# Source code for mindspore.nn.metrics.error

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# ============================================================================
"""Error."""
from __future__ import absolute_import

import numpy as np

from .metric import Metric, rearrange_inputs

[文档]class MAE(Metric):
r"""
Calculates the mean absolute error(MAE).

Creates a criterion that measures the MAE between each element
in the input: :math:x and the target: :math:y.

.. math::
\text{MAE} = \frac{\sum_{i=1}^n \|{y\_pred}_i - y_i\|}{n}

where :math:n is batch size.

Supported Platforms:
Ascend GPU CPU

Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import nn, Tensor
>>>
>>> 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
"""
def __init__(self):
super(MAE, self).__init__()
self.clear()

[文档]    def clear(self):
"""Clears the internal evaluation result."""
self._abs_error_sum = 0
self._samples_num = 0

[文档]    @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result :math:y_{pred} and :math:y.

Args:
inputs: Input y_pred and y for calculating MAE where the shape of
y_pred and y are both N-D and the shape should be the same.

Raises:
ValueError: If the number of the input is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'MAE.update', it needs 2 inputs (predicted value, true value), "
"but got {}.".format(len(inputs)))
y_pred = self._convert_data(inputs[0])
y = self._convert_data(inputs[1])
abs_error_sum = np.abs(y.reshape(y_pred.shape) - y_pred)
self._abs_error_sum += abs_error_sum.sum()
self._samples_num += y.shape[0]

[文档]    def eval(self):
"""
Computes the mean absolute error(MAE).

Returns:
numpy.float64. The computed result.

Raises:
RuntimeError: If the total number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError("The 'MAE' can not be calculated, because the number of samples is 0, "
"or has called update method before calling eval method.")
return self._abs_error_sum / self._samples_num

[文档]class MSE(Metric):
r"""
Measures the mean squared error(MSE).

Creates a criterion that measures the MSE (squared L2 norm) between
each element in the prediction and the ground truth: :math:x and: :math:y.

.. math::
\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n({y\_pred}_i - y_i)^2}{n}

where :math:n is batch size.

Supported Platforms:
Ascend GPU CPU

Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import nn, Tensor
>>>
>>> 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()
>>> print(result)
0.0031250009778887033
"""
def __init__(self):
super(MSE, self).__init__()
self.clear()

[文档]    def clear(self):
"""Clear the internal evaluation result."""
self._squared_error_sum = 0
self._samples_num = 0

[文档]    @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result :math:y_{pred} and :math:y.

Args:
inputs: Input y_pred and y for calculating the MSE where the shape of
y_pred and y are both N-D and the shape should be the same.

Raises:
ValueError: If the number of inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'MSE.update', it needs 2 inputs (predicted value, true value), "
"but got {}.".format(len(inputs)))

y_pred = self._convert_data(inputs[0])
y = self._convert_data(inputs[1])
squared_error_sum = np.power(y.reshape(y_pred.shape) - y_pred, 2)
self._squared_error_sum += squared_error_sum.sum()
self._samples_num += y.shape[0]

[文档]    def eval(self):
"""
Computes the mean squared error(MSE).

Returns:
numpy.float64. The computed result.

Raises:
RuntimeError: If the number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError("The 'MSE' can not be calculated, because the number of samples is 0, "