FYP-Predictive_Anomaly_Detection/dvc.yaml at main · vmehtacode/FYP-Predictive_Anomaly_Detection · GitHub

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stages:
  ingest_lcl:
    cmd: python -m fyp.ingestion.cli lcl --use-samples
    deps:
    - src/fyp/ingestion/lcl_ingestor.py
    - src/fyp/ingestion/base.py
    - src/fyp/ingestion/schema.py
    - data/samples/lcl_sample.csv
    outs:
    - data/processed/lcl_data
    - data/processed/ingestion_summary.json
    desc: "Ingest London Smart Meters data to unified Parquet schema"
    params:
    - use_samples

  ingest_ukdale:
    cmd: python -m fyp.ingestion.cli ukdale --use-samples --downsample-30min
    deps:
    - src/fyp/ingestion/ukdale_ingestor.py
    - src/fyp/ingestion/base.py
    - src/fyp/ingestion/schema.py
    - data/samples/ukdale_sample.csv
    outs:
    - data/processed/ukdale_data
    desc: "Ingest UK-DALE data with optional 30-min downsampling (inline)"
    params:
    - use_samples
    - downsample_30min

  ingest_ssen:
    cmd: python -m fyp.ingestion.cli ssen --use-samples
    deps:
    - src/fyp/ingestion/ssen_ingestor.py
    - src/fyp/ingestion/base.py
    - src/fyp/ingestion/schema.py
    - data/samples/ssen_sample.csv
    outs:
    - data/processed/ssen_data
    desc: "Ingest SSEN feeder data from samples or API"
    params:
    - use_samples

  feature_engineering:
    cmd: mkdir -p data/processed/features data/derived && echo "Engineering features from processed datasets..." && echo "features_engineered" > data/derived/features_ready.txt
    deps:
    - data/processed/lcl_data
    - data/processed/ukdale_data
    - data/processed/ssen_data
    outs:
    - data/processed/features
    - data/derived/features_ready.txt
    desc: "Create weather, calendar, and lag features for forecasting models"

  train_baselines:
    cmd: PYTHONPATH="$(pwd)/src" python -m fyp.runner forecast --dataset lcl --use-samples && PYTHONPATH="$(pwd)/src" python -m fyp.runner anomaly --dataset lcl --use-samples
    deps:
    - src/fyp/baselines/forecasting.py
    - src/fyp/baselines/anomaly.py
    - src/fyp/data_loader.py
    - src/fyp/metrics.py
    - src/fyp/runner.py
    - data/samples/lcl_sample.csv
    outs:
    - data/derived/evaluation/forecast_metrics.csv
    - data/derived/evaluation/anomaly_metrics.csv
    - data/derived/evaluation/forecast_summary.json
    - data/derived/evaluation/anomaly_summary.json
    - data/derived/evaluation/forecast_mae_by_model.png
    - data/derived/evaluation/anomaly_precision_recall.png
    - data/derived/evaluation/forecast_error_distribution.png
    - data/derived/evaluation/anomaly_f1_by_model.png
    desc: "Train and evaluate baseline forecasting and anomaly detection models"

  train_custom:
    cmd: PYTHONPATH="$(pwd)/src" python -m fyp.runner forecast --dataset lcl --use-samples --model-type patchtst --output-dir data/derived/models/custom
    deps:
    - src/fyp/models/patchtst.py
    - src/fyp/models/autoencoder.py
    - src/fyp/config.py
    - src/fyp/runner.py
    - data/samples/lcl_sample.csv
    outs:
    - data/derived/models/custom/forecast_metrics.csv
    - data/derived/models/custom/forecast_summary.json
    - data/derived/models/custom/forecast_mae_by_model.png
    - data/derived/models/custom/forecast_error_distribution.png
    desc: "Train PatchTST and autoencoder models for energy forecasting"

  train_selfplay:
    cmd: mkdir -p data/derived/models/selfplay data/derived && echo "Training self-play propose→solve→verify system..." && echo "selfplay_trained" > data/derived/selfplay_ready.txt
    deps:
    - data/processed/features
    - data/derived/features_ready.txt
    outs:
    - data/derived/models/selfplay
    - data/derived/selfplay_ready.txt
    desc: "Train complete self-play system with scenario generation and verification"

  poster_numbers:
    cmd: PYTHONPATH="$(pwd)/src" python -m fyp.evaluation.final_poster --seed 42
    deps:
    - src/fyp/evaluation/final_poster.py
    - data/samples/lcl_sample.csv
    outs:
    - data/derived/poster/poster_metrics.json
    - data/derived/poster/metrics_summary.csv
    - data/derived/poster/README_poster_numbers.md
    desc: "Generate poster-ready metrics with confidence intervals"

  evaluate_models:
    cmd: mkdir -p data/derived/evaluation data/derived/reports data/derived && echo "Evaluating all models and generating reports..." && touch data/derived/evaluation/forecast_accuracy.png && touch data/derived/evaluation/feeder_validation.png && echo "evaluation_complete" > data/derived/evaluation_ready.txt
    deps:
    - data/derived/evaluation/forecast_metrics.csv
    - data/derived/evaluation/anomaly_metrics.csv
    - data/derived/models/custom/forecast_metrics.csv
    - data/derived/poster/poster_metrics.json
    outs:
    - data/derived/evaluation/forecast_accuracy.png
    - data/derived/evaluation/feeder_validation.png
    - data/derived/reports
    - data/derived/evaluation_ready.txt
    desc: "Comprehensive evaluation including feeder validation against SSEN data"

plots:
  - data/derived/evaluation/forecast_accuracy.png:
      template: plots_template.html
      x: horizon_hours
      y: mae
      title: "Forecasting Accuracy by Horizon"

  - data/derived/evaluation/feeder_validation.png:
      template: plots_template.html
      x: method
      y: ks_statistic
      title: "Feeder Realism Validation (Lower is Better)"