def create_AT_model(self, num_topics):
corpus = self.corpus
dictionary = self.dictionary
author2doc = self.author2doc
model_list = []
for i in range(1):
print(i)
model = AuthorTopicModel(corpus=corpus, num_topics=num_topics, id2word=dictionary.id2token, \
author2doc=author2doc, chunksize=2000, passes=100, gamma_threshold=1e-10, \
eval_every=0, iterations=1, random_state=i)
top_topics = model.top_topics(corpus)
tc = sum([t[1] for t in top_topics])
model_list.append((model, tc))
model, tc = max(model_list, key=lambda x: x[1])
print('Topic coherence: %.3e' % tc)
model.save(self.model_name)
print('AT Model saved as %s' % self.model_name)
self.model = model
print('Creating author Vecs')
self.create_author_vecs()
print('\n Creating Clustering:')
self.create_author_clustering(self.model.num_topics)
# print('\nCreating Classification from cluster Data')
# self.create_classification_from_cluster_data()
print('\nCreating TSNE embeddings')
self.create_tsne_embeddings()
def atm_model(self):
docs = []
author2doc = {}
index = 0
for line in open(self.corpus, encoding="utf-8"):
line = line.strip()
if not line:
continue
author = line.split('\t')[0]
if author not in author2doc:
author2doc[author] = [index]
else:
author2doc[author].append(index)
doc = line.split('\t')[1].replace("，", "").replace("。",
"").split(' ')
docs.append(doc)
index += 1
print(len(docs))
# 构建词典
dictionary = corpora.Dictionary(docs)
# 对文本进行向量化
corpus = [dictionary.doc2bow(doc) for doc in docs]
# 使用atm模型进行训练
model = AuthorTopicModel(corpus,
author2doc=author2doc,
id2word=dictionary,
num_topics=100)
# 保存模型
model.save('topicmodel/author_topic.model')
def train(self):
self.aTM = AuthorTopicModel(self.train_C_,
author2doc=self.Adic,
num_topics=self.K,
passes=100)
self.phi = self.aTM.get_topics().transpose()
self.theta = np.zeros((self.K, self.n_a))
self.A = normalize(self.AMask, 'l1', 0)
for a in range(self.n_a):
self.theta[:, a] = [
b for (c, b) in self.aTM.get_author_topics(str(a), 0)
]
self.D_reb = self.phi.dot(self.theta).dot(self.A)
class aTM():
def __init__(self, K, data, AMask, params, name, dataName):
self.K = K # [int] nb of topics
self.AMask = AMask # [n_a,n_d float] matrix of author participation to each paper (1 if author participated to paper)
self.n_a, self.n_d = self.AMask.shape # [int] nb authors
self.D = data
self.n_dic, self.n_d = self.D.shape
self.name = name
self.train_param = params['train_param']
self.train_C_ = []
for d in range(self.n_d):
self.train_C_.append([(k, self.D[k, d])
for k in range(self.n_dic)])
self.Adic = {}
for a in range(self.n_a):
self.Adic[str(a)] = list(np.where(self.AMask[a, :] > 0)[0])
self.dataName = dataName
def train(self):
self.aTM = AuthorTopicModel(self.train_C_,
author2doc=self.Adic,
num_topics=self.K,
passes=100)
self.phi = self.aTM.get_topics().transpose()
self.theta = np.zeros((self.K, self.n_a))
self.A = normalize(self.AMask, 'l1', 0)
for a in range(self.n_a):
self.theta[:, a] = [
b for (c, b) in self.aTM.get_author_topics(str(a), 0)
]
self.D_reb = self.phi.dot(self.theta).dot(self.A)
def save(self, path):
'''
path example
'''
toSave = {}
toSave['theta'] = self.theta
toSave['phi'] = self.phi
toSave['A'] = self.A
toSave['K'] = self.K
toSave['train_param'] = self.train_param
with open(path + self.name + '_' + self.dataName + '.pkl',
'wb') as output:
pickle.dump(toSave, output, pickle.HIGHEST_PROTOCOL)
def show():
model = AuthorTopicModel.load('model/at_all.atmodel')
author_list = model.id2author.values()
t_res = [[] for i in range(10)]
for a in author_list:
res = model.get_author_topics(a, minimum_probability=0.0)
for i in range(10):
t_res[i].append((a, res[i][1]))
res = []
for i in range(10):
res.append(
sorted(t_res[i], key=lambda item: item[1], reverse=True)[:6])
# for topic in model.show_topics(num_topics=10):
# print('Label: ')
# words = ''
# for word, prob in model.show_topic(topic[0]):
# words += word + ' '
# print('Words: ' + words)
for i in range(len(res)):
print 'topic' + str(i)
for j in res[i]:
print j[0], "%.6f" % (j[1] / 2974)
def calAuthorSim():
conn = sqlite3.connect(config.db_path)
db = conn.cursor()
model = AuthorTopicModel.load(config.author_model128_path)
poets = list(model.id2author.values())
print(len(poets))
# vec = model.get_author_topics('苏轼')
index = MatrixSimilarity(model[list(model.id2author.values())], num_best=30)
index.save(config.author_simMatrix_path)
# index = MatrixSimilarity.load(config.author_simMatrix_path)
for name in poets:
# print(name)
sims = index[model[name]]
sims = sorted(sims, key=lambda item: -item[1])
sims = [ [poets[sim[0]] , sim[1]] for sim in sims]
# print(sims)
# sql_comment = "UPDATE author SET sims=? WHERE id=?"
# db.execute(sql_comment, (toJson(sims), name))
sql_comment = "UPDATE author SET sims=\'{}\' WHERE id=\'{}\'".format(toJson(sims), name)
db.execute(sql_comment)
# print(sql_comment)
# print(len(poets))
conn.commit()
def tsne_clusting(self):
model = AuthorTopicModel.load('topicmodel/author_topic.model')
tsne = TSNE(n_components=2, random_state=0)
smallest_author = 200 # 想看最少写作数量多少的诗人
authors = [
model.author2id[a] for a in model.author2id.keys()
if len(model.author2doc[a]) >= smallest_author
]
print(authors)
embeddings = tsne.fit_transform(model.state.gamma[authors, :])
# print(model.state.gamma[authors, :])
# print(embeddings)
authors_list = [model.id2author[k] for k in authors]
print(authors_list)
# plt.scatter(embeddings[:, 0], embeddings[:, 1], c=y_predict)
#
# plt.show()
# labels = ['李世民', '李白', '白居易', '武则天', '白居易', '杜甫', '刘禹锡', '武元衡', '权德舆']#对应的需要查找某几个诗人的方法
# author_ids = [model.author2id[author] for author in labels]
# print(author_ids)
# author_embs = tsne.fit_transform([embeddings[i] for i in author_ids])
# print(author_embs)
# print(authors, author_ids, author_embs)
self.plot_with_labels(embeddings, authors_list) # 在这里该以下对应诗人的
def build_lda_models(course_corpus, course_dictionary, mapping, course_texts):
# ==== Train Unsupervised LDA ====
lda_model = LdaModel(corpus=course_corpus, id2word=course_dictionary)
# ==== Train Unsupervised HDP-LDA ====
hdp_model = HdpModel(corpus=course_corpus, id2word=course_dictionary)
# ==== Train Author Topic Model ====
author_to_doc = {} # author topic LDA (authors are modules,lessons,items)
for author_type in ["modules", "lessons", "items"]:
entity_to_doc = mapping[author_type]
for entity_name, entity_docs in entity_to_doc.items():
author_to_doc["{}: {}".format(author_type[0].capitalize(),
entity_name)] = entity_docs
at_model = AuthorTopicModel(corpus=course_corpus,
id2word=course_dictionary,
author2doc=author_to_doc)
# ==== Train Labeled LDA ====
# explicitly supervised, labeled LDA
llda_alpha = 0.01
llda_beta = 0.001
llda_iterations = 50
llda_labels = []
llda_corpus = []
labelset = set()
for course_text_id in range(0, len(course_texts)):
doc_labels = []
# get module label name
for module_name, doc_vec in mapping["modules"].items():
if course_text_id in doc_vec:
doc_labels.append("M: {}".format(module_name))
break
# get lesson label name
for lesson_name, doc_vec in mapping["lessons"].items():
if course_text_id in doc_vec:
doc_labels.append("L: {}".format(lesson_name))
break
for item_name, doc_vec in mapping["items"].items():
if course_text_id in doc_vec:
doc_labels.append("I: {}".format(item_name))
break
llda_labels.append(doc_labels)
llda_corpus.append(course_texts[course_text_id])
labelset = labelset.union(doc_labels)
llda_model = LLDA(llda_alpha, llda_beta, K=len(llda_labels))
llda_model.set_corpus(llda_corpus, llda_labels)
llda_model.train(iteration=llda_iterations)
# phi = llda.phi()
# for k, label in enumerate(labelset):
# print ("\n-- label %d : %s" % (k + 1, label))
# for w in argsort(-phi[k + 1])[:10]:
# print("%s: %.4f" % (llda.vocas[w], phi[k + 1,w]))
return lda_model, hdp_model, at_model, llda_model, llda_labels
def ATM(period):
print(period)
if period == "period0":
train_corpus, train_author2doc, train_dictionary = corpus_author2doc(
period)
global ATMModel
ATMModel = AuthorTopicModel(corpus=train_corpus,
num_topics=nb_topics,
id2word=train_dictionary.id2token,
author2doc=train_author2doc,
chunksize=2000,
passes=1,
eval_every=0,
iterations=1,
random_state=1,
minimum_probability=0)
author_list = []
for index, author in enumerate(train_author2doc):
dict = show_author(ATMModel, index, author)
author_list.append(dict)
import json
filename = "User_Topics_Distribution_" + period + ".json"
with open(filename, 'w') as fout:
json.dump(author_list, fout)
else:
global ATMModel
cwd = os.getcwd()
perioddictionary = cwd + "/Dataset/" + period
if os.path.exists(perioddictionary):
new_corpus, new_author2doc, new_dictionary = corpus_author2doc(
period)
ATMModel.update(new_corpus, new_author2doc)
author_list = []
for index, author in enumerate(ATMModel.author2doc):
dict = show_author(ATMModel, index, author)
author_list.append(dict)
import json
filename = "User_Topics_Distribution_" + period + ".json"
with open(filename, 'w') as fout:
json.dump(author_list, fout)
else:
print("No more text information to update!!!!")
def train():
author2doc = {}
corpus = []
count = 0
files = os.listdir(poem_dir) #列出文件夹下所有的目录与文件
for file in files:
path = os.path.join(poem_dir, file)
# if count>100 :
# break
if os.path.isfile(path):
# print(path)
f = open(path, 'r', encoding='utf-8')
file = json.loads(f.read())
f.close()
for poem_id in file:
if count % 100000 == 0 and count != 0:
print(count, len(corpus))
poem = file[poem_id]
# print(poem_id)
for shi_data in poem['ShiData']:
author = shi_data['Author']
if author == '无名氏':
continue
sentences = [
sentence['Content'] for sentence in shi_data['Clauses']
]
content = ' '.join(sentences)
words = seg(content) #+ segAll(content)
# words = dictionary.doc2bow(words)
corpus.append(words)
if author not in author2doc:
author2doc[author] = []
author2doc[author].append(count)
count += 1
# print(count)
dictionary = corpora.Dictionary(corpus)
corpus = [dictionary.doc2bow(doc) for doc in corpus]
print('开始训练', len(corpus), len(author2doc.keys()))
model = AuthorTopicModel(corpus,
author2doc=author2doc,
id2word=dictionary,
iterations=10)
# model = AuthorTopicModel.load(author_model_path)
# model.update(corpus, author2doc=author2doc, iterations=10)
model.save(author_model_path)
def example_1():
"""
Example code from Gensim documentation on author-topic class.
:return:
"""
author2doc = {
'john': [0, 1, 2, 3, 4, 5, 6],
'jane': [2, 3, 4, 5, 6, 7, 8],
'jack': [0, 2, 4, 6, 8]
}
corpus = mmcorpus.MmCorpus(datapath('testcorpus.mm'))
print("Corpus contents:")
print(f"{corpus}\n")
print(f"Documents in the corpus: ")
for document in corpus:
print(f"{document}")
print("\nDictionary contents:")
print(f"{common_dictionary}\n")
print(f"Dictionary contents with word index value:")
print(f"{common_dictionary.token2id}\n")
with temporary_file("serialized") as s_path:
model = AuthorTopicModel(corpus,
author2doc=author2doc,
id2word=common_dictionary,
num_topics=4,
serialized=True,
serialization_path=s_path)
model.update(
corpus, author2doc
) # update the author-topic model with additional documents
# construct vectors for authors
author_vecs = [
model.get_author_topics(author) for author in model.id2author.values()
]
print(f"Vectors for authors:")
print(f"{author_vecs}\n")
def ver():
# 每词单词边界指标
model = AuthorTopicModel.load('model/at_all.atmodel')
dictionary = Dictionary.load('model/atdc.dict')
from gensim.models.coherencemodel import CoherenceModel
atcm = CoherenceModel(model,
corpus=model.corpus,
dictionary=dictionary,
coherence='u_mass')
print atcm.get_coherence()
def calAuthorCat():
model = AuthorTopicModel.load(
'./data_process/model/author2vec/author2vec.model')
conn = sqlite3.connect(db_path)
db = conn.cursor()
rows = db.execute('SELECT id FROM author WHERE potery_num>=5')
ids = set(model.id2author.values())
ids = [row[0] for row in rows if row[0] in ids]
print(len(ids))
def getSpraceVec(id):
topics = model.get_author_topics(id)
vec = np.zeros(128)
for topic in topics:
vec[topic[0]] = topic[1]
return vec
vecs = [getSpraceVec(id) for id in ids]
print('开始计算')
id2label = {}
label2id = {}
labels = KMeans(n_clusters=50, max_iter=1000, n_jobs=-1).fit_predict(vecs)
for index, label in enumerate(labels):
label = str(label)
id = ids[index]
if label not in label2id:
label2id[label] = []
label2id[label].append(id)
id2label[id] = label
# print('开始计算第二层')
# for label in label2id:
# print(label)
# sub_ids = label2id[label]
# sub_vecs = [getSpraceVec(id) for id in sub_ids]
# cluster_num = 10 if len(sub_ids)>10 else len(sub_ids)
# sub_labels = KMeans(n_clusters=cluster_num, max_iter=1000, n_jobs=-1).fit_predict(sub_vecs)
# for index, label in enumerate(sub_labels):
# label = str(label)
# id = sub_ids[index]
# id2label[id] += '-' + label
for id in id2label:
label = id2label[id]
db.execute('UPDATE author SET cat=? WHERE id=?', (label, id))
conn.commit()
conn.close()
def author_cluster(self):
model = AuthorTopicModel.load('author_topic.model')
tsne = TSNE(n_components=2, random_state=0)
smallest_author = 0
authors = [
model.author2id[a] for a in model.author2id.keys()
if len(model.author2doc[a]) >= smallest_author
]
embeddings = tsne.fit_transform(model.state.gamma[authors, :])
authors = list(model.id2author.values())
labels = ['柳永', '晏殊', '欧阳修', '李煜', '李清照', '范仲淹', '苏轼', '辛弃疾', '岳飞']
author_ids = [model.author2id[author] for author in labels]
author_embs = tsne.fit_transform([embeddings[i] for i in author_ids])
print(authors, author_ids, author_embs)
self.plot_with_labels(author_embs, labels)
def test_model(self):
model = AuthorTopicModel.load('topicmodel/author_topic.model')
# 每个作者的向量，每个作者向量维度不一样，对应的主题不一样，主题分别是概率。
author_vecs = [
model.get_author_topics(author)
for author in model.id2author.values()
]
print(len(author_vecs)) # 2610个诗人，每个诗人有几个主题（<100）,之后对应的概率
for author in author_vecs:
print(author, len(author)) # 每个作者身上的主题个数不同
# 介绍每位作者
authors = model.id2author.values()
print(len(authors), authors)
# 显示某位作者的向量
print(model['李白'])
# 显示模型主题
for topic in model.show_topics(num_topics=100):
print(topic)
def __init__(
self,
documents,
attributions,
topics,
iterations,
DOC_KEY,
USER_KEY,
DOC_VALUE='TARGET',
minimum_attributions=2,
maximum_attributions=50,
):
attributions = self._remove_doubles(attributions, [DOC_KEY, USER_KEY])
attributions = self._bound_authors(attributions, minimum_attributions,
maximum_attributions, USER_KEY)
documents, attributions = self._author_document_downselect(
documents, attributions, DOC_KEY)
train, test, vocab = self._get_train_test_and_vocab(
documents, DOC_VALUE)
self.train, self.test, self.vocab = train, test, vocab
train_attr = self._attribution_table(self.train, attributions, DOC_KEY,
USER_KEY)
test_attr = self._attribution_table(self.test, attributions, DOC_KEY,
USER_KEY)
self.train_attr, self.test_attr = train_attr, test_attr
self.train_corpus = self._processed_to_bow(self.train[DOC_VALUE],
self.vocab)
self.test_corpus = self._processed_to_bow(self.train[DOC_VALUE],
self.vocab)
self.topics = topics
self.iterations = iterations
self.model = AuthorTopicModel(
corpus=self.train_corpus,
author2doc=self.train_attr,
num_topics=self.topics,
iterations=self.iterations,
)
def run_model(corpus1, corpus2, dictionary, author2doc1, author2doc2):
model = AuthorTopicModel(corpus=corpus1,
num_topics=10,
id2word=dictionary.id2token,
author2doc=author2doc1,
chunksize=2000,
passes=1,
eval_every=0,
serialized=True,
iterations=1,
random_state=1,
serialization_path='tmp/corpus1.mm')
model.update(corpus=corpus2, author2doc=author2doc2)
model.save('model/at_all.atmodel')
nb_fold = 10
k_list = [10,15,20,30]
nb_k = len(k_list)
aLDA_store_train = np.zeros((nb_fold,nb_k))
aLDA_store_test = np.zeros((nb_fold,nb_k))
aTM_store_train = np.zeros((nb_fold,nb_k))
aTM_store_test = np.zeros((nb_fold,nb_k))
aLDAgen = aLDA_generator(n_dic, n_w, A_mask, K, alpha, beta, gamma)
aLDAgen.itialise()
for f in range(nb_fold):
train_Z,train_C,train_D,train_C_ = aLDAgen.generate()
for k in range(nb_k):
aLDA = aLDA_estimator(k_list[k], train_C, A_mask, alpha, beta,1,False)
aLDA.gd_ll(0.05, 60, 0,0.0,0,1)
aTM = AuthorTopicModel(train_C_ , author2doc=Adic, num_topics=k_list[k])
aTM_phi = aTM.get_topics().transpose()
aTM_theta = 0*aLDA.thetaStar
for a in range(n_a):
aTM_theta[:,a] = [b for (c,b) in aTM.get_author_topics(str(a),0)]
aLDA_store_train[f,k] = aLDA.llgd[-1,0]
aTM_store_train[f,k] = loglikaLDA(aTM_theta, aTM_phi, A_mask, train_D, alpha, beta,1)
aLDA_store_test[f,k] = np.sum(np.sum(np.log(aLDA.phiStar.dot(aLDA.thetaStar).dot(aLDA.AStar))*(aLDAgen.phi.dot(aLDAgen.theta).dot(aLDAgen.A))))*n_w
aTM_store_test[f,k] = np.sum(np.sum(np.log(aTM_phi.dot(aTM_theta).dot(aLDAgen.A))*(aLDAgen.phi.dot(aLDAgen.theta).dot(aLDAgen.A))))*n_w
print(f)
#%%
max_ll = np.sum(np.sum(np.log(aLDAgen.phi.dot(aLDAgen.theta).dot(aLDA.AStar))*(aLDAgen.phi.dot(aLDAgen.theta).dot(aLDAgen.A))))*n_w
plt.Figure()
# In[ ]:
# TODO (Lee) review pat_inv_map workflow
# partitions data_1000 to size of training set (80/20 split so grabs first 800 rows)
data_800 = data_1000[:800]
# create inventor-to-doc mapping from original list of dicts in json api response
pat2inv = pat_inv_map(data_800)
# #### Construct author-topic model
# In[ ]:
# construct author-topic model
model_at = AuthorTopicModel(corpus=corpus_1000train,
doc2author=pat2inv,
id2word=id_to_word_1000train)
# In[ ]:
# construct vectors for authors
author_vecs = [
model_at.get_author_topics(author)
for author in model_at.id2author.values()
]
author_vecs
# In[ ]:
# retrieve topic distribution for author using use model[name] syntax
# each topic has a probability of being expressed given the particular author,
init['phi'] = phiLDA
aLDALDA = aLDA_estimator(K, M_train, np.eye(n_doc), 5, 5, 1, True, init)
aLDALDA.gd_ll(0.00004, 60, 0, 0, 0, 1, 0)
plt.plot(aLDALDA.llgd[:, 0] / aLDALDA.llgd[0, 0])
t1 = time.time()
print('gd on LDA for k = ' + str(K), ', time = ' + str(t1 - t))
t = t1
# aTM ---------------------------------------------------
Adic = {}
for a in range(n_a):
Adic[str(a)] = list(np.where(At[a, :] > 0)[0])
aTM = AuthorTopicModel(train_C_, author2doc=Adic, num_topics=K)
phiaTM = aTM.get_topics().transpose()
thetaaTM = np.zeros((K, n_a))
for a in range(n_a):
thetaaTM[:, a] = [b for (c, b) in aTM.get_author_topics(str(a), 0)]
t1 = time.time()
print('aTM for k = ' + str(K), ', time = ' + str(t1 - t))
t = t1
# aLDA on aTM
init2 = {}
init2['A'] = normalize(At, 'l1', 0)
init2['theta'] = thetaaTM
init2['phi'] = phiaTM
aLDATaTM = aLDA_estimator(K, M_train, At, 10, 10, 1, True, init2)
from joblib import dump, load
# document embedding
from gensim.models import TfidfModel, AuthorTopicModel
from gensim.corpora import Dictionary
from gensim.matutils import corpus2csc
from gensim.test.utils import get_tmpfile
import bz2
from Preprocessing.preprocessing import Preprocessing
import itertools
print('Loading and preprocessing data')
with bz2.open('/gepris_data/train_filtered.csv.bz2', mode='rt') as f:
csvreader = csv.reader(f)
traindata = Preprocessing().fit_transform((row[1] for row in csvreader))
f.seek(0)
doc2author = {i: row[3:] for i, row in enumerate(csvreader)}
print('Building dict and training TfIdf model:')
dct = Dictionary(doc for doc in traindata) # fit dictionary
tfidf_model = TfidfModel((dct.doc2bow(doc) for doc in traindata)) # fit model
dump(tfidf_model, '/models/tfidf/tfidf.joblib')
dump(dct, '/models/dict/dict.joblib')
print('ATM training:')
atm_model = AuthorTopicModel([dct.doc2bow(doc) for doc in traindata],
doc2author=doc2author,
id2word=dct)
dump(atm_model, '/models/atm/atm.joblib')
def topicandresult(num, corpus, id2doc, dic):
"""
model_list = []
for i in range(5):
model = AuthorTopicModel(corpus = corpus, num_topics = num, id2word=dic, \
author2doc = id2doc, chunksize = 2000, passes = 1, eval_every = 0, \
#random_state = int(random.random()*1000),
iterations=100000
)
top_topics = model.top_topics(corpus)
tc = sum([t[1] for t in top_topics])
model_list.append((model, tc))
model, tc = max(model_list, key=lambda x: x[1])
"""
model = AuthorTopicModel(corpus = corpus,
num_topics = num,
id2word = dic,
author2doc = id2doc,
chunksize = 2000,
passes = 55,
eval_every = 0,
gamma_threshold=1e-11,
iterations = 10000000)
with open('NuclearEnergy/Data/' + str(num) + 'topic.model', 'wb') as p:
pickle.dump(model, p)
'''
with open('NuclearEnergy/Data/' + str(num) + 'topic.model', 'rb') as p:
model = pickle.load(p)
'''
pubid = pd.Series(list(set([total['press'][i] + ', ' + total['pubtime'][i]
for i in range(0, len(total))])), name='id', dtype='category')
result = {'publisher': [], 'time': [], 'docnum': [], 'corpusp': []}
for i in range(0, num):
result[i] = []
def proportion(pubid):
docs = model.author2doc[pubid]
return sum([word[1] for doc in docs for word in corpus[doc]]) / vocnum
for i in pubid:
result['publisher'].append(re.split(', ', i)[0])
result['time'].append(re.split(', ', i)[1])
cr1 = total['press'].map(lambda x: x == re.split(', ', i)[0])
cr2 = total['pubtime'].map(lambda x: x == re.split(', ', i)[1])
result['docnum'].append(len(total[cr1 & cr2]))
result['corpusp'].append(proportion(i))
topics = {j[0]: j[1] for j in model[i]}
for j in result:
if j in topics:
result[j].append(topics[j])
elif j is 'publisher' or j is 'time' or j is 'docnum' or j is 'corpusp':
pass
else:
result[j].append(0)
result = pd.DataFrame(result)
result = result.reindex_axis(natsorted(result.columns, alg=ns.IC), axis=1)
ratio = {}
for i in range(0, num):
ratio[i] = 0
for index, row in result.iterrows():
for i in ratio:
ratio[i] += row[i] * row['corpusp']
agg = sum(ratio.values())
ratio = {i: str(ratio[i] * 100 / agg) + '%' for i in ratio}
topics = pd.DataFrame({
i[0]: [re.split('\*', j)[1] + '*' + str(round(float(re.split('\*', j)[0]) * 100, 3)) + '%' for
j in re.split(' \\+ ', re.sub('"', '', i[1]))] for i
in model.show_topics(num_topics= num, num_words=50)})
topics.loc[30] = ratio
topics = topics.reindex([30] + list(range(0, 30)))
topics.index = ['토픽 비율'] + list(range(1, 31))
'''
topic_labels = ['1 사이버 해킹과 공격', '2 ',
'3 원전에 대한 비판적 담론들', '4 원전의 안전과 기술적 문제들',
'5 후쿠시마 원전 사고와 안전', '6 지진과 원자력 발전소 및 정치권', '7 해외 동향',
'8 지진과 원전 위치 지역의 불안', '9 원자력 안전과 지역사회', '10 원자력 관련 기관 인사',
'11 원자력을 둘러싼 정치적 갈등', '12 원전 건설 사업과 관련된 이슈', '13 고리/월성 원전',
'14 영화 판도라 관련', '15 원자력 산업 관리', '16 원전 관련 투자와 에너지 산업',
'17 사회적 이슈들과 일본 지진','18 원전 관련 기술', '19 원전에 대한 사업적 접근',
'20 원전과 북한, 안보']
'''
topic_labels = [str(i) for i in range(1, num + 1)]
topics.columns = topic_labels
result.columns = topic_labels + ['형태소의 비율', '기사 숫자', '발표 기관', '발표 시기']
return(topics, result, model)
print(dict(dictionary.items())) dictionary.id2token dictionary.token2id dictionary.num_pos dictionary.num_nnz dictionary.num_docs # 对文本进行向量化，得到每首诗词的稀疏向量，向量的每一个元素代表了一个 word 在这首诗词中的出现次数 corpus = [dictionary.doc2bow(doc) for doc in docs] ######### atm 模型 ########## ''' author topic model ''' # 使用 atm 模型进行训练, 设置100个主题 model = AuthorTopicModel(corpus, author2doc=author2doc, id2word=dictionary, num_topics=100) # 保存模型 model_file = '/Users/wxy/Documents/Sufepost/Courses/文本挖掘/report/MyPoemMining/atm_model/atm.model' model.save(model_file) ######### 模型信息与降维 ########## ''' 模型信息与降维 ''' model_file = '/Users/wxy/Documents/Sufepost/Courses/文本挖掘/report/MyPoemMining/atm_model/atm.model' model = AuthorTopicModel.load(model_file) # 模型的一些信息 model.id2author model.author2id model.author2doc
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 29 11:33:01 2017
@author: jadm
"""
#%%
from gensim.models import AuthorTopicModel
model = AuthorTopicModel.load('modelo1/model.atmodel')
#%%
top_topics = model.top_topics(model.corpus)
#%%
for i in range(len(top_topics)):
print(top_topics[i][1])
for index, row in df.iterrows():
row = row.tolist()
curr_doc = []
for i in range(len(row)):
curr_item = row[i]
if curr_item > 0:
curr_doc.append((i, curr_item))
corpus.append(curr_doc)
# dictionary = Dictionary(data)
# corpus = [dictionary.doc2bow(text) for text in data]
num_topics = 12
model_POLE = AuthorTopicModel(
corpus=corpus,
author2doc=author2doc_POLE,
#id2word=dictionary,
num_topics=num_topics)
model_MSI = AuthorTopicModel(
corpus=corpus,
author2doc=author2doc_MSI,
#id2word=dictionary,
num_topics=num_topics)
# construct vectors for authors
# author_vecs = [model.get_author_topics(author) for author in model.id2author.values()]
#
# print(author_vecs)
POLE_tops = model_POLE.get_topics()
np.savetxt("authortopic_POLE_output.csv", POLE_tops, delimiter=",")
df, f_ini, f_fin = archivos_csv(sys.argv[1])
else:
df, f_ini, f_fin = archivos_csv()
#%%
corpus, dictionary, author2doc = preprocesamiento(df)
print('# de autores: %d' % len(author2doc))
print('# tokens unicos: %d' % len(dictionary))
print('# de documentos: %d' % len(corpus))
print('Corriendo modelo')
model = AuthorTopicModel(corpus=corpus,
num_topics=100,
id2word=dictionary.id2token,
author2doc=author2doc,
chunksize=2000,
passes=55,
eval_every=0,
iterations=10000000,
gamma_threshold=1e-11)
f = 'modelo' + folder()
os.makedirs(f)
os.makedirs(f + '/LDA')
model.save(f + '/model.atmodel')
print("MODELO TERMINADO Y GUARDADO")
# LDA
print('Corriendo LDA')
ldamodel = LdaModel(corpus=corpus, num_topics=100, id2word=dictionary)
# Remove rare and common tokens.
# Filter out words that occur too frequently or too rarely.
max_freq = 0.5
min_wordcount = 20
dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)
_ = dictionary[0] # This sort of "initializes" dictionary.id2token.
corpus = [dictionary.doc2bow(doc) for doc in docs]
print('Number of authors: %d' % len(author2doc))
print('Number of unique tokens: %d' % len(dictionary))
print('Number of documents: %d' % len(corpus))
from gensim.models import AuthorTopicModel
model = AuthorTopicModel(corpus=corpus, num_topics=10, id2word=dictionary.id2token, \
author2doc=author2doc, chunksize=2000, passes=1, eval_every=0, \
iterations=1, random_state=1)
model_list = []
for i in range(5):
model = AuthorTopicModel(corpus=corpus, num_topics=10, id2word=dictionary.id2token, \
author2doc=author2doc, chunksize=2000, passes=100, gamma_threshold=1e-10, \
eval_every=0, iterations=1, random_state=i)
top_topics = model.top_topics(corpus)
tc = sum([t[1] for t in top_topics])
model_list.append((model, tc))
model, tc = max(model_list, key=lambda x: x[1])
print('Topic coherence: %.3e' % tc)
model.save('/tmp/model.atmodel')
# In[143]:
patdf2inv = dict((df_pat_idx[key], value) for (key, value) in pat2inv.items())
patdf2inv
# #### Construct author-topic model
# In[144]:
# construct author-topic model
model_at = AuthorTopicModel(corpus=corpus_at,
doc2author=patdf2inv,
id2word=id_to_word_at,
num_topics=25)
# In[145]:
# construct vectors for authors
author_vecs = [model_at.get_author_topics(author) for author in model_at.id2author.values()]
author_vecs
# In[146]:
# inspect topic distribution for author with id# 7788103-1
# Parameters:
#
# **num_topics**: The number of topics ion the model. There is no 'correct' value here, and it depends entirely on how many different topics occur in the corpus. 100 is generally a reasonable compromise for a corpus this size. <br />
# **chunksize**: Controls the size of the mini-batches. This depends entirely on the corpus - 2000 is the default, but this obviously makes no sense if a corpus only contains 1000 documents. <br />
# **passes**: 100 by default <br />
# **iterations**: iterations is the maximum number of times the model loops over each document <br />
# **alpha**: Can be set to 'asymmetric' <br />
# **eta**: Can be set to ‘auto’, which learns an asymmetric prior over words directly from the data
# In[23]:
#get_ipython().run_cell_magic('time', '', 'model = AuthorTopicModel(corpus=corpus, num_topics=100, id2word=dictionary.id2token, \\\n author2doc=author2doc_test, chunksize=100, passes=100, gamma_threshold=0.001, \\\n eval_every=0, iterations=1, random_state=1)')
print('Training...')
model = AuthorTopicModel(corpus=corpus, num_topics=100, id2word=dictionary.id2token, \
author2doc=author2doc_test, chunksize=100, passes=100, gamma_threshold=0.001, \
eval_every=0, iterations=1, random_state=1)
# In[24]:
# Save model.
model.save('./results/model_presentation_github.atmodel')
# In[18]:
#import pandas as pd
#import spacy
#from gensim.models import Phrases
#from gensim.corpora import Dictionary
#from gensim.models import AuthorTopicModel
def load_AT_model(self, name='model.atmodel'):
print("\nLoading model from %s" % self.model_name)
self.model = AuthorTopicModel.load(self.model_name)