tinyms.metrics

Metrics module provides functions to measure the performance of the machine learning models on the evaluation dataset. It’s used to choose the best model.

tinyms.metrics.names()

Gets the names of the metric methods.

Returns

List, the name list of metric methods.

tinyms.metrics.get_metric_fn(name, *args, **kwargs)

Gets the metric method based on the input name.

Parameters

• name (str) – The name of metric method. Refer to the ‘__factory__’ object for the currently supported metrics.

• args – Arguments for the metric function.

• kwargs – Keyword arguments for the metric function.

Returns

Metric object, class instance of the metric method.

Examples

>>> metric = nn.get_metric_fn('precision', eval_type='classification')

class tinyms.metrics.Accuracy(eval_type='classification')

Calculates the accuracy for classification and multilabel data.

The accuracy class creates two local variables, the correct number and the total number that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as the accuracy: an idempotent operation that simply divides the correct number by the total number.

\[\text{accuracy} =\frac{\text{true_positive} + \text{true_negative}} {\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}}\]

Parameters

eval_type (str) – Metric to calculate the accuracy over a dataset, for classification (single-label), and multilabel (multilabel classification). Default: ‘classification’.

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
>>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
>>> metric = nn.Accuracy('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> accuracy = metric.eval()
>>> print(accuracy)
0.6666666666666666

clear()

Clears the internal evaluation result.

eval()

Computes the accuracy.

Returns

Float, the computed result.

Raises

RuntimeError – If the sample size is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y. y_pred and y are a Tensor, a list or an array. For the ‘classification’ evaluation type, y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. Shape of y can be \((N, C)\) with values 0 and 1 if one-hot encoding is used or the shape is \((N,)\) with integer values if index of category is used. For ‘multilabel’ evaluation type, y_pred and y can only be one-hot encoding with values 0 or 1. Indices with 1 indicate the positive category. The shape of y_pred and y are both \((N, C)\).

Raises

ValueError – If the number of the inputs is not 2.

class tinyms.metrics.MAE

Calculates the mean absolute error.

Creates a criterion that measures the mean absolute error (MAE) between each element in the input: \(x\) and the target: \(y\).

\[\text{MAE} = \frac{\sum_{i=1}^n \|y_i - x_i\|}{n}\]

Here \(y_i\) is the prediction and \(x_i\) is the true value.

Note

The method update must be called with the form update(y_pred, y).

Examples

>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
>>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
>>> error = nn.MAE()
>>> error.clear()
>>> error.update(x, y)
>>> result = error.eval()
>>> print(result)
0.037499990314245224

clear()

Clears the internal evaluation result.

eval()

Computes the mean absolute error.

Returns

Float, the computed result.

Raises

RuntimeError – If the number of the total samples is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y for calculating mean absolute error where the shape of y_pred and y are both N-D and the shape are the same.

Raises

ValueError – If the number of the input is not 2.

class tinyms.metrics.MSE

Measures the mean squared error.

Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input: \(x\) and the target: \(y\).

\[\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n(y_i - x_i)^2}{n}\]

where \(n\) is batch size.

Examples

>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
>>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
>>> error = nn.MSE()
>>> error.clear()
>>> error.update(x, y)
>>> result = error.eval()

clear()

Clear the internal evaluation result.

eval()

Compute the mean squared error.

Returns

Float, the computed result.

Raises

RuntimeError – If the number of samples is 0.

update(*inputs)

Updates the internal evaluation result \(y_{pred}\) and \(y\).

Parameters

inputs – Input y_pred and y for calculating mean square error where the shape of y_pred and y are both N-D and the shape are the same.

Raises

ValueError – If the number of input is not 2.

class tinyms.metrics.Metric

Base class of metric.

Note

For examples of subclasses, please refer to the definition of class MAE, ‘Recall’ etc.

abstract clear()

An interface describes the behavior of clearing the internal evaluation result.

Note

All subclasses must override this interface.

abstract eval()

An interface describes the behavior of computing the evaluation result.

Note

All subclasses must override this interface.

abstract update(*inputs)

An interface describes the behavior of updating the internal evaluation result.

Note

All subclasses must override this interface.

Parameters

inputs – A variable-length input argument list.

class tinyms.metrics.Precision(eval_type='classification')

Calculates precision for classification and multilabel data.

The precision function creates two local variables, \(\text{true_positive}\) and \(\text{false_positive}\), that are used to compute the precision. This value is ultimately returned as the precision, an idempotent operation that simply divides \(\text{true_positive}\) by the sum of \(\text{true_positive}\) and \(\text{false_positive}\).

\[\text{precision} = \frac{\text{true_positive}}{\text{true_positive} + \text{false_positive}}\]

Note

In the multi-label cases, the elements of \(y\) and \(y_{pred}\) must be 0 or 1.

Parameters

eval_type (str) – Metric to calculate accuracy over a dataset, for classification or multilabel. Default: ‘classification’.

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.Precision('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> precision = metric.eval()
>>> print(precision)
[0.5 1. ]

clear()

Clears the internal evaluation result.

eval(average=False)

Computes the precision.

Parameters

average (bool) – Specify whether calculate the average precision. Default value is False.

Returns

Float, the computed result.

update(*inputs)

Updates the internal evaluation result with y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are Tensor, list or numpy.ndarray. For ‘classification’ evaluation type, y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. Shape of y can be \((N, C)\) with values 0 and 1 if one-hot encoding is used or the shape is \((N,)\) with integer values if index of category is used. For ‘multilabel’ evaluation type, y_pred and y can only be one-hot encoding with values 0 or 1. Indices with 1 indicate positive category. The shape of y_pred and y are both \((N, C)\).

Raises

ValueError – If the number of input is not 2.

class tinyms.metrics.Recall(eval_type='classification')

Calculates recall for classification and multilabel data.

The recall class creates two local variables, \(\text{true_positive}\) and \(\text{false_negative}\), that are used to compute the recall. This value is ultimately returned as the recall, an idempotent operation that simply divides \(\text{true_positive}\) by the sum of \(\text{true_positive}\) and \(\text{false_negative}\).

\[\text{recall} = \frac{\text{true_positive}}{\text{true_positive} + \text{false_negative}}\]

Note

In the multi-label cases, the elements of \(y\) and \(y_{pred}\) must be 0 or 1.

Parameters

eval_type (str) – Metric to calculate the recall over a dataset, for classification or multilabel. Default: ‘classification’.

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.Recall('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> recall = metric.eval()
>>> print(recall)
[1. 0.5]

clear()

Clears the internal evaluation result.

eval(average=False)

Computes the recall.

Parameters

average (bool) – Specify whether calculate the average recall. Default value is False.

Returns

Float, the computed result.

update(*inputs)

Updates the internal evaluation result with y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are a Tensor, a list or an array. For ‘classification’ evaluation type, y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. Shape of y can be \((N, C)\) with values 0 and 1 if one-hot encoding is used or the shape is \((N,)\) with integer values if index of category is used. For ‘multilabel’ evaluation type, y_pred and y can only be one-hot encoding with values 0 or 1. Indices with 1 indicate positive category. The shape of y_pred and y are both \((N, C)\).

Raises

ValueError – If the number of input is not 2.

class tinyms.metrics.Fbeta(beta)

Calculates the fbeta score.

Fbeta score is a weighted mean of precison and recall.

\[F_\beta=\frac{(1+\beta^2) \cdot true\_positive} {(1+\beta^2) \cdot true\_positive +\beta^2 \cdot false\_negative + false\_positive}\]

Parameters

beta (Union[float, int]) – The weight of precision.

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.Fbeta(1)
>>> metric.clear()
>>> metric.update(x, y)
>>> fbeta = metric.eval()
>>> print(fbeta)
[0.66666667 0.66666667]

clear()

Clears the internal evaluation result.

eval(average=False)

Computes the fbeta.

Parameters

average (bool) – Whether to calculate the average fbeta. Default value is False.

Returns

Float, computed result.

update(*inputs)

Updates the internal evaluation result y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are Tensor, list or numpy.ndarray. y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. y contains values of integers. The shape is \((N, C)\) if one-hot encoding is used. Shape can also be \((N,)\) if category index is used.

class tinyms.metrics.F1

Calculates the F1 score. F1 is a special case of Fbeta when beta is 1. Refer to class mindspore.nn.Fbeta for more details.

\[F_1=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}\]

Examples

>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.F1()
>>> metric.update(x, y)
>>> result = metric.eval()
>>> print(result)
[0.66666667 0.66666667]

class tinyms.metrics.TopKCategoricalAccuracy(k)

Calculates the top-k categorical accuracy.

Note

The method update must receive input of the form \((y_{pred}, y)\). If some samples have the same accuracy, the first sample will be chosen.

Parameters

k (int) – Specifies the top-k categorical accuracy to compute.

Raises

• TypeError – If k is not int.

• ValueError – If k is less than 1.

Examples

>>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
...         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
>>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
>>> topk = nn.TopKCategoricalAccuracy(3)
>>> topk.clear()
>>> topk.update(x, y)
>>> output = topk.eval()
>>> print(output)
0.6666666666666666

clear()

Clear the internal evaluation result.

eval()

Computes the top-k categorical accuracy.

Returns

Float, computed result.

update(*inputs)

Updates the internal evaluation result y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are Tensor, list or numpy.ndarray. y_pred is in most cases (not strictly) a list of floating numbers in range \([0, 1]\) and the shape is \((N, C)\), where \(N\) is the number of cases and \(C\) is the number of categories. y contains values of integers. The shape is \((N, C)\) if one-hot encoding is used. Shape can also be \((N,)\) if category index is used.

class tinyms.metrics.Top1CategoricalAccuracy

Calculates the top-1 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy. Refer to class ‘TopKCategoricalAccuracy’ for more details.

Examples

>>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
...         [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
>>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
>>> topk = nn.Top1CategoricalAccuracy()
>>> topk.clear()
>>> topk.update(x, y)
>>> output = topk.eval()
>>> print(output)
0.0

class tinyms.metrics.Top5CategoricalAccuracy

Calculates the top-5 categorical accuracy. This class is a specialized class for TopKCategoricalAccuracy. Refer to class ‘TopKCategoricalAccuracy’ for more details.

Examples

>>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
...            [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
>>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
>>> topk = nn.Top5CategoricalAccuracy()
>>> topk.clear()
>>> topk.update(x, y)
>>> output = topk.eval()
>>> print(output)
1.0

class tinyms.metrics.Loss

Calculates the average of the loss. If method ‘update’ is called every \(n\) iterations, the result of evaluation will be:

\[loss = \frac{\sum_{k=1}^{n}loss_k}{n}\]

Examples

>>> x = Tensor(np.array(0.2), mindspore.float32)
>>> loss = nn.Loss()
>>> loss.clear()
>>> loss.update(x)
>>> result = loss.eval()

clear()

Clears the internal evaluation result.

eval()

Calculates the average of the loss.

Returns

Float, the average of the loss.

Raises

RuntimeError – If the total number is 0.

update(*inputs)

Updates the internal evaluation result.

Parameters

inputs – Inputs contain only one element, the element is loss. The dimension of loss must be 0 or 1.

Raises

• ValueError – If the length of inputs is not 1.

• ValueError – If the dimensions of loss is not 1.