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Merge branch 'feature-normal-science-backport' into legacy

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LSaldyt
2017-11-18 18:44:24 -07:00
parent 1b84b22e3f 4388bede7d
commit be6d1fa495
6 changed files with 182 additions and 137 deletions
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.distributions Normal file
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copycat/__init__.py
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from .copycat import Copycat, Reporter # noqa
from .problem import Problem

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copycat/problem.py Normal file
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from .copycat import Copycat
from pprint import pprint
class Problem:
def __init__(self, initial, modified, target, iterations, distributions=None, formulas=None):
self.formulas = formulas
self.initial = initial
self.modified = modified
self.target = target
self.iterations = iterations
if distributions is None:
self.distributions = self.solve()
else:
self.distributions = distributions
if formulas is not None:
assert hasattr(Copycat().workspace, 'temperature')
def test(self, comparison, expected=None):
print('-' * 120)
print('Testing copycat problem: {} : {} :: {} : _'.format(self.initial,
self.modified,
self.target))
print('expected:')
if expected is None:
expected = self.distributions
pprint(expected)
actual = self.solve()
print('actual:')
pprint(actual)
comparison(actual, expected)
print('-' * 120)
def solve(self):
print('-' * 120)
print('Testing copycat problem: {} : {} :: {} : _'.format(self.initial,
self.modified,
self.target))
copycat = Copycat()
answers = dict()
if self.formulas == None:
if hasattr(copycat.workspace, 'temperature'):
formula = copycat.workspace.temperature.getAdj()
else:
formula = None
answers[formula] = copycat.run(self.initial,
self.modified,
self.target,
self.iterations)
else:
for formula in self.formulas:
copycat.temperature.useAdj(formula)
answers[formulas] = copycat.run(self.initial,
self.modified,
self.target,
self.iterations)
return answers
def generate(self):
self.distributions = self.solve()

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copycat/statistics.py Normal file
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# CHI2 values for n degrees freedom
_chiSquared_table = {
1:3.841,
2:5.991,
3:7.815,
4:9.488,
5:11.071,
6:12.592,
7:14.067,
8:15.507,
9:16.919,
10:18.307
}
class ChiSquaredException(Exception):
pass
def chi_squared(actual, expected):
answerKeys = set(list(actual.keys()) + list(expected.keys()))
degreesFreedom = len(answerKeys)
chiSquared = 0
get_count = lambda k, d : d[k]['count'] if k in d else 0
for k in answerKeys:
E = get_count(k, expected)
O = get_count(k, actual)
if E == 0:
print('Warning! Expected 0 counts of {}, but got {}'.format(k, O))
else:
chiSquared += (O - E) ** 2 / E
return chiSquared
def cross_formula_chi_squared(actualDict, expectedDict):
for ka, actual in actualDict.items():
for ke, expected in expectedDict.items():
print('Comparing {} with {}'.format(ka, ke))
chiSquared = chi_squared(actual, expected)
if chiSquared >= _chiSquared_table[degreesFreedom]:
print('Significant difference between expected and actual answer distributions: \n' +
'Chi2 value: {} with {} degrees of freedom'.format(chiSquared, degreesFreedom))
def cross_chi_squared(problemSets):
for i, problemSetA in enumerate(problemSets):
for problemSetB in problemSets[i + 1:]:
for problemA in problemSetA:
for problemB in problemSetB:
answersA = problemA.distributions
answersB = problemB.distributions
cross_formula_chi_squared(answersA, answersB)
def iso_chi_squared(actualDict, expectedDict):
for key in expectedDict.keys():
assert key in actualDict, 'The key {} was not tested'.format(key)
actual = actualDict[key]
expected = expectedDict[key]

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copycat/tests.py
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import unittest
from .copycat import Copycat
def pnormaldist(p):
table = {
0.80: 1.2815,
0.90: 1.6448,
0.95: 1.9599,
0.98: 2.3263,
0.99: 2.5758,
0.995: 2.8070,
0.998: 3.0902,
0.999: 3.2905,
0.9999: 3.8905,
0.99999: 4.4171,
0.999999: 4.8916,
0.9999999: 5.3267,
0.99999999: 5.7307,
0.999999999: 6.1094,
}
return max(v for k, v in table.items() if k <= p)
def lower_bound_on_probability(hits, attempts, confidence=0.95):
if attempts == 0:
return 0
z = pnormaldist(confidence)
zsqr = z * z
phat = 1.0 * hits / attempts
under_sqrt = (phat * (1 - phat) + zsqr / (4 * attempts)) / attempts
denominator = (1 + zsqr / attempts)
return (phat + zsqr / (2 * attempts) - z * (under_sqrt ** 0.5)) / denominator
def upper_bound_on_probability(hits, attempts, confidence=0.95):
misses = attempts - hits
return 1.0 - lower_bound_on_probability(misses, attempts, confidence)
class TestCopycat(unittest.TestCase):
def setUp(self):
self.longMessage = True # new in Python 2.7
def assertProbabilitiesLookRoughlyLike(self, actual, expected):
actual_count = 0.0 + sum(d['count'] for d in list(actual.values()))
expected_count = 0.0 + sum(d['count'] for d in list(expected.values()))
self.assertGreater(actual_count, 1)
self.assertGreater(expected_count, 1)
for k in set(list(actual.keys()) + list(expected.keys())):
if k not in expected:
self.fail('Key %s was produced but not expected! %r != %r' % (k, actual, expected))
expected_probability = expected[k]['count'] / expected_count
if k in actual:
actual_lo = lower_bound_on_probability(actual[k]['count'], actual_count)
actual_hi = upper_bound_on_probability(actual[k]['count'], actual_count)
if not (actual_lo <= expected_probability <= actual_hi):
print('Failed (%s <= %s <= %s)' % (actual_lo, expected_probability, actual_hi))
self.fail('Count ("obviousness" metric) seems way off! %r != %r' % (actual, expected))
if abs(actual[k]['avgtemp'] - expected[k]['avgtemp']) >= 10.0 + (10.0 / actual[k]['count']):
print('Failed (%s - %s >= %s)' % (actual[k]['avgtemp'], expected[k]['avgtemp'], 10.0 + (10.0 / actual[k]['count'])))
self.fail('Temperature ("elegance" metric) seems way off! %r != %r' % (actual, expected))
else:
actual_hi = upper_bound_on_probability(0, actual_count)
if not (0 <= expected_probability <= actual_hi):
self.fail('No instances of expected key %s were produced! %r != %r' % (k, actual, expected))
def run_testcase(self, initial, modified, target, iterations, expected):
actual = Copycat().run(initial, modified, target, iterations)
self.assertEqual(sum(a['count'] for a in list(actual.values())), iterations)
self.assertProbabilitiesLookRoughlyLike(actual, expected)
def test_simple_cases(self):
self.run_testcase('abc', 'abd', 'efg', 50, {
'efd': {'count': 1, 'avgtemp': 16},
'efh': {'count': 99, 'avgtemp': 19},
})
self.run_testcase('abc', 'abd', 'ijk', 50, {
'ijd': {'count': 4, 'avgtemp': 24},
'ijl': {'count': 96, 'avgtemp': 20},
})
def test_abc_xyz(self):
self.run_testcase('abc', 'abd', 'xyz', 20, {
'xyd': {'count': 100, 'avgtemp': 19},
})
def test_ambiguous_case(self):
self.run_testcase('abc', 'abd', 'ijkk', 50, {
'ijkkk': {'count': 7, 'avgtemp': 21},
'ijll': {'count': 47, 'avgtemp': 28},
'ijkl': {'count': 44, 'avgtemp': 32},
'ijkd': {'count': 2, 'avgtemp': 65},
})
def test_mrrjjj(self):
self.run_testcase('abc', 'abd', 'mrrjjj', 50, {
'mrrjjjj': {'count': 4, 'avgtemp': 16},
'mrrkkk': {'count': 31, 'avgtemp': 47},
'mrrjjk': {'count': 64, 'avgtemp': 51},
'mrrjkk': {'count': 1, 'avgtemp': 52},
'mrrjjd': {'count': 1, 'avgtemp': 54},
})
def test_elongation(self):
# This isn't remotely what a human would say.
self.run_testcase('abc', 'aabbcc', 'milk', 50, {
'milj': {'count': 85, 'avgtemp': 55},
'mikj': {'count': 10, 'avgtemp': 56},
'milk': {'count': 1, 'avgtemp': 56},
'lilk': {'count': 1, 'avgtemp': 57},
'milb': {'count': 3, 'avgtemp': 57},
})
def test_repairing_successor_sequence(self):
# This isn't remotely what a human would say.
self.run_testcase('aba', 'abc', 'xyx', 50, {
'xc': {'count': 9, 'avgtemp': 57},
'xyc': {'count': 82, 'avgtemp': 59},
'cyx': {'count': 7, 'avgtemp': 68},
'xyx': {'count': 2, 'avgtemp': 69},
})
def test_nonsense(self):
self.run_testcase('cat', 'dog', 'cake', 10, {
'cakg': {'count': 99, 'avgtemp': 70},
'gake': {'count': 1, 'avgtemp': 59},
})
self.run_testcase('cat', 'dog', 'kitten', 10, {
'kitteg': {'count': 96, 'avgtemp': 66},
'kitten': {'count': 4, 'avgtemp': 68},
})
if __name__ == '__main__':
unittest.main()

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tests.py Normal file
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import unittest
import os.path
import pickle
import argparse
import sys
from pprint import pprint
from copycat import Problem
from copycat.statistics import iso_chi_squared
# TODO: update test cases to use entropy
def generate():
print('Generating distributions for new file')
iterations = 30
problems = [
Problem('abc', 'abd', 'efg', iterations),
Problem('abc', 'abd', 'ijk', iterations),
Problem('abc', 'abd', 'xyz', iterations),
Problem('abc', 'abd', 'ijkk', iterations),
Problem('abc', 'abd', 'mrrjjj', iterations)]
with open(TestCopycat.Filename, 'wb') as outfile:
pickle.dump(problems, outfile)
return problems
class TestCopycat(unittest.TestCase):
Filename = None
def setUp(self):
self.longMessage = True # new in Python 2.7
def test(self):
print('Testing copycat with input file: {}'.format(TestCopycat.Filename))
try:
with open(TestCopycat.Filename, 'rb') as infile:
problems = pickle.load(infile)
except Exception as e:
print('Generating due to error:')
print(e)
problems = generate()
for problem in problems:
problem.test(iso_chi_squared)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--generate', action='store_true')
parser.add_argument('filename', default='.distributions', nargs='?')
parser.add_argument('unittest_args', default=[], nargs='?')
args = parser.parse_args()
# TODO: Go do something with args.input and args.filename
TestCopycat.Filename = args.filename
if args.generate:
generate()
# Now set the sys.argv to the unittest_args (leaving sys.argv[0] alone)
sys.argv[1:] = args.unittest_args
unittest.main()