scorel = []
for i in range(0,200,10):
rfc = RandomForestClassifier(n_estimators=i+1,
n_jobs=-1,
random_state=90)
score = cross_val_score(rfc,data.data,data.target,cv=10,error_score='raise').mean()
scorel.append(score)
print(max(scorel),(scorel,index(max(scorel))*10)+1)
plt.figure(figsize=[20,5])
plt.plot(range(1,201,10),scorel)
plt.show()
出现了错误提示
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_2692\4090940073.py in <module>
4 n_jobs=-1,
5 random_state=90)
----> 6 score = cross_val_score(rfc,data.data,data.target,cv=10,error_score='raise').mean()
7 scorel.append(score)
8
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
507 scorer = check_scoring(estimator, scoring=scoring)
508
--> 509 cv_results = cross_validate(
510 estimator=estimator,
511 X=X,
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
265 # independent, and that it is pickle-able.
266 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 267 results = parallel(
268 delayed(_fit_and_score)(
269 clone(estimator),
D:\Anaconda3\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
D:\Anaconda3\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
D:\Anaconda3\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
D:\Anaconda3\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)
D:\Anaconda3\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):
D:\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
260 # change the default number of processes to -1
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
D:\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
260 # change the default number of processes to -1
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
D:\Anaconda3\lib\site-packages\sklearn\utils\fixes.py in __call__(self, *args, **kwargs)
214 def __call__(self, *args, **kwargs):
215 with config_context(**self.config):
--> 216 return self.function(*args, **kwargs)
217
218
D:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
678 estimator.fit(X_train, **fit_params)
679 else:
--> 680 estimator.fit(X_train, y_train, **fit_params)
681
682 except Exception:
D:\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in fit(self, X, y, sample_weight)
448 # parallel_backend contexts set at a higher level,
449 # since correctness does not rely on using threads.
--> 450 trees = Parallel(
451 n_jobs=self.n_jobs,
452 verbose=self.verbose,
D:\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
966
967 if not self._managed_backend:
--> 968 n_jobs = self._initialize_backend()
969 else:
970 n_jobs = self._effective_n_jobs()
D:\Anaconda3\lib\site-packages\joblib\parallel.py in _initialize_backend(self)
733 """Build a process or thread pool and return the number of workers"""
734 try:
--> 735 n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
736 **self._backend_args)
737 if self.timeout is not None and not self._backend.supports_timeout:
D:\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
492 SequentialBackend(nesting_level=self.nesting_level))
493
--> 494 self._workers = get_memmapping_executor(
495 n_jobs, timeout=idle_worker_timeout,
496 env=self._prepare_worker_env(n_jobs=n_jobs),
D:\Anaconda3\lib\site-packages\joblib\executor.py in get_memmapping_executor(n_jobs, **kwargs)
18
19 def get_memmapping_executor(n_jobs, **kwargs):
---> 20 return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)
21
22
D:\Anaconda3\lib\site-packages\joblib\executor.py in get_memmapping_executor(cls, n_jobs, timeout, initializer, initargs, env, temp_folder, context_id, **backend_args)
40 _executor_args = executor_args
41
---> 42 manager = TemporaryResourcesManager(temp_folder)
43
44 # reducers access the temporary folder in which to store temporary
D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in __init__(self, temp_folder_root, context_id)
529 # exposes exposes too many low-level details.
530 context_id = uuid4().hex
--> 531 self.set_current_context(context_id)
532
533 def set_current_context(self, context_id):
D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in set_current_context(self, context_id)
533 def set_current_context(self, context_id):
534 self._current_context_id = context_id
--> 535 self.register_new_context(context_id)
536
537 def register_new_context(self, context_id):
D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in register_new_context(self, context_id)
558 new_folder_name, self._temp_folder_root
559 )
--> 560 self.register_folder_finalizer(new_folder_path, context_id)
561 self._cached_temp_folders[context_id] = new_folder_path
562
D:\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in register_folder_finalizer(self, pool_subfolder, context_id)
588 # semaphores and pipes
589 pool_module_name = whichmodule(delete_folder, 'delete_folder')
--> 590 resource_tracker.register(pool_subfolder, "folder")
591
592 def _cleanup():
D:\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in register(self, name, rtype)
189 '''Register a named resource, and increment its refcount.'''
190 self.ensure_running()
--> 191 self._send('REGISTER', name, rtype)
192
193 def unregister(self, name, rtype):
D:\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in _send(self, cmd, name, rtype)
202
203 def _send(self, cmd, name, rtype):
--> 204 msg = '{0}:{1}:{2}\n'.format(cmd, name, rtype).encode('ascii')
205 if len(name) > 512:
206 # posix guarantees that writes to a pipe of less than PIPE_BUF
UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)
