#!/usr/bin/env python
#LIBRARIES:
# Standard library
import math
import random
import unittest
from decimal import *
# Local application
from vose_sampler import vose_sampler
# Common paths and error messages
valid_folder = "tests/file_examples/valid_files/"
invalid_folder = "tests/file_examples/invalid_files/"
empty_file_error = "Please provide a file containing a corpus \(not an empty file\)."
binary_file_error = "Please provide a file containing text-based data."
nonnegative_integer_error = "Please enter a non-negative integer for the number of samples desired\: "
class TestValidation(unittest.TestCase):
""" unittest methods for testing validation checks within vose_sampler
work as expected. """
def test_empty_file(self):
"""Test vose_sampler.get_words against empty files """
self.assertRaisesRegex(IOError, empty_file_error, vose_sampler.get_words, invalid_folder + "empty.txt")
def test_binary_file1(self):
"""Test vose_sampler.get_words against .epub files """
self.assertRaisesRegex(IOError, binary_file_error, vose_sampler.get_words, invalid_folder + "Alice.epub")
def test_binary_file2(self):
"""Test vose_sampler.get_words against .mobi files """
self.assertRaisesRegex(IOError, binary_file_error, vose_sampler.get_words, invalid_folder + "Alice.mobi")
def test_binary_file3(self):
"""Test vose_sampler.get_words against .pdf files """
self.assertRaisesRegex(IOError, binary_file_error, vose_sampler.get_words, invalid_folder + "Alice.pdf")
def test_binary_file4(self):
"""Test vose_sampler.get_words against .wav files """
self.assertRaisesRegex(IOError, binary_file_error, vose_sampler.get_words, invalid_folder + "zero.wav")
def test_negative_integer(self):
"""Test vose_sampler.VoseAlias.alias_generation against a size
specified by a negative integer. """
words = vose_sampler.get_words(valid_folder + "small.txt")
word_dist = vose_sampler.sample2dist(words)
VA_words = vose_sampler.VoseAlias(word_dist)
self.assertRaisesRegex(ValueError, nonnegative_integer_error + "-1", VA_words.sample_n, -1)
def test_zero_integer(self):
"""Test vose_sampler.ProbDistribution.alias_generation against a size
defined by zero. """
words = vose_sampler.get_words(valid_folder + "small.txt")
word_dist = vose_sampler.sample2dist(words)
VA_words = vose_sampler.VoseAlias(word_dist)
self.assertRaisesRegex(ValueError, nonnegative_integer_error + "0", VA_words.sample_n, 0)
class TestAccuracy(unittest.TestCase):
""" unittest methods for testing the accuracy of method within vose_sampler. """
def dbinom(self, x, n, p):
""" Compute the probability of x successes in n flips of a coin that produces
a head with probability p (i.e. the probability density of a Binomial RV). """
f = math.factorial
C = Decimal(f(n) / (f(x) * f(n-x)))
return C * p**x * (1-p)**(n-x)
def test_output_get_word(self):
"""Test vose_sampler.get_words to ensure it correctly produces a list of
words from a given corpus. """
actual = vose_sampler.get_words(valid_folder + "single_word.txt")
expected = ["Alice"]
self.assertEqual(actual, expected)
def test_output_create_dist(self):
"""Test vose_sampler.ProbDistribution.create_dist to ensure it correctly
produces a uniform distribution for a list of words representing a standard die. """
numbers_dist = vose_sampler.sample2dist(["one","two","three","four","five","six"])
VA_numbers = vose_sampler.VoseAlias(numbers_dist)
actual = VA_numbers.dist
prob = Decimal(1)/Decimal(6)
expected = {"one":prob, "two":prob, "three":prob, "four":prob, "five":prob, "six":prob}
self.assertEqual(actual, expected)
def test_output_alias_generation(self):
"""Test vose_sampler.ProbDistribution.alias_generation to ensure it
generates words with same distribution as the original corpus. This
performs a 2-sided hypothesis test at the 1% significance level, that:
H_0: observed proportion a randomly selected word is equal to the
proportion seen in the original corpus (i.e. p_original == p_observed)
H_1: p_original != p_observed
"""
print("WARNING: There is a random element to test_output_alias_generation\n\
so it is likely to occasionally fail, nonetheless if the alias_generation\n\
method is working correctly failures will be very rare (testing at alpha=0.01\n\
implies we should expect a Type I error about 1% of the time).")
# Construct a ProbDistribution
words = vose_sampler.get_words(valid_folder + "small.txt")
word_dist = vose_sampler.sample2dist(words)
VA_words = vose_sampler.VoseAlias(word_dist)
# Generate sample and calculate the number of observations for a randomly selected word
word = random.choice(list(VA_words.dist))
n = 1000
t = 0
for i in range(n):
if VA_words.alias_generation() == word:
t += 1
# Compute the p-value
p_original = VA_words.dist[word]
p_low = math.fsum([self.dbinom(x, n, p_original) for x in range(t,n+1)])
p_high = math.fsum([self.dbinom(x, n, p_original) for x in range(t+1)])
p = 2*min(p_low, p_high)
# Do not accept H_0 if p <= alpha
alpha = 0.01
self.assertGreater(p, alpha)
def test_roundtrip(self):
dist = {"H": Decimal(0.2), "T": Decimal(0.8)}
VA = vose_sampler.VoseAlias(dist)
sample = VA.sample_n(100000)
computed_dist = vose_sampler.sample2dist(sample)
self.assertAlmostEqual(dist.get("H"), computed_dist.get("H"), delta=0.01)
self.assertAlmostEqual(dist.get("T"), computed_dist.get("T"), delta=0.01)
if __name__ == "__main__":
unittest.main()
