对于这个大型代码块，我的歉意。这是我可以提供一个可重复的工作示例的最简洁的方式。
在代码中，我正在尝试使用FeatureUnion从数据框中转换两个列，其中一列是文本数据，因此TfidfVectorizer，另一列是标签列表的列，因此我想使用MultiLabelBinarizer。
ItemSelector变压器用于从数据框架中选择右列。
为什么我要获得TypeError: fit_transform() takes 2 positional arguments but 3 were given ？
我需要在代码中更改什么才能使此示例正确运行？
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)
这是完整的错误：
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:Python34libsite-packagessklearnmodel_selection_search.py", line 945, in fit
return self._fit(X, y, groups, ParameterGrid(self.param_grid))
File "C:Python34libsite-packagessklearnmodel_selection_search.py", line 564, in _fit
for parameters in parameter_iterable
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 326, in __init__
self.results = batch()
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnmodel_selection_validation.py", line 238, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:Python34libsite-packagessklearnmulticlass.py", line 216, in fit
for i, column in enumerate(columns))
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 326, in __init__
self.results = batch()
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnmulticlass.py", line 80, in _fit_binary
estimator.fit(X, y)
File "C:Python34libsite-packagessklearnpipeline.py", line 268, in fit
Xt, fit_params = self._fit(X, y, **fit_params)
File "C:Python34libsite-packagessklearnpipeline.py", line 234, in _fit
Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
File "C:Python34libsite-packagessklearnpipeline.py", line 734, in fit_transform
for name, trans, weight in self._iter())
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "C:Python34libsite-packagessklearnexternalsjoblib_parallel_backends.py", line 326, in __init__
self.results = batch()
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnexternalsjoblibparallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:Python34libsite-packagessklearnpipeline.py", line 577, in _fit_transform_one
res = transformer.fit_transform(X, y, **fit_params)
File "C:Python34libsite-packagessklearnpipeline.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
注意：我看过_transform（）进行了2个位置参数，但给出了3个，但对我来说仍然没有意义。
得到了它。制造了另一个变压器来处理多标签的二进制化。这更像是一个工作，而不是解决方案，因为二进制化发生在转换中而不是管道中。
。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)