Highway2VecEmbedder

srai.embedders.Highway2VecEmbedder(
    hidden_size=64, embedding_size=30
)

Bases: Embedder

Highway2Vec Embedder.

PARAMETER	DESCRIPTION
hidden_size	Hidden size in encoder and decoder. Defaults to 64. TYPE: int DEFAULT: 64
embedding_size	Embedding size. Defaults to 30. TYPE: int DEFAULT: 30

Source code in srai/embedders/highway2vec/embedder.py

def __init__(self, hidden_size: int = 64, embedding_size: int = 30) -> None:
    """
    Init Highway2Vec Embedder.

    Args:
        hidden_size (int, optional): Hidden size in encoder and decoder. Defaults to 64.
        embedding_size (int, optional): Embedding size. Defaults to 30.
    """
    import_optional_dependencies(
        dependency_group="torch", modules=["torch", "pytorch_lightning"]
    )

    self._model: Optional[Highway2VecModel] = None
    self._hidden_size = hidden_size
    self._embedding_size = embedding_size
    self._is_fitted = False

transform(regions_gdf, features_gdf, joint_gdf)

Embed regions using features.

PARAMETER	DESCRIPTION
regions_gdf	Region indexes and geometries. TYPE: GeoDataFrame
features_gdf	Feature indexes, geometries and feature values. TYPE: GeoDataFrame
joint_gdf	Joiner result with region-feature multi-index. TYPE: GeoDataFrame

RETURNS	DESCRIPTION
DataFrame	pd.DataFrame: Embedding and geometry index for each region in regions_gdf.

RAISES	DESCRIPTION
ValueError	If any of the gdfs index names is None.
ValueError	If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
ValueError	If index levels in gdfs don't overlap correctly.

Source code in srai/embedders/highway2vec/embedder.py

def transform(
    self,
    regions_gdf: gpd.GeoDataFrame,
    features_gdf: gpd.GeoDataFrame,
    joint_gdf: gpd.GeoDataFrame,
) -> pd.DataFrame:  # pragma: no cover
    """
    Embed regions using features.

    Args:
        regions_gdf (gpd.GeoDataFrame): Region indexes and geometries.
        features_gdf (gpd.GeoDataFrame): Feature indexes, geometries and feature values.
        joint_gdf (gpd.GeoDataFrame): Joiner result with region-feature multi-index.

    Returns:
        pd.DataFrame: Embedding and geometry index for each region in regions_gdf.

    Raises:
        ValueError: If any of the gdfs index names is None.
        ValueError: If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
        ValueError: If index levels in gdfs don't overlap correctly.
    """
    import torch

    self._validate_indexes(regions_gdf, features_gdf, joint_gdf)
    self._check_is_fitted()
    features_df = self._remove_geometry_if_present(features_gdf)

    self._model.eval()  # type: ignore
    embeddings = self._model(torch.Tensor(features_df.values)).detach().numpy()  # type: ignore
    embeddings_df = pd.DataFrame(embeddings, index=features_df.index)
    embeddings_joint = joint_gdf.join(embeddings_df)
    embeddings_aggregated = embeddings_joint.groupby(level=[0]).mean()

    return embeddings_aggregated

fit(
    regions_gdf,
    features_gdf,
    joint_gdf,
    trainer_kwargs=None,
    dataloader_kwargs=None,
)

Fit the model to the data.

PARAMETER	DESCRIPTION
regions_gdf	Region indexes and geometries. TYPE: GeoDataFrame
features_gdf	Feature indexes, geometries and feature values. TYPE: GeoDataFrame
joint_gdf	Joiner result with region-feature multi-index. TYPE: GeoDataFrame
trainer_kwargs	Trainer kwargs. Defaults to None. TYPE: Optional[Dict[str, Any]] DEFAULT: None
dataloader_kwargs	Dataloader kwargs. Defaults to None. TYPE: Optional[Dict[str, Any]] DEFAULT: None

RAISES	DESCRIPTION
ValueError	If any of the gdfs index names is None.
ValueError	If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
ValueError	If index levels in gdfs don't overlap correctly.

Source code in srai/embedders/highway2vec/embedder.py

def fit(
    self,
    regions_gdf: gpd.GeoDataFrame,
    features_gdf: gpd.GeoDataFrame,
    joint_gdf: gpd.GeoDataFrame,
    trainer_kwargs: Optional[dict[str, Any]] = None,
    dataloader_kwargs: Optional[dict[str, Any]] = None,
) -> None:
    """
    Fit the model to the data.

    Args:
        regions_gdf (gpd.GeoDataFrame): Region indexes and geometries.
        features_gdf (gpd.GeoDataFrame): Feature indexes, geometries and feature values.
        joint_gdf (gpd.GeoDataFrame): Joiner result with region-feature multi-index.
        trainer_kwargs (Optional[Dict[str, Any]], optional): Trainer kwargs. Defaults to None.
        dataloader_kwargs (Optional[Dict[str, Any]], optional): Dataloader kwargs.
            Defaults to None.

    Raises:
        ValueError: If any of the gdfs index names is None.
        ValueError: If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
        ValueError: If index levels in gdfs don't overlap correctly.
    """
    import pytorch_lightning as pl
    import torch
    from torch.utils.data import DataLoader

    self._validate_indexes(regions_gdf, features_gdf, joint_gdf)
    features_df = self._remove_geometry_if_present(features_gdf)

    num_features = len(features_df.columns)
    self._model = Highway2VecModel(
        n_features=num_features, n_hidden=self._hidden_size, n_embed=self._embedding_size
    )

    dataloader_kwargs = dataloader_kwargs or {}
    if "batch_size" not in dataloader_kwargs:
        dataloader_kwargs["batch_size"] = 128

    dataloader = DataLoader(torch.Tensor(features_df.values), **dataloader_kwargs)

    trainer_kwargs = trainer_kwargs or {}
    if "max_epochs" not in trainer_kwargs:
        trainer_kwargs["max_epochs"] = 10

    trainer = pl.Trainer(**trainer_kwargs)
    trainer.fit(self._model, dataloader)
    self._is_fitted = True

fit_transform(
    regions_gdf,
    features_gdf,
    joint_gdf,
    trainer_kwargs=None,
    dataloader_kwargs=None,
)

Fit the model to the data and return the embeddings.

PARAMETER	DESCRIPTION
regions_gdf	Region indexes and geometries. TYPE: GeoDataFrame
features_gdf	Feature indexes, geometries and feature values. TYPE: GeoDataFrame
joint_gdf	Joiner result with region-feature multi-index. TYPE: GeoDataFrame
trainer_kwargs	Trainer kwargs. Defaults to None. TYPE: Optional[Dict[str, Any]] DEFAULT: None
dataloader_kwargs	Dataloader kwargs. Defaults to None. TYPE: Optional[Dict[str, Any]] DEFAULT: None

RETURNS	DESCRIPTION
DataFrame	pd.DataFrame: Region embeddings.

RAISES	DESCRIPTION
ValueError	If any of the gdfs index names is None.
ValueError	If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
ValueError	If index levels in gdfs don't overlap correctly.

Source code in srai/embedders/highway2vec/embedder.py

def fit_transform(
    self,
    regions_gdf: gpd.GeoDataFrame,
    features_gdf: gpd.GeoDataFrame,
    joint_gdf: gpd.GeoDataFrame,
    trainer_kwargs: Optional[dict[str, Any]] = None,
    dataloader_kwargs: Optional[dict[str, Any]] = None,
) -> pd.DataFrame:
    """
    Fit the model to the data and return the embeddings.

    Args:
        regions_gdf (gpd.GeoDataFrame): Region indexes and geometries.
        features_gdf (gpd.GeoDataFrame): Feature indexes, geometries and feature values.
        joint_gdf (gpd.GeoDataFrame): Joiner result with region-feature multi-index.
        trainer_kwargs (Optional[Dict[str, Any]], optional): Trainer kwargs. Defaults to None.
        dataloader_kwargs (Optional[Dict[str, Any]], optional): Dataloader kwargs.
            Defaults to None.

    Returns:
        pd.DataFrame: Region embeddings.

    Raises:
        ValueError: If any of the gdfs index names is None.
        ValueError: If joint_gdf.index is not of type pd.MultiIndex or doesn't have 2 levels.
        ValueError: If index levels in gdfs don't overlap correctly.
    """
    self.fit(regions_gdf, features_gdf, joint_gdf, trainer_kwargs, dataloader_kwargs)
    return self.transform(regions_gdf, features_gdf, joint_gdf)

save(path)

Save the model to a directory.

PARAMETER	DESCRIPTION
path	Path to the directory. TYPE: Path

Source code in srai/embedders/highway2vec/embedder.py

def save(self, path: Union[Path, str]) -> None:
    """
    Save the model to a directory.

    Args:
        path (Path): Path to the directory.
    """
    embedder_config = {"hidden_size": self._hidden_size, "embedding_size": self._embedding_size}
    self._save(path, embedder_config)

load(path)

classmethod

Load the model from a directory.

PARAMETER	DESCRIPTION
path	Path to the directory. TYPE: Path

RETURNS	DESCRIPTION
Hex2VecEmbedder	The loaded embedder. TYPE: Highway2VecEmbedder

Source code in srai/embedders/highway2vec/embedder.py

@classmethod
def load(cls, path: Union[Path, str]) -> "Highway2VecEmbedder":
    """
    Load the model from a directory.

    Args:
        path (Path): Path to the directory.

    Returns:
        Hex2VecEmbedder: The loaded embedder.
    """
    return cls._load(path, Highway2VecModel)