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Helical Base Models

helical.models.base_models.HelicalBaseFoundationModel

Bases: ABC, Logger

Helical Base Foundation Model Class which serves as the base class for all foundation models in the helical package. Each new model should be a subclass of this class.

Parameters:

Name Type Description Default
logging_type LoggingType

The logging type

 CONSOLE
level LoggingLevel

The logging level

 INFO

Returns:

Type Description
None
Source code in helical/models/base_models.py 
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class HelicalBaseFoundationModel(ABC, Logger):
    """Helical Base Foundation Model Class which serves as the base class for all foundation models in the helical package.
    Each new model should be a subclass of this class.

    Parameters
    ----------
    logging_type : LoggingType
        The logging type
    level : LoggingLevel
        The logging level

    Returns
    -------
    None

    """

    def __init__(self, logging_type=LoggingType.CONSOLE, level=LoggingLevel.INFO):

        super().__init__(logging_type, level)

    @abstractmethod
    def process_data():
        pass

    @abstractmethod
    def get_embeddings():
        pass

helical.models.base_models.HelicalRNAModel

Bases: HelicalBaseFoundationModel

Source code in helical/models/base_models.py 
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class HelicalRNAModel(HelicalBaseFoundationModel):
    def ensure_rna_data_validity(
        self, adata: AnnData, gene_names: str, use_raw_counts: bool = True
    ) -> None:
        """Ensures that the data contains the gene_names and has integer counts for adata.X which is saved
        in 'total_counts'.

        Parameters
        ----------
        adata : AnnData
            The data to be checked.
        gene_names : str
            The name of the column containing gene names in adata.var.
        use_raw_counts : bool, default=True
            Whether to use raw counts or not.

        Raises
        ------
        KeyError
            If the data is missing column names.
        """

        if gene_names == "index":

            # as the gene_names is "index" by default, check that the data is strings
            if not all(isinstance(item, str) for item in adata.var.index):
                message = "The data in the index must only contain strings."
                LOGGER.error(message)
                raise ValueError(message)

            adata.var["index"] = adata.var.index

        # verify gene col name is present in adata.var
        if not gene_names in adata.var:
            message = f"Data must have the provided key '{gene_names}' in its 'var' section to be processed by the Helical RNA model."
            LOGGER.error(message)
            raise KeyError(message)

        # verify that the data in X are integers
        adata.obs["total_counts"] = adata.X.sum(axis=1)
        if use_raw_counts and not (adata.obs["total_counts"] % 1 == 0).all():
            message = "The data in X must be integers."
            LOGGER.error(message)
            raise ValueError(message)

    def get_valid_rna_sequence(
        self, sequences: Union[list[str], DataFrame]
    ) -> list[str]:
        """
        Returns valid RNA sequences that only contain the characters A, C, U, G, N and E.

        Parameters
        ----------
        sequences : List[str] or DataFrame
            The RNA sequences to be checked. If a DataFrame is provided, it should have a column named 'Sequence'.

        Raises
        ------
        ValueError
            If the sequences contain characters other than A, C, U, G, N and E.

        Returns
        -------
        List[str]
            Valid RNA sequences.
        """

        if isinstance(sequences, DataFrame):
            if "Sequence" not in sequences.columns:
                message = "The DataFrame must have a column named 'Sequence'."
                LOGGER.error(message)
                raise KeyError(message)
            sequences = sequences["Sequence"].tolist()

        valid_chars = set("ACUGNE")

        for sequence in sequences:
            if not set(sequence.upper()).issubset(valid_chars):
                invalid_chars = set(sequence.upper()) - valid_chars
                message = (
                    f"Invalid RNA sequence: found invalid characters {invalid_chars}"
                )
                LOGGER.error(message)
                raise ValueError(message)

        return sequences

ensure_rna_data_validity(adata, gene_names, use_raw_counts=True)

Ensures that the data contains the gene_names and has integer counts for adata.X which is saved in 'total_counts'.

Parameters:

Name Type Description Default
adata AnnData

The data to be checked.

 required
gene_names str

The name of the column containing gene names in adata.var.

 required
use_raw_counts bool

Whether to use raw counts or not.

 True

Raises:

Type Description
KeyError

If the data is missing column names.
Source code in helical/models/base_models.py 
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def ensure_rna_data_validity(
    self, adata: AnnData, gene_names: str, use_raw_counts: bool = True
) -> None:
    """Ensures that the data contains the gene_names and has integer counts for adata.X which is saved
    in 'total_counts'.

    Parameters
    ----------
    adata : AnnData
        The data to be checked.
    gene_names : str
        The name of the column containing gene names in adata.var.
    use_raw_counts : bool, default=True
        Whether to use raw counts or not.

    Raises
    ------
    KeyError
        If the data is missing column names.
    """

    if gene_names == "index":

        # as the gene_names is "index" by default, check that the data is strings
        if not all(isinstance(item, str) for item in adata.var.index):
            message = "The data in the index must only contain strings."
            LOGGER.error(message)
            raise ValueError(message)

        adata.var["index"] = adata.var.index

    # verify gene col name is present in adata.var
    if not gene_names in adata.var:
        message = f"Data must have the provided key '{gene_names}' in its 'var' section to be processed by the Helical RNA model."
        LOGGER.error(message)
        raise KeyError(message)

    # verify that the data in X are integers
    adata.obs["total_counts"] = adata.X.sum(axis=1)
    if use_raw_counts and not (adata.obs["total_counts"] % 1 == 0).all():
        message = "The data in X must be integers."
        LOGGER.error(message)
        raise ValueError(message)

get_valid_rna_sequence(sequences)

Returns valid RNA sequences that only contain the characters A, C, U, G, N and E.

Parameters:

Name Type Description Default
sequences List[str] or DataFrame

The RNA sequences to be checked. If a DataFrame is provided, it should have a column named 'Sequence'.

 required

Raises:

Type Description
ValueError

If the sequences contain characters other than A, C, U, G, N and E.

Returns:

Type Description
List[str]

Valid RNA sequences.
Source code in helical/models/base_models.py 
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def get_valid_rna_sequence(
    self, sequences: Union[list[str], DataFrame]
) -> list[str]:
    """
    Returns valid RNA sequences that only contain the characters A, C, U, G, N and E.

    Parameters
    ----------
    sequences : List[str] or DataFrame
        The RNA sequences to be checked. If a DataFrame is provided, it should have a column named 'Sequence'.

    Raises
    ------
    ValueError
        If the sequences contain characters other than A, C, U, G, N and E.

    Returns
    -------
    List[str]
        Valid RNA sequences.
    """

    if isinstance(sequences, DataFrame):
        if "Sequence" not in sequences.columns:
            message = "The DataFrame must have a column named 'Sequence'."
            LOGGER.error(message)
            raise KeyError(message)
        sequences = sequences["Sequence"].tolist()

    valid_chars = set("ACUGNE")

    for sequence in sequences:
        if not set(sequence.upper()).issubset(valid_chars):
            invalid_chars = set(sequence.upper()) - valid_chars
            message = (
                f"Invalid RNA sequence: found invalid characters {invalid_chars}"
            )
            LOGGER.error(message)
            raise ValueError(message)

    return sequences

helical.models.base_models.HelicalDNAModel

Bases: HelicalBaseFoundationModel

Source code in helical/models/base_models.py 
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class HelicalDNAModel(HelicalBaseFoundationModel):
    def get_valid_dna_sequence(
        self, sequences: Union[list[str], DataFrame], enforce_characters: bool = True
    ) -> list[str]:
        """
        Returns valid DNA sequences that only contain the characters A, C, T, G, N.

        Parameters
        ----------
        sequences : List[str]
            The DNA sequences to be checked.

        Raises
        ------
        ValueError
            If the sequences contain characters other than A, C, T, G, N and E.

        Returns
        -------
        List[str]
            Valid DNA sequences.
        """

        if isinstance(sequences, DataFrame):
            if "Sequence" not in sequences.columns:
                message = "The DataFrame must have a column named 'Sequence'."
                LOGGER.error(message)
                raise KeyError(message)

            sequences = sequences["Sequence"].tolist()

        if enforce_characters:
            valid_chars = set("ACTGN")

            for sequence in sequences:
                if not set(sequence.upper()).issubset(valid_chars):
                    invalid_chars = set(sequence.upper()) - valid_chars
                    message = f"Invalid DNA sequence: found invalid characters {invalid_chars}"
                    LOGGER.error(message)
                    raise ValueError(message)

        return sequences

get_valid_dna_sequence(sequences, enforce_characters=True)

Returns valid DNA sequences that only contain the characters A, C, T, G, N.

Parameters:

Name Type Description Default
sequences List[str]

The DNA sequences to be checked.

 required

Raises:

Type Description
ValueError

If the sequences contain characters other than A, C, T, G, N and E.

Returns:

Type Description
List[str]

Valid DNA sequences.
Source code in helical/models/base_models.py 
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def get_valid_dna_sequence(
    self, sequences: Union[list[str], DataFrame], enforce_characters: bool = True
) -> list[str]:
    """
    Returns valid DNA sequences that only contain the characters A, C, T, G, N.

    Parameters
    ----------
    sequences : List[str]
        The DNA sequences to be checked.

    Raises
    ------
    ValueError
        If the sequences contain characters other than A, C, T, G, N and E.

    Returns
    -------
    List[str]
        Valid DNA sequences.
    """

    if isinstance(sequences, DataFrame):
        if "Sequence" not in sequences.columns:
            message = "The DataFrame must have a column named 'Sequence'."
            LOGGER.error(message)
            raise KeyError(message)

        sequences = sequences["Sequence"].tolist()

    if enforce_characters:
        valid_chars = set("ACTGN")

        for sequence in sequences:
            if not set(sequence.upper()).issubset(valid_chars):
                invalid_chars = set(sequence.upper()) - valid_chars
                message = f"Invalid DNA sequence: found invalid characters {invalid_chars}"
                LOGGER.error(message)
                raise ValueError(message)

    return sequences

helical.models.base_models.HelicalBaseFineTuningModel

Bases: Module

Helical Base Fine-Tuning Model Class which serves as the base class for all fine-tuning models in the helical package. Each new fine-tuning model should be a subclass of this class.

Parameters:

Name Type Description Default
fine_tuning_head Literal['classification', 'regression'] | HelicalBaseFineTuningHead

The fine-tuning head that is appended to the model. This can either be a string (options available: "classification" and "regression") specifying the task or a custom fine-tuning head inheriting from HelicalBaseFineTuningHead.

 required
output_size Optional[int]

The output size of the fine-tuning model. This is required if the fine_tuning_head is a string specified task. For a classification task this is number of unique classes.

 required
Source code in helical/models/base_models.py 
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class HelicalBaseFineTuningModel(torch.nn.Module):
    """Helical Base Fine-Tuning Model Class which serves as the base class for all fine-tuning models in the helical package.
    Each new fine-tuning model should be a subclass of this class.

    Parameters
    ----------

    fine_tuning_head: Literal["classification", "regression"] | HelicalBaseFineTuningHead
        The fine-tuning head that is appended to the model. This can either be a string (options available: "classification" and "regression") specifying the task or a custom fine-tuning head inheriting from HelicalBaseFineTuningHead.
    output_size : Optional[int]
        The output size of the fine-tuning model. This is required if the fine_tuning_head is a string specified task. For a classification task this is number of unique classes.

    """

    def __init__(
        self,
        fine_tuning_head: (
            Literal["classification", "regression"] | HelicalBaseFineTuningHead
        ),
        output_size: int,
    ):
        super().__init__()
        if isinstance(fine_tuning_head, str):
            if fine_tuning_head == "classification":
                if output_size is None:
                    message = (
                        "The output_size must be specified for a classification head."
                    )
                    LOGGER.error(message)
                    raise ValueError(message)
                fine_tuning_head = ClassificationHead(output_size)
            elif fine_tuning_head == "regression":
                if output_size is None:
                    message = "The output_size must be specified for a regression head."
                    LOGGER.error(message)
                    raise ValueError(message)
                fine_tuning_head = RegressionHead(output_size)
            else:
                message = "Not implemented fine-tuning head."
                LOGGER.error(message)
                raise NotImplementedError(message)

        elif not isinstance(fine_tuning_head, HelicalBaseFineTuningHead):
            message = (
                "The fine_tuning_head must be a valid 'HelicalBaseFineTuningHead'."
            )
            LOGGER.error(message)
            raise ValueError(message)

        self.fine_tuning_head = fine_tuning_head

    @abstractmethod
    def forward():
        pass

    @abstractmethod
    def train():
        pass

    @abstractmethod
    def get_outputs():
        pass

    def save_model(self, path: str):
        """
        Save the model to a file.

        Parameters
        ----------
        path : str
            The path to save the model to.
        """
        torch.save(self.model, path)
        LOGGER.info(f"Model saved to {path}")

    def load_model(self, path: str):
        """
        Load the model from a file.

        Parameters
        ----------
        path : str
            The path to load the model from.
        """
        self.model = torch.load(path, weights_only=False)
        self.model.eval()
        self.fine_tuning_head.eval()
        LOGGER.info(f"Model loaded from {path}")

load_model(path)

Load the model from a file.

Parameters:

Name Type Description Default
path str

The path to load the model from.

 required
Source code in helical/models/base_models.py 
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def load_model(self, path: str):
    """
    Load the model from a file.

    Parameters
    ----------
    path : str
        The path to load the model from.
    """
    self.model = torch.load(path, weights_only=False)
    self.model.eval()
    self.fine_tuning_head.eval()
    LOGGER.info(f"Model loaded from {path}")

save_model(path)

Save the model to a file.

Parameters:

Name Type Description Default
path str

The path to save the model to.

 required
Source code in helical/models/base_models.py 
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def save_model(self, path: str):
    """
    Save the model to a file.

    Parameters
    ----------
    path : str
        The path to save the model to.
    """
    torch.save(self.model, path)
    LOGGER.info(f"Model saved to {path}")

helical.models.base_models.HelicalBaseFineTuningHead

Bases: Module, ABC

Helical Fine-Tuning Head Class which serves as the base class for all fine-tuning heads in the helical package. Each new fine-tuning head should be a subclass of this class.

Source code in helical/models/fine_tune/fine_tuning_heads.py 
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class HelicalBaseFineTuningHead(torch.nn.Module, ABC):
    """Helical Fine-Tuning Head Class which serves as the base class for all fine-tuning heads in the helical package.
    Each new fine-tuning head should be a subclass of this class.
    """

    def __init__(self):
        super().__init__()

    @abstractmethod
    def forward():
        pass

    @abstractmethod
    def set_dim_size():
        pass