Generations: 0%| | 1/10000 [02:24<?, ?gen/s]
Traceback (most recent call last):
File "C:\Users\andre\AppData\Local\Programs\Python\Python39\lib\contextlib.py", line 135, in __exit__
self.gen.throw(type, value, traceback)
File "C:\Users\andre\Documents\GitHub\FEDOT\fedot\api\time.py", line 58, in launch_composing
yield
File "C:\Users\andre\Documents\GitHub\FEDOT\fedot\api\api_utils\api_composer.py", line 130, in compose_pipeline
best_pipelines = gp_composer.compose_pipeline(data=train_data)
File "C:\Users\andre\Documents\GitHub\FEDOT\fedot\core\composer\gp_composer\gp_composer.py", line 67, in compose_pipeline
opt_result = self.optimizer.optimise(objective_function)
File "C:\Users\andre\Documents\GitHub\FEDOT\venv\lib\site-packages\golem\core\optimisers\populational_optimizer.py", line 98, in optimise
new_population = self._evolve_population(evaluator)
File "C:\Users\andre\Documents\GitHub\FEDOT\venv\lib\site-packages\golem\core\optimisers\genetic\gp_optimizer.py", line 112, in _evolve_population
new_population = self.reproducer.reproduce(individuals_to_select, evaluator)
File "C:\Users\andre\Documents\GitHub\FEDOT\venv\lib\site-packages\golem\core\optimisers\genetic\operators\reproduction.py", line 106, in reproduce
collected_next_population.update({ind.uid: ind for ind in partial_next_population})
TypeError: 'NoneType' object is not iterable
Sometimes, it cause Exception
Exception Traceback (most recent call last)
Cell In[30], line 8
4 locs.remove(base_location)
6 print(f'Base TS was choosen randomly: {base_location}')
----> 8 model, pipeline, forecast = fit_predict_model(base_location, plots=True)
10 test['emission'][test['location'] == base_location] = forecast
12 for loc in tqdm(locs):
Cell In[28], line 12, in fit_predict_model(loc_name, plots)
2 ts_train, ts_test, train_data, _, task_parameters = prepare_data(loc_name)
4 auto_model = Fedot(
5 problem='ts_forecasting',
6 task_params=task_parameters,
(...)
9 seed=15,
10 )
---> 12 pipeline = auto_model.fit(features=train_data)
14 pipeline.show(node_size_scale=0.5, dpi=100)
16 if plots:
File /opt/conda/lib/python3.10/site-packages/fedot/api/main.py:247, in Fedot.fit(self, features, target, predefined_model)
243 self.current_pipeline = PredefinedModel(predefined_model, self.train_data, self.log,
244 use_input_preprocessing=self.params.get(
245 'use_input_preprocessing')).fit()
246 else:
--> 247 self.current_pipeline, self.best_models, self.history = self.api_composer.obtain_model(self.train_data)
249 if self.current_pipeline is None:
250 raise ValueError('No models were found')
File /opt/conda/lib/python3.10/site-packages/fedot/api/api_utils/api_composer.py:69, in ApiComposer.obtain_model(self, train_data)
62 self.params.init_params_for_composing(self.timer.timedelta_composing, multi_objective)
64 self.log.message(f"AutoML configured."
65 f" Parameters tuning: {with_tuning}."
66 f" Time limit: {timeout} min."
67 f" Set of candidate models: {self.params.get('available_operations')}.")
---> 69 best_pipeline, best_pipeline_candidates, gp_composer = self.compose_pipeline(
70 train_data,
71 initial_assumption,
72 fitted_assumption
73 )
74 if with_tuning:
75 best_pipeline = self.tune_final_pipeline(train_data, best_pipeline)
File /opt/conda/lib/python3.10/site-packages/fedot/api/api_utils/api_composer.py:130, in ApiComposer.compose_pipeline(self, train_data, initial_assumption, fitted_assumption)
128 self.log.message('Pipeline composition started.')
129 self.was_optimised = False
--> 130 best_pipelines = gp_composer.compose_pipeline(data=train_data)
131 best_pipeline_candidates = gp_composer.best_models
132 self.was_optimised = True
File /opt/conda/lib/python3.10/site-packages/fedot/core/composer/gp_composer/gp_composer.py:67, in GPComposer.compose_pipeline(self, data)
64 self.optimizer.set_evaluation_callback(objective_evaluator.evaluate_intermediate_metrics)
66 # Finally, run optimization process
---> 67 opt_result = self.optimizer.optimise(objective_function)
69 best_model, self.best_models = self._convert_opt_results_to_pipeline(opt_result)
70 self.log.info('GP composition finished')
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/populational_optimizer.py:98, in PopulationalOptimizer.optimise(self, objective)
96 while not self.stop_optimization():
97 try:
---> 98 new_population = self._evolve_population(evaluator)
99 if self.gen_structural_diversity_check != -1 \
100 and self.generations.generation_num % self.gen_structural_diversity_check == 0 \
101 and self.generations.generation_num != 0:
102 new_population = self.get_structure_unique_population(new_population, evaluator)
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/genetic/gp_optimizer.py:112, in EvoGraphOptimizer._evolve_population(self, evaluator)
110 individuals_to_select = self.regularization(self.population, evaluator)
111 # Reproduce from previous pop to get next population
--> 112 new_population = self.reproducer.reproduce(individuals_to_select, evaluator)
114 # Adaptive agent experience collection & learning
115 # Must be called after reproduction (that collects the new experience)
116 experience = self.mutation.agent_experience
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/genetic/operators/reproduction.py:104, in ReproductionController.reproduce(self, population, evaluator)
101 residual_size = min(len(population), residual_size)
103 # Reproduce the required number of individuals that equals residual size
--> 104 partial_next_population = self.reproduce_uncontrolled(population, evaluator, residual_size)
105 # Avoid duplicate individuals that can come unchanged from previous population
106 collected_next_population.update({ind.uid: ind for ind in partial_next_population})
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/genetic/operators/reproduction.py:82, in ReproductionController.reproduce_uncontrolled(self, population, evaluator, pop_size)
80 new_population = self.crossover(selected_individuals)
81 new_population = self.mutation(new_population)
---> 82 new_population = evaluator(new_population)
83 return new_population
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/genetic/evaluation.py:199, in BaseGraphEvaluationDispatcher.evaluate_with_cache(self, population)
197 reversed_population = list(reversed(population))
198 self._remote_compute_cache(reversed_population)
--> 199 evaluated_population = self.evaluate_population(reversed_population)
200 self._reset_eval_cache()
201 return evaluated_population
File /opt/conda/lib/python3.10/site-packages/golem/core/optimisers/genetic/evaluation.py:249, in MultiprocessingDispatcher.evaluate_population(self, individuals)
247 parallel = Parallel(n_jobs=n_jobs, verbose=0, pre_dispatch="2*n_jobs")
248 eval_func = partial(self.evaluate_single, logs_initializer=Log().get_parameters())
--> 249 evaluation_results = parallel(delayed(eval_func)(ind.graph, ind.uid) for ind in individuals_to_evaluate)
250 individuals_evaluated = self.apply_evaluation_results(individuals_to_evaluate, evaluation_results)
251 # If there were no successful evals then try once again getting at least one,
252 # even if time limit was reached
File /opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1098, in Parallel.__call__(self, iterable)
1095 self._iterating = False
1097 with self._backend.retrieval_context():
-> 1098 self.retrieve()
1099 # Make sure that we get a last message telling us we are done
1100 elapsed_time = time.time() - self._start_time
File /opt/conda/lib/python3.10/site-packages/joblib/parallel.py:975, in Parallel.retrieve(self)
973 try:
974 if getattr(self._backend, 'supports_timeout', False):
--> 975 self._output.extend(job.get(timeout=self.timeout))
976 else:
977 self._output.extend(job.get())
File /opt/conda/lib/python3.10/site-packages/joblib/_parallel_backends.py:567, in LokyBackend.wrap_future_result(future, timeout)
564 """Wrapper for Future.result to implement the same behaviour as
565 AsyncResults.get from multiprocessing."""
566 try:
--> 567 return future.result(timeout=timeout)
568 except CfTimeoutError as e:
569 raise TimeoutError from e
File /opt/conda/lib/python3.10/concurrent/futures/_base.py:458, in Future.result(self, timeout)
456 raise CancelledError()
457 elif self._state == FINISHED:
--> 458 return self.__get_result()
459 else:
460 raise TimeoutError()
File /opt/conda/lib/python3.10/concurrent/futures/_base.py:403, in Future.__get_result(self)
401 if self._exception:
402 try:
--> 403 raise self._exception
404 finally:
405 # Break a reference cycle with the exception in self._exception
406 self = None
Exception: Invalid fitness after objective evaluation. Skipping the graph: (/n_lagged_{'window_size': 10};)/n_cgru_{'hidden_size': 200, 'learning_rate': 0.001, 'cnn1_kernel_size': 5, 'cnn1_output_size': 32, 'cnn2_kernel_size': 5, 'cnn2_output_size': 64, 'batch_size': 64, 'num_epochs': 50, 'optimizer': 'adamw', 'loss': 'mse'}
nicl-nno
commented
11 months ago
Fedot crushing after fitting it with ts full of zero values.
Code to reproduce:
Traceback^
Sometimes, it cause Exception