this is the code to reproduce the error:
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from scipy.stats import loguniform
from skopt import BayesSearchCV
from sklearn.datasets import load_iris
import numpy as np
X, y = load_iris(return_X_y=True)
grid = {
'LogisticRegression' : {
'C': loguniform.rvs(0.1, 10000, size = 50),
'solver': ['lbfgs','saga'],
'penalty': ['l2'],
'warm_start': [False, True],
'class_weight' : [None, 'balanced'],
'max_iter': [100, 1000],
'n_jobs': [ 10 ]
},
'RandomForestClassifier' : {
'n_estimators': np.random.randint(5, 200, size=10),
'criterion' : [ 'gini', 'entropy' ],
'max_depth' : np.random.randint(5, 50, size=10),
'min_samples_split': np.random.randint(5, 50, size=10),
'min_samples_leaf': np.random.randint(5, 50, size=10),
'max_features' : loguniform.rvs(0.2, 1.0, size=5),
'n_jobs' : [ 10 ]
}
}
tuner_params = {
'cv': 2,
'n_jobs': 10,
'scoring': 'roc_auc_ovr',
'return_train_score': True,
'refit': True,
'n_iter':3
}
clf = 'LogisticRegression'
search_cv = BayesSearchCV( estimator = eval(clf)(), search_spaces = grid[clf], **tuner_params)
search_cv.fit(X,y)
clf = 'RandomForestClassifier'
search_cv = BayesSearchCV( estimator = eval(clf)(), search_spaces = grid[clf], **tuner_params)
search_cv.fit(X,y)
Using BayesSearchCV on LogisticRegression as classifier gives no error, while using RandomForestClassifier it gives the following error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Input In [8], in <cell line: 2>()
1 search_cv = BayesSearchCV( estimator = eval(clf)(), search_spaces = grid[clf], **tuner_params)
----> 2 search_cv.fit(X,y)
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/searchcv.py:466, in BayesSearchCV.fit(self, X, y, groups, callback, **fit_params)
463 else:
464 self.optimizer_kwargs_ = dict(self.optimizer_kwargs)
--> 466 super().fit(X=X, y=y, groups=groups, **fit_params)
468 # BaseSearchCV never ranked train scores,
469 # but apparently we used to ship this (back-compat)
470 if self.return_train_score:
File ~/.conda/envs/meth/lib/python3.9/site-packages/sklearn/model_selection/_search.py:875, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
869 results = self._format_results(
870 all_candidate_params, n_splits, all_out, all_more_results
871 )
873 return results
--> 875 self._run_search(evaluate_candidates)
877 # multimetric is determined here because in the case of a callable
878 # self.scoring the return type is only known after calling
879 first_test_score = all_out[0]["test_scores"]
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/searchcv.py:512, in BayesSearchCV._run_search(self, evaluate_candidates)
508 while n_iter > 0:
509 # when n_iter < n_points points left for evaluation
510 n_points_adjusted = min(n_iter, n_points)
--> 512 optim_result = self._step(
513 search_space, optimizer,
514 evaluate_candidates, n_points=n_points_adjusted
515 )
516 n_iter -= n_points
518 if eval_callbacks(callbacks, optim_result):
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/searchcv.py:400, in BayesSearchCV._step(self, search_space, optimizer, evaluate_candidates, n_points)
397 """Generate n_jobs parameters and evaluate them in parallel.
398 """
399 # get parameter values to evaluate
--> 400 params = optimizer.ask(n_points=n_points)
402 # convert parameters to python native types
403 params = [[np.array(v).item() for v in p] for p in params]
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/optimizer/optimizer.py:395, in Optimizer.ask(self, n_points, strategy)
393 X = []
394 for i in range(n_points):
--> 395 x = opt.ask()
396 X.append(x)
398 ti_available = "ps" in self.acq_func and len(opt.yi) > 0
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/optimizer/optimizer.py:367, in Optimizer.ask(self, n_points, strategy)
336 """Query point or multiple points at which objective should be evaluated.
337
338 n_points : int or None, default: None
(...)
364
365 """
366 if n_points is None:
--> 367 return self._ask()
369 supported_strategies = ["cl_min", "cl_mean", "cl_max"]
371 if not (isinstance(n_points, int) and n_points > 0):
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/optimizer/optimizer.py:434, in Optimizer._ask(self)
430 if self._n_initial_points > 0 or self.base_estimator_ is None:
431 # this will not make a copy of `self.rng` and hence keep advancing
432 # our random state.
433 if self._initial_samples is None:
--> 434 return self.space.rvs(random_state=self.rng)[0]
435 else:
436 # The samples are evaluated starting form initial_samples[0]
437 return self._initial_samples[
438 len(self._initial_samples) - self._n_initial_points]
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/space.py:900, in Space.rvs(self, n_samples, random_state)
897 columns = []
899 for dim in self.dimensions:
--> 900 columns.append(dim.rvs(n_samples=n_samples, random_state=rng))
902 # Transpose
903 return _transpose_list_array(columns)
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/space.py:698, in Categorical.rvs(self, n_samples, random_state)
696 return self.inverse_transform([(choices)])
697 elif self.transform_ == "normalize":
--> 698 return self.inverse_transform(list(choices))
699 else:
700 return [self.categories[c] for c in choices]
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/space.py:685, in Categorical.inverse_transform(self, Xt)
680 """Inverse transform samples from the warped space back into the
681 original space.
682 """
683 # The concatenation of all transformed dimensions makes Xt to be
684 # of type float, hence the required cast back to int.
--> 685 inv_transform = super(Categorical, self).inverse_transform(Xt)
686 if isinstance(inv_transform, list):
687 inv_transform = np.array(inv_transform)
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/space.py:168, in Dimension.inverse_transform(self, Xt)
164 def inverse_transform(self, Xt):
165 """Inverse transform samples from the warped space back into the
166 original space.
167 """
--> 168 return self.transformer.inverse_transform(Xt)
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/transformers.py:309, in Pipeline.inverse_transform(self, X)
307 def inverse_transform(self, X):
308 for transformer in self.transformers[::-1]:
--> 309 X = transformer.inverse_transform(X)
310 return X
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/transformers.py:216, in LabelEncoder.inverse_transform(self, Xt)
214 else:
215 Xt = np.asarray(Xt)
--> 216 return [
217 self.inverse_mapping_[int(np.round(i))] for i in Xt
218 ]
File ~/.conda/envs/meth/lib/python3.9/site-packages/skopt/space/transformers.py:217, in <listcomp>(.0)
214 else:
215 Xt = np.asarray(Xt)
216 return [
--> 217 self.inverse_mapping_[int(np.round(i))] for i in Xt
218 ]
KeyError: 9
My versions:
python: 3.9.12 sklearn: 1.1.1 skopt: 0.9.0
The same error happen when using XGBClassifier or GradientBoostingClassifier, while there is no error using SVC or KNeighborsClassifier.
