spaCy 和 scikit-learn 向量化器

Question

我根据他们的example 使用 spaCy 为 scikit-learn 编写了一个引理分词器，它可以独立运行：

import spacy
from sklearn.feature_extraction.text import TfidfVectorizer

class LemmaTokenizer(object):
    def __init__(self):
        self.spacynlp = spacy.load('en')
    def __call__(self, doc):
        nlpdoc = self.spacynlp(doc)
        nlpdoc = [token.lemma_ for token in nlpdoc if (len(token.lemma_) > 1) or (token.lemma_.isalnum()) ]
        return nlpdoc

vect = TfidfVectorizer(tokenizer=LemmaTokenizer())
vect.fit(['Apples and oranges are tasty.'])
print(vect.vocabulary_)
### prints {'apple': 1, 'and': 0, 'tasty': 4, 'be': 2, 'orange': 3}

但是，在GridSearchCV 中使用它会出错，下面是一个自包含的示例：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV

wordvect = TfidfVectorizer(analyzer='word', strip_accents='ascii', tokenizer=LemmaTokenizer())
classifier = OneVsRestClassifier(SVC(kernel='linear'))
pipeline = Pipeline([('vect', wordvect), ('classifier', classifier)])
parameters = {'vect__min_df': [1, 2], 'vect__max_df': [0.7, 0.8], 'classifier__estimator__C': [0.1, 1, 10]}
gs_clf = GridSearchCV(pipeline, parameters, n_jobs=7, verbose=1)

from sklearn.datasets import fetch_20newsgroups
categories = ['comp.graphics', 'rec.sport.baseball']
newsgroups = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'), shuffle=True, categories=categories)
X = newsgroups.data
y = newsgroups.target
gs_clf = gs_clf.fit(X, y)

### AttributeError: 'spacy.tokenizer.Tokenizer' object has no attribute '_prefix_re'

当我在分词器的构造函数之外加载 spacy 时没有出现错误，然后 GridSearchCV 运行：

spacynlp = spacy.load('en')
    class LemmaTokenizer(object):
        def __call__(self, doc):
            nlpdoc = spacynlp(doc)
            nlpdoc = [token.lemma_ for token in nlpdoc if (len(token.lemma_) > 1) or (token.lemma_.isalnum()) ]
            return nlpdoc

但这意味着GridSearchCV 中的每个n_jobs 都将访问和调用同一个 spacynlp 对象，它在这些作业之间共享，这就留下了问题：

1. 来自spacy.load('en') 的 spacynlp 对象是否可以安全地用于 GridSearchCV 中的多个作业？
2. 这是在 scikit-learn 的标记器中实现对 spacy 的调用的正确方法吗？

Answer 1

为网格中的每个参数设置运行 Spacy 是在浪费时间。内存开销也很大。您应该通过 Spacy 运行一次所有数据并将其保存到磁盘，然后使用简化的矢量化器来读取预先确定的数据。查看TfidfVectorizer 的tokenizer、analyser 和preprocessor 参数。有很多关于堆栈溢出的示例展示了如何构建自定义矢量化器。

Answer 2

基于 mbatchkarov 的帖子的 cmets，我尝试通过 Spacy 运行我在 pandas 系列中的所有文档进行标记化和词形还原，并首先将其保存到磁盘。 然后，我加载了词形化的spacy Doc 对象，为每个文档提取一个标记列表，并将其作为输入提供给由简化的TfidfVectorizer 和DecisionTreeClassifier 组成的管道。 我用GridSearchCV 运行pipeline 并提取最佳估计器和相应的参数。

看一个例子：

from sklearn import tree
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
import spacy
from spacy.tokens import DocBin
nlp = spacy.load("de_core_news_sm") # define your language model

# adjust attributes to your liking:
doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)

for doc in nlp.pipe(df['articleDocument'].str.lower()):
    doc_bin.add(doc)

# either save DocBin to a bytes object, or...
#bytes_data = doc_bin.to_bytes()

# save DocBin to a file on disc
file_name_spacy = 'output/preprocessed_documents.spacy'
doc_bin.to_disk(file_name_spacy)

#Load DocBin at later time or on different system from disc or bytes object
#doc_bin = DocBin().from_bytes(bytes_data)
doc_bin = DocBin().from_disk(file_name_spacy)

docs = list(doc_bin.get_docs(nlp.vocab))
print(len(docs))

tokenized_lemmatized_texts = [[token.lemma_ for token in doc 
                               if not token.is_stop and not token.is_punct and not token.is_space and not token.like_url and not token.like_email] 
                               for doc in docs]

# classifier to use
clf = tree.DecisionTreeClassifier()

# just some random target response
y = np.random.randint(2, size=len(docs))


vectorizer = TfidfVectorizer(ngram_range=(1, 1), lowercase=False, tokenizer=lambda x: x, max_features=3000)

pipeline = Pipeline([('vect', vectorizer), ('dectree', clf)])
parameters = {'dectree__max_depth':[4, 10]}
gs_clf = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, cv=5)
gs_clf.fit(tokenized_lemmatized_texts, y)
print(gs_clf.best_estimator_.get_params()['dectree'])

一些更有用的资源：

• model and language selection spacy
• DocBin class spacy
• Multiprocessing nlp pipe spacy
• Serializing Doc objects efficiently spacy
• Token class spacy
• GridSearchCV scikit-learn
• Pipeline scikit-learn