qxmt.evaluation.metrics.defaults_classification module

qxmt.evaluation.metrics.defaults_classification module#

class qxmt.evaluation.metrics.defaults_classification.Accuracy(name='accuracy')#

Bases: BaseMetric

Accuracy metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import Accuracy
>>> metric = Accuracy()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
accuracy: 0.67
__init__(name='accuracy')#

Initialize the accuracy metric.

Parameters:

name (str, optional) – name of accuracy metric. Defaults to “accuracy”.

Return type:

None

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

Evaluate the accuracy score.

Parameters:

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

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

  • kwargs (Any)

Returns:

accuracy score

Return type:

float

class qxmt.evaluation.metrics.defaults_classification.F1Score(name='f1_score')#

Bases: BaseMetric

F1 score metric

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import F1Score
>>> metric = F1Score()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
f1_score: 0.8
__init__(name='f1_score')#

Initialize the F1 score metric.

Parameters:

name (str, optional) – name of f1-score metric. Defaults to “f1_score”.

Return type:

None

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

Evaluate the F1 score.

Parameters:

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

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

  • **kwargs (dict) – additional keyword arguments. The following options are supported: - average (str): define averaging method - pos_label (int): positive label for binary classification (default: max value)

Returns:

F1 score

Return type:

float

class qxmt.evaluation.metrics.defaults_classification.Precision(name='precision')#

Bases: BaseMetric

Precision metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import Precision
>>> metric = Precision()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
precision: 0.67
__init__(name='precision')#

Initialize the precision metric.

Parameters:

name (str, optional) – name of precision metric. Defaults to “precision”.

Return type:

None

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

Evaluate the precision score.

Parameters:

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

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

  • **kwargs (dict) – additional keyword arguments. The following options are supported: - average (str): define averaging method - pos_label (int): positive label for binary classification (default: max value)

Returns:

precision score

Return type:

float

class qxmt.evaluation.metrics.defaults_classification.Recall(name='recall')#

Bases: BaseMetric

Recall metric class.

Parameters:

  • BaseMetric (_type_) – Base metric class

  • name (str)

Examples

>>> import numpy as np
>>> from qxmt.evaluation.defaults import Recall
>>> metric = Recall()
>>> metric.set_score(np.array([1, 0, 1]), np.array([1, 1, 1]))
>>> metric.output_score()
recall: 1.0
__init__(name='recall')#

Initialize the recall metric.

Parameters:

name (str, optional) – name of recall metric. Defaults to “recall”.

Return type:

None

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

Evaluate the recall score.

Parameters:

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

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

  • **kwargs (dict) – additional keyword arguments. The following options are supported: - average (str): define averaging method - pos_label (int): positive label for binary classification (default: max value)

Returns:

recall score

Return type:

float

class qxmt.evaluation.metrics.defaults_classification.TargetAlignmet(name='target_alignment')#

Bases: BaseMetric

Parameters:

name (str)

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

define evaluation method for each metric.

Parameters:

  • actual (np.ndarray) – array of actual value

  • predicted (np.ndarray) – array of predicted value

  • **kwargs (dict) – additional arguments

Returns:

evaluated score

Return type:

float