Scikit learn can'；t仅在数据帧的一列上应用sklearn.compose.ColumnTransformer
我已经定义了一个定制的tansformer，它接受一个pandas数据帧，只在一列上应用一个函数，并保留所有剩余列不变。变压器在测试期间工作良好，但在我将其作为管道的一部分时，情况并非如此 这是变压器：Scikit learn can'；t仅在数据帧的一列上应用sklearn.compose.ColumnTransformer,scikit-learn,pipeline,sklearn-pandas,Scikit Learn,Pipeline,Sklearn Pandas,我已经定义了一个定制的tansformer，它接受一个pandas数据帧，只在一列上应用一个函数，并保留所有剩余列不变。变压器在测试期间工作良好，但在我将其作为管道的一部分时，情况并非如此 这是变压器： import re from sklearn.base import BaseEstimator, TransformerMixin class SynopsisCleaner(BaseEstimator, TransformerMixin): def __init__(self):
import re
from sklearn.base import BaseEstimator, TransformerMixin
class SynopsisCleaner(BaseEstimator, TransformerMixin):
def __init__(self):
return None
def fit(self, X, y=None, **fit_params):
# nothing to learn from data.
return self
def clean_text(self, text):
text = text.lower()
text = re.sub(r'@[a-zA-Z0-9_]+', '', text)
text = re.sub(r'https?://[A-Za-z0-9./]+', '', text)
text = re.sub(r'www.[^ ]+', '', text)
text = re.sub(r'[a-zA-Z0-9]*www[a-zA-Z0-9]*com[a-zA-Z0-9]*', '', text)
text = re.sub(r'[^a-zA-Z]', ' ', text)
text = [token for token in text.split() if len(token) > 2]
text = ' '.join(text)
return text
def transform(self, X, y=None, **fit_params):
for i in range(X.shape[0]):
X[i] = self.clean_text(X[i])
return X
train_synopsis = SynopsisCleaner().transform(train_data['Synopsis'])
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
# part 1: defining a column transformer that learns on only one column and transforms it
synopsis_clean_col_tran = ColumnTransformer(transformers=[('synopsis_clean_col_tran', SynopsisCleaner(), ['Synopsis'])],
# set remainder to passthrough to pass along all the un-specified columns untouched to the next steps
remainder='passthrough')
# make a pipeline now with all the steps
pipe_1 = Pipeline(steps=[('synopsis_cleaning', synopsis_clean_col_tran)])
pipe_1.fit(train_data)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2890 try:
-> 2891 return self._engine.get_loc(casted_key)
2892 except KeyError as err:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 0
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
16 frames
<ipython-input-10-3396fa5d6092> in <module>()
6 # make a pipeline now with all the steps
7 pipe_1 = Pipeline(steps=[('synopsis_cleaning', synopsis_clean_col_tran)])
----> 8 pipe_1.fit(train_data)
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
352 self._log_message(len(self.steps) - 1)):
353 if self._final_estimator != 'passthrough':
--> 354 self._final_estimator.fit(Xt, y, **fit_params)
355 return self
356
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in fit(self, X, y)
482 # we use fit_transform to make sure to set sparse_output_ (for which we
483 # need the transformed data) to have consistent output type in predict
--> 484 self.fit_transform(X, y=y)
485 return self
486
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in fit_transform(self, X, y)
516 self._validate_remainder(X)
517
--> 518 result = self._fit_transform(X, y, _fit_transform_one)
519
520 if not result:
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in _fit_transform(self, X, y, func, fitted)
455 message=self._log_message(name, idx, len(transformers)))
456 for idx, (name, trans, column, weight) in enumerate(
--> 457 self._iter(fitted=fitted, replace_strings=True), 1))
458 except ValueError as e:
459 if "Expected 2D array, got 1D array instead" in str(e):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in __call__(self, iterable)
1027 # remaining jobs.
1028 self._iterating = False
-> 1029 if self.dispatch_one_batch(iterator):
1030 self._iterating = self._original_iterator is not None
1031
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
845 return False
846 else:
--> 847 self._dispatch(tasks)
848 return True
849
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in _dispatch(self, batch)
763 with self._lock:
764 job_idx = len(self._jobs)
--> 765 job = self._backend.apply_async(batch, callback=cb)
766 # A job can complete so quickly than its callback is
767 # called before we get here, causing self._jobs to
/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
206 def apply_async(self, func, callback=None):
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
210 callback(result)
/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py in __init__(self, batch)
570 # Don't delay the application, to avoid keeping the input
571 # arguments in memory
--> 572 self.results = batch()
573
574 def get(self):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in __call__(self)
251 with parallel_backend(self._backend, n_jobs=self._n_jobs):
252 return [func(*args, **kwargs)
--> 253 for func, args, kwargs in self.items]
254
255 def __reduce__(self):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in <listcomp>(.0)
251 with parallel_backend(self._backend, n_jobs=self._n_jobs):
252 return [func(*args, **kwargs)
--> 253 for func, args, kwargs in self.items]
254
255 def __reduce__(self):
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
726 with _print_elapsed_time(message_clsname, message):
727 if hasattr(transformer, 'fit_transform'):
--> 728 res = transformer.fit_transform(X, y, **fit_params)
729 else:
730 res = transformer.fit(X, y, **fit_params).transform(X)
/usr/local/lib/python3.6/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
569 if y is None:
570 # fit method of arity 1 (unsupervised transformation)
--> 571 return self.fit(X, **fit_params).transform(X)
572 else:
573 # fit method of arity 2 (supervised transformation)
<ipython-input-6-004ee595d544> in transform(self, X, y, **fit_params)
20 def transform(self, X, y=None, **fit_params):
21 for i in range(X.shape[0]):
---> 22 X[i] = self.clean_text(X[i])
23 return X
/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in __getitem__(self, key)
2900 if self.columns.nlevels > 1:
2901 return self._getitem_multilevel(key)
-> 2902 indexer = self.columns.get_loc(key)
2903 if is_integer(indexer):
2904 indexer = [indexer]
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2891 return self._engine.get_loc(casted_key)
2892 except KeyError as err:
-> 2893 raise KeyError(key) from err
2894
2895 if tolerance is not None:
KeyError: 0
---------------------------------------------------------------------------
KeyError回溯（最近一次呼叫最后一次）
/get_loc中的usr/local/lib/python3.6/dist-packages/pandas/core/index/base.py（self、key、method、tolerance）
2890试试：
->2891返回自发动机。获取定位（铸造钥匙）
2892除KeyError作为错误外：
pandas/_libs/index.pyx在pandas中。_libs.index.IndexEngine.get_loc（）
pandas/_libs/index.pyx在pandas中。_libs.index.IndexEngine.get_loc（）
pandas/_libs/hashtable_class_helper.pxi在pandas._libs.hashtable.PyObjectHashTable.get_item（）中
pandas/_libs/hashtable_class_helper.pxi在pandas._libs.hashtable.PyObjectHashTable.get_item（）中
关键错误：0
上述异常是以下异常的直接原因：
KeyError回溯（最近一次呼叫最后一次）
16帧
在（）
6#现在用所有步骤制作管道
7管道1=管道（步骤=[（‘清洁’概要，清洁’概要）
---->8管道1.安装（列车数据）
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py-in-fit（self，X，y，**fit_参数）
352自我记录信息（len（self.steps）-1）：
353如果自我最终估计器！='逾越节'：
-->354自我最终估计值拟合（Xt，y，**拟合参数）
355返回自我
356
/usr/local/lib/python3.6/dist-packages/sklearn/compose//u column\u transformer.py in fit（self，X，y）
482#我们使用fit_变换来确保设置稀疏的_输出(为此我们
483#需要转换的数据）在预测中具有一致的输出类型
-->484自拟合变换（X，y=y）
485返回自我
486
/usr/local/lib/python3.6/dist-packages/sklearn/compose//u column\u transformer.py in fit\u transform（self，X，y）
516自验证余数（X）
517
-->518结果=自拟合变换（X，y，拟合变换）
519
520如果没有结果：
/usr/local/lib/python3.6/dist-packages/sklearn/compose//u column\u transformer.py in\u fit\u transform（self、X、y、func、fitted）
455 message=self.\u log\u message（名称、idx、len（变压器）））
456对于枚举中的idx（名称、事务、列、权重）(
-->457自测试仪（已安装=已安装，更换字符串=正确），1）
458除e值错误外：
459如果str（e）中的“预期2D数组，改为1D数组”：
/usr/local/lib/python3.6/dist-packages/joblb/parallel.py in_u_调用（self，iterable）
1027#剩余工作。
1028自迭代=错误
->1029如果自行调度一批（迭代器）：
1030 self.\u iterating=self.\u original\u iterator不是None
1031
/usr/local/lib/python3.6/dist-packages/joblb/parallel.py在dispatch\u one\u批处理中（self，iterator）
845返回错误
846其他：
-->847自我派遣（任务）
848返回真值
849
/usr/local/lib/python3.6/dist-packages/joblb/parallel.py in_dispatch（self，batch）
763带自锁：
764作业idx=长度（自作业）
-->765 job=self.\u backend.apply\u async（批处理，回调=cb）
766#一个作业可以比它的回调完成得更快
767#在我们到达这里之前打电话，导致self.#U jobs
/usr/local/lib/python3.6/dist-packages/joblib//\u parallel\u backends.py in apply\u async（self、func、callback）
206 def apply_async（self、func、callback=None）：
207“计划要运行的func”
-->208结果=立即结果（func）
209如果回调：
210回调（结果）
/usr/local/lib/python3.6/dist-packages/joblib//\u parallel\u backends.py in\uuuuu init\uuuu（self，batch）
570#不要延迟应用程序，以免保留输入
571#内存中的参数
-->572自身结果=批次（）
573
574 def get（自我）：
/usr/local/lib/python3.6/dist-packages/joblb/parallel.py in___调用（self）
251具有并行\u后端（self.\u后端，n\u作业=self.\u n\u作业）：
252返回[func（*args，**kwargs）
-->253用于自身项目中的func、ARG、kwargs]
254
255定义减少（自）：
/usr/local/lib/python3.6/dist-packages/joblb/parallel.py in（.0）
251具有并行\u后端（self.\u后端，n\u作业=self.\u n\u作业）：
252返回[func（*args，**kwargs）
-->253用于自身项目中的func、ARG、kwargs]
254
255定义减少（自）：
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in_-fit_-transform_-one（变压器、X、y、重量、消息名称、消息、**fit_参数）
726带有_print_exposed_time（消息名称，消息）：
727如果hasattr（变压器，“拟合变换”）：
-->728 res=变换器。拟合变换（X，y，**拟合参数）
729其他：
730 res=变换器.fit（X，y，**拟合参数）.transform（X）
/拟合变换中的usr/local/lib/python3.6/dist-packages/sklearn/base.py（self，X，y，**拟合参数）
569如果y为无：
570算术1的拟合方法（无监督变换）
-->571返回自拟合（X，**拟合参数）.transform（X）
572其他：
573#第2类拟合方法（监督
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-bdd42b09e2af> in <module>()
6 # make a pipeline now with all the steps
7 pipe_1 = Pipeline(steps=[('synopsis_cleaning', synopsis_clean_col_tran)])
----> 8 pipe_1.fit(train_data)
3 frames
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
352 self._log_message(len(self.steps) - 1)):
353 if self._final_estimator != 'passthrough':
--> 354 self._final_estimator.fit(Xt, y, **fit_params)
355 return self
356
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in fit(self, X, y)
482 # we use fit_transform to make sure to set sparse_output_ (for which we
483 # need the transformed data) to have consistent output type in predict
--> 484 self.fit_transform(X, y=y)
485 return self
486
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in fit_transform(self, X, y)
536
537 self._update_fitted_transformers(transformers)
--> 538 self._validate_output(Xs)
539
540 return self._hstack(list(Xs))
/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py in _validate_output(self, result)
400 raise ValueError(
401 "The output of the '{0}' transformer should be 2D (scipy "
--> 402 "matrix, array, or pandas DataFrame).".format(name))
403
404 def _validate_features(self, n_features, feature_names):
ValueError: The output of the 'synopsis_clean_col_tran' transformer should be 2D (scipy matrix, array, or pandas DataFrame).