인프런 커뮤니티 질문&답변

안녕하세요!

머신러닝 학습 중 랜덤서치의 코드를 실행했을 때 에러가 발생하여 질문 드립니다.

학습 자료의 코드를 그대로 복사해서 실행해도 같은 에러가 발생하는데 어떤 이유인지 궁금하고 해결방안까지 알려주시면 감사하겠습니다🙏🏻

from scipy.stats import randint
param_distribs={'C': randint(low=0.001, high=100)}
from sklearn.model_selection import RandomizedSearchCV
random_search=RandomizedSearchCV(LogisticRegression(), param_distributions=param_distribs, n_iter=100, cv=5)random_search.fit(X_scaled_train, y_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[24], line 1
----> 1 random_search.fit(X_scaled_train, y_train)

File ~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1144     estimator._validate_params()
   1146 with config_context(
   1147     skip_parameter_validation=(
   1148         prefer_skip_nested_validation or global_skip_validation
   1149     )
   1150 ):
-> 1151     return fit_method(estimator, *args, **kwargs)

File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:1806, in RandomizedSearchCV._run_search(self, evaluate_candidates)
   1804 def _run_search(self, evaluate_candidates):
   1805     """Search n_iter candidates from param_distributions"""
-> 1806     evaluate_candidates(
   1807         ParameterSampler(
   1808             self.param_distributions, self.n_iter, random_state=self.random_state
   1809         )
   1810     )

File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:834, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    832 def evaluate_candidates(candidate_params, cv=None, more_results=None):
    833     cv = cv or cv_orig
--> 834     candidate_params = list(candidate_params)
    835     n_candidates = len(candidate_params)
    837     if self.verbose > 0:

File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_search.py:325, in ParameterSampler.__iter__(self)
    323 for k, v in items:
    324     if hasattr(v, "rvs"):
--> 325         params[k] = v.rvs(random_state=rng)
    326     else:
    327         params[k] = v[rng.randint(len(v))]

File ~/anaconda3/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:468, in rv_frozen.rvs(self, size, random_state)
    466 kwds = self.kwds.copy()
    467 kwds.update({'size': size, 'random_state': random_state})
--> 468 return self.dist.rvs(*self.args, **kwds)

File ~/anaconda3/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:3357, in rv_discrete.rvs(self, *args, **kwargs)
   3328 """Random variates of given type.
   3329 
   3330 Parameters
   (...)
   3354 
   3355 """
   3356 kwargs['discrete'] = True
-> 3357 return super().rvs(*args, **kwargs)

File ~/anaconda3/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py:1036, in rv_generic.rvs(self, *args, **kwds)
   1030 if not np.all(cond):
   1031     message = ("Domain error in arguments. The `scale` parameter must "
   1032                "be positive for all distributions, and many "
   1033                "distributions have restrictions on shape parameters. "
   1034                f"Please see the `scipy.stats.{self.name}` "
   1035                "documentation for details.")
-> 1036     raise ValueError(message)
   1038 if np.all(scale == 0):
   1039     return loc*ones(size, 'd')

ValueError: Domain error in arguments. The `scale` parameter must be positive for all distributions, and many distributions have restrictions on shape parameters. Please see the `scipy.stats.randint` documentation for details.