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pydvl.utils.types

This module contains types, protocols, decorators and generic function transformations. Some of it probably belongs elsewhere.

BaseModel

Bases: Protocol

This is the minimal model protocol with the method fit()

fit 

fit(x: NDArray, y: NDArray | None)

Fit the model to the data

PARAMETER DESCRIPTION
x

Independent variables

TYPE: NDArray

y

Dependent variable

TYPE: NDArray | None

Source code in src/pydvl/utils/types.py 
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def fit(self, x: NDArray, y: NDArray | None):
    """Fit the model to the data

    Args:
        x: Independent variables
        y: Dependent variable
    """
    pass

SupervisedModel

Bases: Protocol

This is the standard sklearn Protocol with the methods fit(), predict() and score().

fit 

fit(x: NDArray, y: NDArray | None)

Fit the model to the data

PARAMETER DESCRIPTION
x

Independent variables

TYPE: NDArray

y

Dependent variable

TYPE: NDArray | None

Source code in src/pydvl/utils/types.py 
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def fit(self, x: NDArray, y: NDArray | None):
    """Fit the model to the data

    Args:
        x: Independent variables
        y: Dependent variable
    """
    pass

predict 

predict(x: NDArray) -> NDArray

Compute predictions for the input

PARAMETER DESCRIPTION
x

Independent variables for which to compute predictions

TYPE: NDArray

RETURNS DESCRIPTION
NDArray

Predictions for the input
Source code in src/pydvl/utils/types.py 
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def predict(self, x: NDArray) -> NDArray:
    """Compute predictions for the input

    Args:
        x: Independent variables for which to compute predictions

    Returns:
        Predictions for the input
    """
    pass

score 

score(x: NDArray, y: NDArray | None) -> float

Compute the score of the model given test data

PARAMETER DESCRIPTION
x

Independent variables

TYPE: NDArray

y

Dependent variable

TYPE: NDArray | None

RETURNS DESCRIPTION
float

The score of the model on (x, y)
Source code in src/pydvl/utils/types.py 
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def score(self, x: NDArray, y: NDArray | None) -> float:
    """Compute the score of the model given test data

    Args:
        x: Independent variables
        y: Dependent variable

    Returns:
        The score of the model on `(x, y)`
    """
    pass

ensure_seed_sequence 

ensure_seed_sequence(
    seed: Optional[Union[Seed, SeedSequence]] = None
) -> SeedSequence

If the passed seed is a SeedSequence object then it is returned as is. If it is a Generator the internal protected seed sequence from the generator gets extracted. Otherwise, a new SeedSequence object is created from the passed (optional) seed.

PARAMETER DESCRIPTION
seed

Either an int, a Generator object a SeedSequence object or None.

TYPE: Optional[Union[Seed, SeedSequence]] DEFAULT: None

RETURNS DESCRIPTION
SeedSequence

A SeedSequence object.

New in version 0.7.0

Source code in src/pydvl/utils/types.py 
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def ensure_seed_sequence(
    seed: Optional[Union[Seed, SeedSequence]] = None
) -> SeedSequence:
    """
    If the passed seed is a SeedSequence object then it is returned as is. If it is
    a Generator the internal protected seed sequence from the generator gets extracted.
    Otherwise, a new SeedSequence object is created from the passed (optional) seed.

    Args:
        seed: Either an int, a Generator object a SeedSequence object or None.

    Returns:
        A SeedSequence object.

    !!! tip "New in version 0.7.0"
    """
    if isinstance(seed, SeedSequence):
        return seed
    elif isinstance(seed, Generator):
        return cast(SeedSequence, seed.bit_generator.seed_seq)  # type: ignore
    else:
        return SeedSequence(seed)