预处理管道错误：给定列不是数据框的列

Question

import pandas as pd
new_data = pd.DataFrame({'at': [15967, 290.865, 307.329, 902.444, 700.898, 800, 850, 900, 1000, 5000, 10000, 5000, 30000, 90000, 200000, 10000, 5000, 30000, 90000, 200000], 
                   'cogs': [26094.000, 246.466, 325.912, 124.903, 1044.110, 800, 850, 900, 1000, 5000, 10000, 5000, 30000, 90000, 200000, 10000, 5000, 30000, 90000, 200000],
                   'division': ['Retail Trade', 'Services', 'Manufacturing', 'Services', 'Manufacturing', 'Retail Trade', 'Services', 'Manufacturing', 'Services', 'Manufacturing', 'Retail Trade', 'Services', 'Manufacturing', 'Services', 'Manufacturing', 'Retail Trade', 'Services', 'Manufacturing', 'Services', 'Manufacturing'],
'bankrupt': [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],

})

我有一个包含 22 列的数据集（在上面创建了一个示例数据集）。目标是“破产”，其余列是特征。 我想为分类变量“division”创建一个管道到onehotencoder。对于剩余的特征列，我想通过gridsearch进行standardscaler和minmax来找出最优结果。

#library
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from imblearn.pipeline import Pipeline #i used imblearn pipeline as I will like to do SMOTE later on
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split

categorical_features = new_data['division']
categorical_features.head()

numerical_features = new_data.drop(columns=['division','bankrupt'])
numerical_features.head()

cat_preprocessor = Pipeline(steps=[
    ('oh', OneHotEncoder(handle_unknown='ignore')) 
])

num_preprocessor = Pipeline(steps=[ 
    ('ss', StandardScaler())                                   
]) 

preprocessor = ColumnTransformer(transformers=[ 
    ('cat', cat_preprocessor, categorical_features),
    ('num', num_preprocessor, numerical_features)                                                       
])


model = Pipeline(steps=[
    ('prep', preprocessor)
])

param_grid = {
    'prep__num__ss': [StandardScaler(), MinMaxScaler()]
}

gs = GridSearchCV(
    estimator=model,
    param_grid=param_grid,
    scoring='roc_auc',
    n_jobs=-1,
    cv=2
)

#Split the dataset into training set and test set

X = new_data.drop(columns=['bankrupt'])
Y = new_data['bankrupt']

X_train, X_test, y_train, y_test = train_test_split(X, 
                                     Y, test_size=0.2, 
                                     random_state=2021, stratify=Y)

gs.fit(X_train)

错误信息

KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3079             try:
-> 3080                 return self._engine.get_loc(casted_key)
   3081             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: 'Retail Trade'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
    395             for col in columns:
--> 396                 col_idx = all_columns.get_loc(col)
    397                 if not isinstance(col_idx, numbers.Integral):

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3081             except KeyError as err:
-> 3082                 raise KeyError(key) from err
   3083 

KeyError: 'Retail Trade'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-10-82a9838329a1> in <module>
----> 1 gs.fit(X_train)

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~\anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    880                 self.best_estimator_.fit(X, y, **fit_params)
    881             else:
--> 882                 self.best_estimator_.fit(X, **fit_params)
    883             refit_end_time = time.time()
    884             self.refit_time_ = refit_end_time - refit_start_time

~\anaconda3\lib\site-packages\imblearn\pipeline.py in fit(self, X, y, **fit_params)
    264             if self._final_estimator != "passthrough":
    265                 fit_params_last_step = fit_params_steps[self.steps[-1][0]]
--> 266                 self._final_estimator.fit(Xt, yt, **fit_params_last_step)
    267         return self
    268 

~\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in fit(self, X, y)
    469         # we use fit_transform to make sure to set sparse_output_ (for which we
    470         # need the transformed data) to have consistent output type in predict
--> 471         self.fit_transform(X, y=y)
    472         return self
    473 

~\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)
    504         self._validate_transformers()
    505         self._validate_column_callables(X)
--> 506         self._validate_remainder(X)
    507 
    508         result = self._fit_transform(X, y, _fit_transform_one)

~\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)
    330         cols = []
    331         for columns in self._columns:
--> 332             cols.extend(_get_column_indices(X, columns))
    333 
    334         remaining_idx = sorted(set(range(self._n_features)) - set(cols))

~\anaconda3\lib\site-packages\sklearn\utils\__init__.py in _get_column_indices(X, key)
    403             raise ValueError(
    404                 "A given column is not a column of the dataframe"
--> 405             ) from e
    406 
    407         return column_indices

ValueError: A given column is not a column of the dataframe

我检查了数据框中的所有列。感谢所有帮助。

Answer 1

我想我知道出了什么问题。 Categorical_features 和 numeric_features 应改为：

categorical_features = ['division']

numerical_features = new_data.drop(columns=['division','bankrupt']).columns