本文整理汇总了Python中KaggleWord2VecUtility.KaggleWord2VecUtility.sku_to_sentences方法的典型用法代码示例。如果您正苦于以下问题：Python KaggleWord2VecUtility.sku_to_sentences方法的具体用法？Python KaggleWord2VecUtility.sku_to_sentences怎么用？Python KaggleWord2VecUtility.sku_to_sentences使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类KaggleWord2VecUtility.KaggleWord2VecUtility的用法示例。
在下文中一共展示了KaggleWord2VecUtility.sku_to_sentences方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1:
# 需要导入模块: from KaggleWord2VecUtility import KaggleWord2VecUtility [as 别名]
# 或者: from KaggleWord2VecUtility.KaggleWord2VecUtility import sku_to_sentences [as 别名]
print "Read %d labeled train skucollection " % (train["product_title"].size)
# Load the punkt tokenizer
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
# ****** Split the labeled and unlabeled training sets into clean sentences
#
sentences = [] # Initialize an empty list of sentences
print "Parsing sentences from training set"
for sku in train["product_title"]:
sentences += KaggleWord2VecUtility.sku_to_sentences(sku, tokenizer)
# ****** Set parameters and train the word2vec model
#
# Import the built-in logging module and configure it so that Word2Vec
# creates nice output messages
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\
level=logging.INFO)
# Set values for various parameters
num_features = 300 # Word vector dimensionality
min_word_count = 40 # Minimum word count
num_workers = 4 # Number of threads to run in parallel
context = 10 # Context window size
downsampling = 1e-3 # Downsample setting for frequent words