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pydvl.utils.types

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

SupervisedModel

Bases: Protocol

This is the minimal Protocol that valuation methods require from models in order to work.

All that is needed are the standard sklearn methods fit(), predict() and score().

fit 

fit(x: NDArray, y: NDArray)

Fit the model to the data

PARAMETER DESCRIPTION
x

Independent variables

TYPE: NDArray

y

Dependent variable

TYPE: NDArray

Source code in src/pydvl/utils/types.py 
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def fit(self, x: NDArray, y: NDArray):
    """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) -> float

Compute the score of the model given test data

PARAMETER DESCRIPTION
x

Independent variables

TYPE: NDArray

y

Dependent variable

TYPE: NDArray

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) -> 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)