From 1acde190b2676ecfa45d754667df36d6b9c9cc7e Mon Sep 17 00:00:00 2001 From: Raymond Hettinger Date: Mon, 14 Jan 2008 01:00:53 +0000 Subject: Take Tim's advice and have random.sample() support only sequences and sets. --- Doc/library/random.rst | 4 ++-- Lib/random.py | 43 ++++++++++++++++--------------------------- Lib/test/test_random.py | 21 +-------------------- Misc/NEWS | 3 +++ 4 files changed, 22 insertions(+), 49 deletions(-) diff --git a/Doc/library/random.rst b/Doc/library/random.rst index ff5fb77..f08192d 100644 --- a/Doc/library/random.rst +++ b/Doc/library/random.rst @@ -111,8 +111,8 @@ Functions for sequences: .. function:: sample(population, k) - Return a *k* length list of unique elements chosen from the population sequence. - Used for random sampling without replacement. + Return a *k* length list of unique elements chosen from the population sequence + or set. Used for random sampling without replacement. Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that diff --git a/Lib/random.py b/Lib/random.py index 5e57203..72b422f 100644 --- a/Lib/random.py +++ b/Lib/random.py @@ -267,7 +267,7 @@ class Random(_random.Random): x[i], x[j] = x[j], x[i] def sample(self, population, k): - """Chooses k unique random elements from a population sequence. + """Chooses k unique random elements from a population sequence or set. Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is @@ -284,15 +284,6 @@ class Random(_random.Random): large population: sample(range(10000000), 60) """ - # XXX Although the documentation says `population` is "a sequence", - # XXX attempts are made to cater to any iterable with a __len__ - # XXX method. This has had mixed success. Examples from both - # XXX sides: sets work fine, and should become officially supported; - # XXX dicts are much harder, and have failed in various subtle - # XXX ways across attempts. Support for mapping types should probably - # XXX be dropped (and users should pass mapping.keys() or .values() - # XXX explicitly). - # Sampling without replacement entails tracking either potential # selections (the pool) in a list or previous selections in a set. @@ -303,37 +294,35 @@ class Random(_random.Random): # preferred since the list takes less space than the # set and it doesn't suffer from frequent reselections. + if isinstance(population, (set, frozenset)): + population = tuple(population) + if not hasattr(population, '__getitem__') or hasattr(population, 'keys'): + raise TypeError("Population must be a sequence or set. For dicts, use dict.keys().") + random = self.random n = len(population) if not 0 <= k <= n: - raise ValueError("sample larger than population") - random = self.random + raise ValueError("Sample larger than population") _int = int result = [None] * k setsize = 21 # size of a small set minus size of an empty list if k > 5: setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets - if n <= setsize or hasattr(population, "keys"): - # An n-length list is smaller than a k-length set, or this is a - # mapping type so the other algorithm wouldn't work. + if n <= setsize: + # An n-length list is smaller than a k-length set pool = list(population) for i in range(k): # invariant: non-selected at [0,n-i) j = _int(random() * (n-i)) result[i] = pool[j] pool[j] = pool[n-i-1] # move non-selected item into vacancy else: - try: - selected = set() - selected_add = selected.add - for i in range(k): + selected = set() + selected_add = selected.add + for i in range(k): + j = _int(random() * n) + while j in selected: j = _int(random() * n) - while j in selected: - j = _int(random() * n) - selected_add(j) - result[i] = population[j] - except (TypeError, KeyError): # handle (at least) sets - if isinstance(population, list): - raise - return self.sample(tuple(population), k) + selected_add(j) + result[i] = population[j] return result ## -------------------- real-valued distributions ------------------- diff --git a/Lib/test/test_random.py b/Lib/test/test_random.py index a7fe605..073b0d0 100644 --- a/Lib/test/test_random.py +++ b/Lib/test/test_random.py @@ -84,26 +84,7 @@ class TestBasicOps(unittest.TestCase): self.gen.sample(tuple('abcdefghijklmnopqrst'), 2) def test_sample_on_dicts(self): - self.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2) - - # SF bug #1460340 -- random.sample can raise KeyError - a = dict.fromkeys(list(range(10)) + - list(range(10,100,2)) + - list(range(100,110))) - self.gen.sample(a, 3) - - # A followup to bug #1460340: sampling from a dict could return - # a subset of its keys or of its values, depending on the size of - # the subset requested. - N = 30 - d = dict((i, complex(i, i)) for i in range(N)) - for k in range(N+1): - samp = self.gen.sample(d, k) - # Verify that we got ints back (keys); the values are complex. - for x in samp: - self.assert_(type(x) is int) - samp.sort() - self.assertEqual(samp, list(range(N))) + self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2) def test_gauss(self): # Ensure that the seed() method initializes all the hidden state. In diff --git a/Misc/NEWS b/Misc/NEWS index 6abe08e..9e9aaae 100644 --- a/Misc/NEWS +++ b/Misc/NEWS @@ -355,6 +355,9 @@ Library - Removed defunct parts of the random module (the Wichmann-Hill generator and the jumpahead() method). +- random.sample() now explicitly supports all sequences and sets while + explicitly excluding mappings. + - Patch #467924: add ZipFile.extract() and ZipFile.extractall() in the zipfile module. -- cgit v0.12