Remove the lfu_cache. Add more tests.

3 files changed, 30 insertions, 130 deletions

diff --git a/Doc/whatsnew/3.2.rst b/Doc/whatsnew/3.2.rst
index bc31764..2c625a8 100644
--- a/Doc/whatsnew/3.2.rst
+++ b/Doc/whatsnew/3.2.rst
@@ -66,45 +66,32 @@ Some smaller changes made to the core Python language are:
 New, Improved, and Deprecated Modules
 =====================================
 
-* The functools module now includes two new decorators for caching function
-  calls, :func:`functools.lru_cache` and :func:`functools.lfu_cache`. These can
-  save repeated queries to an external resource whenever the results are
-  expected to be the same.
+* The functools module now includes a new decorator for caching function calls.
+  :func:`functools.lru_cache` can save repeated queries to an external resource
+  whenever the results are expected to be the same.
 
   For example, adding a caching decorator to a database query function can save
   database accesses for popular searches::
 
-      @functools.lfu_cache(maxsize=50)
+      @functools.lru_cache(maxsize=300)
       def get_phone_number(name):
           c = conn.cursor()
           c.execute('SELECT phonenumber FROM phonelist WHERE name=?', (name,))
           return c.fetchone()[0]
 
-  The caches support two strategies for limiting their size to *maxsize*. The
-  LFU (least-frequently-used) cache works bests when popular queries remain the
-  same over time.  In contrast, the LRU (least-recently-used) cache works best
-  query popularity changes over time (for example, the most popular news
-  articles change each day as newer articles are added).
-
-  The two caching decorators can be composed (nested) to handle hybrid cases.
-  For example, music searches can reflect both long-term patterns (popular
-  classics) and short-term trends (new releases)::
-
-        @functools.lfu_cache(maxsize=500)
-        @functools.lru_cache(maxsize=100)
-        def find_lyrics(song):
-            query = 'http://www.example.com/songlist/%s' % urllib.quote(song)
-            page = urllib.urlopen(query).read()
-            return parse_lyrics(page)
-
-  To help with choosing an effective cache size, the wrapped function
-  is instrumented with two attributes *hits* and *misses*::
-
-        >>> for song in user_requests:
-        ...     find_lyrics(song)
-        >>> print(find_lyrics.hits, find_lyrics.misses)
+  To help with choosing an effective cache size, the wrapped function is
+  instrumented with two attributes *hits* and *misses*::
+
+        >>> for name in user_requests:
+        ...     get_phone_number(name)
+        >>> print(get_phone_number.hits, get_phone_number.misses)
         4805 980
 
+  If the phonelist table gets updated, the outdated contents of the cache can be
+  cleared with::
+
+        >>> get_phone_number.clear()
+
   (Contributed by Raymond Hettinger)
 
 * The previously deprecated :func:`contextlib.nested` function has been
diff --git a/Lib/functools.py b/Lib/functools.py
index 9f28004..efc533c 100644
--- a/Lib/functools.py
+++ b/Lib/functools.py
@@ -110,58 +110,6 @@ def cmp_to_key(mycmp):
             raise TypeError('hash not implemented')
     return K
 
-def lfu_cache(maxsize=100):
-    """Least-frequently-used cache decorator.
-
-    Arguments to the cached function must be hashable.
-    Cache performance statistics stored in f.hits and f.misses.
-    Clear the cache using f.clear().
-    http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used
-
-    """
-    def decorating_function(user_function, tuple=tuple, sorted=sorted,
-                            len=len, KeyError=KeyError):
-        cache = {}                      # mapping of args to results
-        use_count = Counter()           # times each key has been accessed
-        kwd_mark = object()             # separate positional and keyword args
-        lock = Lock()
-
-        @wraps(user_function)
-        def wrapper(*args, **kwds):
-            key = args
-            if kwds:
-                key += (kwd_mark,) + tuple(sorted(kwds.items()))
-            try:
-                with lock:
-                    use_count[key] += 1         # count a use of this key
-                    result = cache[key]
-                    wrapper.hits += 1
-            except KeyError:
-                result = user_function(*args, **kwds)
-                with lock:
-                    use_count[key] += 1         # count a use of this key
-                    cache[key] = result
-                    wrapper.misses += 1
-                    if len(cache) > maxsize:
-                        # purge the 10% least frequently used entries
-                        for key, _ in nsmallest(maxsize // 10 or 1,
-                                                use_count.items(),
-                                                key=itemgetter(1)):
-                            del cache[key], use_count[key]
-            return result
-
-        def clear():
-            """Clear the cache and cache statistics"""
-            with lock:
-                cache.clear()
-                use_count.clear()
-                wrapper.hits = wrapper.misses = 0
-
-        wrapper.hits = wrapper.misses = 0
-        wrapper.clear = clear
-        return wrapper
-    return decorating_function
-
 def lru_cache(maxsize=100):
     """Least-recently-used cache decorator.
 
diff --git a/Lib/test/test_functools.py b/Lib/test/test_functools.py
index 4dd6fab..f9e8a97 100644
--- a/Lib/test/test_functools.py
+++ b/Lib/test/test_functools.py
@@ -483,73 +483,38 @@ class TestLRU(unittest.TestCase):
         self.assertEqual(f.misses, 1)
 
         # test size zero (which means "never-cache")
-        f_cnt = 0
         @functools.lru_cache(0)
         def f():
             nonlocal f_cnt
             f_cnt += 1
             return 20
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f_cnt, 3)
+        f_cnt = 0
+        for i in range(5):
+            self.assertEqual(f(), 20)
+        self.assertEqual(f_cnt, 5)
 
         # test size one
-        f_cnt = 0
         @functools.lru_cache(1)
         def f():
             nonlocal f_cnt
             f_cnt += 1
             return 20
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
+        f_cnt = 0
+        for i in range(5):
+            self.assertEqual(f(), 20)
         self.assertEqual(f_cnt, 1)
 
-    def test_lfu(self):
-        def orig(x, y):
-            return 3*x+y
-        f = functools.lfu_cache(maxsize=20)(orig)
-
-        domain = range(5)
-        for i in range(1000):
-            x, y = choice(domain), choice(domain)
-            actual = f(x, y)
-            expected = orig(x, y)
-            self.assertEquals(actual, expected)
-        self.assert_(f.hits > f.misses)
-        self.assertEquals(f.hits + f.misses, 1000)
-
-        f.clear()   # test clearing
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 0)
-        f(x, y)
-        self.assertEqual(f.hits, 0)
-        self.assertEqual(f.misses, 1)
-
-        # test size zero (which means "never-cache")
-        f_cnt = 0
-        @functools.lfu_cache(0)
-        def f():
+        # test size two
+        @functools.lru_cache(2)
+        def f(x):
             nonlocal f_cnt
             f_cnt += 1
-            return 20
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f_cnt, 3)
-
-        # test size one
+            return x*10
         f_cnt = 0
-        @functools.lfu_cache(1)
-        def f():
-            nonlocal f_cnt
-            f_cnt += 1
-            return 20
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f(), 20)
-        self.assertEqual(f_cnt, 1)
+        for x in 7, 9, 7, 9, 7, 9, 8, 8, 8, 9, 9, 9, 8, 8, 8, 7:
+            #    *  *              *                          *
+            self.assertEqual(f(x), x*10)
+        self.assertEqual(f_cnt, 4)
 
 def test_main(verbose=None):
     test_classes = (