Sklearn - FeatureUnion - Transformer：TypeError：fit_transform（）需要2個位置參數，但有3個被給出

Question

我很抱歉提前爲這個大代碼塊。這是我可以提供可重複工作示例的最簡潔的方式。

在代碼中，我試圖用FeatureUnion從數據幀，其中一列是文本數據，以便TfidfVectorizer，另一個是標籤列表的列，所以我想用MultiLabelBinarizer改造兩列。

ItemSelector變壓器用於從數據幀中選擇右列。

爲什麼我會得到TypeError: fit_transform() takes 2 positional arguments but 3 were given？

我需要更改代碼才能讓此示例正常運行？

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) 

這是完全錯誤：

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：我已經看過_transform() takes 2 positional arguments but 3 were given，但它仍然沒有道理給我。

Answer 1

明白了。製作另一臺變壓器來處理多標籤二值化。這更像是解決方案而不是解決方案，因爲二值化發生在變換而不是管道內。

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)