我有一个在本地训练并部署到引擎的模型，这样我就可以进行推断/调用端点。当我尝试进行预测时，我遇到了以下异常。

raise ValueError('Input numpy.ndarray must be 2 dimensional')
ValueError: Input numpy.ndarray must be 2 dimensional

我的model是一个带有一些预处理（变量编码）和超参数调优的xgboost模型。

import pandas as pd
import pickle
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder 

# split df into train and test
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:21], df.iloc[:,-1], test_size=0.1)

X_train.shape
(1000,21)

# Encode categorical variables  
cat_vars = ['cat1','cat2','cat3']
cat_transform = ColumnTransformer([('cat', OneHotEncoder(handle_unknown='ignore'), cat_vars)], remainder='passthrough')

encoder = cat_transform.fit(X_train)
X_train = encoder.transform(X_train)
X_test = encoder.transform(X_test)

X_train.shape
(1000,420)

# Define a xgboost regression model
model = XGBRegressor()

# Do hyper-parameter tuning
.....

# Fit model
model.fit(X_train, y_train)

model对象如下所示：

XGBRegressor(colsample_bytree=xxx, gamma=xxx,
             learning_rate=xxx, max_depth=x, n_estimators=xxx,
             subsample=xxx)

我的测试数据是一个浮点值字符串，它被转换成一个数组，因为数据必须作为numpy数组传递。

testdata = [........., 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 2000, 200, 85, 412412, 123, 41, 552, 50000, 512, 0.1, 10.0, 2.0, 0.05]

我尝试过将numpy数组从1d重新调整为2d，但是，这不起作用，因为测试数据和训练模型之间的特征数量不匹配。
我的问题是如何传递一个长度与trained model中的# of features相同的numpy数组？有什么解决办法吗？我可以通过本地传递测试数据列表来进行预测。
有关推理脚本的详细信息，请单击此处：https://github.com/aws-samples/amazon-sagemaker-local-mode/blob/main/xgboost_script_mode_local_training_and_serving/code/inference.py

Traceback (most recent call last):
File "/miniconda3/lib/python3.6/site-packages/sagemaker_containers/_functions.py", line 93, in wrapper
return fn(*args, **kwargs)
File "/opt/ml/code/inference.py", line 75, in predict_fn
prediction = model.predict(input_data)
File "/miniconda3/lib/python3.6/site-packages/xgboost/sklearn.py", line 448, in predict
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
File "/miniconda3/lib/python3.6/site-packages/xgboost/core.py", line 404, in __init__
self._init_from_npy2d(data, missing, nthread)
File "/miniconda3/lib/python3.6/site-packages/xgboost/core.py", line 474, in _init_from_npy2d
raise ValueError('Input numpy.ndarray must be 2 dimensional')
ValueError: Input numpy.ndarray must be 2 dimensional

当我尝试使用testdata.reshape(-1,1)将测试数据重新整形为2d numpy数组时，遇到feature_names不匹配异常。

File "/opt/ml/code/inference.py", line 75, in predict_fn
3n0u6hucsr-algo-1-qbiyg  |     prediction = model.predict(input_data)
3n0u6hucsr-algo-1-qbiyg  |   File "/miniconda3/lib/python3.6/site-packages/xgboost/sklearn.py", line 456, in predict
3n0u6hucsr-algo-1-qbiyg  |     validate_features=validate_features)
3n0u6hucsr-algo-1-qbiyg  |   File "/miniconda3/lib/python3.6/site-packages/xgboost/core.py", line 1284, in predict
3n0u6hucsr-algo-1-qbiyg  |     self._validate_features(data)
3n0u6hucsr-algo-1-qbiyg  |   File "/miniconda3/lib/python3.6/site-packages/xgboost/core.py", line 1690, in _validate_features
3n0u6hucsr-algo-1-qbiyg  |     data.feature_names))
3n0u6hucsr-algo-1-qbiyg  | ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15',

更新：我可以通过运行model.get_booster().feature_names来检索模型的特性名称。有没有方法可以使用这些名称并分配给测试数据点，以使它们保持一致？

['f0', 'f1', 'f2', 'f3', 'f4', 'f5',......'f417','f418','f419']