pydvl.influence.base_influence_function_model ¶

InfluenceFunctionModel ¶

Bases: Generic[TensorType, DataLoaderType], ABC

Generic abstract base class for computing influence related quantities. For a specific influence algorithm and tensor framework, inherit from this base class

n_parameters `abstractmethod` `property` ¶

n_parameters

Number of trainable parameters of the underlying model

is_thread_safe `abstractmethod` `property` ¶

is_thread_safe: bool

Whether the influence computation is thread safe

is_fitted `abstractmethod` `property` ¶

is_fitted

Override this, to expose the fitting status of the instance.

fit `abstractmethod` ¶

fit(data: DataLoaderType) -> InfluenceFunctionModel

Override this method to fit the influence function model to training data, e.g. pre-compute hessian matrix or matrix decompositions

PARAMETER	DESCRIPTION
`data`	TYPE: `DataLoaderType`

RETURNS	DESCRIPTION
`InfluenceFunctionModel`	The fitted instance

Source code in src/pydvl/influence/base_influence_function_model.py

@abstractmethod
def fit(self, data: DataLoaderType) -> InfluenceFunctionModel:
    """
    Override this method to fit the influence function model to training data,
    e.g. pre-compute hessian matrix or matrix decompositions

    Args:
        data:

    Returns:
        The fitted instance
    """

fit_required `staticmethod` ¶

fit_required(method)

Decorator to enforce the fitted check

Source code in src/pydvl/influence/base_influence_function_model.py

@staticmethod
def fit_required(method):
    """Decorator to enforce the fitted check"""

    @wraps(method)
    def wrapper(self, *args, **kwargs):
        if not self.is_fitted:
            raise NotFittedException(type(self))
        return method(self, *args, **kwargs)

    return wrapper

influence_factors ¶

influence_factors(x: TensorType, y: TensorType) -> TensorType

Computes the approximation of

\[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

where the gradient is meant to be per sample of the batch \((x, y)\). For all input tensors it is assumed, that the first dimension is the batch dimension.

PARAMETER	DESCRIPTION
`x`	model input to use in the gradient computations TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise inverse Hessian matrix vector products

Source code in src/pydvl/influence/base_influence_function_model.py

def influence_factors(self, x: TensorType, y: TensorType) -> TensorType:
    r"""
    Computes the approximation of

    \[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

    where the gradient is meant to be per sample of the batch $(x, y)$.
    For all input tensors it is assumed,
    that the first dimension is the batch dimension.

    Args:
        x: model input to use in the gradient computations
        y: label tensor to compute gradients

    Returns:
        Tensor representing the element-wise inverse Hessian matrix vector products

    """
    if not self.is_fitted:
        raise NotFittedException(type(self))
    return self._influence_factors(x, y)

influences ¶

influences(
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType

Computes the approximation of

\[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}})), \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle H^{-1}\nabla_{\theta} \ell(y_{test}, f_{\theta}(x_{test})), \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case.

PARAMETER	DESCRIPTION
`x_test`	model input to use in the gradient computations of \(H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))\) TYPE: `TensorType`
`y_test`	label tensor to compute gradients TYPE: `TensorType`
`x`	optional model input to use in the gradient computations \(\nabla_{theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{test}\) TYPE: `Optional[TensorType]` DEFAULT: `None`
`y`	optional label tensor to compute gradients TYPE: `Optional[TensorType]` DEFAULT: `None`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise scalar products for the provided batch

Source code in src/pydvl/influence/base_influence_function_model.py

def influences(
    self,
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType:
    r"""
    Computes the approximation of

    \[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
        f_{\theta}(x_{\text{test}})),
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle H^{-1}\nabla_{\theta} \ell(y_{test}, f_{\theta}(x_{test})),
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case.

    Args:
        x_test: model input to use in the gradient computations
            of $H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))$
        y_test: label tensor to compute gradients
        x: optional model input to use in the gradient computations
            $\nabla_{theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{test}$
        y: optional label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Tensor representing the element-wise scalar products for the provided batch

    """
    if not self.is_fitted:
        raise NotFittedException(type(self))

    if x is None and y is not None:
        raise ValueError(
            "Providing labels y, without providing model input x "
            "is not supported"
        )

    if x is not None and y is None:
        raise ValueError(
            "Providing model input x, without providing labels y "
            "is not supported"
        )

    return self._influences(x_test, y_test, x, y, mode)

influences_from_factors `abstractmethod` ¶

influences_from_factors(
    z_test_factors: TensorType,
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType

Override this method to implement the computation of

\[ \langle z_{\text{test_factors}}, \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle z_{\text{test_factors}}, \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case. The gradient is meant to be per sample of the batch \((x, y)\).

PARAMETER	DESCRIPTION
`z_test_factors`	pre-computed array, approximating \(H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}}))\) TYPE: `TensorType`
`x`	model input to use in the gradient computations \(\nabla_{\theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{\text{test}}\) TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise scalar products for the provided batch

Source code in src/pydvl/influence/base_influence_function_model.py

@abstractmethod
def influences_from_factors(
    self,
    z_test_factors: TensorType,
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType:
    r"""
    Override this method to implement the computation of

    \[ \langle z_{\text{test_factors}},
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle z_{\text{test_factors}},
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case. The gradient is meant to be per sample
    of the batch $(x, y)$.

    Args:
        z_test_factors: pre-computed array, approximating
            $H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
            f_{\theta}(x_{\text{test}}))$
        x: model input to use in the gradient computations
            $\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{\text{test}}$
        y: label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Tensor representing the element-wise scalar products for the provided batch

    """

ComposableInfluence ¶

Bases: InfluenceFunctionModel, Generic[TensorType, BatchType, DataLoaderType, BlockMapperType], ABC

Generic abstract base class, that allow for block-wise computation of influence quantities. Inherit from this base class for specific influence algorithms and tensor frameworks.

n_parameters `abstractmethod` `property` ¶

n_parameters

Number of trainable parameters of the underlying model

is_thread_safe `abstractmethod` `property` ¶

is_thread_safe: bool

Whether the influence computation is thread safe

fit_required `staticmethod` ¶

fit_required(method)

Decorator to enforce the fitted check

Source code in src/pydvl/influence/base_influence_function_model.py

@staticmethod
def fit_required(method):
    """Decorator to enforce the fitted check"""

    @wraps(method)
    def wrapper(self, *args, **kwargs):
        if not self.is_fitted:
            raise NotFittedException(type(self))
        return method(self, *args, **kwargs)

    return wrapper

influence_factors ¶

influence_factors(x: TensorType, y: TensorType) -> TensorType

Computes the approximation of

\[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

where the gradient is meant to be per sample of the batch \((x, y)\). For all input tensors it is assumed, that the first dimension is the batch dimension.

PARAMETER	DESCRIPTION
`x`	model input to use in the gradient computations TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise inverse Hessian matrix vector products

Source code in src/pydvl/influence/base_influence_function_model.py

def influence_factors(self, x: TensorType, y: TensorType) -> TensorType:
    r"""
    Computes the approximation of

    \[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

    where the gradient is meant to be per sample of the batch $(x, y)$.
    For all input tensors it is assumed,
    that the first dimension is the batch dimension.

    Args:
        x: model input to use in the gradient computations
        y: label tensor to compute gradients

    Returns:
        Tensor representing the element-wise inverse Hessian matrix vector products

    """
    if not self.is_fitted:
        raise NotFittedException(type(self))
    return self._influence_factors(x, y)

influences ¶

influences(
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType

Computes the approximation of

\[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}})), \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle H^{-1}\nabla_{\theta} \ell(y_{test}, f_{\theta}(x_{test})), \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case.

PARAMETER	DESCRIPTION
`x_test`	model input to use in the gradient computations of \(H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))\) TYPE: `TensorType`
`y_test`	label tensor to compute gradients TYPE: `TensorType`
`x`	optional model input to use in the gradient computations \(\nabla_{theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{test}\) TYPE: `Optional[TensorType]` DEFAULT: `None`
`y`	optional label tensor to compute gradients TYPE: `Optional[TensorType]` DEFAULT: `None`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise scalar products for the provided batch

Source code in src/pydvl/influence/base_influence_function_model.py

def influences(
    self,
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType:
    r"""
    Computes the approximation of

    \[ \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
        f_{\theta}(x_{\text{test}})),
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle H^{-1}\nabla_{\theta} \ell(y_{test}, f_{\theta}(x_{test})),
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case.

    Args:
        x_test: model input to use in the gradient computations
            of $H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))$
        y_test: label tensor to compute gradients
        x: optional model input to use in the gradient computations
            $\nabla_{theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{test}$
        y: optional label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Tensor representing the element-wise scalar products for the provided batch

    """
    if not self.is_fitted:
        raise NotFittedException(type(self))

    if x is None and y is not None:
        raise ValueError(
            "Providing labels y, without providing model input x "
            "is not supported"
        )

    if x is not None and y is None:
        raise ValueError(
            "Providing model input x, without providing labels y "
            "is not supported"
        )

    return self._influences(x_test, y_test, x, y, mode)

fit ¶

fit(data: DataLoaderType) -> InfluenceFunctionModel

Fitting to provided data, by internally creating a block mapper instance from it. Args: data: iterable of tensors

RETURNS	DESCRIPTION
`InfluenceFunctionModel`	Fitted instance

Source code in src/pydvl/influence/base_influence_function_model.py

@log_duration(log_level=logging.INFO)
def fit(self, data: DataLoaderType) -> InfluenceFunctionModel:
    """
    Fitting to provided data, by internally creating a block mapper instance from
    it.
    Args:
        data: iterable of tensors

    Returns:
        Fitted instance
    """
    self.block_mapper = self._create_block_mapper(data)
    return self

influences_by_block ¶

influences_by_block(
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> OrderedDict[str, TensorType]

Compute the block-wise influence values for the provided data, i.e. an approximation of

\[ \langle H^{-1}\nabla_{theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}})), \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test})), \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case.

PARAMETER	DESCRIPTION
`x_test`	model input to use in the gradient computations of the approximation of \(H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))\) TYPE: `TensorType`
`y_test`	label tensor to compute gradients TYPE: `TensorType`
`x`	optional model input to use in the gradient computations \(\nabla_{theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{test}\) TYPE: `Optional[TensorType]` DEFAULT: `None`
`y`	optional label tensor to compute gradients TYPE: `Optional[TensorType]` DEFAULT: `None`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`OrderedDict[str, TensorType]`	Ordered dictionary of tensors representing the element-wise scalar products
`OrderedDict[str, TensorType]`	for the provided batch per block.

Source code in src/pydvl/influence/base_influence_function_model.py

@InfluenceFunctionModel.fit_required
def influences_by_block(
    self,
    x_test: TensorType,
    y_test: TensorType,
    x: Optional[TensorType] = None,
    y: Optional[TensorType] = None,
    mode: InfluenceMode = InfluenceMode.Up,
) -> OrderedDict[str, TensorType]:
    r"""
    Compute the block-wise influence values for the provided data, i.e. an
    approximation of

    \[ \langle H^{-1}\nabla_{theta} \ell(y_{\text{test}},
        f_{\theta}(x_{\text{test}})),
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test})),
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case.

    Args:
        x_test: model input to use in the gradient computations
            of the approximation of
            $H^{-1}\nabla_{theta} \ell(y_{test}, f_{\theta}(x_{test}))$
        y_test: label tensor to compute gradients
        x: optional model input to use in the gradient computations
            $\nabla_{theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{test}$
        y: optional label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Ordered dictionary of tensors representing the element-wise scalar products
        for the provided batch per block.

    """
    left_batch = self._create_batch(x_test, y_test)

    if x is None:
        if y is not None:
            raise ValueError(
                "Providing labels y, without providing model input x "
                "is not supported"
            )
        right_batch = left_batch
    else:
        if y is None:
            raise ValueError(
                "Providing model input x, without providing labels y "
                "is not supported"
            )
        right_batch = self._create_batch(x, y)

    return self.block_mapper.interactions(left_batch, right_batch, mode)

influence_factors_by_block ¶

influence_factors_by_block(
    x: TensorType, y: TensorType
) -> OrderedDict[str, TensorType]

Compute the block-wise approximation of

\[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

where the gradient is meant to be per sample of the batch \((x, y)\).

PARAMETER	DESCRIPTION
`x`	model input to use in the gradient computations TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`

RETURNS	DESCRIPTION
`OrderedDict[str, TensorType]`	Ordered dictionary of tensors representing the element-wise
`OrderedDict[str, TensorType]`	approximate inverse Hessian matrix vector products per block.

Source code in src/pydvl/influence/base_influence_function_model.py

@InfluenceFunctionModel.fit_required
def influence_factors_by_block(
    self, x: TensorType, y: TensorType
) -> OrderedDict[str, TensorType]:
    r"""
    Compute the block-wise approximation of

    \[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]

    where the gradient is meant to be per sample of the batch $(x, y)$.

    Args:
        x: model input to use in the gradient computations
        y: label tensor to compute gradients

    Returns:
        Ordered dictionary of tensors representing the element-wise
        approximate inverse Hessian matrix vector products per block.

    """
    return self.block_mapper.transformed_grads(self._create_batch(x, y))

influences_from_factors_by_block ¶

influences_from_factors_by_block(
    z_test_factors: OrderedDict[str, TensorType],
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> OrderedDict[str, TensorType]

Block-wise computation of

\[ \langle z_{\text{test_factors}}, \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle z_{\text{test_factors}}, \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case. The gradient is meant to be per sample of the batch \((x, y)\).

PARAMETER	DESCRIPTION
`z_test_factors`	pre-computed array, approximating \(H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}}))\) TYPE: `OrderedDict[str, TensorType]`
`x`	model input to use in the gradient computations \(\nabla_{\theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{\text{test}}\) TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`OrderedDict[str, TensorType]`	Ordered dictionary of tensors representing the element-wise scalar products
`OrderedDict[str, TensorType]`	for the provided batch per block

Source code in src/pydvl/influence/base_influence_function_model.py

@InfluenceFunctionModel.fit_required
def influences_from_factors_by_block(
    self,
    z_test_factors: OrderedDict[str, TensorType],
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> OrderedDict[str, TensorType]:
    r"""
    Block-wise computation of

    \[ \langle z_{\text{test_factors}},
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle z_{\text{test_factors}},
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case. The gradient is meant to be per sample
    of the batch $(x, y)$.

    Args:
        z_test_factors: pre-computed array, approximating
            $H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
            f_{\theta}(x_{\text{test}}))$
        x: model input to use in the gradient computations
            $\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{\text{test}}$
        y: label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Ordered dictionary of tensors representing the element-wise scalar products
        for the provided batch per block

    """
    return self.block_mapper.interactions_from_transformed_grads(
        z_test_factors, self._create_batch(x, y), mode
    )

influences_from_factors ¶

influences_from_factors(
    z_test_factors: TensorType,
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType

Computation of

\[ \langle z_{\text{test_factors}}, \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the case of up-weighting influence, resp.

\[ \langle z_{\text{test_factors}}, \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

for the perturbation type influence case. The gradient is meant to be per sample of the batch \((x, y)\).

PARAMETER	DESCRIPTION
`z_test_factors`	pre-computed array, approximating \(H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}}))\) TYPE: `TensorType`
`x`	model input to use in the gradient computations \(\nabla_{\theta}\ell(y, f_{\theta}(x))\), resp. \(\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))\), if None, use \(x=x_{\text{test}}\) TYPE: `TensorType`
`y`	label tensor to compute gradients TYPE: `TensorType`
`mode`	enum value of InfluenceMode TYPE: `InfluenceMode` DEFAULT: `Up`

RETURNS	DESCRIPTION
`TensorType`	Tensor representing the element-wise scalar products for the provided batch

Source code in src/pydvl/influence/base_influence_function_model.py

@InfluenceFunctionModel.fit_required
def influences_from_factors(
    self,
    z_test_factors: TensorType,
    x: TensorType,
    y: TensorType,
    mode: InfluenceMode = InfluenceMode.Up,
) -> TensorType:
    r"""
    Computation of

    \[ \langle z_{\text{test_factors}},
        \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the case of up-weighting influence, resp.

    \[ \langle z_{\text{test_factors}},
        \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]

    for the perturbation type influence case. The gradient is meant to be per sample
    of the batch $(x, y)$.

    Args:
        z_test_factors: pre-computed array, approximating
            $H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
            f_{\theta}(x_{\text{test}}))$
        x: model input to use in the gradient computations
            $\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            resp. $\nabla_{x}\nabla_{\theta}\ell(y, f_{\theta}(x))$,
            if None, use $x=x_{\text{test}}$
        y: label tensor to compute gradients
        mode: enum value of [InfluenceMode]
            [pydvl.influence.base_influence_function_model.InfluenceMode]

    Returns:
        Tensor representing the element-wise scalar products for the provided batch

    """
    tensors = self.block_mapper.generate_interactions_from_transformed_grads(
        z_test_factors,
        self._create_batch(x, y),
        mode,
    )
    result: TensorType = next(tensors)
    for tensor in tensors:
        result = result + tensor
    return result

pydvl.influence.base_influence_function_model ¶

InfluenceFunctionModel ¶

n_parameters abstractmethod property ¶

is_thread_safe abstractmethod property ¶

is_fitted abstractmethod property ¶

fit abstractmethod ¶

fit_required staticmethod ¶

influence_factors ¶

influences ¶

influences_from_factors abstractmethod ¶

ComposableInfluence ¶

n_parameters abstractmethod property ¶

is_thread_safe abstractmethod property ¶

fit_required staticmethod ¶

influence_factors ¶

influences ¶

fit ¶

influences_by_block ¶

influence_factors_by_block ¶

influences_from_factors_by_block ¶

influences_from_factors ¶

n_parameters `abstractmethod` `property` ¶

is_thread_safe `abstractmethod` `property` ¶

is_fitted `abstractmethod` `property` ¶

fit `abstractmethod` ¶

fit_required `staticmethod` ¶

influences_from_factors `abstractmethod` ¶

n_parameters `abstractmethod` `property` ¶

is_thread_safe `abstractmethod` `property` ¶

fit_required `staticmethod` ¶