continuous data, Y response not support in the cross_val_score() binary|multiclass for IterativeImputer for BayesianRidge

Problem Defined, Continuous Challenge

This new imputer_bayesian_ridge() function is for Iterative Imputer to impute training data. Sending in data frame training data, then immediately get data.values for numpy array variable. This send or passes a training data with many features, and Y response variable. This effort is only seeking to impute on one single feature.

Apparently my continuous data, Y response data, which is price $$$$ continuous data, is not supported in the cross_val_score(interative_imputer, data_array).

So what advise on how to work with continuous data in Y response variable to work with Iterative Imputer and satisfy the cross_val_score for the object 'interativea_imputer'

To support the target type, should I cast my continuous data in Y response variable to binary? No. For this is not a binary classification, so multiclass is more in line. So how to handle price data when it is the response variable?

Error Received

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.

CODE

   
def imputer_regressor_bay_ridge(data, y):
    data_array = data.values. ##looks OK

    interative_imputer = IterativeImputer(BayesianRidge()). ## runs OK

    interative_imputer_fit = interative_imputer.fit(data_array) ## runs OK

    data_imputed = interative_imputer_fit.transform(data_array) ## runs OK

    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) ## runs OK

    scores = cross_val_score(interative_imputer, data_array, y, 
                             scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')
    
    return scores, data_imputed

DATA SAMPLE

print(train_data.shape)
data_array = train_data.values
data_array

(1460, 250)
array([[-1.73086488, -0.20803433, -0.20714171, ..., -0.11785113,
         0.4676514 , -0.30599503],
       [-1.7284922 ,  0.40989452, -0.09188637, ..., -0.11785113,
         0.4676514 , -0.30599503],
       [-1.72611953, -0.08444856,  0.07347998, ..., -0.11785113,
         0.4676514 , -0.30599503],
       ...,
       [ 1.72611953, -0.16683907, -0.14781027, ..., -0.11785113,
         0.4676514 , -0.30599503],
       [ 1.7284922 , -0.08444856, -0.08016039, ..., -0.11785113,
         0.4676514 , -0.30599503],
       [ 1.73086488,  0.20391824, -0.05811155, ..., -0.11785113,
         0.4676514 , -0.30599503]])

y = train_data['ResponseY'].values
y.shape  

(1460,)
array([ 0.34727322,  0.00728832,  0.53615372, ...,  1.07761115,
       -0.48852299, -0.42084081])

Value Error

Apparently my continuous data, which is price $ data, is not supported in cross_val_score(interative_imputer, data_array on:

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.

Empty                                     Traceback (most recent call last)
File ~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py:820, in Parallel.dispatch_one_batch(self, iterator)
819 try:
--> 820     tasks = self._ready_batches.get(block=False)
821 except queue.Empty:
822     # slice the iterator n_jobs * batchsize items at a time. If the
823     # slice returns less than that, then the current batchsize puts
(...)
826     # accordingly to distribute evenly the last items between all
827     # workers.
File ~/opt/anaconda3/lib/python3.9/queue.py:168, in Queue.get(self, block, timeout)
167     if not self._qsize():
--> 168         raise Empty
169 elif timeout is None:
Empty: 
During handling of the above exception, another exception occurred:
ValueError                                Traceback (most recent call last)
Cell In[27], line 5
3 #train_data, test_data = minmaxscaler(train_data, test_data)  # alternate run for min-max scaler
4 columns, imputed_df = imputer_regressor(train_data)
----> 5 scores, data_imputed = imputer_regressor_bay_ridge(train_data, y)
7 misTrain = whichColumnsMissing(train_data)
8 misTest = whichColumnsMissing(test_data)
Cell In[24], line 110, in imputer_regressor_bay_ridge(data, y)
108 data_imputed = interative_imputer_fit.transform(data_array)
109 cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
--> 110 scores = cross_val_score(interative_imputer, data_array, 
111                          y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')
113 return scores, data_imputed
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:509, in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
506 # To ensure multimetric format is not supported
507 scorer = check_scoring(estimator, scoring=scoring)
--> 509 cv_results = cross_validate(
510     estimator=estimator,
511     X=X,
512     y=y,
513     groups=groups,
514     scoring={"score": scorer},
515     cv=cv,
516     n_jobs=n_jobs,
517     verbose=verbose,
518     fit_params=fit_params,
519     pre_dispatch=pre_dispatch,
520     error_score=error_score,
521 )
522 return cv_results["test_score"]
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:267, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
264 # We clone the estimator to make sure that all the folds are
265 # independent, and that it is pickle-able.
266 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 267 results = parallel(
268     delayed(_fit_and_score)(
269         clone(estimator),
270         X,
271         y,
272         scorers,
273         train,
274         test,
275         verbose,
276         None,
277         fit_params,
278         return_train_score=return_train_score,
279         return_times=True,
280         return_estimator=return_estimator,
281         error_score=error_score,
282     )
283     for train, test in cv.split(X, y, groups)
284 )
286 _warn_about_fit_failures(results, error_score)
288 # For callabe scoring, the return type is only know after calling. If the
289 # return type is a dictionary, the error scores can now be inserted with
290 # the correct key.
File ~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py:1041, in Parallel.__call__(self, iterable)
1032 try:
1033     # Only set self._iterating to True if at least a batch
1034     # was dispatched. In particular this covers the edge
(...)
1038     # was very quick and its callback already dispatched all the
1039     # remaining jobs.
1040     self._iterating = False
-> 1041     if self.dispatch_one_batch(iterator):
1042         self._iterating = self._original_iterator is not None
1044     while self.dispatch_one_batch(iterator):
File ~/opt/anaconda3/lib/python3.9/site-packages/joblib/parallel.py:831, in Parallel.dispatch_one_batch(self, iterator)
828 n_jobs = self._cached_effective_n_jobs
829 big_batch_size = batch_size * n_jobs
--> 831 islice = list(itertools.islice(iterator, big_batch_size))
832 if len(islice) == 0:
833     return False
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:267, in <genexpr>(.0)
264 # We clone the estimator to make sure that all the folds are
265 # independent, and that it is pickle-able.
266 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 267 results = parallel(
268     delayed(_fit_and_score)(
269         clone(estimator),
270         X,
271         y,
272         scorers,
273         train,
274         test,
275         verbose,
276         None,
277         fit_params,
278         return_train_score=return_train_score,
279         return_times=True,
280         return_estimator=return_estimator,
281         error_score=error_score,
282     )
283     for train, test in cv.split(X, y, groups)
284 )
286 _warn_about_fit_failures(results, error_score)
288 # For callabe scoring, the return type is only know after calling. If the
289 # return type is a dictionary, the error scores can now be inserted with
290 # the correct key.
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:1411, in _RepeatedSplits.split(self, X, y, groups)
1409 for idx in range(n_repeats):
1410     cv = self.cv(random_state=rng, shuffle=True, **self.cvargs)
-> 1411     for train_index, test_index in cv.split(X, y, groups):
1412         yield train_index, test_index
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:340, in _BaseKFold.split(self, X, y, groups)
332 if self.n_splits > n_samples:
333     raise ValueError(
334         (
335             "Cannot have number of splits n_splits={0} greater"
336             " than the number of samples: n_samples={1}."
337         ).format(self.n_splits, n_samples)
338     )
--> 340 for train, test in super().split(X, y, groups):
341     yield train, test
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:86, in BaseCrossValidator.split(self, X, y, groups)
84 X, y, groups = indexable(X, y, groups)
85 indices = np.arange(_num_samples(X))
---> 86 for test_index in self._iter_test_masks(X, y, groups):
87     train_index = indices[np.logical_not(test_index)]
88     test_index = indices[test_index]
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:709, in StratifiedKFold._iter_test_masks(self, X, y, groups)
708 def _iter_test_masks(self, X, y=None, groups=None):
--> 709     test_folds = self._make_test_folds(X, y)
710     for i in range(self.n_splits):
711         yield test_folds == i
File ~/opt/anaconda3/lib/python3.9/site-packages/sklearn/model_selection/_split.py:652, in StratifiedKFold._make_test_folds(self, X, y)
650 allowed_target_types = ("binary", "multiclass")
651 if type_of_target_y not in allowed_target_types:
--> 652     raise ValueError(
653         "Supported target types are: {}. Got {!r} instead.".format(
654             allowed_target_types, type_of_target_y
655         )
656     )
658 y = column_or_1d(y)
660 _, y_idx, y_inv = np.unique(y, return_index=True, return_inverse=True)
ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.

In cross_val_score, the use of scoring='accuracy' is only for binary or multiclass targets.
You should use instead scoring=None or some other scoring adequate for continuous targets. See regression ('neg_mean_absolute_error', 'neg_mean_squared_error', ...).