X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
automl = AutoML()
automl.fit(X_train, y_train)
after running the code, it raises error like this
AutoML directory: AutoML_9
The task is regression with evaluation metric rmse
AutoML will use algorithms: ['Baseline', 'Linear', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']
AutoML will ensemble availabe models
2020-09-10 18:59:15,591 supervised.preprocessing.eda ERROR There was an issue when running EDA. [Errno 22] Invalid argument: 'AutoML_9\EDA\Spd*LSBW.png'
AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']
- Step simple_algorithms will try to check up to 3 models
1_Baseline rmse 2.013368 trained in 0.11 seconds
2_DecisionTree rmse 0.686167 trained in 9.43 seconds
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
'''
Traceback (most recent call last):
File "D:\Anaconda3\envs\mljar\lib\site-packages\joblib\externals\loky\process_executor.py", line 391, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
File "D:\Anaconda3\envs\mljar\lib\multiprocessing\queues.py", line 113, in get
return ForkingPickler.loads(res)
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised_init.py", line 3, in
from supervised.automl import AutoML
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\automl.py", line 3, in
from supervised.base_automl import BaseAutoML
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py", line 17, in
from supervised.algorithms.registry import AlgorithmsRegistry
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\registry.py", line 63, in
import supervised.algorithms.random_forest
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\random_forest.py", line 8, in
from supervised.algorithms.algorithm import BaseAlgorithm
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\algorithm.py", line 3, in
from supervised.utils.importance import PermutationImportance
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\utils\importance.py", line 7, in
import matplotlib.pyplot as plt
File "D:\Anaconda3\envs\mljar\lib\site-packages\matplotlib\pyplot.py", line 43, in
from matplotlib.figure import Figure, figaspect
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 665, in _load_unlocked
File "", line 674, in exec_module
File "", line 764, in get_code
File "", line 833, in get_data
MemoryError
'''
The above exception was the direct cause of the following exception:
BrokenProcessPool Traceback (most recent call last)
in
5 # explain_level=0
6 )
----> 7 automl.fit(X_train, y_train)
~\AppData\Roaming\Python\Python36\site-packages\supervised\automl.py in fit(self, X, y)
276 self : AutoML object
277 """
--> 278 return self._fit(X, y)
279
280 def predict(self, X):
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in _fit(self, X, y)
668
669 except Exception as e:
--> 670 raise e
671 finally:
672 if self._X_path is not None:
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in _fit(self, X, y)
655 trained = self.ensemble_step(is_stacked=params["is_stacked"])
656 else:
--> 657 trained = self.train_model(params)
658
659 params["status"] = "trained" if trained else "skipped"
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in train_model(self, params)
227 f"Train model #{len(self._models)+1} / Model name: {params['name']}"
228 )
--> 229 mf.train(model_path)
230
231 # save the model
~\AppData\Roaming\Python\Python36\site-packages\supervised\model_framework.py in train(self, model_path)
176 metric_name=self.get_metric_name(),
177 ml_task=self._ml_task,
--> 178 explain_level=self._explain_level,
179 )
180
~\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\linear.py in interpret(self, X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level)
137 metric_name,
138 ml_task,
--> 139 explain_level,
140 )
141 if explain_level == 0:
~\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\algorithm.py in interpret(self, X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level)
77 learner_name,
78 metric_name,
---> 79 ml_task,
80 )
81 if explain_level > 1:
~\AppData\Roaming\Python\Python36\site-packages\supervised\utils\importance.py in compute_and_plot(model, X_validation, y_validation, model_file_path, learner_name, metric_name, ml_task)
58 n_jobs=-1, # all cores
59 random_state=12,
---> 60 n_repeats=5, # default
61 )
62
D:\Anaconda3\envs\mljar\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72 return f(**kwargs)
73 return inner_f
74
D:\Anaconda3\envs\mljar\lib\site-packages\sklearn\inspection_permutation_importance.py in permutation_importance(estimator, X, y, scoring, n_repeats, n_jobs, random_state)
135 scores = Parallel(n_jobs=n_jobs)(delayed(_calculate_permutation_scores)(
136 estimator, X, y, col_idx, random_seed, n_repeats, scorer
--> 137 ) for col_idx in range(X.shape[1]))
138
139 importances = baseline_score - np.array(scores)
D:\Anaconda3\envs\mljar\lib\site-packages\joblib\parallel.py in call(self, iterable)
1015
1016 with self._backend.retrieval_context():
-> 1017 self.retrieve()
1018 # Make sure that we get a last message telling us we are done
1019 elapsed_time = time.time() - self._start_time
D:\Anaconda3\envs\mljar\lib\site-packages\joblib\parallel.py in retrieve(self)
907 try:
908 if getattr(self._backend, 'supports_timeout', False):
--> 909 self._output.extend(job.get(timeout=self.timeout))
910 else:
911 self._output.extend(job.get())
D:\Anaconda3\envs\mljar\lib\site-packages\joblib_parallel_backends.py in wrap_future_result(future, timeout)
560 AsyncResults.get from multiprocessing."""
561 try:
--> 562 return future.result(timeout=timeout)
563 except LokyTimeoutError:
564 raise TimeoutError()
D:\Anaconda3\envs\mljar\lib\concurrent\futures_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
D:\Anaconda3\envs\mljar\lib\concurrent\futures_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
automl = AutoML()
automl.fit(X_train, y_train)
after running the code, it raises error like this
AutoML directory: AutoML_9
The task is regression with evaluation metric rmse
AutoML will use algorithms: ['Baseline', 'Linear', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']
AutoML will ensemble availabe models
2020-09-10 18:59:15,591 supervised.preprocessing.eda ERROR There was an issue when running EDA. [Errno 22] Invalid argument: 'AutoML_9\EDA\Spd*LSBW.png'
AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']
1_Baseline rmse 2.013368 trained in 0.11 seconds
2_DecisionTree rmse 0.686167 trained in 9.43 seconds
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
'''
Traceback (most recent call last):
File "D:\Anaconda3\envs\mljar\lib\site-packages\joblib\externals\loky\process_executor.py", line 391, in _process_worker
call_item = call_queue.get(block=True, timeout=timeout)
File "D:\Anaconda3\envs\mljar\lib\multiprocessing\queues.py", line 113, in get
return ForkingPickler.loads(res)
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised_init.py", line 3, in
from supervised.automl import AutoML
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\automl.py", line 3, in
from supervised.base_automl import BaseAutoML
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py", line 17, in
from supervised.algorithms.registry import AlgorithmsRegistry
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\registry.py", line 63, in
import supervised.algorithms.random_forest
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\random_forest.py", line 8, in
from supervised.algorithms.algorithm import BaseAlgorithm
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\algorithm.py", line 3, in
from supervised.utils.importance import PermutationImportance
File "C:\Users\ZW\AppData\Roaming\Python\Python36\site-packages\supervised\utils\importance.py", line 7, in
import matplotlib.pyplot as plt
File "D:\Anaconda3\envs\mljar\lib\site-packages\matplotlib\pyplot.py", line 43, in
from matplotlib.figure import Figure, figaspect
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 665, in _load_unlocked
File "", line 674, in exec_module
File "", line 764, in get_code
File "", line 833, in get_data
MemoryError
'''
The above exception was the direct cause of the following exception:
BrokenProcessPool Traceback (most recent call last)
in
5 # explain_level=0
6 )
----> 7 automl.fit(X_train, y_train)
~\AppData\Roaming\Python\Python36\site-packages\supervised\automl.py in fit(self, X, y)
276 self : AutoML object
277 """
--> 278 return self._fit(X, y)
279
280 def predict(self, X):
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in _fit(self, X, y)
668
669 except Exception as e:
--> 670 raise e
671 finally:
672 if self._X_path is not None:
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in _fit(self, X, y)
655 trained = self.ensemble_step(is_stacked=params["is_stacked"])
656 else:
--> 657 trained = self.train_model(params)
658
659 params["status"] = "trained" if trained else "skipped"
~\AppData\Roaming\Python\Python36\site-packages\supervised\base_automl.py in train_model(self, params)
227 f"Train model #{len(self._models)+1} / Model name: {params['name']}"
228 )
--> 229 mf.train(model_path)
230
231 # save the model
~\AppData\Roaming\Python\Python36\site-packages\supervised\model_framework.py in train(self, model_path)
176 metric_name=self.get_metric_name(),
177 ml_task=self._ml_task,
--> 178 explain_level=self._explain_level,
179 )
180
~\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\linear.py in interpret(self, X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level)
137 metric_name,
138 ml_task,
--> 139 explain_level,
140 )
141 if explain_level == 0:
~\AppData\Roaming\Python\Python36\site-packages\supervised\algorithms\algorithm.py in interpret(self, X_train, y_train, X_validation, y_validation, model_file_path, learner_name, target_name, class_names, metric_name, ml_task, explain_level)
77 learner_name,
78 metric_name,
---> 79 ml_task,
80 )
81 if explain_level > 1:
~\AppData\Roaming\Python\Python36\site-packages\supervised\utils\importance.py in compute_and_plot(model, X_validation, y_validation, model_file_path, learner_name, metric_name, ml_task)
58 n_jobs=-1, # all cores
59 random_state=12,
---> 60 n_repeats=5, # default
61 )
62
D:\Anaconda3\envs\mljar\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72 return f(**kwargs)
73 return inner_f
74
D:\Anaconda3\envs\mljar\lib\site-packages\sklearn\inspection_permutation_importance.py in permutation_importance(estimator, X, y, scoring, n_repeats, n_jobs, random_state)
135 scores = Parallel(n_jobs=n_jobs)(delayed(_calculate_permutation_scores)(
136 estimator, X, y, col_idx, random_seed, n_repeats, scorer
--> 137 ) for col_idx in range(X.shape[1]))
138
139 importances = baseline_score - np.array(scores)
D:\Anaconda3\envs\mljar\lib\site-packages\joblib\parallel.py in call(self, iterable)
1015
1016 with self._backend.retrieval_context():
-> 1017 self.retrieve()
1018 # Make sure that we get a last message telling us we are done
1019 elapsed_time = time.time() - self._start_time
D:\Anaconda3\envs\mljar\lib\site-packages\joblib\parallel.py in retrieve(self)
907 try:
908 if getattr(self._backend, 'supports_timeout', False):
--> 909 self._output.extend(job.get(timeout=self.timeout))
910 else:
911 self._output.extend(job.get())
D:\Anaconda3\envs\mljar\lib\site-packages\joblib_parallel_backends.py in wrap_future_result(future, timeout)
560 AsyncResults.get from multiprocessing."""
561 try:
--> 562 return future.result(timeout=timeout)
563 except LokyTimeoutError:
564 raise TimeoutError()
D:\Anaconda3\envs\mljar\lib\concurrent\futures_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
D:\Anaconda3\envs\mljar\lib\concurrent\futures_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.