def build_weight(self):
n = V.shape[0]
m = V.shape[1]
self.weight = matrix((n, n))
self.weight_sum = matrix((n, n))
for f in self.F:
for i in range(m):
self.assign_weight(f[i], f[(i+1) % 3])
def test__centroid(self):
vectors = [
matrix([0,0,8]),
matrix([6,0,2]),
matrix([3,0,-5]),
]
expected_vec = matrix([3,0,5.0/3])
actual_vec = math_utils.get_vectors_centroid(vectors)
numpy.testing.assert_array_almost_equal(expected_vec.todense(), actual_vec.todense(), err_msg="Centroid wrong calculations!")
def test__centroid(self):
vectors = [
matrix([0, 0, 8]),
matrix([6, 0, 2]),
matrix([3, 0, -5]),
]
expected_vec = matrix([3, 0, 5.0 / 3])
actual_vec = math_utils.get_vectors_centroid(vectors)
numpy.testing.assert_array_almost_equal(
expected_vec.todense(),
actual_vec.todense(),
err_msg="Centroid wrong calculations!")
def simple_wf():
wf = WorkFlow()
# declare docs:
wf.docs = [
Doc("Testing advanced 1", "a b c c c d d d d e"),
Doc("Testing advanced 2", "a a a a a b c c c d d d e e"),
Doc("Testing advanced 3", "b b b b f f f f"),
]
# declare preprocessing
wf.title_index = [doc.title for doc in wf.docs]
wf.word_index = ['a', 'b', 'c', 'd', 'e', 'f']
# prepare tf matrix
wf.count_matrix = array([
[1, 5, 0], #a
[1, 1, 4], #b
[3, 3, 0], #c
[4, 3, 0], #d
[1, 2, 0], #e
[0, 0, 4], #f
])
count_to_tf = vectorize(math_utils.count_to_tf)
wf.tf_mat = count_to_tf(wf.count_matrix)
# prepare lidf vector: log of inverted df
wf.n_docs = float(len(wf.docs))
wf.df_vec = array([2.0, 3.0, 2.0, 2.0, 2.0, 1.0])
wf.idf_vec = wf.n_docs / wf.df_vec
wf.log_idf_vec = log(wf.idf_vec)
wf.wieghts_mat = matrix(wf.tf_mat * wf.log_idf_vec[:, None])
return wf
def generate_Stream_GM():
sparse = matrix((Stream,GM),dtype=np.bool_)
for i in tqdm(range(Stream)):
connected = np.random.randint(low=0, high=GM, size=20)
for j in connected:
sparse[i,j]=1
return sparse
def simple_wf():
wf = WorkFlow()
# declare docs:
wf.docs = [
Doc("Testing advanced 1", "a b c c c d d d d e"),
Doc("Testing advanced 2", "a a a a a b c c c d d d e e"),
Doc("Testing advanced 3", "b b b b f f f f"),
]
# declare preprocessing
wf.title_index = [ doc.title for doc in wf.docs]
wf.word_index = ['a', 'b', 'c', 'd', 'e', 'f']
# prepare tf matrix
wf.count_matrix = array([
[1, 5, 0], #a
[1, 1, 4], #b
[3, 3, 0], #c
[4, 3, 0], #d
[1, 2, 0], #e
[0, 0, 4], #f
])
count_to_tf = vectorize(math_utils.count_to_tf)
wf.tf_mat = count_to_tf(wf.count_matrix)
# prepare lidf vector: log of inverted df
wf.n_docs = float(len(wf.docs))
wf.df_vec = array([2.0, 3.0, 2.0, 2.0, 2.0, 1.0])
wf.idf_vec = wf.n_docs / wf.df_vec
wf.log_idf_vec = log(wf.idf_vec)
wf.wieghts_mat = matrix(wf.tf_mat * wf.log_idf_vec[:,None])
return wf
def getSimpleDb(self):
concepts_index=['c1','c2']
words_index=['a','b','c']
wieght_matrix =matrix(
[[0.5, 0.5],
[0.2, 0.8],
[1.0, 0.0]])
db = DatabaseWrapper( wieght_matrix, concepts_index, words_index, StopWordsStemmer([]))
return db
def generate_WM_Stream():
# Specific to DOK matrix
sparse = matrix((WM,Stream),dtype=np.bool_)
for i in tqdm(range(WM)):
connected = np.random.randint(low=0, high=Stream, size=average_stream_per_vox)
for j in connected:
sparse[i,j] = 1
return sparse
def test_simple(self):
# arrange
db = self.getSimpleDb()
text = "a b c"
expected = matrix([1.7/3, 1.3/3])
# act
actual = db.get_text_centroid(text)
numpy.testing.assert_array_almost_equal(expected.todense(), actual.todense(), err_msg="wrong centroid")
def get_vectors_centroid(list_of_vectors):
""" gets a list of scipy vectors with same dimensions and returns their centroid"""
n = len(list_of_vectors)
if n == 0: return
#on 1d vector, shape holds the length
shape = list_of_vectors[0].shape
ret_vec = matrix(shape)
for vector in list_of_vectors:
#_log.debug("get_vectors_centroid: Adding vector {}".format(vector))
ret_vec = ret_vec + vector
ret_vec = ret_vec * (1.0 / n)
return ret_vec
def test_migration(self):
"""tests that new form of test equals to old one from test__advanced_doc"""
expected = matrix([[0.40546511, 1.05803603, 0.], [0., 0., 0.],
[0.85091406, 0.85091406, 0.],
[0.9675591, 0.85091406, 0.],
[0.40546511, 0.6865121, 0.], [0., 0., 2.62161231]])
wf = simple_wf()
actual = wf.wieghts_mat
assert_allclose(actual.todense(), expected.todense())
def get_vectors_centroid(list_of_vectors):
""" gets a list of scipy vectors with same dimensions and returns their centroid"""
n = len(list_of_vectors)
if n == 0: return
#on 1d vector, shape holds the length
shape = list_of_vectors[0].shape
ret_vec = matrix(shape)
for vector in list_of_vectors:
#_log.debug("get_vectors_centroid: Adding vector {}".format(vector))
ret_vec = ret_vec + vector
ret_vec = ret_vec * (1.0 / n)
return ret_vec
def get_word_vector(self, word):
"""
Row representation of the word in Wiki concepts
@returns: the text vector in wikipedia space.
"""
vector = None
if self.index_by_word.has_key(word):
index = self.index_by_word[word]
vector = self.wieght_matrix[index, :]
else:
#if word is not in corpus: return empty vector
vector = matrix((1,self.concepts_num))
return vector
def get_word_vector(self, word):
"""
Row representation of the word in Wiki concepts
@returns: the text vector in wikipedia space.
"""
vector = None
if self.index_by_word.has_key(word):
index = self.index_by_word[word]
vector = self.wieght_matrix[index, :]
else:
#if word is not in corpus: return empty vector
vector = matrix((1, self.concepts_num))
return vector
def test_migration(self):
"""tests that new form of test equals to old one from test__advanced_doc"""
expected = matrix([
[ 0.40546511, 1.05803603, 0. ],
[ 0. , 0. , 0. ],
[ 0.85091406, 0.85091406, 0. ],
[ 0.9675591 , 0.85091406, 0. ],
[ 0.40546511, 0.6865121 , 0. ],
[ 0. , 0. , 2.62161231]])
wf = simple_wf()
actual = wf.wieghts_mat
assert_allclose(actual.todense(), expected.todense())
def test__advanced_doc(self):
"""tests that new form of test equals to old one from test__advanced_doc"""
expected_wf = simple_wf()
builder = DbBuilder(StopWordsStemmer([]))
for doc in expected_wf.docs:
builder.add_document(doc)
actual_wf = WorkFlow()
builder.build(wf=actual_wf, normalization=False)
#workaround to handle dimensions mismatch
expected_wf.df_vec = matrix(expected_wf.df_vec)
assert_allclose(actual_wf.df_vec.todense(), expected_wf.df_vec.todense())
assert_allclose(actual_wf.wieghts_mat.todense(), expected_wf.wieghts_mat.todense())
def test__advanced_doc(self):
"""tests that new form of test equals to old one from test__advanced_doc"""
expected_wf = simple_wf()
builder = DbBuilder(StopWordsStemmer([]))
for doc in expected_wf.docs:
builder.add_document(doc)
actual_wf = WorkFlow()
builder.build(wf=actual_wf, normalization=False)
#workaround to handle dimensions mismatch
expected_wf.df_vec = matrix(expected_wf.df_vec)
assert_allclose(actual_wf.df_vec.todense(),
expected_wf.df_vec.todense())
assert_allclose(actual_wf.wieghts_mat.todense(),
expected_wf.wieghts_mat.todense())
def build(self, wf=None, normalization=True):
''' Builds DatabaseWrapper according to algorithm
@param wf: workflow for debug purpuses
@returns: DatabaseWrapper
'''
_log.info("Start building inverted index")
_log.info("Normalization={}".format(normalization))
_log.info("Building word index")
#unique enumeration of words (list of words and index is a posiioin of the word in list)
self.word_index = build_word_index(self.concepts_list)
_log.info("Number of terms={}".format(len(self.word_index)))
_log.info("Number of concepts={}".format(len(self.concepts_list)))
#word => index in word_index
index_by_word = build_index_by_words(self.word_index)
# docs per word
df_vec = build_df(index_by_word, self.concepts_list)
_log.info("DF vector build is DONE")
# weight table not normalized
T = build_wieght_table_dok(df_vec, index_by_word, self.concepts_list)
_log.info("ID-TDF vector build is DONE")
if normalization:
normalize(T)
_log.info("Normalization is DONE")
db = DatabaseWrapper(T, self.concepts_list, self.word_index,
self.stemmer)
_log.info("Database wrapper created")
if wf:
wf.word_index = self.word_index
#workaround to force returned wf to be sparse
wf.df_vec = matrix(df_vec)
wf.wieghts_mat = T
return db
def build(self, wf=None, normalization=True):
''' Builds DatabaseWrapper according to algorithm
@param wf: workflow for debug purpuses
@returns: DatabaseWrapper
'''
_log.info("Start building inverted index")
_log.info("Normalization={}".format(normalization))
_log.info("Building word index")
#unique enumeration of words (list of words and index is a posiioin of the word in list)
self.word_index = build_word_index(self.concepts_list)
_log.info("Number of terms={}".format(len(self.word_index)))
_log.info("Number of concepts={}".format(len(self.concepts_list)))
#word => index in word_index
index_by_word = build_index_by_words(self.word_index)
# docs per word
df_vec = build_df(index_by_word, self.concepts_list)
_log.info("DF vector build is DONE")
# weight table not normalized
T = build_wieght_table_dok(df_vec, index_by_word, self.concepts_list)
_log.info("ID-TDF vector build is DONE")
if normalization:
normalize(T)
_log.info("Normalization is DONE")
db = DatabaseWrapper(T, self.concepts_list, self.word_index, self.stemmer)
_log.info("Database wrapper created")
if wf:
wf.word_index = self.word_index
#workaround to force returned wf to be sparse
wf.df_vec = matrix(df_vec)
wf.wieghts_mat = T
return db
