我正在创建一个机器学习算法，用于情绪分析，但一直遇到这个错误
TypeError：'int'和'str'的实例之间不支持'<'
我已经看到了其他问题，但只有一种解决方法，比如“str'和'int'实例之间不支持”TypeError：'<'
train_data = "C:/Users/User/Abhinav/TrumpStuff/trumpwords.csv"
Xwords = pd.read_csv(train_data, usecols=[2], header=None)
ywords_pos = pd.read_csv(train_data, usecols=[3], header=None)
ywords_neg = pd.read_csv(train_data, usecols=[4], header=None)
ywords_bad = pd.read_csv(train_data, usecols=[5], header=None)
count_vect = CountVectorizer()
Xtrain_counts = count_vect.fit_transform(getStringArray(Xwords))
tfidf_transformer = TfidfTransformer()
Xtrain_tfidf = tfidf_transformer.fit_transform(Xtrain_counts)
clf_positive = MultinomialNB().fit(Xtrain_tfidf, ywords_pos)
clf_negative = MultinomialNB().fit(Xtrain_tfidf, ywords_neg)
clf_bad = MultinomialNB().fit(Xtrain_tfidf, ywords_bad)
"""
My data is from https://data.world/lovesdata/trump-tweets-5-4-09-12-5-16/workspace/file?filename=trumpwords.xlsx
"""
我希望代码运行并给我一个情绪，但目前，我无法通过这个错误。这是错误：
D:\WPy-3702\python-3.7.0\lib\site-packages\sklearn\utils\validation.py:578:
DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-5775276c3452> in <module>()
----> 1 clf_positive = MultinomialNB().fit(Xtrain_tfidf, ywords_pos)
2 clf_negative = MultinomialNB().fit(Xtrain_tfidf, ywords_neg)
3 clf_bad = MultinomialNB().fit(Xtrain_tfidf, ywords_bad)
D:\WPy-3702\python-3.7.0\lib\site-packages\sklearn\naive_bayes.py in fit(self, X, y, sample_weight)
581
582 labelbin = LabelBinarizer()
--> 583 Y = labelbin.fit_transform(y)
584 self.classes_ = labelbin.classes_
585 if Y.shape[1] == 1:
D:\WPy-3702\python-3.7.0\lib\site-packages\sklearn\preprocessing\label.py in fit_transform(self, y)
305 Shape will be [n_samples, 1] for binary problems.
306 """
--> 307 return self.fit(y).transform(y)
308
309 def transform(self, y):
D:\WPy-3702\python-3.7.0\lib\site-packages\sklearn\preprocessing\label.py in fit(self, y)
274 self : returns an instance of self.
275 """
--> 276 self.y_type_ = type_of_target(y)
277 if 'multioutput' in self.y_type_:
278 raise ValueError("Multioutput target data is not supported with "
D:\WPy-3702\python-3.7.0\lib\site-packages\sklearn\utils\multiclass.py in type_of_target(y)
285 return 'continuous' + suffix
286
--> 287 if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1):
288 return 'multiclass' + suffix # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]
289 else:
D:\WPy-3702\python-3.7.0\lib\site-packages\numpy\lib\arraysetops.py in unique(ar, return_index, return_inverse, return_counts, axis)
221 ar = np.asanyarray(ar)
222 if axis is None:
--> 223 return _unique1d(ar, return_index, return_inverse, return_counts)
224 if not (-ar.ndim <= axis < ar.ndim):
225 raise ValueError('Invalid axis kwarg specified for unique')
D:\WPy-3702\python-3.7.0\lib\site-packages\numpy\lib\arraysetops.py in _unique1d(ar, return_index, return_inverse, return_counts)
281 aux = ar[perm]
282 else:
--> 283 ar.sort()
284 aux = ar
285 flag = np.concatenate(([True], aux[1:] != aux[:-1]))
TypeError: '<' not supported between instances of 'int' and 'str'
您正在使用CountVectorizer来矢量化您的数据，然后将该结果用于TfIdfVectorizer。你不能为TfIdfVectorizer提供整数数据。如果你想使用TfIdfVectorizer，那么直接在你的文字上使用它。 CountVectorizer和TfIdfVectorizer是两种不同的方法来矢量化文本数据，可以输入到您的模型。我建议你阅读那些了解它的作用。
希望能帮助到你！