自定义 sklearn 管道变压器给出“pickle.PicklingError”

我正在尝试根据本教程的指导为 Python sklearn 管道创建自定义转换器：http://danielhnyk.cz/creating-your-own-estimator-scikit-learn/

现在我的自定义类/变压器如下所示：

class SelectBestPercFeats(BaseEstimator, TransformerMixin):
    def __init__(self, model=RandomForestRegressor(), percent=0.8,
                 random_state=52):
        self.model = model
        self.percent = percent
        self.random_state = random_state


    def fit(self, X, y, **fit_params):
        """
        Find features with best predictive power for the model, and
        have cumulative importance value less than self.percent
        """
        # Check parameters
        if not isinstance(self.percent, float):
            print("SelectBestPercFeats.percent is not a float, it should be...")
        elif not isinstance(self.random_state, int):
            print("SelectBestPercFeats.random_state is not a int, it should be...")

        # If checks are good proceed with fitting...
        else:
            try:
                self.model.fit(X, y)
            except:
                print("Error fitting model inside SelectBestPercFeats object")
                return self

            # Get feature importance
            try:
                feat_imp = list(self.model.feature_importances_)
                feat_imp_cum = pd.Series(feat_imp, index=X.columns) \
                    .sort_values(ascending=False).cumsum()

                # Get features whose cumulative importance is <= `percent`
                n_feats = len(feat_imp_cum[feat_imp_cum <= self.percent].index) + 1
                self.bestcolumns_ = list(feat_imp_cum.index)[:n_feats]
            except:
                print ("ERROR: SelectBestPercFeats can only be used with models with"\
                       " .feature_importances_ parameter")
        return self


    def transform(self, X, y=None, **fit_params):
        """
        Filter out only the important features (based on percent threshold)
        for the model supplied.

        :param X: Dataframe with features to be down selected
        """
        if self.bestcolumns_ is None:
            print("Must call fit function on SelectBestPercFeats object before transforming")
        else:
            return X[self.bestcolumns_]

我正在将此类集成到 sklearn 管道中，如下所示：

# Define feature selection and model pipeline components
rf_simp = RandomForestRegressor(criterion='mse', n_jobs=-1,
                                n_estimators=600)
bestfeat = SelectBestPercFeats(rf_simp, feat_perc)
rf = RandomForestRegressor(n_jobs=-1,
                           criterion='mse',
                           n_estimators=200,
                           max_features=0.4,
                           )

# Build Pipeline
master_model = Pipeline([('feat_sel', bestfeat), ('rf', rf)])

# define GridSearchCV parameter space to search, 
#   only listing one parameter to simplify troubleshooting
param_grid = {
    'feat_select__percent': [0.8],
}

# Fit pipeline model
grid = GridSearchCV(master_model, cv=3, n_jobs=-1,
                    param_grid=param_grid)

# Search grid using CV, and get the best estimator
grid.fit(X_train, y_train)

每当我运行最后一行代码时（grid.fit(X_train, y_train)）我收到以下“PicklingError”。任何人都可以看到我的代码中导致此问题的原因是什么？

EDIT:

或者，我的 Python 设置中是否存在错误......我可能缺少一个包或类似的东西吗？我刚刚检查过我可以import pickle成功地

回溯（最近一次调用）：文件“”，第 5 行，位于 文件 “C：\ Users \ jjaaae \ AppData \ Local \ Programs \ Python \ Python36 \ lib \ site-packages \ sklearn \ model_selection_search.py​​”， 945 行，适合 返回 self._fit(X, y, groups, ParameterGrid(self.param_grid)) 文件 “C：\ Users \ jjaaae \ AppData \ Local \ Programs \ Python \ Python36 \ lib \ site-packages \ sklearn \ model_selection_search.py​​”， 第 564 行，在 _fit 中 对于parameter_iterable文件“C：\ Users \ jjaaae \ AppData \ Local \ Programs \ Python \ Python36 \ lib \ site-packages \ sklearn \ externals \ joblib \ parallel.py”中的参数， 768 号线，在callself.retrieve() 文件“C:\Users\jjaaae\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\externals\joblib\parallel.py”， 第 719 行，在检索中 引发异常文件“C：\ Users \ jjaaae \ AppData \ Local \ Programs \ Python \ Python36 \ lib \ site-packages \ sklearn \ externals \ joblib \ parallel.py”， 第 682 行，在检索中 self._output.extend(job.get(timeout=self.timeout)) 文件“C:\Users\jjaaae\AppData\Local\Programs\Python\Python36\lib\multiprocessing\pool.py”, 第 608 行，在 get 中 提高 self._value 文件“C:\Users\jjaaae\AppData\Local\Programs\Python\Python36\lib\multiprocessing\pool.py”， 第 385 行，在 _handle_tasks 中 put（任务）文件“C：\ Users \ jjaaae \ AppData \ Local \ Programs \ Python \ Python36 \ lib \ site-packages \ sklearn \ externals \ joblib \ pool.py”， 第 371 行，发送中 CustomizedPickler(buffer, self._reducers).dump(obj) _pickle.PicklingError：无法 pickle：内置函数上的属性查找 SelectBestPercFeats 失败

pickle 包需要在另一个模块中定义自定义类，然后导入。因此，创建另一个 python 包文件（例如transformation.py）然后像这样导入from transformation import SelectBestPercFeats。这将解决酸洗错误。