add pythainlp.word_vector.thai2vec

Author: wannaphong

pythainlp/tools/__init__.py

@@ -3,6 +3,7 @@
 import os
 import dill
 from pythainlp.tokenize import tcc
+import marisa_trie
 def file_trie(data):
 	'''
 	ใช้สร้างไฟล์ข้อมูลสำหรับระบบที่ใช้ trie
@@ -18,7 +19,6 @@ def file_trie(data):
 		path = os.path.join(path, 'pythainlp_trie2.data')
 	if not os.path.exists(path):
 		#ถ้าไม่มีไฟล์
-		import marisa_trie
 		if data=="newmm":
 			from pythainlp.corpus.thaiword import get_data # ข้อมูลเก่า
 			data2=get_data()

pythainlp/word_vector/__init__.py

@@ -1,65 +1,2 @@
 # -*- coding: utf-8 -*-
-'''
-Code by https://github.com/cstorm125/thai2vec/blob/master/notebooks/examples.ipynb
-'''
-from __future__ import absolute_import,unicode_literals
-import six
-import sys
-if six.PY2:
-	print("Thai sentiment in pythainlp. Not support python 2.7")
-	sys.exit(0)
-try:
-	from gensim.models import KeyedVectors
-	import numpy as np
-except ImportError:
-	import pip
-	pip.main(['install','gensim','numpy'])
-	try:
-		from gensim.models import KeyedVectors
-		import numpy as np
-	except ImportError:
-		print("Error ! using 'pip install gensim numpy'")
-		sys.exit(0)
-from pythainlp.tokenize import word_tokenize
-import os
-
-def download():
-	path = os.path.join(os.path.expanduser("~"), 'pythainlp-data')
-	if not os.path.exists(path):
-		os.makedirs(path)
-	path = os.path.join(path, 'thai2vec.vec')
-	if not os.path.exists(path):
-		print("Download models...")
-		from urllib import request
-		request.urlretrieve("https://github.com/cstorm125/thai2vec/raw/master/data/thaiwiki/models/thai2vec.vec",path)
-		print("OK.")
-	return path
-def get_model():
-	return KeyedVectors.load_word2vec_format(download(),binary=False)
-def most_similar_cosmul(positive,negative):
-	'''
-	การใช้งาน
-	input list
-	'''
-	return get_model().most_similar_cosmul(positive=positive, negative=negative)
-def doesnt_match(listdata):
-	return get_model().doesnt_match(listdata)
-def similarity(word1,word2):
-	return get_model().similarity(word1,word2)
-def sentence_vectorizer(ss,dim=300,use_mean=False):
-    s = word_tokenize(ss)
-    vec = np.zeros((1,dim))
-    for word in s:
-        if word in get_model().wv.index2word:
-            vec+= get_model().wv.word_vec(word)
-        else: pass
-    if use_mean: vec /= len(s)
-    return(vec)
-def about():
-	return '''
-	thai2vec
-	Language Modeling, Word2Vec and Text Classification in Thai Language. Created as part of pyThaiNLP.
-	
-	Development : Charin Polpanumas
-	GitHub : https://github.com/cstorm125/thai2vec
-	'''
+from __future__ import absolute_import,unicode_literals

pythainlp/word_vector/thai2vec.py

@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+'''
+Code by https://github.com/cstorm125/thai2vec/blob/master/notebooks/examples.ipynb
+'''
+from __future__ import absolute_import,unicode_literals
+import six
+import sys
+if six.PY2:
+	print("Thai sentiment in pythainlp. Not support python 2.7")
+	sys.exit(0)
+try:
+	from gensim.models import KeyedVectors
+	import numpy as np
+except ImportError:
+	import pip
+	pip.main(['install','gensim','numpy'])
+	try:
+		from gensim.models import KeyedVectors
+		import numpy as np
+	except ImportError:
+		print("Error ! using 'pip install gensim numpy'")
+		sys.exit(0)
+from pythainlp.tokenize import word_tokenize
+import os
+
+def download():
+	path = os.path.join(os.path.expanduser("~"), 'pythainlp-data')
+	if not os.path.exists(path):
+		os.makedirs(path)
+	path = os.path.join(path, 'thai2vec.vec')
+	if not os.path.exists(path):
+		print("Download models...")
+		from urllib import request
+		request.urlretrieve("https://github.com/cstorm125/thai2vec/raw/master/data/thaiwiki/models/thai2vec.vec",path)
+		print("OK.")
+	return path
+def get_model():
+	return KeyedVectors.load_word2vec_format(download(),binary=False)
+def most_similar_cosmul(positive,negative):
+	'''
+	การใช้งาน
+	input list
+	'''
+	return get_model().most_similar_cosmul(positive=positive, negative=negative)
+def doesnt_match(listdata):
+	return get_model().doesnt_match(listdata)
+def similarity(word1,word2):
+	return get_model().similarity(word1,word2)
+def sentence_vectorizer(ss,dim=300,use_mean=False):
+    s = word_tokenize(ss)
+    vec = np.zeros((1,dim))
+    for word in s:
+        if word in get_model().wv.index2word:
+            vec+= get_model().wv.word_vec(word)
+        else: pass
+    if use_mean: vec /= len(s)
+    return(vec)
+def about():
+	return '''
+	thai2vec
+	Language Modeling, Word2Vec and Text Classification in Thai Language. Created as part of pyThaiNLP.
+	
+	Development : Charin Polpanumas
+	GitHub : https://github.com/cstorm125/thai2vec
+	'''