mvpy.metrics package

Submodules

mvpy.metrics.accuracy module

class mvpy.metrics.accuracy.Accuracy(name: str = 'accuracy', request: str | ~typing.Tuple[str] = ('y', 'predict'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function accuracy>)

Bases: Metric

Implements mvpy.math.accuracy() as a Metric.

Warning

This class extends Metric. If you would like to apply this metric, please use the instance exposed under mvpy.metrics.accuracy.

For more information on this, please consult the documentation of Metric and score().

Parameters:

namestr, default=’accuracy’

The name of this metric.

requeststr | tuple[str], default=(‘y’, ‘predict’)

The values to request for scoring.

reduceint | tuple[int], default= (0,)

The dimension(s) to reduce over.

fCallable, default=mvpy.math.accuracy

The function to call.

Examples

>>> import torch
>>> from mvpy.dataset import make_meeg_categorical
>>> from mvpy.estimators import RidgeClassifier
>>> from mvpy.crossvalidation import cross_val_score
>>> from mvpy.metric import accuracy
>>> X, y = make_meeg_categorical()
>>> clf = RidgeClassifier()
>>> cross_val_score(clf, X, y, metric = accuracy)

f(y: ndarray | Tensor) → ndarray | Tensor

Compute accuracy between x and y. Note that accuracy is always computed over the final dimension.

Parameters:

xUnion[np.ndarray, torch.Tensor]

Vector/Matrix/Tensor

yUnion[np.ndarray, torch.Tensor]

Vector/Matrix/Tensor

Returns:

Union[np.ndarray, torch.Tensor]

Accuracy

Notes

Accuracy is defined as:

\[\text{accuracy}(x, y) = \frac{1}{N}\sum_i^N{1(x_i = y_i)}\]

Examples

>>> import torch
>>> from mvpy.math import accuracy
>>> x = torch.tensor([1, 0])
>>> y = torch.tensor([-1, 0])
>>> accuracy(x, y)
tensor([0.5])

name: str = 'accuracy'

reduce: int | Tuple[int] = (0,)

request: str | Tuple[str] = ('y', 'predict')

Implements mvpy.math.accuracy() as a Metric.

The torch package contains data structures for multi-dimensional

mvpy.metrics.metric module

Base metric class.

class mvpy.metrics.metric.Metric(name: str = 'metric', request: ~typing.Tuple[str] = ('y', 'predict'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function Metric.<lambda>>)

Bases: object

f()

mutate(name: str | None = None, request: str | None = None, reduce: int | Tuple[int] | None = None, f: Callable | None = None) → Metric

name: str = 'metric'

reduce: int | Tuple[int] = (0,)

request: Tuple[str] = ('y', 'predict')

The torch package contains data structures for multi-dimensional

mvpy.metrics.pearsonr module

class mvpy.metrics.pearsonr.Pearsonr(name: str = 'pearsonr', request: str | ~typing.Tuple[str] = ('y', 'predict'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function pearsonr>)

Bases: Metric

Implements mvpy.math.pearsonr() as a Metric.

Warning

This class extends Metric. If you would like to apply this metric, please use the instance exposed under mvpy.metrics.pearsonr.

For more information on this, please consult the documentation of Metric and score().

Parameters:

namestr, default=’pearsonr’

The name of this metric.

requeststr | tuple[str], default=(‘y’, ‘predict’)

The values to request for scoring.

reduceint | tuple[int], default= (0,)

The dimension(s) to reduce over.

fCallable, default=mvpy.math.pearsonr

The function to call.

Examples

>>> import torch
>>> from mvpy.dataset import make_meeg_categorical
>>> from mvpy.estimators import RidgeDecoder
>>> from mvpy.crossvalidation import cross_val_score
>>> from mvpy.metric import pearsonr
>>> X, y = make_meeg_continuous()
>>> clf = RidgeClassifier()
>>> cross_val_score(clf, X, y, metric = pearsonr)

f(y: ndarray | Tensor, *args: Any) → ndarray | Tensor

Computes pearson correlations between x and y. Note that correlations are always computed over the final dimension.

Parameters:

xUnion[np.ndarray, torch.Tensor]

Matrix ([samples …] x features)

yUnion[np.ndarray, torch.Tensor]

Matrix ([samples …] x features)

Returns:

Union[np.ndarray, torch.Tensor]

Vector or matrix of pearson correlations

Notes

Pearson correlations are defined as:

\[r = \frac{\sum{(x_i - \bar{x})(y_i - \bar{y})}}{\sqrt{\sum{(x_i - \bar{x})^2} \sum{(y_i - \bar{y})^2}}}\]

where \(x_i\) and \(y_i\) are the \(i\)-th elements of \(x\) and \(y\), respectively.

Examples

>>> import torch
>>> from mvpy.math import pearsonr
>>> x = torch.tensor([1, 2, 3])
>>> y = torch.tensor([4, 5, 6])
>>> pearsonr(x, y)
tensor(1., dtype=torch.float64)

name: str = 'pearsonr'

reduce: int | Tuple[int] = (0,)

request: str | Tuple[str] = ('y', 'predict')

Implements mvpy.math.pearsonr() as a Metric.

The torch package contains data structures for multi-dimensional

mvpy.metrics.r2 module

class mvpy.metrics.r2.R2(name: str = 'r2', request: str | ~typing.Tuple[str] = ('y', 'predict'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function r2>)

Bases: Metric

Implements mvpy.math.r2() as a Metric.

Warning

This class extends Metric. If you would like to apply this metric, please use the instance exposed under mvpy.metrics.r2.

For more information on this, please consult the documentation of Metric and score().

Parameters:

namestr, default=’r2’

The name of this metric.

requeststr | tuple[str], default=(‘y’, ‘predict’)

The values to request for scoring.

reduceint | tuple[int], default= (0,)

The dimension(s) to reduce over.

fCallable, default=mvpy.math.r2

The function to call.

Examples

>>> import torch
>>> from mvpy.dataset import make_meeg_categorical
>>> from mvpy.estimators import RidgeClassifier
>>> from mvpy.crossvalidation import cross_val_score
>>> from mvpy.metric import r2
>>> X, y = make_meeg_categorical()
>>> clf = RidgeClassifier()
>>> cross_val_score(clf, X, y, metric = r2)

f(y_h: ndarray | Tensor) → ndarray | Tensor

Rank data in x along its final feature dimension. Ties are computed as averages.

Parameters:

yUnion[np.ndarray, torch.Tensor]

True outcomes of shape ([n_samples, ...,] n_features).

y_hUnion[np.ndarray, torch.Tensor]

Predicted outcomes of shape ([n_samples, ...,] n_features).

Returns:

rUnion[np.ndarray, torch.Tensor]

R2 scores of shape ([n_samples, ...]).

Examples

>>> import torch
>>> from mvpy.math import rank
>>> y = torch.tensor([1.0, 2.0, 3.0])
>>> y_h = torch.tensor([1.0, 2.0, 3.0])
>>> r2(x)
tensor([1.0])

name: str = 'r2'

reduce: int | Tuple[int] = (0,)

request: str | Tuple[str] = ('y', 'predict')

Implements mvpy.math.r2() as a Metric.

The torch package contains data structures for multi-dimensional

mvpy.metrics.roc_auc module

class mvpy.metrics.roc_auc.Roc_auc(name: str = 'roc_auc', request: str | ~typing.Tuple[str] = ('y', 'decision_function'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function roc_auc>)

Bases: Metric

Implements mvpy.math.roc_auc() as a Metric.

Warning

This class extends Metric. If you would like to apply this metric, please use the instance exposed under mvpy.metrics.roc_auc.

For more information on this, please consult the documentation of Metric and score().

Parameters:

namestr, default=’roc_auc’

The name of this metric.

requeststr | tuple[str], default=(‘y’, ‘decision_function’)

The values to request for scoring.

reduceint | tuple[int], default= (0,)

The dimension(s) to reduce over.

fCallable, default=mvpy.math.roc_auc

The function to call.

Examples

>>> import torch
>>> from mvpy.dataset import make_meeg_categorical
>>> from mvpy.estimators import RidgeClassifier
>>> from mvpy.crossvalidation import cross_val_score
>>> from mvpy.metric import roc_auc
>>> X, y = make_meeg_categorical()
>>> clf = RidgeClassifier()
>>> cross_val_score(clf, X, y, metric = roc_auc)

f(y_score: ndarray | Tensor) → ndarray | Tensor

Compute ROC AUC score between y_true and y_score. Note that ROC AUC is always computed over the final dimension.

Parameters:

y_trueUnion[np.ndarray, torch.Tensor]

Vector/Matrix/Tensor

y_scoreUnion[np.ndarray, torch.Tensor]

Vector/Matrix/Tensor

Returns:

Union[np.ndarray, torch.Tensor]

Accuracy

Notes

ROC AUC is computed using the Mann-Whitney U formula:

\[\text{ROCAUC}(y, \hat{y}) = \frac{R_{+} - \frac{P * (P + 1)}{2}}}{P * N}\]

where \(R_{+}\) is the sum of ranks for positive classes, \(P\) is the number of positive samples, and \(N\) is the number of negative samples.

In the case that labels are not binary, we create unique binary labels by one-hot encoding the labels and then take the macro-average over classes.

Examples

>>> import torch
>>> from mvpy.math import roc_auc
>>> y_true = torch.tensor([1., 0.])
>>> y_score = torch.tensor([-1., 1.])
>>> roc_auc(y_true, y_score)
tensor(0.)

>>> import torch
>>> from mvpy.math import roc_auc
>>> y_true = torch.tensor([0., 1., 2.])
>>> y_score = torch.tensor([[-1., 1., 1.], [1., -1., 1.], [1., 1., -1.]])
>>> roc_auc(y_true, y_score)
tensor(0.)

name: str = 'roc_auc'

reduce: int | Tuple[int] = (0,)

request: str | Tuple[str] = ('y', 'decision_function')

Implements mvpy.math.roc_auc() as a Metric.

The torch package contains data structures for multi-dimensional

mvpy.metrics.score module

Base metric class.

mvpy.metrics.score.reduce_(X: ndarray | Tensor, dims: int | Tuple[int]) → ndarray | Tensor

mvpy.metrics.score.score(model: Pipeline | BaseEstimator, metric: Metric | Tuple[Metric], X: ndarray | Tensor, y: ndarray | Tensor | None = None) → List[ndarray | Tensor]

Configure global settings and get information about the working environment.

The torch package contains data structures for multi-dimensional

mvpy.metrics.spearmanr module

class mvpy.metrics.spearmanr.Spearmanr(name: str = 'spearmanr', request: str | ~typing.Tuple[str] = ('y', 'predict'), reduce: int | ~typing.Tuple[int] = (0, ), f: ~typing.Callable = <function spearmanr>)

Bases: Metric

Implements mvpy.math.spearmanr() as a Metric.

Warning

This class extends Metric. If you would like to apply this metric, please use the instance exposed under mvpy.metrics.spearmanr.

For more information on this, please consult the documentation of Metric and score().

Parameters:

namestr, default=’spearmanr’

The name of this metric.

requeststr | tuple[str], default=(‘y’, ‘predict’)

The values to request for scoring.

reduceint | tuple[int], default= (0,)

The dimension(s) to reduce over.

fCallable, default=mvpy.math.spearmanr

The function to call.

Examples

>>> import torch
>>> from mvpy.dataset import make_meeg_categorical
>>> from mvpy.estimators import RidgeDecoder
>>> from mvpy.crossvalidation import cross_val_score
>>> from mvpy.metric import spearmanr
>>> X, y = make_meeg_continuous()
>>> clf = RidgeClassifier()
>>> cross_val_score(clf, X, y, metric = spearmanr)

f(y: ndarray | Tensor, *args: Any) → ndarray | Tensor

Compute Spearman correlation between x and y. Note that correlations are always computed over the final dimension in your inputs.

Parameters:

xUnion[np.ndarray, torch.Tensor]

Matrix to compute correlation with.

yUnion[np.ndarray, torch.Tensor]

Matrix to compute correlation with.

Returns:

Union[np.ndarray, torch.Tensor]

Spearman correlations.

See also

mvpy.math.pearsonr()

mvpy.math.rank()

Notes

Spearman correlations are defined as Pearson correlations between the ranks of x and y.

Examples

>>> import torch
>>> from mvpy.math import spearmanr
>>> x = torch.tensor([1, 5, 9])
>>> y = torch.tensor([1, 50, 60])
>>> spearmanr(x, y)
tensor(1., dtype=torch.float64)

name: str = 'spearmanr'

reduce: int | Tuple[int] = (0,)

request: str | Tuple[str] = ('y', 'predict')

Implements mvpy.math.spearmanr() as a Metric.

The torch package contains data structures for multi-dimensional

Module contents

Base metric class.