def get_normed_author_theorem_matrix(setting):
if (not force_gen
and os.path.isfile("derived_data/" + setting_string(**setting) + "__normed_author_theorem_matrix.npz")
and os.path.isfile("derived_data/" + setting_string(**setting) + "__normed_theorem_author_matrix.npz")):
normed_author_theorem_mat = load_csr_matrix("derived_data/" + setting_string(**setting) + "__normed_author_theorem_matrix.npz")
normed_theorem_author_mat = load_csr_matrix("derived_data/" + setting_string(**setting) + "__normed_theorem_author_matrix.npz")
return normed_author_theorem_mat, normed_theorem_author_mat
else:
mat, r, c = get_author_theorem_matrix(setting)
normed_author_theorem_mat = normalize(mat)
normed_theorem_author_mat = normalize(mat.transpose())
save_csr_matrix(normed_author_theorem_mat, "derived_data/" + setting_string(**setting) + "__normed_author_theorem_matrix")
save_csr_matrix(normed_theorem_author_mat, "derived_data/" + setting_string(**setting) + "__normed_theorem_author_matrix")
return normed_author_theorem_mat, normed_theorem_author_mat
def get_raw_tdm(setting):
if not force_gen and all(os.path.isfile(filename) for filename in [
"derived_data/" + setting_string(**setting) + "__raw_tdm.npz",
"derived_data/" + setting_string(**setting) + "__ids"]):
mat = load_csr_matrix("derived_data/" + setting_string(**setting) + "__raw_tdm.npz")
ids = []
with open("derived_data/" + setting_string(**setting) + "__ids") as f:
count = 0
for line in f:
x = line.split(";")
if setting['granularity'] == 'paragraphs':
ids.append((count, (x[0], x[1].strip())))
elif setting['granularity'] == 'documents':
ids.append((count, x[0].strip()))
else:
raise ValueError("granularity must be either 'documents' or 'paragraphs'")
row2id_map = dict(ids)
token2index_map = get_token2index_map(setting)
column2token_map = {index: token for token, index in token2index_map.items()}
return mat, row2id_map, column2token_map
else:
token2index_map = get_token2index_map(setting)
column2token_map = {index: token for token, index in token2index_map.items()}
if setting['granularity'] == "paragraphs":
paragraph_generator = get_all_docs_paragrahps_as_token_list(setting['token_method'], setting['data_basis'])
mat, id_log = build_raw_csr_matrix(paragraph_generator, token2index_map)
elif setting['granularity'] == "documents":
document_generator = get_all_documents_as_feature_map(setting['token_method'], setting['data_basis'])
mat, id_log = build_raw_csr_matrix(document_generator, token2index_map)
else:
raise ValueError("granularity must be either paragraphs or documents")
save_csr_matrix(mat, "derived_data/" + setting_string(**setting) + "__raw_tdm")
f = open("derived_data/" + setting_string(**setting) + "__ids", "w")
if setting['granularity'] == "paragraphs":
for id in id_log:
f.write(id[0] + ";" + id[1] + "\n")
elif setting['granularity'] == "documents":
for id in id_log:
f.write(id + "\n")
f.close()
row2id_map = dict(zip(range(len(id_log)), id_log))
return mat, row2id_map, column2token_map
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 get_processed_tdm(setting, intended_amount_of_text_tokens=None, intended_amount_of_formula_tokens=None):
if not force_gen and all(os.path.isfile(filename) for filename in [
"derived_data/" + setting_string(**setting) + "__processed_tdm.npz",
"derived_data/" + setting_string(**setting) + "__processed_ids",
"derived_data/" + setting_string(**setting) + "__processed_token2index_map.json"]):
mat = load_csr_matrix("derived_data/" + setting_string(**setting) + "__processed_tdm.npz")
ids = []
with open("derived_data/" + setting_string(**setting) + "__processed_ids") as f:
count = 0
for line in f:
x = line.split(";")
if setting['granularity'] == 'paragraphs':
ids.append((count, (x[0], x[1].strip())))
elif setting['granularity'] == 'documents':
ids.append((count, x[0].strip()))
else:
raise ValueError("granularity must be either 'documents' or 'paragraphs'")
row2id_map = dict(ids)
with open("derived_data/" + setting_string(**setting) + "__processed_token2index_map.json") as f:
token2index_map = json.load(f)
column2token_map = {index: token for token, index in token2index_map.items()}
return mat, row2id_map, column2token_map
else:
# retrieve best tf-idf terms
raw_tdm, row2id_map, column2token_map = get_raw_tdm(setting)
nz_row_indexes = non_zero_row_indexes(raw_tdm)
raw_tdm = raw_tdm[nz_row_indexes, :]
token2index_map = get_token2index_map(setting)
text_token_scores, formula_token_scores = tf_idf_scores(raw_tdm, token2index_map)
best_text_token_indexes, best_formula_token_indexes = select_best_tokens(text_token_scores, formula_token_scores, intended_amount_of_text_tokens, intended_amount_of_formula_tokens)
text_tdm = raw_tdm[:, best_text_token_indexes]
formula_tdm = raw_tdm[:, best_formula_token_indexes]
if text_tdm.shape[1] == 0:
processed_tdm = formula_tdm
elif formula_tdm == 0:
processed_tdm = text_tdm
else:
float_text_tdm = element_wise_multiply(text_tdm, 1.0)
pruned_formula_tdm = element_wise_multiply(formula_tdm, avg_row_norm(text_tdm) / avg_row_norm(formula_tdm))
processed_tdm = vertically_append_matrix(float_text_tdm, pruned_formula_tdm)
new_index2old_index_map = {new_index: old_index for new_index, old_index in enumerate(best_text_token_indexes)}
new_index2old_index_map.update({new_index+len(best_text_token_indexes): old_index for new_index, old_index in enumerate(best_formula_token_indexes)})
new_token2index_map = {}
for new_index, old_index in new_index2old_index_map.items():
new_token2index_map[column2token_map[old_index]] = new_index
new_column2token_map = {index: token for token, index in new_token2index_map.items()}
new_row2id_map = {}
count = 0
for index, id in row2id_map.items():
if index in nz_row_indexes:
new_row2id_map[count] = id
count += 1
# save processed tdm
save_csr_matrix(processed_tdm, "derived_data/" + setting_string(**setting) + "__processed_tdm")
# save respective ids
with open("derived_data/" + setting_string(**setting) + "__processed_ids", "w") as f:
for index, id in sorted(new_row2id_map.items(), key=lambda x: x[0]):
f.write(id + "\n")
# save token2index map
with open("derived_data/" + setting_string(**setting) + "__processed_token2index_map.json", "w") as outfile:
json.dump(new_token2index_map, outfile)
return processed_tdm, new_row2id_map, new_column2token_map
count += 1
theoremTDM = horizontally_combine_matrixes(matrixList)
save_csr_matrix(theoremTDM, "derived_data/combined_theorem_text_formula_tdm")"""
# train lsa
"""theoremTDM = load_csr_matrix("derived_data/combined_theorem_text_formula_tdm.npz")
svd = TruncatedSVD(n_components=250)
svd.fit(theoremTDM)
joblib.dump(svd, "models/combined_theorem_text_formula_lsi250_model")
"""
# perform clustering
theoremTDM = load_csr_matrix("derived_data/combined_theorem_text_formula_tdm.npz")
svd = joblib.load("models/combined_theorem_text_formula_lsi250_model")
LSI_TDM = svd.transform(theoremTDM)
ap = AffinityPropagation(
damping=0.5,
max_iter=200,
convergence_iter=15,
copy=True,
preference=None,
affinity='euclidean',
verbose=False
)
ap.fit(LSI_TDM)
joblib.dump(ap, "models/combined_theorem_text_formula_ap_model")
import json
from scipy.sparse import csr_matrix
from util import save_csr_matrix, load_csr_matrix, get_dirpath, get_filenames_and_filepaths, DocumentParser, filesInDict
from util import connectToDb, bin2NumpyArr
import numpy as np
from time import time
from main.arffJson.ArffJsonCorpus import ArffJsonCorpus, ArffJsonDocument
from string import digits, ascii_letters
from os.path import isfile, join
dirpath = get_dirpath()
filenames, filepaths = get_filenames_and_filepaths("raw_data/ntcir_filenames")
tdm = load_csr_matrix("derived_data/zb_math_full_text_tdm2.npz")
translateMap = json.load(open("derived_data/zb_math_full_texts_tokens2IndexMap"))
row_number2fulltext_id_map = json.load(open("derived_data/row_number2fulltext_id_map.json"))
phrase = "theorem"
tokenizer = DocumentParser.TextTokenizer()
tokens = tokenizer.tokenize(phrase)
tokenIds = map(lambda token: translateMap[token], tokens)
candidateIds = []
index = 0
m = tdm[:, tokenIds]
candidateInd = []
currInd = 0
for i in range(len(m.indptr) - 1):
diff = m.indptr[i + 1] - m.indptr[i]
if diff == len(tokenIds):
target_class = "81"
ordered_document_assignments = map(
lambda doc_id: doc2msc[str(doc_id)]
if doc_id in doc2msc else None, document_ids)
ordered_document_labels = map(
lambda lab: None
if lab is None else (1 if lab[:len(target_class)] == target_class else 0),
ordered_document_assignments)
test_doc_ind = indexes_in_list(document_ids,
readFileLinewise2Array("raw_data/test_doc_ids"))
train_doc_ind = indexes_in_list(
document_ids, readFileLinewise2Array("raw_data/train_doc_ids"))
mat = load_csr_matrix("derived_data/tfidf_theorem_tdm_grouped_by_docs.npz")
train_mat = mat[train_doc_ind, :]
train_labels = itemgetter(*train_doc_ind)(ordered_document_labels)
svd = TruncatedSVD(n_components=1000)
svd.fit(train_mat)
test_mat = mat[test_doc_ind, :]
test_labels = itemgetter(*test_doc_ind)(ordered_document_labels)
clf = svm.LinearSVC()
clf.fit(svd.transform(train_mat), train_labels)
# eval results
predictions = clf.predict(svd.transform(test_mat)).tolist()
# Save CSR-Matrix
"""c = corpus.toCsrMatrix()
save_csr_matrix(c, "abschlussbericht-csr")"""
# Save Labels
"""corpus = ArffJsonCorpus("../zb_math_cluster_experiments/raw_data/abschlussbericht-corpus.json")
labels_list = (doc.classes for doc in corpus)
with open("abschlussbericht-labels", "w") as f:
for labels in labels_list:
top_class_labels = set(map(lambda x: x[:2], labels))
f.write(",".join(top_class_labels) + "\n")"""
tdm = load_csr_matrix("corpus.npz")
labels = read_labels("abschlussbericht-labels")
mats = {}
def get_transformed_mat(mat, transform_id, transformer_list, test_train):
global mats
if mats.get((transform_id, test_train)) is None:
if transformer_list is not None or not len(transformer_list) == 0:
mat_copy = mat
for transformer in transformer_list:
mat_copy = transformer.transform(mat_copy)
mats[(transform_id, test_train)] = mat_copy
else:
mats[(transform_id, test_train)] = mat
document_ids = json.load(open("derived_data/theorem_tdm_grouped_by_docs_doc_ids"))
doc2msc = {}
f = open("raw_data/doc2msc")
for line in f:
x = line.split(";")
doc2msc[str(x[0])] = x[1].strip()
f.close()
target_class = "81"
ordered_document_assignments = map(lambda doc_id: doc2msc[str(doc_id)] if doc_id in doc2msc else None, document_ids)
ordered_document_labels = map(lambda lab: None if lab is None else (1 if lab[:len(target_class)] == target_class else 0), ordered_document_assignments)
test_doc_ind = indexes_in_list(document_ids, readFileLinewise2Array("raw_data/test_doc_ids"))
train_doc_ind = indexes_in_list(document_ids, readFileLinewise2Array("raw_data/train_doc_ids"))
mat = load_csr_matrix("derived_data/tfidf_theorem_tdm_grouped_by_docs.npz")
train_mat = mat[train_doc_ind, :]
train_labels = itemgetter(*train_doc_ind)(ordered_document_labels)
svd = TruncatedSVD(n_components=1000)
svd.fit(train_mat)
test_mat = mat[test_doc_ind, :]
test_labels = itemgetter(*test_doc_ind)(ordered_document_labels)
clf = svm.LinearSVC()
clf.fit(svd.transform(train_mat), train_labels)
# eval results
predictions = clf.predict(svd.transform(test_mat)).tolist()
import json
from scipy.sparse import csr_matrix
from util import save_csr_matrix, load_csr_matrix, get_dirpath, get_filenames_and_filepaths, DocumentParser, filesInDict
from util import connectToDb, bin2NumpyArr
import numpy as np
from time import time
from main.arffJson.ArffJsonCorpus import ArffJsonCorpus, ArffJsonDocument
from string import digits, ascii_letters
from os.path import isfile, join
dirpath = get_dirpath()
filenames, filepaths = get_filenames_and_filepaths("raw_data/ntcir_filenames")
tdm = load_csr_matrix("derived_data/zb_math_full_text_tdm2.npz")
translateMap = json.load(
open("derived_data/zb_math_full_texts_tokens2IndexMap"))
row_number2fulltext_id_map = json.load(
open("derived_data/row_number2fulltext_id_map.json"))
phrase = "theorem"
tokenizer = DocumentParser.TextTokenizer()
tokens = tokenizer.tokenize(phrase)
tokenIds = map(lambda token: translateMap[token], tokens)
candidateIds = []
index = 0
m = tdm[:, tokenIds]
candidateInd = []
currInd = 0
for i in range(len(m.indptr) - 1):
matrixList.append(combinedFeatures)
count += 1
theoremTDM = horizontally_combine_matrixes(matrixList)
save_csr_matrix(theoremTDM, "derived_data/combined_theorem_text_formula_tdm")"""
# train lsa
"""theoremTDM = load_csr_matrix("derived_data/combined_theorem_text_formula_tdm.npz")
svd = TruncatedSVD(n_components=250)
svd.fit(theoremTDM)
joblib.dump(svd, "models/combined_theorem_text_formula_lsi250_model")
"""
# perform clustering
theoremTDM = load_csr_matrix(
"derived_data/combined_theorem_text_formula_tdm.npz")
svd = joblib.load("models/combined_theorem_text_formula_lsi250_model")
LSI_TDM = svd.transform(theoremTDM)
ap = AffinityPropagation(damping=0.5,
max_iter=200,
convergence_iter=15,
copy=True,
preference=None,
affinity='euclidean',
verbose=False)
ap.fit(LSI_TDM)
joblib.dump(ap, "models/combined_theorem_text_formula_ap_model")