These models are:

• Guarded by optional imports
• Exposed only when installable
• Never removed from source code

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Architecture Overview

SmartML is organized into modular components:

• Dataset loading and encoding
• Model registry and availability detection
• Training and benchmarking engine
• Evaluation and inference measurement
• Inspection and reporting

Each component is deterministic and reproducible.

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Part of SmartEco

SmartML is one component of the SmartEco ecosystem