ayodeji63/multiclass_model_

How ayodeji63/multiclass_model_ works

This MATLAB project is an end-to-end electricity-theft research workflow: load SGCC or synthetic data, prepare and split it, train a multi-class CNN-BiLSTM with dual attention, then assess and explain its results. The README documents the main stages and optional analyses, but no source excerpts were provided, so internal call wiring is shown only where the documentation supports it; in particular, the diagram treats main.m as the workflow orchestrator rather than asserting unverified helper calls.

Main entry points: main.m Run workflow · preprocess_pipeline.m Preprocess data · load_sgcc_dataset.m Load SGCC · generate_synthetic_sgcc_data.m Generate synthetic

Components 21

Data preparation

Load SGCC data/load_sgcc_dataset.m
Generate synthetic data/generate_synthetic_sgcc_data.m
Preprocess data preprocessing/preprocess_pipeline.m
Filter consumers preprocessing/filter_low_quality_consumers.m
Inject theft types preprocessing/inject_multiclass_theft_signatures.m
Compute signatures preprocessing/compute_signature_features.m
Create data splits splitting/data_splits.m
Balance classes preprocessing/apply_smote.m

Modeling

Proposed model models/proposed_model.m
Baseline models models/baseline_models.m
Train deep model training/train_deep_model.m
Tune hyperparameters optimization/run_hyperparameter_search.m

Assessment

Binary benchmark evaluation/run_binary_holdout_benchmark.m
Comparative study evaluation/run_comparative_and_ablation_study.m
Stratified CV evaluation/run_stratified_kfold.m
Temporal validation evaluation/run_temporal_split_validation.m

Interpretation

SHAP analysis explainability/run_shap_analysis.m
Attention maps explainability/temporal_attention_map.m
Generate figures visualization/generate_all_figures.m

Other components

Researcher
Run workflow main.m

Connections 20

Researcher → Run workflow: runs
Run workflow → Load SGCC: loads data
Load SGCC → Generate synthetic: falls back
Run workflow → Preprocess data: runs preprocessing
Run workflow → Create data splits: creates splits
Run workflow → Balance classes: balances classes
Run workflow → Proposed model: selects model
Run workflow → Train deep model: trains model
Run workflow → Tune hyperparameters: optionally tunes
Run workflow → Binary benchmark: benchmarks holdout
Run workflow → Comparative study: runs comparison
Run workflow → Stratified CV: runs cross-validation
Run workflow → Temporal validation: validates temporally
Run workflow → SHAP analysis: explains predictions
Run workflow → Attention maps: maps attention
Run workflow → Generate figures: generates figures
Preprocess data → Filter consumers: filters consumers
Preprocess data → Inject theft types: creates classes
Preprocess data → Compute signatures: computes features
Run workflow → Baseline models: uses baselines

More Jupyter Notebook diagrams: kinivi/hand-gesture-recognition-mediapipe · d3blocks/d3blocks · mizuhoaoki/python_simple_mppi · pavelkomarov/exportify · kazuhito00/hand-gesture-recognition-using-mediapipe

Drawn by GitDiagram on September 26, 2026 from the repository's file tree, README and source files. Read as Markdown ·