---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[57], line 72
70 # Perform GridSearchCV with custom scoring function
71 grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=custom_scoring, cv=tscv, verbose=2)
---> 72 grid_result = grid.fit(x_train, y_train)
74 # Extract and store results
75 results_dict[f'Dataset_{i}_x{i}_y{i}'] = {
76 'Best Score': grid_result.best_score_,
77 'Best Params': grid_result.best_params_,
78 'MSE': mean_squared_error(y_test, grid_result.predict(x_test))
79 }
File ~/tf2/lib/python3.10/site-packages/sklearn/base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
1466 estimator._validate_params()
1468 with config_context(
1469 skip_parameter_validation=(
1470 prefer_skip_nested_validation or global_skip_validation
1471 )
1472 ):
-> 1473 return fit_method(estimator, *args, **kwargs)
File ~/tf2/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968, in BaseSearchCV.fit(self, X, y, **params)
962 results = self._format_results(
963 all_candidate_params, n_splits, all_out, all_more_results
964 )
966 return results
--> 968 self._run_search(evaluate_candidates)
970 # multimetric is determined here because in the case of a callable
971 # self.scoring the return type is only known after calling
972 first_test_score = all_out[0]["test_scores"]
File ~/tf2/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1543, in GridSearchCV._run_search(self, evaluate_candidates)
1541 def _run_search(self, evaluate_candidates):
1542 """Search all candidates in param_grid"""
-> 1543 evaluate_candidates(ParameterGrid(self.param_grid))
File ~/tf2/lib/python3.10/site-packages/sklearn/model_selection/_search.py:914, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
906 if self.verbose > 0:
907 print(
908 "Fitting {0} folds for each of {1} candidates,"
909 " totalling {2} fits".format(
910 n_splits, n_candidates, n_candidates * n_splits
911 )
912 )
--> 914 out = parallel(
915 delayed(_fit_and_score)(
916 clone(base_estimator),
917 X,
918 y,
919 train=train,
920 test=test,
921 parameters=parameters,
922 split_progress=(split_idx, n_splits),
923 candidate_progress=(cand_idx, n_candidates),
924 **fit_and_score_kwargs,
925 )
926 for (cand_idx, parameters), (split_idx, (train, test)) in product(
927 enumerate(candidate_params),
928 enumerate(cv.split(X, y, **routed_params.splitter.split)),
929 )
930 )
932 if len(out) < 1:
933 raise ValueError(
934 "No fits were performed. "
935 "Was the CV iterator empty? "
936 "Were there no candidates?"
937 )
File ~/tf2/lib/python3.10/site-packages/sklearn/utils/parallel.py:67, in Parallel.__call__(self, iterable)
62 config = get_config()
63 iterable_with_config = (
64 (_with_config(delayed_func, config), args, kwargs)
65 for delayed_func, args, kwargs in iterable
66 )
---> 67 return super().__call__(iterable_with_config)
File ~/tf2/lib/python3.10/site-packages/joblib/parallel.py:1863, in Parallel.__call__(self, iterable)
1861 output = self._get_sequential_output(iterable)
1862 next(output)
-> 1863 return output if self.return_generator else list(output)
1865 # Let's create an ID that uniquely identifies the current call. If the
1866 # call is interrupted early and that the same instance is immediately
1867 # re-used, this id will be used to prevent workers that were
1868 # concurrently finalizing a task from the previous call to run the
1869 # callback.
1870 with self._lock:
File ~/tf2/lib/python3.10/site-packages/joblib/parallel.py:1792, in Parallel._get_sequential_output(self, iterable)
1790 self.n_dispatched_batches += 1
1791 self.n_dispatched_tasks += 1
-> 1792 res = func(*args, **kwargs)
1793 self.n_completed_tasks += 1
1794 self.print_progress()
File ~/tf2/lib/python3.10/site-packages/sklearn/utils/parallel.py:129, in _FuncWrapper.__call__(self, *args, **kwargs)
127 config = {}
128 with config_context(**config):
--> 129 return self.function(*args, **kwargs)
File ~/tf2/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:876, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
869 score_params_test = _check_method_params(X, params=score_params, indices=test)
871 if parameters is not None:
872 # here we clone the parameters, since sometimes the parameters
873 # themselves might be estimators, e.g. when we search over different
874 # estimators in a pipeline.
875 # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 876 estimator = estimator.set_params(**clone(parameters, safe=False))
878 start_time = time.time()
880 X_train, y_train = _safe_split(estimator, X, y, train)
File ~/tf2/lib/python3.10/site-packages/scikeras/wrappers.py:1175, in BaseWrapper.set_params(self, **params)
1171 super().set_params(**{param: value})
1172 except ValueError:
1173 # Give a SciKeras specific user message to aid
1174 # in moving from the Keras wrappers
-> 1175 raise ValueError(
1176 f"Invalid parameter {param} for estimator {self.__name__}."
1177 "\nThis issue can likely be resolved by setting this parameter"
1178 f" in the {self.__name__} constructor:"
1179 f"\n`{self.__name__}({param}={value})`"
1180 "\nCheck the list of available parameters with"
1181 " `estimator.get_params().keys()`"
1182 ) from None
1183 return self
ValueError: Invalid parameter activation for estimator KerasRegressor.
This issue can likely be resolved by setting this parameter in the KerasRegressor constructor:
`KerasRegressor(activation=relu)`
Check the list of available parameters with `estimator.get_params().keys()`
ys7yoo
commented
3 months ago