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.
I believe that's related to how skopt encodes the hyperparameter space: it seems having identical points generated by your random lists are required to trigger the error, though sometimes it fits regardless. Either there are collisions or it makes the grid to be processed erroneously.
At least the issue stopped reproducing for me after changing all random lists to list(range(...)).
Might be worth a bug report.