带有计数和 tfidf 矢量化器的管道产生 TypeError: expected string or bytes-like object

【问题标题】：Pipeline with count and tfidf vectorizer produces TypeError: expected string or bytes-like object带有计数和 tfidf 矢量化器的管道产生 TypeError: expected string or bytes-like object
【发布时间】：2021-03-24 04:27:23
【问题描述】：

我有一个如下的语料库 'CCC 0 0 0 X 0 1 0 0 0 0'、'CCC 0 0 0 X 0 1 0 0 0 0'、'CCC 0 0 0 X 0 1 0 0 0 0'、'XX X'、'XX X '，'XX'， 我想使用 count 和 tfidf 矢量化器以及逻辑回归作为分类器。 下面的代码我改编自sklearn的示例。

from pprint import pprint
from time import time
import logging
import pickle

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

print(__doc__)

# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s %(levelname)s %(message)s')


# #############################################################################
# Define a pipeline combining a text feature extractor with a simple
# classifier
pipeline = Pipeline([
    ('vect', CountVectorizer(analyzer='char',lowercase=False)),
    ('tfidf', TfidfVectorizer(analyzer='char',lowercase=False)),
    ('clf', LogisticRegression()),
])

# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
parameters = {
    'vect__max_df': (0.5, 0.75, 1.0),
    # 'vect__max_features': (None, 5000, 10000, 50000),
    'vect__ngram_range': ((1, 1), (1, 2)),  # unigrams or bigrams
    # 'tfidf__use_idf': (True, False),
    # 'tfidf__norm': ('l1', 'l2'),
    'clf__max_iter': (1000,),
    'clf__C': (0.00001, 0.000001),
    'clf__penalty': ('l2', 'elasticnet'),
    # 'clf__max_iter': (10, 50, 80),
}

if __name__ == "__main__":
    # multiprocessing requires the fork to happen in a __main__ protected
    # block

    # find the best parameters for both the feature extraction and the
    # classifier
    grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
    corpus =['C C C 0 0 0 X 0 1 0 0 0 0', 'C C C 0 0 0 X 0 1 0 0 0 0', 'C C C 0 0 0 X 0 1 0 0 0 0', 'X X X', 'X X X',
             'X X X', 'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X',             
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 X', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0',
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0',
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 0', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X',
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X', 
             'C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C X X X X 0 0 0 X 0 X X']    
    y_train = [0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

    
    print(len(corpus),len(y_train))
    print("Performing grid search...")
    print("pipeline:", [name for name, _ in pipeline.steps])
    print("parameters:")
    pprint(parameters)
    t0 = time()
    #print(type(data.data),type(data.target))
    #print(data.data[:1])
    #print(data.data[:2])

    grid_search.fit(corpus,y_train)
    print("done in %0.3fs" % (time() - t0))
    print()

    print("Best score: %0.3f" % grid_search.best_score_)
    print("Best parameters set:")
    best_parameters = grid_search.best_estimator_.get_params()
    for param_name in sorted(parameters.keys()):
        print("\t%s: %r" % (param_name, best_parameters[param_name]))

我的堆栈跟踪如下

Automatically created module for IPython interactive environment
50 50
Performing grid search...
pipeline: ['vect', 'tfidf', 'clf']
parameters:
{'clf__C': (1e-05, 1e-06),
 'clf__max_iter': (1000,),
 'clf__penalty': ('l2', 'elasticnet'),
 'vect__max_df': (0.5, 0.75, 1.0),
 'vect__ngram_range': ((1, 1), (1, 2))}
Fitting 5 folds for each of 24 candidates, totalling 120 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:    0.1s finished
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-114-0d47590b1279> in <module>
    107     #print(data.data[:2])
    108 
--> 109     grid_search.fit(corpus,y_train)
    110     print("done in %0.3fs" % (time() - t0))
    111     print()

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    737             refit_start_time = time.time()
    738             if y is not None:
--> 739                 self.best_estimator_.fit(X, y, **fit_params)
    740             else:
    741                 self.best_estimator_.fit(X, **fit_params)

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
    348             This estimator
    349         """
--> 350         Xt, fit_params = self._fit(X, y, **fit_params)
    351         with _print_elapsed_time('Pipeline',
    352                                  self._log_message(len(self.steps) - 1)):

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
    313                 message_clsname='Pipeline',
    314                 message=self._log_message(step_idx),
--> 315                 **fit_params_steps[name])
    316             # Replace the transformer of the step with the fitted
    317             # transformer. This is necessary when loading the transformer

E:\anaconda\envs\appliedaicourse\lib\site-packages\joblib\memory.py in __call__(self, *args, **kwargs)
    350 
    351     def __call__(self, *args, **kwargs):
--> 352         return self.func(*args, **kwargs)
    353 
    354     def call_and_shelve(self, *args, **kwargs):

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    726     with _print_elapsed_time(message_clsname, message):
    727         if hasattr(transformer, 'fit_transform'):
--> 728             res = transformer.fit_transform(X, y, **fit_params)
    729         else:
    730             res = transformer.fit(X, y, **fit_params).transform(X)

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y)
   1857         """
   1858         self._check_params()
-> 1859         X = super().fit_transform(raw_documents)
   1860         self._tfidf.fit(X)
   1861         # X is already a transformed view of raw_documents so

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y)
   1218 
   1219         vocabulary, X = self._count_vocab(raw_documents,
-> 1220                                           self.fixed_vocabulary_)
   1221 
   1222         if self.binary:

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\feature_extraction\text.py in _count_vocab(self, raw_documents, fixed_vocab)
   1129         for doc in raw_documents:
   1130             feature_counter = {}
-> 1131             for feature in analyze(doc):
   1132                 try:
   1133                     feature_idx = vocabulary[feature]

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\feature_extraction\text.py in _analyze(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)
    108                 doc = ngrams(doc, stop_words)
    109             else:
--> 110                 doc = ngrams(doc)
    111     return doc
    112 

E:\anaconda\envs\appliedaicourse\lib\site-packages\sklearn\feature_extraction\text.py in _char_ngrams(self, text_document)
    255         """Tokenize text_document into a sequence of character n-grams"""
    256         # normalize white spaces
--> 257         text_document = self._white_spaces.sub(" ", text_document)
    258 
    259         text_len = len(text_document)

TypeError: expected string or bytes-like object

我单独运行了 tfidf 矢量化器，得到以下结果

vectorizer = TfidfVectorizer(analyzer='char',lowercase=False,ngram_range=(6, 6))
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())

print(X.shape)
print(X)

结果

<class 'list'>
[' 0 0 0', ' 0 0 X', ' 0 1 0', ' 0 X 0', ' 0 X X', ' 1 0 0', ' C 0 0', ' C C 0', ' C C C', ' C C X', ' C X X', ' X 0 0', ' X 0 1', ' X 0 X', ' X X 0', ' X X X', '0 0 0 ', '0 0 X ', '0 1 0 ', '0 X 0 ', '1 0 0 ', 'C 0 0 ', 'C C 0 ', 'C C C ', 'C C X ', 'C X X ', 'X 0 0 ', 'X 0 1 ', 'X 0 X ', 'X X 0 ', 'X X X ']
(50, 31)
  (0, 20)   0.31810783213188626
  (0, 5)    0.31810783213188626
  (0, 18)   0.31810783213188626
  (0, 2)    0.31810783213188626
  (0, 27)   0.31810783213188626
  (0, 12)   0.31810783213188626
  (0, 19)   0.16116825632411622
  (0, 3)    0.16116825632411622
  (0, 17)   0.16116825632411622
  (0, 1)    0.11378963445554637
  (0, 16)   0.22757926891109273
  (0, 0)    0.3413689033666391
  (0, 21)   0.17370780684495662
  (0, 6)    0.17370780684495662
  (0, 22)   0.17370780684495662
  (0, 7)    0.17370780684495662
  (0, 23)   0.11378963445554637
  (1, 20)   0.31810783213188626
  (1, 5)    0.31810783213188626
  (1, 18)   0.31810783213188626
...
...
...
  (49, 1)   0.01436413072356797
  (49, 16)  0.01436413072356797
  (49, 0)   0.01436413072356797
  (49, 23)  0.6894782747312626

我的问题

为什么独立矢量化器可以工作，但是当放置在 Gridsearch 使用的管道中时，我得到类型错误

【问题讨论】：

    标签： python scikit-learn pipeline tf-idf gridsearchcv


    【解决方案1】：

    默认情况下，CountVectorizer 和 TfidfVectorizer 都需要一个可以是字符串或字节类型的项目序列。在您的管道中，CountVectorizer 接收语料库并使用 scipy.sparse.csr_matrix 向 TfidfVectorizer 输出计数的稀疏表示。由于 TfidfVectorizer 的输入不是预期的类型，因此您会收到类型错误“TypeError：预期的字符串或类似字节的对象”。如果您使用其中一个但不是两个矢量化器，您的管道就可以工作。例如，

    pipeline = Pipeline([
    #('vect', CountVectorizer(analyzer='char',lowercase=False)),
    ('tfidf', TfidfVectorizer(analyzer='char',lowercase=False)),
    ('clf', LogisticRegression())
])

# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
parameters = {
    #'vect__max_df': (0.5, 0.75, 1.0),
    # 'vect__max_features': (None, 5000, 10000, 50000),
    #'vect__ngram_range': [(1, 1), (1, 2)],  # unigrams or bigrams
    'tfidf__use_idf': [True, False],
    'tfidf__norm': ['l1', 'l2'],
    'clf__max_iter': [1000],
    'clf__C': [0.00001, 0.000001],
    'clf__penalty': ['l2'],
    # 'clf__max_iter': (10, 50, 80),
}

    产生以下输出：

    50 50
Performing grid search...
pipeline: ['tfidf', 'clf']
parameters:
{'clf__C': [1e-05, 1e-06],
 'clf__max_iter': [1000],
 'clf__penalty': ['l2'],
 'tfidf__norm': ['l1', 'l2'],
 'tfidf__use_idf': [True, False]}
Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
done in 0.347s

Best score: 0.680
Best parameters set:
    clf__C: 1e-05
    clf__max_iter: 1000
    clf__penalty: 'l2'
    tfidf__norm: 'l1'
    tfidf__use_idf: True
[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:    0.2s finished

    【讨论】：

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