Python sklearn 分类器获取 ValueError：输入形状错误
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sklearn classifier get ValueError: bad input shape
提问by Mithril
I have a csv, struct is
CAT1,CAT2,TITLE,URL,CONTENT, CAT1, CAT2, TITLE ,CONTENT are in chinese.
我有一个 csv, struct is
CAT1,CAT2,TITLE,URL,CONTENT, CAT1, CAT2, TITLE ,CONTENT 都是中文的。
I want train LinearSVCor MultinomialNBwith X(TITLE) and feature(CAT1,CAT2), both get this error. below is my code:
我想要训练LinearSVC或MultinomialNB使用 X(TITLE) 和功能 (CAT1,CAT2)，都会出现此错误。下面是我的代码：
PS: I write below code through this example scikit-learn text_analytics
PS：我通过这个例子写了下面的代码scikit-learn text_analytics
import numpy as np
import csv
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
label_list = []
def label_map_target(label):
''' map chinese feature name to integer '''
try:
idx = label_list.index(label)
except ValueError:
idx = len(label_list)
label_list.append(label)
return idx
c1_list = []
c2_list = []
title_list = []
with open(csv_file, 'r') as f:
# row_from_csv is for shorting this example
for row in row_from_csv(f):
c1_list.append(label_map_target(row[0])
c2_list.append(label_map_target(row[1])
title_list.append(row[2])
data = np.array(title_list)
target = np.array([c1_list, c2_list])
print target.shape
# (2, 4405)
target = target.reshape(4405,2)
print target.shape
# (4405, 2)
docs_train, docs_test, y_train, y_test = train_test_split(
data, target, test_size=0.25, random_state=None)
# vect = TfidfVectorizer(tokenizer=jieba_tokenizer, min_df=3, max_df=0.95)
# use custom chinese tokenizer get same error
vect = TfidfVectorizer(min_df=3, max_df=0.95)
docs_train= vect.fit_transform(docs_train)
clf = LinearSVC()
clf.fit(docs_train, y_train)
error:
错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-904eb9af02cd> in <module>()
1 clf = LinearSVC()
----> 2 clf.fit(docs_train, y_train)
C:\Python27\lib\site-packages\sklearn\svm\classes.pyc in fit(self, X, y)
198
199 X, y = check_X_y(X, y, accept_sparse='csr',
--> 200 dtype=np.float64, order="C")
201 self.classes_ = np.unique(y)
202
C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)
447 dtype=None)
448 else:
--> 449 y = column_or_1d(y, warn=True)
450 _assert_all_finite(y)
451 if y_numeric and y.dtype.kind == 'O':
C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in column_or_1d(y, warn)
483 return np.ravel(y)
484
--> 485 raise ValueError("bad input shape {0}".format(shape))
486
487
ValueError: bad input shape (3303, 2)
采纳答案by Mithril
Thanks to @meelo, I solved this problem.
As he said: in my code, datais a feature vector, targetis target value. I mixed up two things.
感谢@meelo，我解决了这个问题。正如他所说：在我的代码中，data是特征向量，target是目标值。我混淆了两件事。
I learned that TfidfVectorizerprocesses data to [data, feature], and each data should map to just one target.
我了解到将TfidfVectorizer数据处理为 [数据，特征]，并且每个数据应该只映射到一个目标。
If I want to predict two type targets, I need two distinct targets:
如果我想预测两种类型的目标，我需要两个不同的目标：
target_C1with all C1 valuetarget_C2with all C2 value.
target_C1与所有 C1 值target_C2与所有 C2 值。
Then use the two targets and original data to train two classifier for each target.
然后使用两个目标和原始数据为每个目标训练两个分类器。
回答by eslam samy
I had the same issue.
我遇到过同样的问题。
So if you are facing the same problem you should check the shape of clf.fit(X,y)parameters:
因此，如果您面临同样的问题，您应该检查clf.fit(X,y)参数的形状：
X : Training vector {array-like, sparse matrix}, shape (n_samples, n_features).
X : 训练向量 {array-like, sparse matrix}, shape (n_samples, n_features)。
y : Target vector relative to X array-like, shape (n_samples,).
y : 相对于 X 数组的目标向量，形状 (n_samples,)。
as you can see the y width should be 1, to make sure your target vector is shaped correctly try command
如您所见，y 宽度应为 1，以确保您的目标向量形状正确，请尝试命令
y.shape
should be (n_samples,)
应该是 (n_samples,)
In my case, for my training vector I was concatenating 3 separate vectors from 3 different vectorizers to use all as my final training vector.
The problem was that each vector had the ['Label']column in it so the final training vector contained 3 ['Label']columns.
Then when I used final_trainingVect['Label']as my Target vector it's shape was n_samples,3).
就我而言，对于我的训练向量，我将来自 3 个不同向量化器的 3 个独立向量连接起来，以将所有向量都用作我的最终训练向量。问题是每个向量都有一['Label']列，所以最终的训练向量包含 3['Label']列。然后当我用作final_trainingVect['Label']我的目标向量时，它的形状是 n_samples,3)。
