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# ============================================================================
"""Precision."""
from __future__ import absolute_import
import sys
import numpy as np
from mindspore._checkparam import Validator as validator
from mindspore.nn.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
[docs]class Precision(EvaluationBase):
r"""
Calculates precision for classification and multilabel data.
The precision function creates two local variables, :math:`\text{true_positive}` and
:math:`\text{false_positive}`, which are used to compute the precision. The calculation formula is:
.. math::
\text{precision} = \frac{\text{true_positive}}{\text{true_positive} + \text{false_positive}}
Note:
In the multi-label cases, the elements of :math:`y` and :math:`y_{pred}` must be 0 or 1.
Args:
eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> 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. ]
"""
def __init__(self, eval_type='classification'):
super(Precision, self).__init__(eval_type)
self.eps = sys.float_info.min
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self._class_num = 0
if self._type == "multilabel":
self._true_positives = np.empty(0)
self._positives = np.empty(0)
self._true_positives_average = 0
self._positives_average = 0
else:
self._true_positives = 0
self._positives = 0
[docs] @rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result with `y_pred` and `y`.
Args:
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 :math:`[0, 1]`
and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
is the number of categories. Shape of `y` can be :math:`(N, C)` with values 0 and 1 if one-hot
encoding is used or the shape is :math:`(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 :math:`(N, C)`.
Raises:
ValueError: If the number of inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'Precision.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])
if self._type == 'classification' and y_pred.ndim == y.ndim and _check_onehot_data(y):
y = y.argmax(axis=1)
self._check_shape(y_pred, y)
self._check_value(y_pred, y)
if self._class_num == 0:
self._class_num = y_pred.shape[1]
elif y_pred.shape[1] != self._class_num:
raise ValueError("For 'Precision.update', class number not match, last input predicted data contain {} "
"classes, but current predicted data contain {} classes, please check your predicted "
"value(inputs[0])".format(self._class_num, y_pred.shape[1]))
class_num = self._class_num
if self._type == "classification":
if y.max() + 1 > class_num:
raise ValueError("For 'Precision.update', predicted value (input[0]) should have the same classes "
"number as true value (input[1]), but got predicted value classes {}, true value "
"classes {}.".format(class_num, y.max() + 1))
y = np.eye(class_num)[y.reshape(-1)]
indices = y_pred.argmax(axis=1).reshape(-1)
y_pred = np.eye(class_num)[indices]
elif self._type == "multilabel":
y_pred = y_pred.swapaxes(1, 0).reshape(class_num, -1)
y = y.swapaxes(1, 0).reshape(class_num, -1)
positives = y_pred.sum(axis=0)
true_positives = (y * y_pred).sum(axis=0)
if self._type == "multilabel":
self._true_positives_average += np.sum(true_positives / (positives + self.eps))
self._positives_average += len(positives)
self._true_positives = np.concatenate((self._true_positives, true_positives), axis=0)
self._positives = np.concatenate((self._positives, positives), axis=0)
else:
self._true_positives += true_positives
self._positives += positives
[docs] def eval(self, average=False):
"""
Computes the precision.
Args:
average (bool): Specify whether calculate the average precision. Default: False.
Returns:
numpy.float64, the computed result.
"""
if self._class_num == 0:
raise RuntimeError("The 'Precision' can not be calculated, because the number of samples is 0, "
"please check whether your inputs (predicted value, true value) are empty, or "
"has called update method before calling eval method.")
validator.check_value_type("average", average, [bool], self.__class__.__name__)
result = self._true_positives / (self._positives + self.eps)
if average:
if self._type == "multilabel":
result = self._true_positives_average / (self._positives_average + self.eps)
return result.mean()
return result