"""Topk."""
import numpy as np
from .metric import Metric, rearrange_inputs
class TopKCategoricalAccuracy(Metric):
"""
Calculates the top-k categorical accuracy.
Note:
The method `update` must receive input of the form :math:`(y_{pred}, y)`. If some samples have
the same accuracy, the first sample will be chosen.
Args:
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:
>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> 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
"""
def __init__(self, k):
super(TopKCategoricalAccuracy, self).__init__()
if not isinstance(k, int):
raise TypeError('k should be integer type, but got {}'.format(type(k)))
if k < 1:
raise ValueError('k must be at least 1, but got {}'.format(k))
self.k = k
self.clear()
def clear(self):
"""Clear the internal evaluation result."""
self._correct_num = 0
self._samples_num = 0
@rearrange_inputs
def update(self, *inputs):
"""
Updates the internal evaluation result y_pred and y.
Args:
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 :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. y contains values of integers. The shape is :math:`(N, C)`
if one-hot encoding is used. Shape can also be :math:`(N,)` if category index is used.
"""
if len(inputs) != 2:
raise ValueError('The topk 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 y_pred.ndim == y.ndim and self._check_onehot_data(y):
y = y.argmax(axis=1)
indices = np.argsort(-y_pred, axis=1)[:, :self.k]
repeated_y = y.reshape(-1, 1).repeat(self.k, axis=1)
correct = np.equal(indices, repeated_y).sum(axis=1)
self._correct_num += correct.sum()
self._samples_num += repeated_y.shape[0]
def eval(self):
"""
Computes the top-k categorical accuracy.
Returns:
Float, computed result.
"""
if self._samples_num == 0:
raise RuntimeError('The total number of samples must not be 0.')
return self._correct_num / self._samples_num
class Top1CategoricalAccuracy(TopKCategoricalAccuracy):
"""
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
"""
def __init__(self):
super(Top1CategoricalAccuracy, self).__init__(1)
class Top5CategoricalAccuracy(TopKCategoricalAccuracy):
"""
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
"""
def __init__(self):
super(Top5CategoricalAccuracy, self).__init__(5)