GridSearchCV.fit() 返回 TypeError: 预期序列或类似数组，得到估计器

Question

我正在尝试按照《在Python中构建机器学习系统》一书的第6章对Twitter数据进行情感分析。

我正在使用数据集：https://raw.githubusercontent.com/zfz/twitter_corpus/master/full-corpus.csv

它使用 tfidf 矢量化器和朴素贝叶斯分类器的管道作为估计器。

然后我使用 GridSearchCV(( 来查找估计器的最佳参数。

代码如下：

from load_data import load_data
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
def pipeline_tfidf_nb():
    tfidf_vect = TfidfVectorizer( analyzer = "word")
    naive_bayes_clf = MultinomialNB()
    return Pipeline([('vect', tfidf_vect),('nbclf',naive_bayes_clf)])
input_file = "full-corpus.csv"
X,y = load_data(input_file)
print X.shape,y.shape
clf = pipeline_tfidf_nb()
cv = ShuffleSplit(n = len(X), test_size = .3, n_iter = 1, random_state = 0)
clf_param_grid = dict(vect__ngram_range = [(1,1),(1,2),(1,3)],
                   vect__min_df = [1,2],
                    vect__smooth_idf = [False, True],
                    vect__use_idf = [False, True],
                    vect__sublinear_tf = [False, True],
                    vect__binary = [False, True],
                    nbclf__alpha = [0, 0.01, 0.05, 0.1, 0.5, 1],
                  )
grid_search = GridSearchCV(estimator = clf, param_grid = clf_param_grid, cv = cv, scoring = f1_score)
grid_search.fit(X, y)
print grid_search.best_estimator_

load_data(( 从具有正面或负面情绪的 csv 文件中提取值。

X 是一个字符串数组(TweetText(，y 是一个布尔值数组(True 表示积极情绪(。

错误是：

runfile('C:/Users/saurabh.s1/Downloads/Python_ml/ch6/main.py', wdir='C:/Users/saurabh.s1/Downloads/Python_ml/ch6')
Reloaded modules: load_data
negative : 572
positive : 519
(1091,) (1091,)
Traceback (most recent call last):
  File "<ipython-input-25-823b07c4ff26>", line 1, in <module>
    runfile('C:/Users/saurabh.s1/Downloads/Python_ml/ch6/main.py', wdir='C:/Users/saurabh.s1/Downloads/Python_ml/ch6')
  File "C:anaconda2libsite-packagesspyderutilssitesitecustomize.py", line 866, in runfile
    execfile(filename, namespace)
  File "C:anaconda2libsite-packagesspyderutilssitesitecustomize.py", line 87, in execfile
    exec(compile(scripttext, filename, 'exec'), glob, loc)
  File "C:/Users/saurabh.s1/Downloads/Python_ml/ch6/main.py", line 31, in <module>
    grid_search.fit(X, y)
  File "C:anaconda2libsite-packagessklearngrid_search.py", line 804, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:anaconda2libsite-packagessklearngrid_search.py", line 553, in _fit
    for parameters in parameter_iterable
  File "C:anaconda2libsite-packagessklearnexternalsjoblibparallel.py", line 800, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:anaconda2libsite-packagessklearnexternalsjoblibparallel.py", line 658, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:anaconda2libsite-packagessklearnexternalsjoblibparallel.py", line 566, in _dispatch
    job = ImmediateComputeBatch(batch)
  File "C:anaconda2libsite-packagessklearnexternalsjoblibparallel.py", line 180, in __init__
    self.results = batch()
  File "C:anaconda2libsite-packagessklearnexternalsjoblibparallel.py", line 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:anaconda2libsite-packagessklearncross_validation.py", line 1550, in _fit_and_score
    test_score = _score(estimator, X_test, y_test, scorer)
  File "C:anaconda2libsite-packagessklearncross_validation.py", line 1606, in _score
    score = scorer(estimator, X_test, y_test)
  File "C:anaconda2libsite-packagessklearnmetricsclassification.py", line 639, in f1_score
    sample_weight=sample_weight)
  File "C:anaconda2libsite-packagessklearnmetricsclassification.py", line 756, in fbeta_score
    sample_weight=sample_weight)
  File "C:anaconda2libsite-packagessklearnmetricsclassification.py", line 956, in precision_recall_fscore_support
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "C:anaconda2libsite-packagessklearnmetricsclassification.py", line 72, in _check_targets
    check_consistent_length(y_true, y_pred)
  File "C:anaconda2libsite-packagessklearnutilsvalidation.py", line 173, in check_consistent_length
    uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
  File "C:anaconda2libsite-packagessklearnutilsvalidation.py", line 112, in _num_samples
    'estimator %s' % x)
TypeError: Expected sequence or array-like, got estimator Pipeline(steps=[('vect', TfidfVectorizer(analyzer='word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 1), norm=u'l2', preprocessor=None,
        smooth_i...e_idf=False, vocabulary=None)), ('nbclf', MultinomialNB(alpha=0, class_prior=None, fit_prior=True))])

我尝试过重塑 X，y，但这不起作用。

如果您需要更多数据或我错过了什么，请告诉我。

谢谢！

Answer 1

此错误

是因为您使用scoring=f1_score传递了错误的参数到 GridSearchCV 构造函数中。查看 GridSearchCV 的文档。

在评分参数中，它要求：

一个字符串(参见模型评估文档(或带有签名记分器的记分器可调用对象/函数(估计器，X，y(。如果为"无"，则使用估计器的评分方法。

您正在传递一个带有签名(y_true, y_pred[, ...])的可调用函数，这是错误的。这就是您收到错误的原因。应使用此处定义的字符串来传入评分，或传递带有签名(estimator, X, y)的可调用对象。这可以通过使用make_scorer来完成。

更改代码中的此行：

grid_search = GridSearchCV(estimator = clf, param_grid = clf_param_grid, 
                           cv = cv, scoring = f1_score)

对此：

grid_search = GridSearchCV(estimator = clf, param_grid = clf_param_grid,
                           cv = cv, scoring = 'f1')
              OR
grid_search = GridSearchCV(estimator = clf, param_grid = clf_param_grid,
                           cv = cv, scoring = make_scorer(f1_score))

我在这里回答了相同类型的问题