FeatureUnion，具有tfidf特征抛出错误的管道分类特征

Question

I am trying to concat features from tfidf and other categorical features to perform classification on the resultant dataset. From various blogs I understand that FeatureUnion can be used to concat the features and then pipeline the same to algorithm (in my case Naive bayes).

I have followed the code from this link - http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html

When I try to execute the code it is giving error

TypeError: no supported conversion for types: (dtype('O'),)

Below is the code which I am trying to execute:

class textdata():
    def transform(self, X, Y):
        return X[desc]

    def fit(self, X, Y):
        return self

class one_hot_trans():
    def transform(self, X, Y):
        X = pd.get_dummies(X, columns=obj_cols)
        return X
    def fit(self, X, Y):
        return self

pipeline = Pipeline([
                  ('features', FeatureUnion([

                          ('ngram_tf_idf', Pipeline([
                                                      ('text', textdata()),
                                                      ('tf_idf', TfidfTransformer())
                                                    ])), 
                          ('one_hot', one_hot_trans())
                        ])),
                  ('classifier', MultinomialNB())
            ])


d_train, d_test, y_train, y_test = train_test_split(data, data[target], test_size=0.2, random_state = 2018)
pipeline.fit(d_train, y_train)

Can anyone help me in resolving this error.

Note: data has total 9 columns with 1 target variable (categorical) and 1 text column (on which I want to perform tfidf) and rest are categorical (obj_cols in above code).

Edit: Thanks Vivek. I did not notice that. It was by mistake i have put transformer instead of Vectorizer. Even after replacing I am getting below error.

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, weight, X, y, **fit_params)
    579                        **fit_params):
    580     if hasattr(transformer, 'fit_transform'):
--> 581         res = transformer.fit_transform(X, y, **fit_params)
    582     else:
    583         res = transformer.fit(X, y, **fit_params).transform(X)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
    745         self._update_transformer_list(transformers)
    746         if any(sparse.issparse(f) for f in Xs):
--> 747             Xs = sparse.hstack(Xs).tocsr()
    748         else:
    749             Xs = np.hstack(Xs)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\scipy\sparse\construct.py in hstack(blocks, format, dtype)
    462 
    463     """
--> 464     return bmat([blocks], format=format, dtype=dtype)
    465 
    466 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\scipy\sparse\construct.py in bmat(blocks, format, dtype)
    598     if dtype is None:
    599         all_dtypes = [blk.dtype for blk in blocks[block_mask]]
--> 600         dtype = upcast(*all_dtypes) if all_dtypes else None
    601 
    602     row_offsets = np.append(0, np.cumsum(brow_lengths))

~\AppData\Local\Continuum\anaconda3\lib\site-packages\scipy\sparse\sputils.py in upcast(*args)
    50             return t
    51 
---> 52     raise TypeError('no supported conversion for types: %r' % (args,))
    53 
    54 

TypeError: no supported conversion for types: (dtype('float64'), dtype('O'))    

Edit::

I have checked for the unique values in all the categorical variables except for description column and I found none of the values appearing in test data which are not there in train. Am I doing something wrong.

for col in d_train.columns.drop(desc):
    ext = set(d_test[col].unique().tolist()) - set(d_train[col].unique().tolist())
    if ext: print ("extra columns: \n\n", ext)

Edit2:: Additional info - details of the d_train, d_test features mentioned. Can anyone help I am still getting "dimension mismatch" error on predict method.

obj cols::  ['priority', 'ticket_type', 'created_group', 'Classification', 'Component', 'ATR_OWNER_PLANT', 'created_day']
d_train cols::  Index(['priority', 'ticket_type', 'created_group', 'Description_ticket', 'Classification', 'Component', 'ATR_OWNER_PLANT', 'created_day'], dtype='object')
d_test cols::  Index(['priority', 'ticket_type', 'created_group', 'Description_ticket','Classification', 'Component', 'ATR_OWNER_PLANT', 'created_day'], dtype='object')

d_train shape::  (95080, 8)
d_test shape::  (23770, 8)
desc::  Description_ticket

Answer 1

I think, you are passing text column also through one_hot_trans function.

Can you try making the output of one_hot_trans as following.

class one_hot_trans():
    def transform(self, X, Y):
        X = pd.get_dummies(X.drop(desc,axis=1), obj_cols])
        return X
    def fit(self, X, Y):
        return self