# 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.