Sklearn - FeatureUnion - Transformer：TypeError：fit_transform（）は2つの位置的な引数をとりますが、3が与えられました

Question

この大きなコードブロックは事前に申し訳ありません。繰り返し可能な作業例を提供することができる最も簡潔な方法です。コードで

、私は1つの列がテキストデータでとてもTfidfVectorizerであると私はMultiLabelBinarizerを使用したいので、他のタグのリストの列であるデータフレームから2つの列を変換するためにFeatureUnionを使用しようとしています。

ItemSelectorトランスフォーマは、データフレームから右の列を選択するためのものです。

なぜ私はTypeError: fit_transform() takes 2 positional arguments but 3 were givenを取得していますか？

この例を正しく実行するには、コードを変更する必要がありますか？

from sklearn.preprocessing import MultiLabelBinarizer 
from sklearn.base import TransformerMixin, BaseEstimator 
from sklearn.pipeline import Pipeline, FeatureUnion 
from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.model_selection import GridSearchCV 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.linear_model import SGDClassifier 

import pandas as pd 
import numpy as np 

d = {'label': ['Help', 'Help', 'Other', 'Sale/Coupon', 'Other', 'Help', 'Help', 
      'Other', 'Sale/Coupon', 'Other', 'Help', 'Help', 'Other', 
      'Sale/Coupon', 'Other', 'Help', 'Help', 'Other', 'Sale/Coupon', 
      'Other', 'Help', 'Help', 'Other', 'Sale/Coupon', 'Other'], 
    'multilabels': ["['Samples']", "['Deck']", "['Deck', 'Deck Over', 'Stain']", 
        "['Coupons']", "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']"], 
    'response': ['this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done', 'this is some text', 
        'this is some more text', 'and here is some more', 
        'and some more', 'and here we go some more yay done', 
        'this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done', 'this is some text', 
        'this is some more text', 'and here is some more', 
        'and some more', 'and here we go some more yay done', 
        'this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done']} 

class ItemSelector(BaseEstimator, TransformerMixin): 
    def __init__(self, key): 
    self.key = key 

    def fit(self, X, y=None): 
    return self 

    def transform(self, df): 
    return df[self.key] 

feature_union = FeatureUnion(
    transformer_list=[ 
    ('step1', Pipeline([ 
     ('selector', ItemSelector(key='response')), 
     ('tfidf', TfidfVectorizer()), 
     ])), 
    ('step2', Pipeline([ 
     ('selector', ItemSelector(key='multilabels')), 
     ('multilabel', MultiLabelBinarizer()) 
     ])) 
    ]) 

pipeline = OneVsRestClassifier(
    Pipeline([('union', feature_union),('sgd', SGDClassifier())]) 
) 

grid = GridSearchCV(pipeline, {}, verbose=5) 

df = pd.DataFrame(d, columns=['response', 'multilabels', 'label']) 
X = df[['response', 'multilabels']] 
y = df['label'] 
grid.fit(X, y) 

これは完全な誤りである：

Traceback (most recent call last): 
    File "C:/Users/owner/Documents/my files/Account Tracking/Client/Foresee Analysis/SOQuestion.py", line 72, in <module> 
    grid.fit(X, y) 
    File "C:\Python34\lib\site-packages\sklearn\model_selection\_search.py", line 945, in fit 
    return self._fit(X, y, groups, ParameterGrid(self.param_grid)) 
    File "C:\Python34\lib\site-packages\sklearn\model_selection\_search.py", line 564, in _fit 
    for parameters in parameter_iterable 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__ 
    while self.dispatch_one_batch(iterator): 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 608, in dispatch_one_batch 
    self._dispatch(tasks) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch 
    job = self._backend.apply_async(batch, callback=cb) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 109, in apply_async 
    result = ImmediateResult(func) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 326, in __init__ 
    self.results = batch() 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__ 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp> 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\model_selection\_validation.py", line 238, in _fit_and_score 
    estimator.fit(X_train, y_train, **fit_params) 
    File "C:\Python34\lib\site-packages\sklearn\multiclass.py", line 216, in fit 
    for i, column in enumerate(columns)) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__ 
    while self.dispatch_one_batch(iterator): 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 608, in dispatch_one_batch 
    self._dispatch(tasks) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch 
    job = self._backend.apply_async(batch, callback=cb) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 109, in apply_async 
    result = ImmediateResult(func) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 326, in __init__ 
    self.results = batch() 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__ 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp> 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\multiclass.py", line 80, in _fit_binary 
    estimator.fit(X, y) 
    File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 268, in fit 
    Xt, fit_params = self._fit(X, y, **fit_params) 
    File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 234, in _fit 
    Xt = transform.fit_transform(Xt, y, **fit_params_steps[name]) 
    File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 734, in fit_transform 
    for name, trans, weight in self._iter()) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__ 
    while self.dispatch_one_batch(iterator): 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 608, in dispatch_one_batch 
    self._dispatch(tasks) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch 
    job = self._backend.apply_async(batch, callback=cb) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 109, in apply_async 
    result = ImmediateResult(func) 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 326, in __init__ 
    self.results = batch() 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__ 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp> 
    return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 577, in _fit_transform_one 
    res = transformer.fit_transform(X, y, **fit_params) 
    File "C:\Python34\lib\site-packages\sklearn\pipeline.py", line 303, in fit_transform 
    return last_step.fit_transform(Xt, y, **fit_params) 
TypeError: fit_transform() takes 2 positional arguments but 3 were given 

注：私は_transform() takes 2 positional arguments but 3 were givenを見てきましたが、それはまだ私には意味がありません。

Answer 1

マルチラベル二値化に対応する別のトランスを製作しました。これは、ソリューションの代わりに回避策に似ています。これは、パイプラインではなくトランスフォーム内で2値化が行われるためです。

from sklearn.preprocessing import MultiLabelBinarizer 
from sklearn.base import TransformerMixin, BaseEstimator 
from sklearn.pipeline import Pipeline, FeatureUnion 
from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.model_selection import GridSearchCV 
from sklearn.multiclass import OneVsRestClassifier 
from sklearn.linear_model import SGDClassifier 

import pandas as pd 
import numpy as np 

d = {'label': ['Help', 'Help', 'Other', 'Sale/Coupon', 'Other', 'Help', 'Help', 
      'Other', 'Sale/Coupon', 'Other', 'Help', 'Help', 'Other', 
      'Sale/Coupon', 'Other', 'Help', 'Help', 'Other', 'Sale/Coupon', 
      'Other', 'Help', 'Help', 'Other', 'Sale/Coupon', 'Other'], 
    'multilabels': ["['Samples']", "['Deck']", "['Deck', 'Deck Over', 'Stain']", 
        "['Coupons']", "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']", "['Samples']", "['Deck']", 
        "['Deck', 'Deck Over', 'Stain']", "['Coupons']", 
        "['Bathroom']"], 
    'response': ['this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done', 'this is some text', 
        'this is some more text', 'and here is some more', 
        'and some more', 'and here we go some more yay done', 
        'this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done', 'this is some text', 
        'this is some more text', 'and here is some more', 
        'and some more', 'and here we go some more yay done', 
        'this is some text', 'this is some more text', 
        'and here is some more', 'and some more', 
        'and here we go some more yay done']} 

class ItemSelector(BaseEstimator, TransformerMixin): 
    def __init__(self, column): 
    self.column = column 

    def fit(self, X, y=None, **fit_params): 
    return self 

    def transform(self, X, y=None, **fit_params): 
    return X[self.column] 

class MultiLabelTransformer(BaseEstimator, TransformerMixin): 

    def __init__(self, column): 
    self.column = column 

    def fit(self, X, y=None): 
    return self 

    def transform(self, X): 
    mlb = MultiLabelBinarizer() 
    return mlb.fit_transform(X[self.column]) 

pipeline = OneVsRestClassifier(
    Pipeline([ 
    ('union', FeatureUnion(
    transformer_list=[ 
     ('step1', Pipeline([ 
     ('selector', ItemSelector(column='response')), 
     ('tfidf', TfidfVectorizer()) 
     ])), 
     ('step2', Pipeline([ 
     ('selector', MultiLabelTransformer(column='multilabels')) 
     ])) 
     ])), 
    ('sgd', SGDClassifier()) 
    ]) 
) 

grid = GridSearchCV(pipeline, {}, verbose=5) 

df = pd.DataFrame(d, columns=['response', 'multilabels', 'label']) 
df['multilabels'] = df['multilabels'].apply(lambda s: eval(s)) 
X = df[['response', 'multilabels']] 
y = df['label'] 
grid.fit(X, y)