Add new daemons and debug scripts for Sigenergy and Oracle functionalities
- Implement `sigen_daemon.py` to poll Sigenergy plant metrics and store snapshots. - Create `web_daemon.py` for serving a web interface with various endpoints. - Add debug scripts: - `debug_duplicates.py` to find duplicate target times in forecast data. - `debug_energy_forecast.py` to print baseline energy forecast curves. - `debug_oracle_evaluations.py` to run the oracle evaluator. - `debug_sigen.py` to inspect stored Sigenergy plant snapshots. - `debug_weather.py` to trace resolved truth data. - `modbus_test.py` for exploring Sigenergy plants or inverters over Modbus TCP. - Introduce `oracle_evaluator.py` for evaluating stored oracle predictions against actuals. - Add TCN training scripts in `tcn` directory for training usage sequence models.
This commit is contained in:
rpotter6298
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"""TCN training and inspection scripts."""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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from random import Random
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from gibil.classes.env_loader import EnvLoader
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from gibil.classes.predictors.usage_hybrid_tcn import (
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UsageHybridTCNConfig,
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build_usage_hybrid_tcn,
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pinball_loss,
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)
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from gibil.classes.predictors.usage_sequence_dataset import (
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UsageSequenceExample,
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UsageSequenceDatasetBuilder,
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UsageSequenceDatasetConfig,
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)
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def main() -> None:
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EnvLoader().load()
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args = parse_args()
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config = UsageSequenceDatasetConfig.from_env()
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builder = UsageSequenceDatasetBuilder(config=config)
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examples = builder.build(limit=args.limit)
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print(f"usage_sequence_examples={len(examples)}")
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print(
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"minimum_history_hours="
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f"{builder.max_past_hours + config.future_hours}"
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)
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print(f"past_features={len(builder.past_feature_names)}")
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for scale in config.past_scales:
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print(
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f"past_scale={scale.name} "
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f"hours={scale.hours} "
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f"step_seconds={scale.step_seconds} "
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f"steps={builder.past_steps(scale)}"
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)
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print(f"future_steps={builder.future_steps}")
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print(f"future_features={len(builder.future_feature_names)}")
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if examples:
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first = examples[0]
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last = examples[-1]
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print(f"first_issued_at={first.issued_at.isoformat()}")
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print(f"last_issued_at={last.issued_at.isoformat()}")
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for name, rows in first.past_by_scale.items():
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print(f"first_past_{name}_shape={len(rows)}x{len(rows[0])}")
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token_count = sum(
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len(tokens)
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for tokens in first.past_tokens_by_scale[name]
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)
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print(f"first_past_{name}_tokens={token_count}")
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print(
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"first_future_feature_shape="
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f"{len(first.future_features)}x{len(first.future_features[0])}"
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)
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print(
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"first_future_tokens="
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f"{sum(len(tokens) for tokens in first.future_tokens)}"
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)
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print(f"first_targets={len(first.targets)}")
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print(
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"first_target_preview="
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+ ",".join(f"{value:.0f}" for value in first.targets[:8])
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)
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if args.dry_run:
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return
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if not examples:
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raise SystemExit("No usage sequence examples available for training yet.")
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train_model(
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examples=examples,
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builder=builder,
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epochs=args.epochs,
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batch_size=args.batch_size,
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learning_rate=args.learning_rate,
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artifact_path=args.artifact_path,
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seed=args.seed,
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Build training windows for the sequence usage oracle."
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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help="Only build examples and print dataset shape.",
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)
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parser.add_argument(
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"--limit",
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type=int,
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default=None,
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help="Optional maximum number of examples to build.",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=20,
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help="Training epochs.",
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=32,
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help="Training batch size.",
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)
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parser.add_argument(
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"--learning-rate",
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type=float,
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default=0.001,
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help="Adam learning rate.",
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)
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parser.add_argument(
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"--artifact-path",
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type=Path,
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default=Path("models/usage_sequence_tcn_v1.pt"),
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help="Where to save the trained TCN artifact.",
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=7,
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help="Deterministic shuffle seed.",
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)
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return parser.parse_args()
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def train_model(
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examples: list[UsageSequenceExample],
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builder: UsageSequenceDatasetBuilder,
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epochs: int,
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batch_size: int,
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learning_rate: float,
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artifact_path: Path,
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seed: int,
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) -> None:
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try:
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import torch
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except ImportError as error:
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raise SystemExit(
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"PyTorch is required for training. Install it with "
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"`python3 -m pip install -r requirements.txt`."
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) from error
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torch.backends.mkldnn.enabled = False
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if hasattr(torch.backends, "nnpack"):
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torch.backends.nnpack.enabled = False
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scale_names = tuple(scale.name for scale in builder.config.past_scales)
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model_config = UsageHybridTCNConfig(
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past_feature_count=len(builder.past_feature_names),
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future_feature_count=len(builder.future_feature_names),
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future_steps=builder.future_steps,
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scale_names=scale_names,
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)
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model = build_usage_hybrid_tcn(model_config)
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optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
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shuffled = examples[:]
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Random(seed).shuffle(shuffled)
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validation_count = max(1, len(shuffled) // 5) if len(shuffled) >= 5 else 0
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validation_examples = shuffled[:validation_count]
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training_examples = shuffled[validation_count:] or shuffled
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for epoch in range(1, epochs + 1):
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model.train()
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training_losses = []
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for batch in batches(training_examples, batch_size):
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past_by_scale, future_features, targets = examples_to_tensors(
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batch,
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scale_names,
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torch,
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)
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prediction = model(past_by_scale, future_features)
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loss = pinball_loss(prediction, targets, model_config.quantiles)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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training_losses.append(float(loss.detach()))
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validation_loss = None
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if validation_examples:
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model.eval()
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with torch.no_grad():
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validation_losses = []
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for batch in batches(validation_examples, batch_size):
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past_by_scale, future_features, targets = examples_to_tensors(
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batch,
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scale_names,
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torch,
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)
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prediction = model(past_by_scale, future_features)
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loss = pinball_loss(prediction, targets, model_config.quantiles)
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validation_losses.append(float(loss.detach()))
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validation_loss = sum(validation_losses) / len(validation_losses)
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train_loss = sum(training_losses) / len(training_losses)
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message = f"epoch={epoch} train_pinball_loss={train_loss:.4f}"
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if validation_loss is not None:
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message += f" validation_pinball_loss={validation_loss:.4f}"
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print(message)
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artifact_path.parent.mkdir(parents=True, exist_ok=True)
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torch.save(
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{
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"model_version": "sequence_usage_tcn_v1",
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"model_config": model_config.__dict__,
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"past_feature_names": builder.past_feature_names,
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"future_feature_names": builder.future_feature_names,
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"state_dict": model.state_dict(),
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},
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artifact_path,
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)
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print(f"saved_artifact={artifact_path}")
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def examples_to_tensors(
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examples: list[UsageSequenceExample],
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scale_names: tuple[str, ...],
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torch,
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):
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past_by_scale = {
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name: torch.tensor(
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[example.past_by_scale[name] for example in examples],
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dtype=torch.float32,
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)
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for name in scale_names
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}
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future_features = torch.tensor(
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[example.future_features for example in examples],
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dtype=torch.float32,
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)
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targets = torch.tensor(
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[example.targets for example in examples],
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dtype=torch.float32,
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)
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return past_by_scale, future_features, targets
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def batches(
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examples: list[UsageSequenceExample],
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batch_size: int,
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):
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for start in range(0, len(examples), batch_size):
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yield examples[start : start + batch_size]
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if __name__ == "__main__":
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main()
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