pycsamt.agents.ensemble_agent#

pycsamt.agents.ensemble_agent#

EnsembleAgent — Ensemble 1-D inversion with uncertainty quantification.

Wraps EnsembleInverter:

  • Trains N independent EMInverter1D models on a shared synthetic dataset using different random seeds.

  • Predicts mean resistivity and uncertainty (std / confidence intervals) for every observed station.

  • Optionally calibrates the intervals using a held-out conformal set.

  • Reports empirical coverage (fraction of true values inside the interval) as a reliability metric.

The outputs feed directly into the ReportAgent and are used as a rigorous uncertainty-aware alternative to single-model AI inversion.

Requires PyTorch or TensorFlow.

Classes

EnsembleAgent(*[, api_key, model, ...])

Ensemble 1-D MT inversion with uncertainty bands.
class pycsamt.agents.ensemble_agent.EnsembleAgent(*, api_key=None, model=None, llm_provider='claude', n_estimators=5, arch='resnet', n_layers=5, n_train_samples=2000, epochs=30, calibrate=True)[source]#

Bases: BaseAgent

Ensemble 1-D MT inversion with uncertainty bands.

Parameters:

  • api_key (str)

  • model (str)

  • llm_provider (str)

  • n_estimators (int) – Number of independent models in the ensemble (default 5).

  • arch (str) – Network architecture for each estimator (default "resnet").

  • n_layers (int) – Number of model layers (default 5).

  • n_train_samples (int) – Synthetic training samples per estimator (default 2 000).

  • epochs (int) – Training epochs per estimator (default 30).

  • calibrate (bool) – Apply conformal calibration using 20 % of training data (default True).

  • keys (Output data)

  • ----------

  • path (sites /)

  • output_dir (str, optional)

  • keys

  • ----------------

  • EnsembleInverter (ensemble)

  • {station (pred_hi dict)

  • {station

  • {station

  • {station

  • coverage (coverage float — empirical 90 % interval)

  • float (rms_global)

  • dict (figure_paths)

  • dict

Examples

>>> agent  = EnsembleAgent(n_estimators=3, epochs=20)
>>> result = agent.execute({"path": "/data/L22PLT", "output_dir": "/out/ens"})
>>> result["coverage"]   # should be ≈ 0.90 after calibration
0.88
SYSTEM_PROMPT: str = 'You are an expert in Bayesian and ensemble methods for geophysical inversion.\nGiven an ensemble inversion result with uncertainty quantification, write 4-5\nsentences that:\n1. Describe the ensemble configuration (N models, architecture, training data).\n2. State the prediction quality (mean RMS, uncertainty magnitude).\n3. Assess the calibration: are the confidence intervals reliable?\n4. Identify depth ranges or stations where uncertainty is largest.\n5. Recommend whether the uncertainty is small enough for geological interpretation.\nReply in plain scientific English.\n'#

Override in subclasses to give the LLM its domain expertise.

execute(input_data)[source]#

Run this agent on input_data and return an AgentResult.

Subclasses must implement this method. The contract:

  • Reset self._last_cost = 0.0 at the top.

  • Record wall-clock time with t0 = time.time().

  • Return AgentResult(elapsed_seconds=time.time()-t0, cost_estimate_usd=self._last_cost, ...).

Parameters:

input_data (dict[str, Any])

Return type:

AgentResult