Creates normal science backport
This commit is contained in:
@ -1 +1,2 @@
|
||||
from .copycat import Copycat, Reporter # noqa
|
||||
from .problem import Problem
|
||||
|
||||
58
copycat/problem.py
Normal file
58
copycat/problem.py
Normal file
@ -0,0 +1,58 @@
|
||||
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):
|
||||
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()
|
||||
57
copycat/statistics.py
Normal file
57
copycat/statistics.py
Normal file
@ -0,0 +1,57 @@
|
||||
# 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]
|
||||
62
tests.py
Normal file
62
tests.py
Normal file
@ -0,0 +1,62 @@
|
||||
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()
|
||||
Reference in New Issue
Block a user