您不需要 scikit-learn 的开发人员版本 - 只需安装 scikit-learn通常的方式通过pip or conda.
要访问由 word2vec 创建的词向量,只需使用词词典作为模型的索引:
X = model[model.wv.vocab]
以下是一个简单但完整的代码示例,它加载一些新闻组数据,应用非常基本的数据准备(清理和分解句子),训练 word2vec 模型,使用 t-SNE 减少维度,并可视化输出。
from gensim.models.word2vec import Word2Vec
from sklearn.manifold import TSNE
from sklearn.datasets import fetch_20newsgroups
import re
import matplotlib.pyplot as plt
# download example data ( may take a while)
train = fetch_20newsgroups()
def clean(text):
"""Remove posting header, split by sentences and words, keep only letters"""
lines = re.split('[?!.:]\s', re.sub('^.*Lines: \d+', '', re.sub('\n', ' ', text)))
return [re.sub('[^a-zA-Z]', ' ', line).lower().split() for line in lines]
sentences = [line for text in train.data for line in clean(text)]
model = Word2Vec(sentences, workers=4, size=100, min_count=50, window=10, sample=1e-3)
print (model.wv.most_similar('memory'))
X = model.wv[model.wv.vocab]
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X)
plt.scatter(X_tsne[:, 0], X_tsne[:, 1])
plt.show()