empml.base¶

Object	Description
`DataDownloader`	Abstract class for downloading data into Polars LazyFrames.
`CVGenerator`	Abstract base class for cross-validation splitting strategies.
`Metric`	Abstract base class for performance metrics.
`BaseTransformer`	Abstract base class for transformers that work with Polars LazyFrames.
`BaseEstimator`	Abstract base class for estimators that work with Polars LazyFrames.
`SKlearnEstimator`	Protocol for sklearn-like estimators.

DataDownloader¶

Abstract class for downloading data into Polars LazyFrames.

Abstract Methods¶

@abstractmethod
def get_data(self) -> pl.LazyFrame:
    pass

CVGenerator¶

Abstract base class for cross-validation splitting strategies.

Abstract Methods¶

@abstractmethod
def split(self, lf : pl.LazyFrame, row_id : str) -> List[Tuple[np.array]]:
    """Generate a list of tuple with two elements: the first one is an array containing the row indexes for the train dataset, while the second contains the row indexes for the validation dataset"""
    pass

Metric¶

Abstract base class for performance metrics.

Abstract Methods¶

@abstractmethod
def compute_metric(self, lf: pl.LazyFrame, target: str, preds: str) -> float:
    """
    Computes the metric, strictly requiring a Polars LazyFrame as input.
    The final calculation executes the lazy plan to return a scalar float.
    """
    pass

BaseTransformer¶

Abstract base class for transformers that work with Polars LazyFrames.

Abstract Methods¶

@abstractmethod
def fit(self, lf: pl.LazyFrame):
    """Fit the transformer on the data."""
    pass

@abstractmethod
def transform(self, lf: pl.LazyFrame) -> pl.LazyFrame:
    """Transform the data."""
    pass

BaseEstimator¶

Abstract base class for estimators that work with Polars LazyFrames.

Abstract Methods¶

@abstractmethod
def fit(self, df : pl.LazyFrame):
    """Fit the estimator on the data."""
    pass

@abstractmethod
def predict(self, df : pl.LazyFrame):
    """Predict by using the fitted estimator."""
    pass

SKlearnEstimator¶

Protocol for sklearn-like estimators.

Protocol Methods¶

def fit(self, X: np.ndarray, y: np.ndarray, **kwargs) -> Any:
    """Fit the estimator."""
    ...

def predict(self, X: np.ndarray) -> np.ndarray:
    """Make predictions."""
    ...