Python Impute using BayesianRidge() sklearn impute.IterativeImputer regression impute analysis value error

Question

PROBLEM

Use interativeImputer from sklearn.impute.IterativeImputer, to get regression model fit for for BayesianRidge() for impute missing data in variable 'Frontage'.

After the interative_imputer_fit = interative_imputer.fit(data) run, the interative_imputer_fit.transform(X) runs but invoke on function, imputer_bay_ridge(data), the transform() function from interative_imputer, e.g., interative_imputer_fit.transform(X) error on value error. Passed in two variables, Frontage and Area. But only Frontage was inside the numpy.array.

Python CODE using sklearn

from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.linear_model import BayesianRidge

def imputer_bay_ridge(data):
    data_array = data.to_numpy()
    data_array.reshape(1, -1)
    interative_imputer = IterativeImputer(BayesianRidge())
    interative_imputer_fit = interative_imputer.fit(data_array)
    X = data['LotFrontage']
    data_imputed = interative_imputer_fit.transform(X)

  
train_data[['Frontage', 'Area']]    

fit_tranformed_imputed = imputer_bay_ridge(train_data[['Frontage', 'Area']])

DATA EXAMPLE

train_data[['Frontage', 'Area']]   

    Frontage    Area
0   65.0    8450
1   80.0    9600
2   68.0    11250
3   60.0    9550
4   84.0    14260
... ... ...
1455    62.0    7917
1456    85.0    13175
1457    66.0    9042
1458    68.0    9717
1459    75.0    9937
1460 rows × 2 columns

ERROR

ValueError                                Traceback (most recent call last)
Cell In[243], line 1
----> 1 fit_tranformed_imputed = imputer_bay_ridge(train_data[['LotFrontage', 'LotArea']])

Cell In[242], line 12, in imputer_bay_ridge(data)
     10 interative_imputer_fit = interative_imputer.fit(data_array)
     11 X = data['LotFrontage']
---> 12 data_imputed = interative_imputer_fit.transform(X)

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/impute/_iterative.py:724, in IterativeImputer.transform(self, X)
    707 """Impute all missing values in `X`.
    708 
    709 Note that this is stochastic, and that if `random_state` is not fixed,
   (...)
    720      The imputed input data.
    721 """
    722 check_is_fitted(self)
--> 724 X, Xt, mask_missing_values, complete_mask = self._initial_imputation(X)
    726 X_indicator = super()._transform_indicator(complete_mask)
    728 if self.n_iter_ == 0 or np.all(mask_missing_values):

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/impute/_iterative.py:514, in IterativeImputer._initial_imputation(self, X, in_fit)
    511 else:
    512     force_all_finite = True
--> 514 X = self._validate_data(
    515     X,
    516     dtype=FLOAT_DTYPES,
    517     order="F",
    518     reset=in_fit,
    519     force_all_finite=force_all_finite,
    520 )
    521 _check_inputs_dtype(X, self.missing_values)
    523 X_missing_mask = _get_mask(X, self.missing_values)

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:566, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    564     raise ValueError("Validation should be done on X, y or both.")
    565 elif not no_val_X and no_val_y:
--> 566     X = check_array(X, **check_params)
    567     out = X
    568 elif no_val_X and not no_val_y:

File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/utils/validation.py:769, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    767     # If input is 1D raise error
    768     if array.ndim == 1:
--> 769         raise ValueError(
    770             "Expected 2D array, got 1D array instead:\narray={}.\n"
    771             "Reshape your data either using array.reshape(-1, 1) if "
    772             "your data has a single feature or array.reshape(1, -1) "
    773             "if it contains a single sample.".format(array)
    774         )
    776 # make sure we actually converted to numeric:
    777 if dtype_numeric and array.dtype.kind in "OUSV":

ValueError: Expected 2D array, got 1D array instead:
array=[65. 80. 68. ... 66. 68. 75.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
```