From 860e929bf895aa305d18f84027803292419086e5 Mon Sep 17 00:00:00 2001 From: Robyn Speer Date: Thu, 28 May 2015 14:01:11 -0400 Subject: [PATCH] update Japanese data; test Japanese and token combining Former-commit-id: 611a6a35de798e7033e33fa4085be9919a53a503 --- README.txt => README.md | 0 tests/test_japanese.py | 26 +++++++++++++++++++++++ tests/test_math.py | 30 +++++++++++++++++++++++++++ wordfreq/__init__.py | 13 +++++++++++- wordfreq/data/combined_ja.msgpack.gz | Bin 164987 -> 145885 bytes 5 files changed, 68 insertions(+), 1 deletion(-) rename README.txt => README.md (100%) create mode 100644 tests/test_japanese.py create mode 100644 tests/test_math.py diff --git a/README.txt b/README.md similarity index 100% rename from README.txt rename to README.md diff --git a/tests/test_japanese.py b/tests/test_japanese.py new file mode 100644 index 0000000..a21eedd --- /dev/null +++ b/tests/test_japanese.py @@ -0,0 +1,26 @@ +from nose.tools import eq_, assert_almost_equal +from wordfreq import tokenize, word_frequency, half_harmonic_mean + + +def test_tokens(): + eq_(tokenize('おはようございます', 'ja'), + ['おはよう', 'ござい', 'ます']) + + +def test_combination(): + ohayou_freq = word_frequency('おはよう', 'ja') + gozai_freq = word_frequency('ござい', 'ja') + masu_freq = word_frequency('ます', 'ja') + + assert_almost_equal( + word_frequency('おはようおはよう', 'ja'), + ohayou_freq / 2 + ) + assert_almost_equal( + word_frequency('おはようございます', 'ja'), + half_harmonic_mean( + half_harmonic_mean(ohayou_freq, gozai_freq), + masu_freq + ) + ) + diff --git a/tests/test_math.py b/tests/test_math.py new file mode 100644 index 0000000..c2b3746 --- /dev/null +++ b/tests/test_math.py @@ -0,0 +1,30 @@ +from nose.tools import assert_less_equal, assert_almost_equal +from wordfreq import half_harmonic_mean +from functools import reduce +import random + + +def check_hm_properties(inputs): + # I asserted that the half-harmonic-mean formula is associative, + # commutative, monotonic, and less than or equal to its inputs. + # (Less if its inputs are strictly positive, in fact.) + # + # So let's test that what I said is true. + hm1 = reduce(half_harmonic_mean, inputs) + random.shuffle(inputs) + hm2 = reduce(half_harmonic_mean, inputs) + assert_almost_equal(hm1, hm2) + + inputs[0] *= 2 + hm3 = reduce(half_harmonic_mean, inputs) + assert_less_equal(hm2, hm3) + + +def test_half_harmonic_mean(): + for count in range(2, 6): + for rep in range(10): + # get some strictly positive arbitrary numbers + inputs = [random.expovariate(0.01) + for i in range(count)] + yield check_hm_properties, inputs + diff --git a/wordfreq/__init__.py b/wordfreq/__init__.py index a0d0955..7fdb774 100644 --- a/wordfreq/__init__.py +++ b/wordfreq/__init__.py @@ -164,6 +164,17 @@ def iter_wordlist(lang, wordlist='combined'): yield word +def half_harmonic_mean(a, b): + """ + An associative, commutative, monotonic function that returns a value + less than or equal to both a and b. + + Used for estimating the frequency of terms made of multiple tokens, given + the assumption that the tokens very frequently appear together. + """ + return (a * b) / (a + b) + + @lru_cache(maxsize=CACHE_SIZE) def word_frequency(word, lang, wordlist='combined', default=0.): """ @@ -199,7 +210,7 @@ def word_frequency(word, lang, wordlist='combined', default=0.): else: # Combine word values using the half-harmonic-mean formula, # (a * b) / (a + b). 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