def wordsim(self, path = "wordsim/wordsim353/combined.tab"):
(pairs, scores) = self.loadCorpus(path)
#m = self.loadSenna("../senna/embeddings/embeddings.txt","../senna/hash/words.lst") #dict{str: np.array()}
m = Word2Vec.load_word2vec_format("../google_data/GoogleNews-vectors-negative300.bin.gz", binary=True)
print "--- Original Pairs: ---"
for pair in pairs:
print pair
words = set(m.index2word)
(pairs,nums) = self.checkWords(m, pairs)
print "--- After Matching: ---"
### For WS dataset.
#nums = [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 16, 17, 19, 23, 24, 25, 27, 28, 29, 30, 31, 32, 36, 37, 40, 43, 44, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 65, 70, 74, 75, 83, 84, 85, 86, 88, 90, 94, 96, 97, 98, 99, 100, 102, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 141, 142, 146, 147, 148, 150, 151, 152, 153, 154, 155, 156, 161, 162, 163, 164, 165, 169, 171, 173, 174, 177, 178, 183, 184, 188, 190, 191, 194, 197, 198, 206, 210, 213, 214, 218, 219, 220, 221, 224, 225, 226, 227, 228, 230, 235, 238, 242, 247, 255, 256, 257, 259, 260, 267, 269, 273, 275, 277, 278, 279, 280, 282, 285, 286, 287, 288, 289, 291, 296, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 317, 318, 320, 321, 324, 325, 332, 334, 335, 336, 340, 343, 344, 347, 348, 350, 351, 352]
print nums
print "Original Number of Words",len(pairs)
for pair in pairs:
print pair
matched_pairs = [pairs[num] for num in nums]
matched_scores = [scores[num] for num in nums]
print "--- After deleting unmatched: ---"
print "Number of remaining words", len(matched_pairs)
print matched_pairs
print matched_scores
cosine_scores = []
for tmp in matched_pairs:
cosine = 1 - spatial.distance.cosine(m[tmp[0]], m[tmp[1]])
cosine_scores.append(cosine)
print "--- After calculating cosine scores:--- "
print cosine_scores
print "--- Spearman Corelation ---"
print stats.spearmanr(matched_scores, cosine_scores)
print stats.pearsonr(matched_scores, cosine_scores)
def wordsim(self, path, vectorPath):
(pairs, scores) = self.loadCorpus(path)
m = Word2Vec.load_word2vec_format(vectorPath, binary=True)
print "--- Original Pairs: ---"
for pair in pairs:
print pair
logging.warn("Loading completed")
words = m.index2word
(pairs,nums) = self.checkWords(m, pairs)
print "--- After Matching: ---"
print nums
print "Original Number of Words",len(pairs)
for pair in pairs:
print pair
matched_pairs = [pairs[num] for num in nums]
matched_scores = [scores[num] for num in nums]
print "--- After deleting unmatched: ---"
print "Number of remaining words", len(matched_pairs)
print matched_pairs
print matched_scores
cosine_scores = []
for tmp in matched_pairs:
cosine = 1 - spatial.distance.cosine(m[tmp[0]], m[tmp[1]])
cosine_scores.append(cosine)
print "--- After calculating cosine scores:--- "
print cosine_scores
print "--- Spearman Corelation ---"
s = stats.spearmanr(matched_scores, cosine_scores)
p = stats.pearsonr(matched_scores, cosine_scores)
print s
print p
return (s,p)
def wordsim(self, path="wordsim/wordsim353/combined.tab"):
(pairs, scores) = self.loadCorpus(path)
#m = self.loadSenna("../senna/embeddings/embeddings.txt","../senna/hash/words.lst") #dict{str: np.array()}
m = Word2Vec.load_word2vec_format(
"../google_data/GoogleNews-vectors-negative300.bin.gz",
binary=True)
print "--- Original Pairs: ---"
for pair in pairs:
print pair
words = set(m.index2word)
(pairs, nums) = self.checkWords(m, pairs)
print "--- After Matching: ---"
### For WS dataset.
#nums = [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 16, 17, 19, 23, 24, 25, 27, 28, 29, 30, 31, 32, 36, 37, 40, 43, 44, 49, 54, 55, 56, 57, 58, 59, 60, 61, 62, 65, 70, 74, 75, 83, 84, 85, 86, 88, 90, 94, 96, 97, 98, 99, 100, 102, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 141, 142, 146, 147, 148, 150, 151, 152, 153, 154, 155, 156, 161, 162, 163, 164, 165, 169, 171, 173, 174, 177, 178, 183, 184, 188, 190, 191, 194, 197, 198, 206, 210, 213, 214, 218, 219, 220, 221, 224, 225, 226, 227, 228, 230, 235, 238, 242, 247, 255, 256, 257, 259, 260, 267, 269, 273, 275, 277, 278, 279, 280, 282, 285, 286, 287, 288, 289, 291, 296, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 314, 317, 318, 320, 321, 324, 325, 332, 334, 335, 336, 340, 343, 344, 347, 348, 350, 351, 352]
print nums
print "Original Number of Words", len(pairs)
for pair in pairs:
print pair
matched_pairs = [pairs[num] for num in nums]
matched_scores = [scores[num] for num in nums]
print "--- After deleting unmatched: ---"
print "Number of remaining words", len(matched_pairs)
print matched_pairs
print matched_scores
cosine_scores = []
for tmp in matched_pairs:
cosine = 1 - spatial.distance.cosine(m[tmp[0]], m[tmp[1]])
cosine_scores.append(cosine)
print "--- After calculating cosine scores:--- "
print cosine_scores
print "--- Spearman Corelation ---"
print stats.spearmanr(matched_scores, cosine_scores)
print stats.pearsonr(matched_scores, cosine_scores)
def conceptcat(self, path = INPUTPATH):
#m = Word2Vec.load_word2vec_format("vectors/entity_vectors_880000", binary=True)
m = Word2Vec.load_word2vec_format("../google_data/GoogleNews-vectors-negative300.bin.gz", binary=True)
#m = Word2Vec.load_word2vec_format("../google_data/freebase-vectors-skipgram1000-en.bin.gz", binary=True)
(concepts, labels) = self.loadData(path, m)
#concepts = map(lambda x:"e_"+x, concepts)
#labels = map(lambda x:"c_"+x, labels)
print "Total number of concepts:", len(concepts)
print "Total number of categories:", len(set(labels))
#m = self.loadVector("../senna/embeddings/embeddings.txt","../senna/hash/words.lst")
#m = Word2Vec.load_word2vec_format("vectors/whole/new_c_e_train_neg10size400min_count1", binary=True)
#m = self.loadVector("vectors/whole/new_c_e_train_neg10size400min_count1.embedding", "vectors/whole/new_c_e_train_neg10size400min_count1.list")
words = set(m.index2word)
for concept in concepts:
if concept not in words:
print concept
for label in set(labels):
if label not in words:
print label
pre_labels = self.cat_predict(concepts, labels, m)
kmeans_pre_labels = self.kmeans(concepts, labels, m, method = 'agg_ward')
print "Gold Standard:",labels
print "Predicted by nearest vectors:",pre_labels
print "Predicted by K-means:",kmeans_pre_labels
purity = self.purity(labels, pre_labels)
accuracy = self.accuracy(labels, pre_labels)
print "Purity:", purity
print "Accuracy:", accuracy
print "K-means Purity:", self.purity(labels, kmeans_pre_labels)
print "K-means Accuracy:", self.accuracy(labels, kmeans_pre_labels)
def __init__(self, inter_filePath = "inter/technology_companies_of_the_united_states/"):
# [[cat,cat...]...]
self.m = Word2Vec.load_word2vec_format("vectors/technology_companies_of_the_united_states/cat_train_neg5size400min_count5", binary=True)
self.dim = 400
(correct_categories_train, context_categories_train) = self.load_category_page(inter_filePath + "category_page.txt")
(correct_categories_test, context_categories_test) = self.load_category_page(inter_filePath + "category_page_test.txt")
## ---- By mean ---
Xvectors = np.array(self.predict_vector_by_mean(context_categories_train))
Xvectors_test = np.array(self.predict_vector_by_mean(context_categories_test))
## ---- By mean --- *
## ---- By SVM ---
corpus_train = [" ".join(i) for i in context_categories_train]
corpus_test = [" ".join(i) for i in context_categories_test]
cv = CountVectorizer(min_df = 1)
X = cv.fit_transform(corpus_train)
##TFIDF
transformer = TfidfTransformer()
X_tfidf = transformer.fit_transform(X)
#Labels
mlb = MultiLabelBinarizer()
mlb.fit(correct_categories_train + correct_categories_test)
Y = mlb.transform(correct_categories_train) ###Transform to multilabel indicator
#predict test labels
X_test = cv.transform(corpus_test)
Y_test = mlb.transform(correct_categories_test)
#Y_predict_ovr = self.ovrSVM(X, Y, X_test)
Y_predict_ovr = self.ovrSVM(Xvectors, Y, Xvectors_test)
#Y_predict_ovo = self.ovoSVM(X, Y, X_test)
print "---One versus rest---"
print "Macro F-1:", f1_score(Y_test, Y_predict_ovr, average='macro')
print "Micro F-1:", f1_score(Y_test, Y_predict_ovr, average='micro')
def convert(fileName):
m = Word2Vec.load_word2vec_format(fileName, binary=True)
words = m.index2word
# print words
list_file = codecs.open(fileName + '.list', 'w', encoding='utf-8')
embedding_file = codecs.open(fileName + '.embedding', 'w', encoding = 'utf-8')
for word in words:
list_file.write(word + '\n')
em = " ".join(map(str, list(m[word])))
embedding_file.write(em + '\n')
def __init__(self, vectorfile, category_page_file, dim):
#self.m = gensim.models.Word2Vec.load_word2vec_format(vectorfile, binary=True)
self.m = Word2Vec.load_word2vec_format(vectorfile, binary=True)
self.dim = dim # number of dimension
self.correct_categories = [] # [[cat,cat,...]...]
self.predict_categories = []
self.context_categories = [] # [[cat,cat...]...]
self.predict_vectors = [] #[vector, vector ... ]
self.entities = [] #[entity, entity ...]
self.load_category_page(category_page_file)
self.load_entity_page("inter/entity_page_name.txt")
self.cal_predict_vector()
def conceptcat(self, vectorPath, path=INPUTPATH):
(concepts, labels) = self.loadData(path)
concepts = map(lambda x: "e_" + x, concepts)
labels = map(lambda x: "c_" + x, labels)
print "Total number of concepts:", len(concepts)
print "Total number of categories:", len(set(labels))
#m = self.loadVector("../senna/embeddings/embeddings.txt","../senna/hash/words.lst")
m = Word2Vec.load_word2vec_format(vectorPath, binary=True)
#m = self.loadVector("vectors/whole/new_c_e_train_neg10size400min_count1.embedding", "vectors/whole/new_c_e_train_neg10size400min_count1.list")
words = set(m.index2word)
for concept in concepts:
if concept not in words:
print concept
for label in set(labels):
if label not in words:
print label
pre_labels = self.cat_predict(concepts, labels, m)
kmeans_pre_labels = self.kmeans(concepts, labels, m, method='agg_ward')
### for paper: draw form of predicting result
pre_labels_ori = map(lambda x: x[2:], pre_labels)
labels_ori = map(lambda x: x[2:], labels)
concepts_ori = map(lambda x: x[2:], concepts)
d = defaultdict(list)
f = open('form_result', 'w')
print len(concepts_ori)
for i in xrange(len(concepts_ori)):
if pre_labels_ori[i] != labels_ori[i]:
f.write(concepts_ori[i] + " " + pre_labels_ori[i] + " " +
labels_ori[i] + "\n")
d[kmeans_pre_labels[i]].append(concepts_ori[i])
print d
f2 = open('form_result_clustering', 'w')
for key, value in d.iteritems():
f2.write(str(key) + " ")
f2.write(" ".join(value))
f2.write('\n')
f.close()
f2.close()
###
print "Gold Standard:", labels
print "Predicted by nearest vectors:", pre_labels
print "Predicted by K-means:", kmeans_pre_labels
purity = self.purity(labels, pre_labels)
accuracy = self.accuracy(labels, pre_labels)
print "Purity:", purity
print "Accuracy:", accuracy
print "K-means Purity:", self.purity(labels, kmeans_pre_labels)
print "K-means Accuracy:", self.accuracy(labels, kmeans_pre_labels)
def __init__(
self,
inter_filePath="inter/technology_companies_of_the_united_states/"):
# [[cat,cat...]...]
self.m = Word2Vec.load_word2vec_format(
"vectors/technology_companies_of_the_united_states/cat_train_neg5size400min_count5",
binary=True)
self.dim = 400
(correct_categories_train, context_categories_train
) = self.load_category_page(inter_filePath + "category_page.txt")
(correct_categories_test, context_categories_test
) = self.load_category_page(inter_filePath + "category_page_test.txt")
## ---- By mean ---
Xvectors = np.array(
self.predict_vector_by_mean(context_categories_train))
Xvectors_test = np.array(
self.predict_vector_by_mean(context_categories_test))
## ---- By mean --- *
## ---- By SVM ---
corpus_train = [" ".join(i) for i in context_categories_train]
corpus_test = [" ".join(i) for i in context_categories_test]
cv = CountVectorizer(min_df=1)
X = cv.fit_transform(corpus_train)
##TFIDF
transformer = TfidfTransformer()
X_tfidf = transformer.fit_transform(X)
#Labels
mlb = MultiLabelBinarizer()
mlb.fit(correct_categories_train + correct_categories_test)
Y = mlb.transform(
correct_categories_train) ###Transform to multilabel indicator
#predict test labels
X_test = cv.transform(corpus_test)
Y_test = mlb.transform(correct_categories_test)
#Y_predict_ovr = self.ovrSVM(X, Y, X_test)
Y_predict_ovr = self.ovrSVM(Xvectors, Y, Xvectors_test)
#Y_predict_ovo = self.ovoSVM(X, Y, X_test)
print "---One versus rest---"
print "Macro F-1:", f1_score(Y_test, Y_predict_ovr, average='macro')
print "Micro F-1:", f1_score(Y_test, Y_predict_ovr, average='micro')
def classify(self, filePath, vectorPath):
# 1. load Corpus from files
(corpus, labels) = self.loadFile(filePath)
#(corpus, labels) = self.loadFile("dataless/20NG/20ng-train-no-stop.txt")
#d = defaultdict(Counter)
#for i in xrange(len(corpus)):
# d[labels[i]].update(corpus[i].split())
#for key,value in d.iteritems():
# print key
# print value.most_common(10)
labels = self.transform_labels(labels)
candidate_labels = set(labels)
most_freq_label = max(set(labels), key=labels.count)
print "candidate labels:",candidate_labels
print "most freq label:",most_freq_label
#m = self.loadSenna("../senna/embeddings/embeddings.txt","../senna/hash/words.lst") #dict{str: np.array()}
m = Word2Vec.load_word2vec_format(vectorPath, binary=True)
# 2. Encode Feature Matrix
cv = CountVectorizer(min_df=1)
X = cv.fit_transform(corpus) # Frequency
#print "Frequency:",X
#print cv.get_feature_names()
#transformer = TfidfTransformer()
#X = transformer.fit_transform(X) # TF-IDF weighted entities
#print "Tf-idf:",X
# 3. calculate final vectors to predict labels
# print X[0]for x in X[0]:
pre_vectors = self.pre_vectors(X, cv ,m)
#print pre_vectors
# 4. find predict labels from candidate labels closest to pre_vectors
pre_labels = self.pre_labels(pre_vectors, candidate_labels,most_freq_label, m)
# print pre_labels
# 5. calculate micro-f1 score
micro_f1 = f1_score(labels, pre_labels, average='micro')
macro_f1 = f1_score(labels, pre_labels, average='macro')
print "Micro-F1:",micro_f1
print "Macro-F1:",macro_f1
print "candidate labels", list(candidate_labels)
print "Micro-F1 for each label:", precision_recall_fscore_support(labels, pre_labels, labels=list(candidate_labels), average=None)
from pyemd import emd
import os
import numpy as np
import codecs
from sklearn.metrics import euclidean_distances
from sklearn.feature_extraction.text import CountVectorizer
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
W2VFilePath = "../we/w2v/GoogleNews-vectors-negative300.bin.gz"
DATAFilePath = "../we_data/embed.dat"
VOCABFilePath = "../we_data/embed.vocab"
if not os.path.exists(DATAFilePath):
print("Caching word embeddings in memmapped format...")
from word2vec_music import Word2Vec
wv = Word2Vec.load_word2vec_format(W2VFilePath, binary=True)
fp = np.memmap(DATAFilePath, dtype=np.double, mode='w+', shape=wv.syn0norm.shape)
fp[:] = wv.syn0norm[:]
with codecs.open(VOCABFilePath, "w", encoding='utf-8') as f:
for _, w in sorted((voc.index, word) for word, voc in wv.vocab.items()):
f.write(w + '\n')
del fp, wv
logging.warning("Loading Word Embedding")
W = np.memmap(DATAFilePath, dtype=np.double, mode="r", shape=(3000000, 300))
vocab_list = []
with codecs.open(VOCABFilePath, "r", encoding = 'utf-8') as f:
for w in f:
vocab_list.append(w.strip())
#vocab_list = map(str.strip, f.readlines())
vocab_dict = {w: k for k, w in enumerate(vocab_list)}
logging.warning("Word Embedding Loaded")
from pyemd import emd
import os
import numpy as np
import codecs
from sklearn.metrics import euclidean_distances
from sklearn.feature_extraction.text import CountVectorizer
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
W2VFilePath = "../we/w2v/GoogleNews-vectors-negative300.bin.gz"
DATAFilePath = "../we_data/embed.dat"
VOCABFilePath = "../we_data/embed.vocab"
if not os.path.exists(DATAFilePath):
print("Caching word embeddings in memmapped format...")
from word2vec_music import Word2Vec
wv = Word2Vec.load_word2vec_format(W2VFilePath, binary=True)
fp = np.memmap(DATAFilePath,
dtype=np.double,
mode='w+',
shape=wv.syn0norm.shape)
fp[:] = wv.syn0norm[:]
with codecs.open(VOCABFilePath, "w", encoding='utf-8') as f:
for _, w in sorted(
(voc.index, word) for word, voc in wv.vocab.items()):
f.write(w + '\n')
del fp, wv
logging.warning("Loading Word Embedding")
W = np.memmap(DATAFilePath, dtype=np.double, mode="r", shape=(3000000, 300))
vocab_list = []
with codecs.open(VOCABFilePath, "r", encoding='utf-8') as f:
for w in f:
from word2vec_music import Word2Vec
from pre_process import Pre_process
from time import time
import numpy as np
import random
import logging
import sys
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
f = open('dota1000','r')
categories = []
for l in f:
categories.append('c_'+l.split(' ')[0].strip())
print categories
print len(categories)
m = Word2Vec.load_word2vec_format("vectors/whole/new_c_e_train_neg10size400min_count1", binary=True)
for category in categories:
if category not in m:
print category, "not in vectors"
for category in categories:
x = map(lambda x: x[0],m.most_similar(positive = [category], topn = 500))
x = [entry for entry in x if entry[0]=='e' and "_" not in entry[2:] and "(" not in entry] ##get nearest entities
print "-------category:--------"
print category
print "-------entities:--------"
print x
from matplotlib import offsetbox
from sklearn import manifold, datasets, decomposition, ensemble, discriminant_analysis, random_projection
logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO)
if len(sys.argv) != 3:
print "Usage : python tsne.py vectorfile savePath"
print "e.g. : python tsne.py vectors/technology_companies_of_the_united_states/cat_train_neg5size400min_count5 tsne.png"
print "e.g. : python tsne.py vectors/technology_companies_based_in_california/cat_train_neg5size400min_count5 tsne.png"
sys.exit(1)
reload(sys)
sys.setdefaultencoding("utf-8")
vectorFile = sys.argv[1]
# digits = datasets.load_digits(n_class=6)
## Add my own data
m = Word2Vec.load_word2vec_format(vectorFile, binary=True)
words = m.index2word
### Random sample 100 categories
categories = [word for word in words if word[0] == "c"]
# print categories
samples = random.sample(categories, 3)
y = []
y.extend(samples)
for sample in samples:
x = map(lambda x: x[0], m.most_similar(positive=[sample], topn=10))
y.extend(x)
### Get 10 nearest neighbors for 100 categories
X = m[y]
n_samples, n_features = X.shape
# n_neighbors = 30
from sklearn import (manifold, datasets, decomposition, ensemble,
discriminant_analysis, random_projection)
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
if len(sys.argv) != 3:
print "Usage : python tsne.py vectorfile savePath"
print "e.g. : python tsne.py vectors/technology_companies_of_the_united_states/cat_train_neg5size400min_count5 tsne.png"
print "e.g. : python tsne.py vectors/technology_companies_based_in_california/cat_train_neg5size400min_count5 tsne.png"
sys.exit(1)
reload(sys)
sys.setdefaultencoding('utf-8')
vectorFile = sys.argv[1]
#digits = datasets.load_digits(n_class=6)
## Add my own data
m = Word2Vec.load_word2vec_format(vectorFile, binary=True)
words = m.index2word
### Random sample 100 categories
categories = [word for word in words if word[0] == "c"]
#print categories
samples = random.sample(categories, 3)
y = []
y.extend(samples)
for sample in samples:
x = map(lambda x: x[0], m.most_similar(positive=[sample], topn=10))
y.extend(x)
### Get 10 nearest neighbors for 100 categories
X = m[y]
n_samples, n_features = X.shape
from pre_process import Pre_process
from time import time
import numpy as np
import random
import logging
import sys
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
f = open('dota1000', 'r')
categories = []
for l in f:
categories.append('c_' + l.split(' ')[0].strip())
print categories
print len(categories)
m = Word2Vec.load_word2vec_format(
"vectors/whole/new_c_e_train_neg10size400min_count1", binary=True)
for category in categories:
if category not in m:
print category, "not in vectors"
for category in categories:
x = map(lambda x: x[0], m.most_similar(positive=[category], topn=500))
x = [
entry for entry in x
if entry[0] == 'e' and "_" not in entry[2:] and "(" not in entry
] ##get nearest entities
print "-------category:--------"
print category
print "-------entities:--------"
print x
