def clusterDocument(title, abstract, tokenizer, token2indexMap, tfidf_model, lsi_model, gmm_model):
tokens = tokenize(title + " " + abstract, tokenizer)
tokenCounts = groupAndCount(tokens)
matrix = build_csr_matrix(listOfMaps=[tokenCounts], token2IndexMap=token2indexMap)
transformedMatrix = lsi_model.transform(tfidf_model.transform(matrix))
prediction = gmm_model.predict(transformedMatrix)[0]
return prediction
def horizontally_combine_matrixes(matrixList):
if len(matrixList) == 0:
return build_csr_matrix(listOfMaps = [], numAttributes = 0)
assert len(matrixList) >= 1
newData = []
newIndices = []
newIndptr = [0]
numNonZerosInMat = map(lambda matrix: len(matrix.data), matrixList)
numCols = matrixList[0].shape[1]
numRows = 0
count = 0
offset = 0
for matrix in matrixList:
if matrix.shape[1] != numCols:
raise ValueError("Num attributes of matrixes for hirizontal combination must be identical")
numRows += matrix.shape[0]
newData.extend(matrix.data)
newIndices.extend(matrix.indices)
newIndptr.extend(map(lambda x: x+offset, matrix.indptr[1:]))
offset += numNonZerosInMat[count]
count += 1
return csr_matrix((np.array(newData), np.array(newIndices), np.array(newIndptr)), shape=(numRows, numCols))
def doc2mat(cls, doc, tokenizer, token2index_map):
tokens = zbMathTokenizer.doc2tokens(doc, tokenizer)
feature_vector = group_and_count(tokens)
mat = build_csr_matrix([feature_vector], token2index_map=token2index_map)
return mat
def horizontally_combine_matrixes(matrixList):
if len(matrixList) == 0:
return build_csr_matrix(listOfMaps=[], numAttributes=0)
assert len(matrixList) >= 1
newData = []
newIndices = []
newIndptr = [0]
numNonZerosInMat = map(lambda matrix: len(matrix.data), matrixList)
numCols = matrixList[0].shape[1]
numRows = 0
count = 0
offset = 0
for matrix in matrixList:
if matrix.shape[1] != numCols:
raise ValueError(
"Num attributes of matrixes for hirizontal combination must be identical"
)
numRows += matrix.shape[0]
newData.extend(matrix.data)
newIndices.extend(matrix.indices)
newIndptr.extend(map(lambda x: x + offset, matrix.indptr[1:]))
offset += numNonZerosInMat[count]
count += 1
return csr_matrix(
(np.array(newData), np.array(newIndices), np.array(newIndptr)),
shape=(numRows, numCols))
def clusterDocument(title, abstract, tokenizer, token2indexMap, tfidf_model,
lsi_model, gmm_model):
tokens = tokenize(title + " " + abstract, tokenizer)
tokenCounts = groupAndCount(tokens)
matrix = build_csr_matrix(listOfMaps=[tokenCounts],
token2IndexMap=token2indexMap)
transformedMatrix = lsi_model.transform(tfidf_model.transform(matrix))
prediction = gmm_model.predict(transformedMatrix)[0]
return prediction
def build_raw_csr_matrix(items, token2index_map):
item_maps = []
item_id_log = []
item_count = 1
for item_id, tokens in items:
print str(item_id) + " (" + str(item_count) + ")"
item_maps.append(group_and_count(tokens))
item_id_log.append(item_id)
item_count += 1
m = build_csr_matrix(list_of_maps=item_maps, token_2_index_map=token2index_map)
return m, item_id_log
def get_author_msc_matrix():
if not force_gen and all(os.path.isfile(filename) for filename in [
"derived_data/author_msc_map.npz",
"derived_data/author_msc_map__row2author_name.json",
"derived_data/author_msc_map__col2msc_code.json"]):
mat = load_csr_matrix("derived_data/author_msc_map.npz")
with open("derived_data/author_msc_map__row2author_name.json") as f:
row2author_map = json.load(f)
with open("derived_data/author_msc_map__col2msc_code.json") as f:
col2msc_map = json.load(f)
return mat, row2author_map, col2msc_map
else:
author2msc_map = defaultdict(lambda: defaultdict(int))
cursor().execute("""SELECT display_name, msc, COUNT(*) FROM authorship
JOIN msc_assignment ON authorship.document = msc_assignment.document
WHERE authorship.rank <= 2 AND msc_assignment.pos <= 3
GROUP BY display_name, msc
ORDER BY display_name""")
for row in cursor():
author2msc_map[row[0]][row[1][:2]] += row[2]
author_names, msc_counts = zip(*author2msc_map.items())
msc_code2index_map = dict(zip(msc_classes, range(len(msc_classes))))
col2msc_map = {index: msc for msc, index in msc_code2index_map.items()}
mat = build_csr_matrix(msc_counts, token2index_map=msc_code2index_map)
save_csr_matrix(mat, "derived_data/author_msc_map")
row2author_map = dict(zip(range(len(author_names)), author_names))
with open("derived_data/author_msc_map__row2author_name.json", "w") as f:
json.dump(row2author_map, f)
with open("derived_data/author_msc_map__col2msc_code.json", "w") as f:
json.dump(col2msc_map, f)
return mat, row2author_map, col2msc_map
def get_author_theorem_matrix(setting):
if not force_gen and all(os.path.isfile(filename) for filename in [
"derived_data/" + setting_string(**setting) + "__raw_author_matrix.npz",
"derived_data/" + setting_string(**setting) + "__raw_author_matrix_row2author.json",
"derived_data/" + setting_string(**setting) + "__raw_author_matrix_col2item.json"]):
mat = load_csr_matrix("derived_data/" + setting_string(**setting) + "__raw_author_matrix.npz")
with open("derived_data/" + setting_string(**setting) + "__raw_author_matrix_row2author.json") as f:
row2author_map = json.load(f)
with open("derived_data/" + setting_string(**setting) + "__raw_author_matrix_col2item.json") as f:
col2item_map = json.load(f)
return mat, row2author_map, col2item_map
else:
author_set = set()
item_id_set = set()
with open("derived_data/" + setting_string(**setting) + "__ids_with_authors") as f:
for line in f:
x = line.split(";")
if setting['granularity'] == 'documents':
item_id_set.add(x[0])
offset = 1
elif setting['granularity'] == 'paragraphs':
item_id_set.add((x[0], x[1]))
offset = 2
else:
raise
for i in range(offset, len(x)):
author_set.add(x[i].strip())
count = 0
item2index_map = {}
with open("derived_data/" + setting_string(**setting) + "__processed_ids") as f:
for line in f:
item2index_map[line.strip()] = count
count += 1
# item2index_map = dict(zip(sorted(list(item_id_set)), range(len(item_id_set))))
author2index_map = dict(zip(sorted(list(author_set)), range(len(author_set))))
author_item_indexes = map(lambda x: {}, range(len(author2index_map)))
with open("derived_data/" + setting_string(**setting) + "__ids_with_authors") as f:
for line in f:
x = line.split(";")
if setting['granularity'] == 'documents':
item_index = item2index_map.get(x[0])
offset = 1
elif setting['granularity'] == 'paragraphs':
item_index = item2index_map.get((x[0], x[1]))
offset = 2
else:
raise
if item_index is not None:
for i in range(offset, len(x)):
author_index = author2index_map[x[i].strip()]
author_item_indexes[author_index][item_index] = 1.0
mat = build_csr_matrix(list_of_dicts=author_item_indexes, num_attributes=len(item2index_map))
save_csr_matrix(mat, "derived_data/" + setting_string(**setting) + "__raw_author_matrix")
row2author_map = {index: author for author, index in author2index_map.items()}
with open("derived_data/" + setting_string(**setting) + "__raw_author_matrix_row2author.json", "w") as f:
json.dump(row2author_map, f)
col2item_map = {index: item for item, index in item2index_map.items()}
with open("derived_data/" + setting_string(**setting) + "__raw_author_matrix_col2item.json", "w") as f:
json.dump(col2item_map, f)
return mat, row2author_map, col2item_map
def processFeatureCounts(featureCounts, token2Id, tfidfModel):
m = build_csr_matrix(listOfMaps=[featureCounts], token2IndexMap=token2Id)
return Normalizer().transform(tfidfModel.transform(m))
import numpy as np
from sklearn.cluster import KMeans
from sklearn.mixture import GMM
from util import build_csr_matrix
corpus = ArffJsonCorpus("raw_data/raw_vector.json")
TDM = corpus.toCsrMatrix(shapeCols=54334)
# build lsi model
"""lsi_model = TruncatedSVD(n_components=250)
lsi_model.fit(TDM)
joblib.dump(lsi_model, "models/raw_vector-lsi250_model")"""
# build gmm model
"""lsi_model = joblib.load("models/raw_vector-lsi250_model")
gmm_model = GMM(n_components=64)
gmm_model.fit(lsi_model.transform(TDM))
joblib.dump(gmm_model, "models/gmm-raw_vector-lsi250")"""
#cluster documents
f = open("results/clusters-gmm-raw_vector-lsi250", "w")
lsi_model = joblib.load("models/raw_vector-lsi250_model")
cl_model = joblib.load("models/gmm-raw_vector-lsi250")
count = 0
for doc in corpus:
doc_vector = lsi_model.transform(
build_csr_matrix(dict(doc.data), numAttributes=54334))
f.write(doc.id + ";" + str(cl_model.predict(doc_vector)[0]) + "\n")
count += 1
f.close()
def processFeatureCounts(featureCounts, token2Id, tfidfModel):
m = build_csr_matrix(listOfMaps=[featureCounts], token2IndexMap=token2Id)
return Normalizer().transform(tfidfModel.transform(m))
import joblib
import numpy as np
from sklearn.cluster import KMeans
from sklearn.mixture import GMM
from util import build_csr_matrix
corpus = ArffJsonCorpus("raw_data/raw_vector.json")
TDM = corpus.toCsrMatrix(shapeCols = 54334)
# build lsi model
"""lsi_model = TruncatedSVD(n_components=250)
lsi_model.fit(TDM)
joblib.dump(lsi_model, "models/raw_vector-lsi250_model")"""
# build gmm model
"""lsi_model = joblib.load("models/raw_vector-lsi250_model")
gmm_model = GMM(n_components=64)
gmm_model.fit(lsi_model.transform(TDM))
joblib.dump(gmm_model, "models/gmm-raw_vector-lsi250")"""
#cluster documents
f = open("results/clusters-gmm-raw_vector-lsi250", "w")
lsi_model = joblib.load("models/raw_vector-lsi250_model")
cl_model = joblib.load("models/gmm-raw_vector-lsi250")
count = 0
for doc in corpus:
doc_vector = lsi_model.transform(build_csr_matrix(dict(doc.data), numAttributes=54334))
f.write(doc.id + ";" + str(cl_model.predict(doc_vector)[0]) + "\n")
count += 1
f.close()
