"""Precision."""
import sys
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import EvaluationBase, rearrange_inputs
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}`, that are used to compute the precision. This value is
ultimately returned as the precision, an idempotent operation that simply divides
:math:`\text{true_positive}` by the sum of :math:`\text{true_positive}` and :math:`\text{false_positive}`.
.. 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): Metric to calculate accuracy over a dataset, for classification or
multilabel. Default: 'classification'.
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()
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
@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 input is not 2.
"""
if len(inputs) != 2:
raise ValueError('The precision needs 2 inputs (y_pred, y), 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 self._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('Class number not match, last input data contain {} classes, but current data contain {} '
'classes'.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('y_pred contains {} classes less than y contains {} 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
def eval(self, average=False):
"""
Computes the precision.
Args:
average (bool): Specify whether calculate the average precision. Default value is False.
Returns:
Float, the computed result.
"""
if self._class_num == 0:
raise RuntimeError('The input number of samples can not be 0.')
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