Value Error in EarlyStoppingShapRFECV when I use LGBM as clf

[Alcoholrithm]

Describe the bug

Whenever I use lgbm as clf of EarlyStoppingShapRFECV the following error is occur

'ValueError: Model type not supported'

Environment (please complete the following information):

• probatus version - 1.8.9
• python version - 3.7
• OS: windows and linux
• lightgbm - 3.2.1

To Reproduce

from lightgbm import LGBMClassifier
import pandas as pd
from probatus.feature_elimination import EarlyStoppingShapRFECV
from sklearn.datasets import make_classification

feature_names = [
    'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7',
    'f8', 'f9', 'f10', 'f11', 'f12', 'f13',
    'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20']

# Prepare two samples
X, y = make_classification(n_samples=200, class_sep=0.05, n_informative=6, n_features=20,
                           random_state=0, n_redundant=10, n_clusters_per_class=1)
X = pd.DataFrame(X, columns=feature_names)

# Prepare model
clf = LGBMClassifier(n_estimators=200, max_depth=3)

# Run feature elimination
shap_elimination = EarlyStoppingShapRFECV(
    clf=clf, step=0.2, cv=10, scoring='roc_auc', early_stopping_rounds=10, n_jobs=3)
report = shap_elimination.fit_compute(X, y)

# Make plots
performance_plot = shap_elimination.plot()

# Get final feature set
final_features_set = shap_elimination.get_reduced_features_set(num_features=3)

Error traceback

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_25508/1460954107.py in <module>
     21 shap_elimination = EarlyStoppingShapRFECV(
     22     clf=clf, step=0.2, cv=10, scoring='roc_auc', early_stopping_rounds=10, n_jobs=1)
---> 23 report = shap_elimination.fit_compute(X, y)
     24 
     25 # Make plots

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\probatus\feature_elimination\feature_elimination.py in fit_compute(self, X, y, sample_weight, columns_to_keep, column_names, **shap_kwargs)
    655             columns_to_keep=columns_to_keep,
    656             column_names=column_names,
--> 657             **shap_kwargs,
    658         )
    659         return self.compute()

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\probatus\feature_elimination\feature_elimination.py in fit(self, X, y, sample_weight, columns_to_keep, column_names, **shap_kwargs)
    547                     **shap_kwargs,
    548                 )
--> 549                 for train_index, val_index in self.cv.split(current_X, self.y)
    550             )
    551 

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1041             # remaining jobs.
   1042             self._iterating = False
-> 1043             if self.dispatch_one_batch(iterator):
   1044                 self._iterating = self._original_iterator is not None
   1045 

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    859                 return False
    860             else:
--> 861                 self._dispatch(tasks)
    862                 return True
    863 

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    777         with self._lock:
    778             job_idx = len(self._jobs)
--> 779             job = self._backend.apply_async(batch, callback=cb)
    780             # A job can complete so quickly than its callback is
    781             # called before we get here, causing self._jobs to

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\parallel.py in __call__(self)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\probatus\feature_elimination\feature_elimination.py in _get_feature_shap_values_per_fold(self, X, y, clf, train_index, val_index, sample_weight, **shap_kwargs)
   1235             sample_weight=sample_weight,
   1236             train_index=train_index,
-> 1237             val_index=val_index,
   1238         )
   1239 

c:\Users\kmiii\.conda\envs\py37\lib\site-packages\probatus\feature_elimination\feature_elimination.py in _get_fit_params(self, clf, X_train, y_train, X_val, y_val, sample_weight, train_index, val_index)
   1184             pass
   1185 
-> 1186         raise ValueError("Model type not supported")
   1187 
   1188     def _get_feature_shap_values_per_fold(self, X, y, clf, train_index, val_index, sample_weight=None, **shap_kwargs):

ValueError: Model type not supported

[Alcoholrithm]

This was just because print_evaluation of lgbm was renamed to log_evaluation after lgbm 3.3.0 I sent a pull request for this.

[ReinierKoops]

Please see #188