from chainer import cuda
from chainer import function
from chainer.utils import type_check
class R2_score(function.Function):
def __init__(self, sample_weight, multioutput):
if sample_weight is not None:
raise NotImplementedError()
if multioutput in ['uniform_average', 'raw_values']:
self.multioutput = multioutput
else:
raise ValueError("invalid multioutput argument")
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
pred_type, true_type = in_types
type_check.expect(
pred_type.dtype.kind == 'f',
true_type.dtype.kind == 'f'
)
type_check.expect(
pred_type.shape == true_type.shape,
)
def forward(self, inputs):
xp = cuda.get_array_module(*inputs)
pred, true = inputs
SS_res = xp.sum((pred - true) ** 2, axis=0)
SS_tot = xp.sum((true - xp.mean(true, axis=0)) ** 2, axis=0)
ret = xp.where(SS_tot != 0, 1 - SS_res / SS_tot, 0.0)\
.astype(pred.dtype)
if self.multioutput == 'uniform_average':
return xp.asarray(ret.mean()),
elif self.multioutput == 'raw_values':
return ret,
[docs]def r2_score(pred, true, sample_weight=None, multioutput='uniform_average'):
"""Computes R^2(coefficient of determination) regression score function.
Args:
pred(Variable): Variable holding a vector, matrix or tensor of
estimated target values.
true(Variable): Variable holding a vector, matrix or tensor of
correct target values.
sample_weight: This argument is for compatibility with scikit-learn's
implementation of r2_score. Current implementation admits None
only.
multioutput(string): ['uniform_average', 'raw_values']. if
'uniform_average', this function returns an average of R^2
score of multiple output. If 'raw_average', this function
return a set of R^2 score of multiple output.
Returns:
Variable: A Variable holding a scalar array of the R^2 score if
'multioutput' is 'uniform_average' or a vector of R^2 scores if
'multioutput' is 'raw_values'.
.. note:: This function is non-differentiable.
"""
return R2_score(sample_weight=sample_weight,
multioutput=multioutput)(pred, true)