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@internaut internaut/cooc.py
Last active Nov 8, 2019

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Function to calculate word co-occurrence from document-term matrix and a test using the hypothesis package
import numpy as np
def word_cooccurrence(dtm):
"""
Calculate the co-document frequency (aka word co-occurrence) matrix for a document-term matrix `dtm`, i.e. how often
each pair of tokens occurs together at least once in the same document.
:param dtm: (sparse) document-term-matrix of size NxM (N docs, M is vocab size) with raw term counts.
:return: co-document frequency (aka word co-occurrence) matrix with shape MxM
"""
if dtm.ndim != 2:
raise ValueError('`dtm` must be a 2D array/matrix')
bin_dtm = (dtm >= 1).astype(np.int)
return bin_dtm.T @ bin_dtm
import numpy as np
from hypothesis import given, strategies as st
from hypothesis.extra.numpy import arrays, array_shapes
from cooc import word_cooccurrence
@given(dtm=arrays(np.int, array_shapes(2, 2), elements=st.integers(min_value=0, max_value=1000)))
def test_word_cooccurrence(dtm):
res = word_cooccurrence(dtm)
n_docs, vocab_size = dtm.shape
assert isinstance(res, np.ndarray)
assert res.dtype == np.int
assert res.ndim == 2
assert res.shape == (vocab_size, vocab_size)
assert np.all((res >= 0) & (res <= n_docs))
assert np.array_equal(res, res.T)
if np.array_equal(dtm, np.zeros(dtm.shape, dtype=np.int)):
assert np.array_equal(res, np.zeros(res.shape, dtype=np.int))
ident = np.eye(n_docs)
if n_docs == vocab_size and np.array_equal(dtm, ident):
assert np.array_equal(res, ident)
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