suse-model-store/models/linear_regression

Linear Regression Model

Linear regression model for time series analysis, with optional polynomial features and clipping support.

Model Parameters (LinearRegressionModel)

These parameters are defined in the model.yaml under model.input_map and are also represented in schemas.yaml under components.schemas.model with concrete example values used during automated validation:

Parameter	Example	Description
degree	2	Degree of the polynomial used to create polynomial features.
interaction_only	true	Whether to include only interaction features in polynomial features.
verbose	true	Enable verbose output during model execution.
clipping_max	100	Maximum value for clipping prediction output.
clipping_min	0	Minimum value for clipping prediction output.

Data Preprocessor (DataPreprocessor)

These parameters are defined in the model.yaml under data_model.input_map and are also represented in schemas.yaml under components.schemas.data_model with concrete example values used during automated validation:

Parameter	Example	Description
scaler__name	'Standard Scaler'	Name of the scaler to use for scaling the data. Options: 'Standard Scaler'
verbose	true	Enable verbose output during model execution.
steps_order	['Discontinuity Treatment', 'Lag Selection', 'Range Selection & Data Removal', 'Static Window Removal', 'Define Variables Limits', 'Normalization', 'Feature Creation', 'Lag Creation']	Order of the steps to be executed in the pipeline.
nan_treatment	'drop'	The treatment for missing values. Options: 'drop', 'linear interpolation'
lag_train	{'variable_name': 1}	The lags for each variable to be applyed during training.
lag_transform	{'variable_name': 1}	The lags for each variable to be applyed during transformation.
start_date	'2020-01-01 00:00:00'	The start date for the dataset. Format: 'YYYY-MM-DD HH:MM:SS'
end_date	'2020-01-01 00:00:00'	The end date for the dataset. Format: 'YYYY-MM-DD HH:MM:SS'
removed_intervals	[(2020-01-01 00:00:00, 2020-01-01 00:00:00)]	The intervals to be removed from the dataset.
static_threshold	10	The number of repeated values to be considered as static.
lower_limits	{'variable_name': 0}	The lower limits for each variable.
upper_limits	{'variable_name': 1}	The upper limits for each variable.
scaler_name	'Standard Scaler'	The scaler name. Options: 'None', 'Standard Scaler'
scaler_params	{'variable_name': {'mean': 0, 'variance': 1}}	The parameters for the scaler object, if it is used.
self_operations	['variable_name_exp_scalar']	The operations for feature creation using the same variable.
cross_operations	['variable_name1_mul_variable_name2']	The operations for feature creation using two variables.
created_lags	{'variable_name': [1, 2]}	Variables created by lagging existing ones.

Lifecycle (train / retrain)

The wrapper follows the SientiaModel interface. Data is always passed as full DataFrames (features + target).

• train(train_data, val_data, target)

  • train_data: full training DataFrame (features + target column).
  • val_data: full validation DataFrame (features + target column).
  • target: name of the target column (must exist in both).
    The base class fits the transformer on the full datasets, then transforms and fits the model on transformed features and target.

• retrain(data)

  • data: full dataset (features + target column).
    Uses self.target set during train(). Call only after train() has been run.

Neither method mutates the passed DataFrames.

Usage Notes

• If variable_columns is not set, it is inferred during fit as all columns except the target.
• Polynomial features are created automatically when degree > 1.
• clipping uses Q1/Q3 of the training target when replacing out-of-bounds predictions.
• The wrapper enforces that wrapper.target is set before training or retraining; otherwise, a ValueError is raised to fail fast when the configuration is incomplete.

All parameters documented above must stay in sync between:

• This README
• model.yaml (model.input_map and data_model.input_map)
• schemas.yaml (components.schemas.model and components.schemas.data_model)

In particular, schemas.yaml must contain complete and coherent examples for every parameter that affects runtime behaviour, since those examples are used to build model_kwargs and transformer_kwargs during validation. Parameters without examples may not be fully exercised by the automated validation flow.