First version of Physionet.

skfda/datasets/__init__.py

@@ -17,6 +17,7 @@
             "fetch_nox",
             "fetch_octane",
             "fetch_phoneme",
+            "fetch_physionet",
             "fetch_tecator",
             "fetch_ucr",
             "fetch_weather",
@@ -45,6 +46,7 @@
         fetch_nox as fetch_nox,
         fetch_octane as fetch_octane,
         fetch_phoneme as fetch_phoneme,
+        fetch_physionet as fetch_physionet,
         fetch_tecator as fetch_tecator,
         fetch_ucr as fetch_ucr,
         fetch_weather as fetch_weather,

skfda/datasets/_real_datasets.py

@@ -10,6 +10,7 @@
 from typing_extensions import Literal
 
 import rdata
+import skdatasets
 
 from ..representation import FDataGrid
 from ..typing._numpy import NDArrayFloat, NDArrayInt
@@ -213,6 +214,126 @@ def fetch_ucr(
     return dataset
 
 
+def _physionet_to_fdatagrid(
+    name: str,
+    data: DataFrame,
+    mode: Literal[
+        None,
+        "pad_left",
+        "pad_right",
+        "truncate_left",
+        "truncate_right",
+    ],
+) -> FDataGrid:
+
+    column = data.loc[:, "signal"]
+    n_samples = len(column)
+    dim_codomain = column[0].shape[1]
+
+    min_len = min(s.shape[0] for s in column)
+    max_len = max(s.shape[0] for s in column)
+
+    if mode is None and min_len != max_len:
+        raise ValueError(
+            f"Dataset {name} has signals of different lengths. Use the "
+            f"'mode' parameter to set a common lenght",
+        )
+
+    n_points = max_len if mode in {"pad_left", "pad_right"} else min_len
+
+    data_matrix = np.full(
+        shape=(n_samples, n_points, dim_codomain),
+        fill_value=np.nan,
+        dtype=column[0].dtype,
+    )
+
+    for i, sample in enumerate(column):
+        copy_len = min(sample.shape[0], n_points)
+
+        if mode in {None, "pad_right", "truncate_right"}:
+            data_matrix[i, :copy_len, :] = sample[:copy_len, :]
+        else:
+            data_matrix[i, -copy_len:, :] = sample[-copy_len:, :]
+
+    grid_points = np.linspace(
+        0,
+        column.attrs["fs"] * (n_points - 1),
+        n_points,
+    )
+
+    coordinate_names = [
+        f"{sig_name}({unit})"
+        for sig_name, unit in zip(
+            column.attrs["sig_name"],
+            column.attrs["units"],
+        )
+    ]
+
+    sample_names = list(data.index)
+
+    return FDataGrid(
+        data_matrix=data_matrix,
+        grid_points=grid_points,
+        dataset_name=name,
+        coordinate_names=coordinate_names,
+        sample_names=sample_names,
+    )
+
+
+def fetch_physionet(
+    name: str,
+    *,
+    return_X_y: bool = False,
+    as_frame: bool = True,
+    target_column: str | Sequence[str] | None = None,
+    mode: Literal[
+        None,
+        "pad_left",
+        "pad_right",
+        "truncate_left",
+        "truncate_right",
+    ] = None,
+    **kwargs: Any,
+) -> (
+    Bunch
+    | Tuple[NDArrayAny, NDArrayAny | None]
+    | Tuple[DataFrame, Series | DataFrame | None]
+):
+    """
+    Fetch a dataset from Physionet.
+
+    Args:
+        name: Dataset name.
+        kwargs: Additional parameters for the function
+            :func:`skdatasets.repositories.ucr.fetch`.
+
+    Returns:
+        The dataset requested.
+
+    Examples:
+        >>> import skfda
+        >>> X, y = skfda.datasets.fetch_physionet("ctu-uhb-ctgdb", return_X_y=True, mode="truncate_right")
+
+    """
+    repositories = _get_skdatasets_repositories()
+
+    dataset = repositories.physionet.fetch(name, as_frame=True, **kwargs)
+
+    fdatagrid = _physionet_to_fdatagrid(name, data=dataset.frame, mode=mode)
+
+    dataset.frame.loc[:, "signal"] = pd.Series(
+        fdatagrid,
+        index=dataset.frame.index,
+    )
+
+    return repositories.base.dataset_from_dataframe(
+        dataset.frame,
+        return_X_y=return_X_y,
+        as_frame=as_frame,
+        target_column=target_column,
+    )
+
+
 def _fetch_cran_no_encoding_warning(*args: Any, **kwargs: Any) -> Any:
     # Probably non thread safe
     with warnings.catch_warnings():

skfda/preprocessing/__init__.py

@@ -6,6 +6,7 @@
     __name__,
     submodules=[
         "feature_construction",
+        "missing",
         "registration",
         "smoothing",
         "dim_reduction",

skfda/preprocessing/missing/__init__.py

@@ -0,0 +1 @@
+from ._interpolate import MissingValuesInterpolation

skfda/preprocessing/missing/_interpolate.py

@@ -0,0 +1,80 @@
+from typing import Any, TypeVar
+
+import numpy as np
+from scipy.interpolate import InterpolatedUnivariateSpline
+from scipy.interpolate.interpnd import LinearNDInterpolator
+
+from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin
+from ...representation import FDataGrid
+from ...typing._base import GridPoints
+from ...typing._numpy import NDArrayFloat, NDArrayInt
+
+T = TypeVar("T", bound=FDataGrid)
+
+
+def _coords_from_indices(
+    coord_indices: NDArrayInt,
+    grid_points: GridPoints,
+) -> NDArrayFloat:
+    return np.stack([
+        grid_points[i][coord_index]
+        for i, coord_index in enumerate(coord_indices.T)
+    ]).T
+
+
+def _interpolate_nans(
+    fdatagrid: T,
+) -> T:
+
+    data_matrix = fdatagrid.data_matrix.copy()
+
+    for n_sample in range(fdatagrid.n_samples):
+        for n_coord in range(fdatagrid.dim_codomain):
+
+            data_points = data_matrix[n_sample, ..., n_coord]
+            nan_pos = np.isnan(data_points)
+            valid_pos = ~nan_pos
+            coord_indices = np.argwhere(valid_pos)
+            desired_coord_indices = np.argwhere(nan_pos)
+            coords = _coords_from_indices(
+                coord_indices,
+                fdatagrid.grid_points,
+            )
+            desired_coords = _coords_from_indices(
+                desired_coord_indices,
+                fdatagrid.grid_points,
+            )
+            values = data_points[valid_pos]
+
+            if fdatagrid.dim_domain == 1:
+                interpolation = InterpolatedUnivariateSpline(
+                    coords,
+                    values,
+                    k=1,
+                    ext=3,
+                )
+            else:
+                interpolation = LinearNDInterpolator(
+                    coords,
+                    values,
+                )
+
+            new_values = interpolation(
+                desired_coords,
+            )
+
+            data_matrix[n_sample, nan_pos, n_coord] = new_values.ravel()
+
+    return fdatagrid.copy(data_matrix=data_matrix)
+
+
+class MissingValuesInterpolation(
+    BaseEstimator,
+    InductiveTransformerMixin[T, T, Any],
+):
+
+    def transform(
+        self,
+        X: T,
+    ) -> T:
+        return _interpolate_nans(X)