# Source code for mindspore.nn.metrics.error

# Copyright 2020 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."""
import numpy as np
from .metric import Metric

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

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

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

Here :math:y_i is the prediction and :math: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()
"""
def __init__(self):
super(MAE, self).__init__()
self.clear()

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

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

Args:
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.
"""
if len(inputs) != 2:
raise ValueError('Mean absolute error need 2 inputs (y_pred, y), 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]

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

Returns:
Float, the computed result.

Raises:
RuntimeError: If the number of the total samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError('Total samples num must not be 0.')
return self._abs_error_sum / self._samples_num

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

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

.. math::
\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n(y_i - x_i)^2}{n},
where :math: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()
"""
def __init__(self):
super(MSE, self).__init__()
self.clear()

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

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

Args:
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.
"""
if len(inputs) != 2:
raise ValueError('Mean squared error need 2 inputs (y_pred, y), 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]

[docs]    def eval(self):
"""
Compute the mean squared error.

Returns:
Float, the computed result.

Raises:
RuntimeError: If the number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError('The number of input samples must not be 0.')
return self._squared_error_sum / self._samples_num