monarch-initiative/N2V

"""Module providing RUINE implementation."""
from typing import Optional, Dict, Any
from ensmallen import Graph
import pandas as pd
from ensmallen import models
from embiggen.embedders.ensmallen_embedders.ensmallen_embedder import EnsmallenEmbedder
from embiggen.utils import EmbeddingResult


class RUINE(EnsmallenEmbedder):
    """Class implementing the RUINE algorithm."""

    def __init__(
        self,
        embedding_size: int = 100,
        dtype: Optional[str] = "u8",
        number_of_convolutions: int = 2,
        path: Optional[str] = None,
        verbose: bool = False,
        ring_bell: bool = False,
        enable_cache: bool = False
    ):
        """Create new RUINE method.

        Parameters
        --------------------------
        embedding_size: int = 100
            Dimension of the embedding.
        dtype: Optional[str] = "u8"
            Dtype to use for the embedding.
        number_of_convolutions: int = 2
            Number of convolutions to execute
        path: Optional[str] = None
            Path where to store the mmap-ed embedding.
            This parameter is necessary to embed very large graphs.
        verbose: bool = False
            Whether to show loading bars.
        ring_bell: bool = False,
            Whether to play a sound when embedding completes.
        enable_cache: bool = False
            Whether to enable the cache, that is to
            store the computed embedding.
        """
        self._dtype = dtype
        self._verbose = verbose
        self._number_of_convolutions = number_of_convolutions
        self._path = path
        self._model = models.RUINE(
            embedding_size=embedding_size,
            verbose=self._verbose,
            number_of_convolutions=self._number_of_convolutions,
            path=self._path
        )

        super().__init__(
            embedding_size=embedding_size,
            ring_bell=ring_bell,
            enable_cache=enable_cache
        )

    def parameters(self) -> Dict[str, Any]:
        """Returns parameters of the model."""
        return dict(
            **super().parameters(),
            **dict(
                dtype=self._dtype,
                number_of_convolutions=self._number_of_convolutions,
                path=self._path,
            )
        )
    
    @classmethod
    def smoke_test_parameters(cls) -> Dict[str, Any]:
        """Returns parameters for smoke test."""
        return dict(
            embedding_size=5,
        )

    def _fit_transform(
        self,
        graph: Graph,
        return_dataframe: bool = True,
    ) -> EmbeddingResult:
        """Return node embedding."""
        node_embedding = self._model.fit_transform(
            graph,
            dtype=self._dtype,
        )
        if return_dataframe:
            node_embedding = pd.DataFrame(
                node_embedding,
                index=graph.get_node_names()
            )
        return EmbeddingResult(
            embedding_method_name=self.model_name(),
            node_embeddings=node_embedding
        )

    @classmethod
    def model_name(cls) -> str:
        """Returns name of the model."""
        return "RUINE"

    @classmethod
    def can_use_node_types(cls) -> bool:
        """Returns whether the model can optionally use node types."""
        return False

    @classmethod
    def can_use_edge_weights(cls) -> bool:
        """Returns whether the model can optionally use edge weights."""
        return False

    @classmethod
    def can_use_edge_types(cls) -> bool:
        """Returns whether the model can optionally use edge types."""
        return False

    @classmethod
    def is_stocastic(cls) -> bool:
        """Returns whether the model is stocastic and has therefore a random state."""
        return False