Merge pull request numpy#6131 from argriffing/choice-precision

MAINT: adjust tolerance for validating the sum of probs in random.choice

numpy/random/mtrand/mtrand.pyx

@@ -1084,6 +1084,12 @@ cdef class RandomState:
 
         if p is not None:
             d = len(p)
+
+            atol = np.sqrt(np.finfo(np.float64).eps)
+            if isinstance(p, np.ndarray):
+                if np.issubdtype(p.dtype, np.floating):
+                    atol = max(atol, np.sqrt(np.finfo(p.dtype).eps))
+
             p = <ndarray>PyArray_ContiguousFromObject(p, NPY_DOUBLE, 1, 1)
             pix = <double*>PyArray_DATA(p)
 
@@ -1093,7 +1099,7 @@ cdef class RandomState:
                 raise ValueError("a and p must have same size")
             if np.logical_or.reduce(p < 0):
                 raise ValueError("probabilities are not non-negative")
-            if abs(kahan_sum(pix, d) - 1.) > 1e-8:
+            if abs(kahan_sum(pix, d) - 1.) > atol:
                 raise ValueError("probabilities do not sum to 1")
 
         shape = size

numpy/random/tests/test_regression.py

@@ -2,7 +2,7 @@
 
 import sys
 from numpy.testing import (TestCase, run_module_suite, assert_,
-                           assert_array_equal)
+                           assert_array_equal, assert_raises)
 from numpy import random
 from numpy.compat import long
 import numpy as np
@@ -100,6 +100,18 @@ def test_beta_small_parameters(self):
         x = np.random.beta(0.0001, 0.0001, size=100)
         assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
 
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        np.random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = np.random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
 
 if __name__ == "__main__":
     run_module_suite()