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Convert test_heapq.py to unittests.

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Raymond Hettinger 2004-06-10 05:07:18 +00:00
parent 33ecffb65a
commit bce036b49e
1 changed files with 92 additions and 88 deletions
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180
Lib/test/test_heapq.py
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@ -1,101 +1,105 @@
"""Unittests for heapq."""
from test.test_support import verify, vereq, verbose, TestFailed
from heapq import heappush, heappop, heapify, heapreplace, nlargest, nsmallest
import random
import unittest
from test import test_support
def check_invariant(heap):
# Check the heap invariant.
for pos, item in enumerate(heap):
if pos: # pos 0 has no parent
parentpos = (pos-1) >> 1
verify(heap[parentpos] <= item)
# An iterator returning a heap's elements, smallest-first.
class heapiter(object):
def __init__(self, heap):
self.heap = heap
def heapiter(heap):
# An iterator returning a heap's elements, smallest-first.
try:
while 1:
yield heappop(heap)
except IndexError:
pass
def next(self):
try:
return heappop(self.heap)
except IndexError:
raise StopIteration
class TestHeap(unittest.TestCase):
def __iter__(self):
return self
def test_push_pop(self):
# 1) Push 256 random numbers and pop them off, verifying all's OK.
heap = []
data = []
self.check_invariant(heap)
for i in range(256):
item = random.random()
data.append(item)
heappush(heap, item)
self.check_invariant(heap)
results = []
while heap:
item = heappop(heap)
self.check_invariant(heap)
results.append(item)
data_sorted = data[:]
data_sorted.sort()
self.assertEqual(data_sorted, results)
# 2) Check that the invariant holds for a sorted array
self.check_invariant(results)
def check_invariant(self, heap):
# Check the heap invariant.
for pos, item in enumerate(heap):
if pos: # pos 0 has no parent
parentpos = (pos-1) >> 1
self.assert_(heap[parentpos] <= item)
def test_heapify(self):
for size in range(30):
heap = [random.random() for dummy in range(size)]
heapify(heap)
self.check_invariant(heap)
def test_naive_nbest(self):
data = [random.randrange(2000) for i in range(1000)]
heap = []
for item in data:
heappush(heap, item)
if len(heap) > 10:
heappop(heap)
heap.sort()
self.assertEqual(heap, sorted(data)[-10:])
def test_nbest(self):
# Less-naive "N-best" algorithm, much faster (if len(data) is big
# enough <wink>) than sorting all of data. However, if we had a max
# heap instead of a min heap, it could go faster still via
# heapify'ing all of data (linear time), then doing 10 heappops
# (10 log-time steps).
data = [random.randrange(2000) for i in range(1000)]
heap = data[:10]
heapify(heap)
for item in data[10:]:
if item > heap[0]: # this gets rarer the longer we run
heapreplace(heap, item)
self.assertEqual(list(heapiter(heap)), sorted(data)[-10:])
def test_heapsort(self):
# Exercise everything with repeated heapsort checks
for trial in xrange(100):
size = random.randrange(50)
data = [random.randrange(25) for i in range(size)]
if trial & 1: # Half of the time, use heapify
heap = data[:]
heapify(heap)
else: # The rest of the time, use heappush
heap = []
for item in data:
heappush(heap, item)
heap_sorted = [heappop(heap) for i in range(size)]
self.assertEqual(heap_sorted, sorted(data))
def test_nsmallest(self):
data = [random.randrange(2000) for i in range(1000)]
self.assertEqual(nsmallest(data, 400), sorted(data)[:400])
def test_largest(self):
data = [random.randrange(2000) for i in range(1000)]
self.assertEqual(nlargest(data, 400), sorted(data, reverse=True)[:400])
def test_main():
# 1) Push 100 random numbers and pop them off, verifying all's OK.
heap = []
data = []
check_invariant(heap)
for i in range(256):
item = random.random()
data.append(item)
heappush(heap, item)
check_invariant(heap)
results = []
while heap:
item = heappop(heap)
check_invariant(heap)
results.append(item)
data_sorted = data[:]
data_sorted.sort()
vereq(data_sorted, results)
# 2) Check that the invariant holds for a sorted array
check_invariant(results)
# 3) Naive "N-best" algorithm
heap = []
for item in data:
heappush(heap, item)
if len(heap) > 10:
heappop(heap)
heap.sort()
vereq(heap, data_sorted[-10:])
# 4) Test heapify.
for size in range(30):
heap = [random.random() for dummy in range(size)]
heapify(heap)
check_invariant(heap)
# 5) Less-naive "N-best" algorithm, much faster (if len(data) is big
# enough <wink>) than sorting all of data. However, if we had a max
# heap instead of a min heap, it could go faster still via
# heapify'ing all of data (linear time), then doing 10 heappops
# (10 log-time steps).
heap = data[:10]
heapify(heap)
for item in data[10:]:
if item > heap[0]: # this gets rarer the longer we run
heapreplace(heap, item)
vereq(list(heapiter(heap)), data_sorted[-10:])
# 6) Exercise everything with repeated heapsort checks
for trial in xrange(100):
size = random.randrange(50)
data = [random.randrange(25) for i in range(size)]
if trial & 1: # Half of the time, use heapify
heap = data[:]
heapify(heap)
else: # The rest of the time, use heappush
heap = []
for item in data:
heappush(heap,item)
data.sort()
sorted = [heappop(heap) for i in range(size)]
vereq(data, sorted)
# 7) Check nlargest() and nsmallest()
data = [random.randrange(2000) for i in range(1000)]
copy = data[:]
copy.sort(reverse=True)
vereq(nlargest(data, 400), copy[:400])
copy.sort()
vereq(nsmallest(data, 400), copy[:400])
# Make user happy
if verbose:
print "All OK"
test_support.run_unittest(TestHeap)
if __name__ == "__main__":
test_main()