如何将交叉验证目标输入管道中的自定义转换器
How to feed cross-validation targets into custom transformers in pipeline
我一直在努力解决使用 sklearn 的管道和 FeatureUnion classes 使一些自定义转换器工作的问题。我最终想使用 GridsSearchCV 来尝试一些不同的参数，但一开始我就卡在这里了。我有以下管道：
feature_selection = FeatureUnion([
("fprfeatures", SelectFprAttrib()),
("modelfeatures",
SelectModelAttrib(clf=RandomForestClassifier(n_estimators=150), on=True)),
])
full_pipeline = Pipeline([
("dataselector", DataSelector(numcolumns)),
("scaler", ScalerFlip()),
("features", feature_selection),
("estimators",estimator_pipe),
])
此处是我的自定义示例 class（它们本质上相同）：
#Custom SelectFromModel that allows me to mess with attribute numbers and
toggle
class SelectModelAttrib(BaseEstimator, TransformerMixin):
from sklearn.feature_selection import SelectFromModel
def __init__(self, clf, attrib_number=20, on=True):
self.attrib_number = attrib_number
self.clf = clf
self.on = on
def fit(self, X, y=None):
self.y = y
return self
def transform(self, X):
if self.on:
self.model = SelectFromModel(self.clf)
return self.model.fit_transform(X,self.y)[:,:self.attrib_number]
else:
return np.empty_like(X)
def get_support(self):
return self.model.get_support()
如果我打电话给
full_pipeline.fit(features, targets)
我没有问题。事实上，如果我注释掉估算器和 运行 以下内容：
full_pipeline.fit_transform(features, targets)
我得到了我想要的一系列功能。但是，当我 运行 full_pipeline 通过 GridSearchCV 时：
#X is in format rows=instances, columns=features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
attrib_number = ss.randint(1,100)
param_grid = {"estimators":[SVC(kernel="rbf"),
SVC(kernel="poly"),LinearSVC(), LogisticRegression()],}
pipe_grd = GridSearchCV(full_pipeline, param_grid, cv=4, scoring =
"accuracy", verbose=2)
pipe_grd.fit(X_train, y_train)
我得到以下回溯....
ValueError Traceback (most recent call last)
<ipython-input-72-86f7cf8d7839> in <module>()
16
17 pipe_grd = GridSearchCV(full_pipeline, param_grid, cv=4, scoring = "accuracy", verbose=2, n_jobs=1)
---> 18 pipe_grd.fit(X_train, y_train)
19 #full_pipeline.predict(X_test)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_search.pyc in fit(self, X, y, groups)
943 train/test set.
944 """
--> 945 return self._fit(X, y, groups, ParameterGrid(self.param_grid))
946
947
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_search.pyc in _fit(self, X, y, groups, parameter_iterable)
562 return_times=True, return_parameters=True,
563 error_score=self.error_score)
--> 564 for parameters in parameter_iterable
565 for train, test in cv_iter)
566
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
258 else:
259 fit_time = time.time() - start_time
--> 260 test_score = _score(estimator, X_test, y_test, scorer)
261 score_time = time.time() - start_time - fit_time
262 if return_train_score:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_validation.pyc in _score(estimator, X_test, y_test, scorer)
286 score = scorer(estimator, X_test)
287 else:
--> 288 score = scorer(estimator, X_test, y_test)
289 if hasattr(score, 'item'):
290 try:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\metrics\scorer.pyc in __call__(self, estimator, X, y_true, sample_weight)
89 super(_PredictScorer, self).__call__(estimator, X, y_true,
90 sample_weight=sample_weight)
---> 91 y_pred = estimator.predict(X)
92 if sample_weight is not None:
93 return self._sign * self._score_func(y_true, y_pred,
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\metaestimators.pyc in <lambda>(*args, **kwargs)
52
53 # lambda, but not partial, allows help() to work with update_wrapper
---> 54 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
55 # update the docstring of the returned function
56 update_wrapper(out, self.fn)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in predict(self, X)
324 for name, transform in self.steps[:-1]:
325 if transform is not None:
--> 326 Xt = transform.transform(Xt)
327 return self.steps[-1][-1].predict(Xt)
328
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in transform(self, X)
761 Xs = Parallel(n_jobs=self.n_jobs)(
762 delayed(_transform_one)(trans, name, weight, X)
--> 763 for name, trans, weight in self._iter())
764 if not Xs:
765 # All transformers are None
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in _transform_one(transformer, name, weight, X)
565
566 def _transform_one(transformer, name, weight, X):
--> 567 res = transformer.transform(X)
568 # if we have a weight for this transformer, multiply output
569 if weight is None:
<ipython-input-64-e7d7de2d62c1> in transform(self, X)
37 if self.on:
38 self.fpr = SelectFpr()
---> 39 return self.fpr.fit_transform(X,self.y)
40 else:
41 return np.empty_like(X)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\base.pyc in fit_transform(self, X, y, **fit_params)
495 else:
496 # fit method of arity 2 (supervised transformation)
--> 497 return self.fit(X, y, **fit_params).transform(X)
498
499
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\feature_selection\univariate_selection.pyc in fit(self, X, y)
320 Returns self.
321 """
--> 322 X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
323
324 if not callable(self.score_func):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
529 y = y.astype(np.float64)
530
--> 531 check_consistent_length(X, y)
532
533 return X, y
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_consistent_length(*arrays)
179 if len(uniques) > 1:
180 raise ValueError("Found input variables with inconsistent numbers of"
--> 181 " samples: %r" % [int(l) for l in lengths])
182
183
ValueError: Found input variables with inconsistent numbers of samples: [14, 38]
根据我通过在管道中放置 Print 函数收集到的信息，问题集中在自定义转换器上。我用 self.y = y.
做了一些可能有点古怪的事情
据我所知，当 GridSearchCV 开始进行交叉验证时，会发生以下情况[示例]：
#Non-cv X.shape is (65,700)
#DataSelector is called
X.shape = (38, 700)
y.shape =(38L,)
#It passes to SelectModelAttrib the same shapes
X.shape = (38, 500)
y.shape = (38L,)
#DataSelector is called a second time
X.shape = (14, 700)
y.shape = (38L,)
#error occurs
这就是错误...有什么方法可以让 y 在此管道中使用 X 进行更新吗？如果我这样做并用 sklearn 上的实际 SelectFromModel class 替换我的自定义转换器，整个事情将 运行。他们是怎么做到的？我查看了他们的源代码，但它超出了我的范围。
我不敢相信我花了一整天试图弄清楚这个问题，只是在试图找出替代方案时偶然发现了答案。
答案是 here 感谢@Vivek Kumar 让我走上正轨。
本质上，将主要的 sklearn 转换器视为父级 class 并继承您的自定义 class。然后我 copy/pasted 来自源 here and from the source here 的代码，并添加了我想要的 extra-tidbits [attrib_number，toggle]。
例如，以下自定义 class 在前一个不可用的地方起作用（不再使用只起作用一次的 self.y）。
from sklearn.utils import check_X_y
from sklearn.feature_selection import f_classif
from sklearn.utils import check_array, safe_mask
from warnings import warn
class SelectFprCustom(SelectFpr):
def __init__(self, score_func=f_classif, attrib_number=20, on=True):
super(SelectFprCustom,self).__init__(alpha=0.05)
self.score_func = score_func
self.attrib_number = attrib_number
self.on = on
def fit(self, X, y):
X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
if not callable(self.score_func):
raise TypeError("The score function should be a callable, %s (%s) "
"was passed."
% (self.score_func, type(self.score_func)))
self._check_params(X, y)
score_func_ret = self.score_func(X, y)
if isinstance(score_func_ret, (list, tuple)):
self.scores_, self.pvalues_ = score_func_ret
self.pvalues_ = np.asarray(self.pvalues_)
else:
self.scores_ = score_func_ret
self.pvalues_ = None
self.scores_ = np.asarray(self.scores_)
return self
def transform(self, X):
X = check_array(X, accept_sparse='csr')
mask = self.get_support()
if (not mask.any()) or (self.on==False):
warn("No features were selected: either the data is"
" too noisy or the selection test too strict or you have on=False.",
UserWarning)
return np.empty(0).reshape((X.shape[0], 0))
if len(mask) != X.shape[1]:
raise ValueError("X has a different shape than during fitting.")
print (self.attrib_number)
return X[:, safe_mask(X, mask)][:,:self.attrib_number]
def _check_params(self, X, y):
pass
我一直在努力解决使用 sklearn 的管道和 FeatureUnion classes 使一些自定义转换器工作的问题。我最终想使用 GridsSearchCV 来尝试一些不同的参数，但一开始我就卡在这里了。我有以下管道：
feature_selection = FeatureUnion([
("fprfeatures", SelectFprAttrib()),
("modelfeatures",
SelectModelAttrib(clf=RandomForestClassifier(n_estimators=150), on=True)),
])
full_pipeline = Pipeline([
("dataselector", DataSelector(numcolumns)),
("scaler", ScalerFlip()),
("features", feature_selection),
("estimators",estimator_pipe),
])
此处是我的自定义示例 class（它们本质上相同）：
#Custom SelectFromModel that allows me to mess with attribute numbers and
toggle
class SelectModelAttrib(BaseEstimator, TransformerMixin):
from sklearn.feature_selection import SelectFromModel
def __init__(self, clf, attrib_number=20, on=True):
self.attrib_number = attrib_number
self.clf = clf
self.on = on
def fit(self, X, y=None):
self.y = y
return self
def transform(self, X):
if self.on:
self.model = SelectFromModel(self.clf)
return self.model.fit_transform(X,self.y)[:,:self.attrib_number]
else:
return np.empty_like(X)
def get_support(self):
return self.model.get_support()
如果我打电话给
full_pipeline.fit(features, targets)
我没有问题。事实上，如果我注释掉估算器和 运行 以下内容：
full_pipeline.fit_transform(features, targets)
我得到了我想要的一系列功能。但是，当我 运行 full_pipeline 通过 GridSearchCV 时：
#X is in format rows=instances, columns=features
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
attrib_number = ss.randint(1,100)
param_grid = {"estimators":[SVC(kernel="rbf"),
SVC(kernel="poly"),LinearSVC(), LogisticRegression()],}
pipe_grd = GridSearchCV(full_pipeline, param_grid, cv=4, scoring =
"accuracy", verbose=2)
pipe_grd.fit(X_train, y_train)
我得到以下回溯....
ValueError Traceback (most recent call last)
<ipython-input-72-86f7cf8d7839> in <module>()
16
17 pipe_grd = GridSearchCV(full_pipeline, param_grid, cv=4, scoring = "accuracy", verbose=2, n_jobs=1)
---> 18 pipe_grd.fit(X_train, y_train)
19 #full_pipeline.predict(X_test)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_search.pyc in fit(self, X, y, groups)
943 train/test set.
944 """
--> 945 return self._fit(X, y, groups, ParameterGrid(self.param_grid))
946
947
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_search.pyc in _fit(self, X, y, groups, parameter_iterable)
562 return_times=True, return_parameters=True,
563 error_score=self.error_score)
--> 564 for parameters in parameter_iterable
565 for train, test in cv_iter)
566
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
258 else:
259 fit_time = time.time() - start_time
--> 260 test_score = _score(estimator, X_test, y_test, scorer)
261 score_time = time.time() - start_time - fit_time
262 if return_train_score:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\model_selection\_validation.pyc in _score(estimator, X_test, y_test, scorer)
286 score = scorer(estimator, X_test)
287 else:
--> 288 score = scorer(estimator, X_test, y_test)
289 if hasattr(score, 'item'):
290 try:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\metrics\scorer.pyc in __call__(self, estimator, X, y_true, sample_weight)
89 super(_PredictScorer, self).__call__(estimator, X, y_true,
90 sample_weight=sample_weight)
---> 91 y_pred = estimator.predict(X)
92 if sample_weight is not None:
93 return self._sign * self._score_func(y_true, y_pred,
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\metaestimators.pyc in <lambda>(*args, **kwargs)
52
53 # lambda, but not partial, allows help() to work with update_wrapper
---> 54 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
55 # update the docstring of the returned function
56 update_wrapper(out, self.fn)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in predict(self, X)
324 for name, transform in self.steps[:-1]:
325 if transform is not None:
--> 326 Xt = transform.transform(Xt)
327 return self.steps[-1][-1].predict(Xt)
328
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in transform(self, X)
761 Xs = Parallel(n_jobs=self.n_jobs)(
762 delayed(_transform_one)(trans, name, weight, X)
--> 763 for name, trans, weight in self._iter())
764 if not Xs:
765 # All transformers are None
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\_parallel_backends.pyc in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\pipeline.pyc in _transform_one(transformer, name, weight, X)
565
566 def _transform_one(transformer, name, weight, X):
--> 567 res = transformer.transform(X)
568 # if we have a weight for this transformer, multiply output
569 if weight is None:
<ipython-input-64-e7d7de2d62c1> in transform(self, X)
37 if self.on:
38 self.fpr = SelectFpr()
---> 39 return self.fpr.fit_transform(X,self.y)
40 else:
41 return np.empty_like(X)
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\base.pyc in fit_transform(self, X, y, **fit_params)
495 else:
496 # fit method of arity 2 (supervised transformation)
--> 497 return self.fit(X, y, **fit_params).transform(X)
498
499
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\feature_selection\univariate_selection.pyc in fit(self, X, y)
320 Returns self.
321 """
--> 322 X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
323
324 if not callable(self.score_func):
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
529 y = y.astype(np.float64)
530
--> 531 check_consistent_length(X, y)
532
533 return X, y
C:\Users\philg\Anaconda2\lib\site-packages\sklearn\utils\validation.pyc in check_consistent_length(*arrays)
179 if len(uniques) > 1:
180 raise ValueError("Found input variables with inconsistent numbers of"
--> 181 " samples: %r" % [int(l) for l in lengths])
182
183
ValueError: Found input variables with inconsistent numbers of samples: [14, 38]
根据我通过在管道中放置 Print 函数收集到的信息，问题集中在自定义转换器上。我用 self.y = y.
做了一些可能有点古怪的事情据我所知，当 GridSearchCV 开始进行交叉验证时，会发生以下情况[示例]：
#Non-cv X.shape is (65,700)
#DataSelector is called
X.shape = (38, 700)
y.shape =(38L,)
#It passes to SelectModelAttrib the same shapes
X.shape = (38, 500)
y.shape = (38L,)
#DataSelector is called a second time
X.shape = (14, 700)
y.shape = (38L,)
#error occurs
这就是错误...有什么方法可以让 y 在此管道中使用 X 进行更新吗？如果我这样做并用 sklearn 上的实际 SelectFromModel class 替换我的自定义转换器，整个事情将 运行。他们是怎么做到的？我查看了他们的源代码，但它超出了我的范围。
我不敢相信我花了一整天试图弄清楚这个问题，只是在试图找出替代方案时偶然发现了答案。
答案是 here 感谢@Vivek Kumar 让我走上正轨。
本质上，将主要的 sklearn 转换器视为父级 class 并继承您的自定义 class。然后我 copy/pasted 来自源 here and from the source here 的代码，并添加了我想要的 extra-tidbits [attrib_number，toggle]。
例如，以下自定义 class 在前一个不可用的地方起作用（不再使用只起作用一次的 self.y）。
from sklearn.utils import check_X_y
from sklearn.feature_selection import f_classif
from sklearn.utils import check_array, safe_mask
from warnings import warn
class SelectFprCustom(SelectFpr):
def __init__(self, score_func=f_classif, attrib_number=20, on=True):
super(SelectFprCustom,self).__init__(alpha=0.05)
self.score_func = score_func
self.attrib_number = attrib_number
self.on = on
def fit(self, X, y):
X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
if not callable(self.score_func):
raise TypeError("The score function should be a callable, %s (%s) "
"was passed."
% (self.score_func, type(self.score_func)))
self._check_params(X, y)
score_func_ret = self.score_func(X, y)
if isinstance(score_func_ret, (list, tuple)):
self.scores_, self.pvalues_ = score_func_ret
self.pvalues_ = np.asarray(self.pvalues_)
else:
self.scores_ = score_func_ret
self.pvalues_ = None
self.scores_ = np.asarray(self.scores_)
return self
def transform(self, X):
X = check_array(X, accept_sparse='csr')
mask = self.get_support()
if (not mask.any()) or (self.on==False):
warn("No features were selected: either the data is"
" too noisy or the selection test too strict or you have on=False.",
UserWarning)
return np.empty(0).reshape((X.shape[0], 0))
if len(mask) != X.shape[1]:
raise ValueError("X has a different shape than during fitting.")
print (self.attrib_number)
return X[:, safe_mask(X, mask)][:,:self.attrib_number]
def _check_params(self, X, y):
pass