sklearn classificateur obtenir ValueError: la mauvaise qualité des entrées de la forme

J'ai un fichier csv, struct est
CAT1,CAT2,TITLE,URL,CONTENT, CAT1, CAT2, le TITRE ,le CONTENU sont en chinois.

Je veux former LinearSVC ou MultinomialNB avec X(TITRE) et la fonction(CAT1,CAT2), à la fois que vous obtenez cette erreur. ci-dessous mon code:

PS: j'écris ci-dessous le code à travers cet exemple 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)

erreur:

---------------------------------------------------------------------------
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)

OriginalL'auteur Mithril | 2015-07-09