qxmt.evaluation.metrics.defaults_regression module

qxmt.evaluation.metrics.defaults_regression module#

class qxmt.evaluation.metrics.defaults_regression.MeanAbsoluteError(name='mean_absolute_error')#

Bases: BaseMetric

Mean absolute error metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import MeanAbsoluteError
>>> metric = MeanAbsoluteError()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
mean_absolute_error: 0.33
__init__(name='mean_absolute_error')#

Initialize the mean absolute error metric.

Parameters:

name (str, optional) – name of mean absolute error metric. Defaults to “mean_absolute_error”.

Return type:

None

static evaluate(actual, predicted, **kwargs)#

Evaluate the mean absolute error.

Parameters:

  • actual (np.ndarray) – numpy array of actual values

  • predicted (np.ndarray) – numpy array of predicted values

  • kwargs (Any)

Returns:

mean absolute error score

Return type:

float

class qxmt.evaluation.metrics.defaults_regression.R2Score(name='r2_score')#

Bases: BaseMetric

R2 score metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import R2Score
>>> metric = R2Score()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
r2_score: -0.5
__init__(name='r2_score')#

Initialize the R2 score metric.

Parameters:

name (str, optional) – name of R2 score metric. Defaults to “r2_score”.

Return type:

None

static evaluate(actual, predicted, **kwargs)#

Evaluate the R2 score.

Parameters:

  • actual (np.ndarray) – numpy array of actual values

  • predicted (np.ndarray) – numpy array of predicted values

  • kwargs (Any)

Returns:

R2 score

Return type:

float

class qxmt.evaluation.metrics.defaults_regression.RootMeanSquaredError(name='root_mean_squared_error')#

Bases: BaseMetric

Root mean squared error metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import RootMeanSquaredError
>>> metric = RootMeanSquaredError()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
root_mean_squared_error: 0.58
__init__(name='root_mean_squared_error')#

Initialize the root mean squared error metric.

Parameters:

name (str, optional) – name of root mean squared error metric. Defaults to “root_mean_squared_error”.

Return type:

None

static evaluate(actual, predicted, **kwargs)#

Evaluate the root mean squared error.

Parameters:

  • actual (np.ndarray) – numpy array of actual values

  • predicted (np.ndarray) – numpy array of predicted values

  • kwargs (Any)

Returns:

root mean squared error score

Return type:

float