energy_fault_detector.config.quickstart_config

generate_quickstart_config(output_path='base_config.yaml', *, max_nan_frac=0.05, min_unique_value_count=2, max_col_zero_frac=1.0, angle_columns=None, counter_columns=None, imputer_strategy='mean', scaler='standard', early_stopping=False, validation_split=0.2, threshold_quantile=0.99, batch_size=128, code_size=20, epochs=10, layers=None, learning_rate=0.001)

Generate a minimal, valid configuration for EnergyFaultDetector.

This function returns a configuration dictionary that uses the steps-based DataPreprocessor and sensible defaults for training. It can also write the configuration to YAML if an output path is supplied.

Example

from energy_fault_detector import FaultDetector, Config cfg = generate_quickstart_config(output_path=None) fault_detector = FaultDetector(config=Config(config_dict=cfg))

Parameters:

  • output_path (Optional[Union[str, Path]]) – YAML output path; set None to return only the dict.

  • max_nan_frac (float) – Max fraction of missing values per column for selection. Default: 0.05

  • min_unique_value_count (int) – Minimal unique values required to keep a column. Default: 2

  • max_col_zero_frac (float) – Max fraction of zeros allowed in a column. Default: 1.0

  • angle_columns (Optional[List[str]]) – Optional columns to transform as angles (sin/cos). Default: None

  • counter_columns (Optional[List[str]]) – Optional counter columns to convert to differences. Default: None

  • imputer_strategy (str) – Strategy for SimpleImputer (“mean”, “median”, etc.). Default: mean

  • scaler (str) – Scaler selection (“standard” or “minmax”; common aliases allowed). Default: standard

  • early_stopping (bool) – Enable early stopping in the autoencoder training. Default: False

  • validation_split (float) – Fraction for validation in sklearn splitter (0 < val < 1).

  • threshold_quantile (float) – Quantile for the “quantile” threshold selector. Default: 0.99

  • batch_size (int) – Autoencoder batch size. Default: 128

  • code_size (int) – Bottleneck code size. Default: 20

  • epochs (int) – Number of training epochs. Default: 10

  • layers (Optional[List[int]]) – Autoencoder layer sizes; defaults to [200, 100, 50] if None.

  • learning_rate (float) – Optimizer learning rate.

Returns:

Configuration ready to use: FaultDetector(config).

Return type:

Config

Raises:

ValueError – If early_stopping is True but validation_split is not in (0, 1).