目标：当模型输入是整数、浮点数和对象（根据pandas数据框）时，使用sklearn预测给定类集的概率。

我使用的是来自UCI仓库的以下数据集：汽车数据集

我已经创建了一个几乎可以工作的管道：

# 为不同类型的变量创建转换器from sklearn.pipeline import Pipelinefrom sklearn.impute import SimpleImputerfrom sklearn.preprocessing import StandardScaler, OneHotEncoderimport pandas as pdimport numpy as npdata = pd.read_csv(r"C:\Auto Dataset.csv")target = 'aspiration'X = data.drop([target], axis = 1)y = data[target]integer_transformer = Pipeline(steps = [   ('imputer', SimpleImputer(strategy = 'most_frequent')),   ('scaler', StandardScaler())])continuous_transformer = Pipeline(steps = [   ('imputer', SimpleImputer(strategy = 'most_frequent')),   ('scaler', StandardScaler())])categorical_transformer = Pipeline(steps = [   ('imputer', SimpleImputer(strategy = 'most_frequent')),   ('lab_enc', OneHotEncoder(handle_unknown='ignore'))])# 使用ColumnTransformer将转换应用到数据框中的正确列integer_features = X.select_dtypes(include=['int64'])continuous_features = X.select_dtypes(include=['float64'])categorical_features = X.select_dtypes(include=['object'])import numpy as npfrom sklearn.compose import ColumnTransformerpreprocessor = ColumnTransformer(   transformers=[       ('ints', integer_transformer, integer_features),       ('cont', continuous_transformer, continuous_features),       ('cat', categorical_transformer, categorical_features)])# 创建一个结合上述预处理器和分类器的管道from sklearn.neighbors import KNeighborsClassifierbase = Pipeline(steps=[('preprocessor', preprocessor),                     ('classifier', KNeighborsClassifier())])

当然，我希望使用predict_proba()，但这给我带来了一些麻烦。我尝试了以下方法：

model = base.fit(X,y )preds = model.predict_proba(X)

然而，我收到了一个错误：

ValueError: No valid specification of the columns. Only a scalar, list or slice of all integers or all strings, or boolean mask is allowed

当然，这里是完整的错误追溯：

---------------------------------------------------------------------------ValueError                                Traceback (most recent call last)<ipython-input-37-a1a29a8b3623> in <module>()----> 1 base_learner.fit(X)D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)    263             This estimator    264         """--> 265         Xt, fit_params = self._fit(X, y, **fit_params)    266         if self._final_estimator is not None:    267             self._final_estimator.fit(Xt, y, **fit_params)D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)    228                 Xt, fitted_transformer = fit_transform_one_cached(    229                     cloned_transformer, Xt, y, None,--> 230                     **fit_params_steps[name])    231                 # Replace the transformer of the step with the fitted    232                 # transformer. This is necessary when loading the transformerD:\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py in __call__(self, *args, **kwargs)    327     328     def __call__(self, *args, **kwargs):--> 329         return self.func(*args, **kwargs)    330     331     def call_and_shelve(self, *args, **kwargs):D:\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, X, y, weight, **fit_params)    612 def _fit_transform_one(transformer, X, y, weight, **fit_params):    613     if hasattr(transformer, 'fit_transform'):--> 614         res = transformer.fit_transform(X, y, **fit_params)    615     else:    616         res = transformer.fit(X, y, **fit_params).transform(X)D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in fit_transform(self, X, y)    445         self._validate_transformers()    446         self._validate_column_callables(X)--> 447         self._validate_remainder(X)    448     449         result = self._fit_transform(X, y, _fit_transform_one)D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _validate_remainder(self, X)    299         cols = []    300         for columns in self._columns:--> 301             cols.extend(_get_column_indices(X, columns))    302         remaining_idx = sorted(list(set(range(n_columns)) - set(cols))) or None    303 D:\Anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _get_column_indices(X, key)    654         return list(np.arange(n_columns)[key])    655     else:--> 656         raise ValueError("No valid specification of the columns. Only a "    657                          "scalar, list or slice of all integers or all "    658                          "strings, or boolean mask is allowed")

不确定我错过了什么，但会感谢任何可能的帮助。

编辑：我使用的是sklearn版本0.20。

回答：

错误消息指出了正确的方向。列应该按名称或索引指定，但您传递了数据列作为DataFrame。

df.select_dtypes()不输出列索引。它输出一个包含匹配列的DataFrame子集。您的代码应该是

# 使用ColumnTransformer将转换应用到数据框中的正确列integer_features = list(X.columns[X.dtypes == 'int64'])continuous_features = list(X.columns[X.dtypes == 'float64'])categorical_features = list(X.columns[X.dtypes == 'object'])

这样，例如，整数列将作为列表传递['curb-weight', 'engine-size', 'city-mpg', 'highway-mpg']