我在一个数据集上实现了一个决策树。在此之前，我想使用CountVectorizer转换一个特定的列。为此，我使用管道来简化操作。
但出现了不兼容行维度的错误。
代码
# 导入了库....from sklearn.feature_extraction.text import CountVectorizer as cvfrom sklearn.preprocessing import OneHotEncoder as ohefrom sklearn.compose import ColumnTransformer as ctfrom sklearn.pipeline import make_pipeline as mpfrom sklearn.tree import DecisionTreeClassifier as dtctransformer=ct(transformers=[('review_counts',cv(),['verified_reviews']), ('variation_dummies', ohe(),['variation']) ],remainder='passthrough')pipe= mp(transformer,dtc(random_state=42))x= data[['rating','variation','verified_reviews']].copy()y= data.feedbackx_train,x_test,y_train,y_test= tts(x,y,test_size=0.3,random_state=42,stratify=y)print(x_train.shape,y_train.shape) # ((2205, 3), (2205,))pipe.fit(x_train,y_train) # 这一行报错错误
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-79-a981c354b190> in <module>()----> 1 pipe.fit(x_train,y_train)7 frames/usr/local/lib/python3.6/dist-packages/scipy/sparse/construct.py in bmat(blocks, format, dtype) 584 exp=brow_lengths[i], 585 got=A.shape[0]))--> 586 raise ValueError(msg) 587 588 if bcol_lengths[j] == 0:ValueError: blocks[0,:] has incompatible row dimensions. Got blocks[0,1].shape[0] == 2205, expected 1.
问题
- 这个不兼容行维度的错误是如何形成的？
- 如何解决这个问题？
回答：
尝试将所需列作为列表传递给ohe，而将简单的字符串传递给cv
from sklearn.feature_extraction.text import CountVectorizer as cvfrom sklearn.preprocessing import OneHotEncoder as ohefrom sklearn.compose import ColumnTransformer as ctfrom sklearn.pipeline import make_pipeline as mpfrom sklearn.tree import DecisionTreeClassifier as dtcdata = pd.DataFrame({'rating':np.random.randint(0,10,6),'variation':['a','b','c','a','b','c'], 'verified_reviews':['adnf asd','sdf dsa','das j s','asd jd s','sad jds a','sajd'], 'feedback':np.random.randint(0,2,6)})transformer=ct(transformers=[('review_counts',cv(),'verified_reviews'), ('variation_dummies', ohe(),['variation'])], remainder='passthrough')pipe= mp(transformer, dtc(random_state=42))x= data[['rating','variation','verified_reviews']].copy()y= data.feedbackpipe.fit(x,y)根据文档，每当变换器期望输入为一维数组时，列应指定为字符串(“xxx”)。对于期望二维数据的变换器，我们需要将列指定为字符串列表([“xxx”])。
