Python ValueError : ColumnTransformer, 列顺序不相等

Question

我将读取 csv、训练模型和预测请求数据的以下函数放在一起。

我有以下 ValueError:使用剩余关键字时，列顺序必须相等才能适合和转换

训练数据和用于预测的数据具有完全相同的列数，例如 15。我不确定列的“排序”如何改变。

~/.local/lib/python3.5/site-packages/sklearn/pipeline.py in predict(self, X, **predict_params)
    417         Xt = X
    418         for _, name, transform in self._iter(with_final=False):
--> 419             Xt = transform.transform(Xt)
    420         return self.steps[-1][-1].predict(Xt, **predict_params)
    421 

~/.local/lib/python3.5/site-packages/sklearn/compose/_column_transformer.py in transform(self, X)
    581             if (n_cols_transform >= n_cols_fit and
    582                     any(X.columns[:n_cols_fit] != self._df_columns)):
--> 583                 raise ValueError('Column ordering must be equal for fit '
    584                                  'and for transform when using the '
    585                                  'remainder keyword')

ValueError: Column ordering must be equal for fit and for transform when using the remainder keyword

功能:

numeric_transformer = Pipeline(steps=[

    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

#Putting data transformation and the model in a pipeline
rf = Pipeline(steps=[('preprocessor', preprocessor),
                     ('classifier', RandomForestClassifier(
                                        n_estimators=500,
                                        criterion="gini",
                                        max_features="sqrt",
                                        min_samples_leaf=4))])

rf.fit(X_train, y_train)

request_data = {'A': [request.A],
                'B': [request.B],
                'C': [request.C],
                'D': [request.D],
                'E': [request.E],
                'F': [request.F],
                'G': [request.G],
                'H': [request.H],
                'I': [request.I],
                'J': [request.J],
                'K': [request.K],
                'L': [request.L],
                'M': [request.M],
                'N': [request.N],
                'O': [request.O]}

df_resp = pd.DataFrame(data=request_data)
response = rf.predict(df_resp)

output = {"Safety Rating": response[0]}

return output

Answer 1

我从错误消息中了解到，X_train.columns 和df_resp.columns 不一样，但是.predict() 需要

为了强制实现这种相等性，您可以在创建数据帧时将 X_train 的列列表作为参数传递:

pd.DataFrame(data=request_data, columns=X_train.columns)