Issues with negative values in sample_weight

Description

I am not sure what the interpretation of a negative value in sample_weight might be and why this should be supported, but I believe that there should be constraints in using non-negative values in several cases; the use of negative ones can lead to some very strange results.

See an example below for r2_score where the use of negative weights yields a value larger than one, which really does not make sense.

Steps/Code to Reproduce

import numpy as np

from sklearn.metrics import r2_score

np.random.seed(seed=2)
x = np.random.randn(100,)
y = x + 0.3*np.random.randn(*x.shape)
w = np.random.randn(*x.shape)

r2_score(x, y, sample_weight=w)

Expected Results

Something smaller or equal to 1.0

Actual Results

1.1919195778883198

Versions

System: python: 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] executable: C:\Users\nak142\Miniconda3\envs\sklearn_contrib\pythonw.exe machine: Windows-10-10.0.17134-SP0

BLAS: macros: SCIPY_MKL_H=None, HAVE_CBLAS=None lib_dirs: C:/Users/nak142/Miniconda3/envs/sklearn_contrib\Library\lib cblas_libs: mkl_rt

Python deps: pip: 10.0.1 setuptools: 40.0.0 sklearn: 0.21.dev0 numpy: 1.15.0 scipy: 1.1.0 Cython: 0.28.5 pandas: None

[alexpearce]

Thanks for the ping @amueller.

I still think the feature is very useful. Samples with negative weights play a critical role in the area of high energy physics that I work in. But one does need to understand that one can easily arrive at nonsensical results when using them, so having a warning/error is probably a good sanity check for many use-cases.

I think a global flag is a nice idea, as then the user has to admit “I think I know what I’m doing” and has to interpret any results accordingly.

[kmqanda]

Would like to check the status of this open item, having a global flag enabling negative sample weight is helpful in my use case. Alternatively, I uses a direct implementation to allow -ve weights:

xw = x * w
xwx = np.dot(xw.T, x)
xwx_inv = np.linalg.inv(xwx)
coef = np.dot(xwx_inv,np.dot(xw.T,y))
xf = np.dot(x, coef)
res = y - xf
res_wss = np.dot(w.T, (res ** 2))
tot_wss = np.dot(w.T, (y ** 2))
wr2 = 1 - res_wss / tot_wss