from tpot import TPOTClassifier
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
X, y = make_blobs(n_samples=100, centers=2, n_features=3, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)
clf = TPOTClassifier(config_dict='TPOT NN', template='Selector-Transformer-PytorchLRClassifier',
verbosity=2, population_size=10, generations=10)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))
clf.export('tpot_nn_demo_pipeline.py')
Error trace:
Generation 1 - Current best internal CV score: -inf
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:817, in TPOTBase.fit(self, features, target, sample_weight, groups)
816 warnings.simplefilter("ignore")
--> 817 self._pop, _ = eaMuPlusLambda(
818 population=self._pop,
819 toolbox=self._toolbox,
820 mu=self.population_size,
821 lambda_=self._lambda,
822 cxpb=self.crossover_rate,
823 mutpb=self.mutation_rate,
824 ngen=self.generations,
825 pbar=self._pbar,
826 halloffame=self._pareto_front,
827 verbose=self.verbosity,
828 per_generation_function=self._check_periodic_pipeline,
829 log_file=self.log_file_,
830 )
832 # Allow for certain exceptions to signal a premature fit() cancellation
File ~/anaconda3/lib/python3.11/site-packages/tpot/gp_deap.py:285, in eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, pbar, stats, halloffame, verbose, per_generation_function, log_file)
284 if per_generation_function is not None:
--> 285 per_generation_function(gen)
287 # Update the statistics with the new population
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:1183, in TPOTBase._check_periodic_pipeline(self, gen)
1173 """If enough time has passed, save a new optimized pipeline. Currently used in the per generation hook in the optimization loop.
1174 Parameters
1175 ----------
(...)
1181 None
1182 """
-> 1183 self._update_top_pipeline()
1184 if self.periodic_checkpoint_folder is not None:
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:925, in TPOTBase._update_top_pipeline(self)
923 from sklearn.model_selection import cross_val_score
--> 925 cv_scores = cross_val_score(
926 sklearn_pipeline,
927 self.pretest_X,
928 self.pretest_y,
929 cv=self.cv,
930 scoring=self.scoring_function,
931 verbose=0,
932 error_score="raise",
933 )
934 break
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:562, in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
560 scorer = check_scoring(estimator, scoring=scoring)
--> 562 cv_results = cross_validate(
563 estimator=estimator,
564 X=X,
565 y=y,
566 groups=groups,
567 scoring={"score": scorer},
568 cv=cv,
569 n_jobs=n_jobs,
570 verbose=verbose,
571 fit_params=fit_params,
572 pre_dispatch=pre_dispatch,
573 error_score=error_score,
574 )
575 return cv_results["test_score"]
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:211, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
206 with config_context(
207 skip_parameter_validation=(
208 prefer_skip_nested_validation or global_skip_validation
209 )
210 ):
--> 211 return func(*args, **kwargs)
212 except InvalidParameterError as e:
213 # When the function is just a wrapper around an estimator, we allow
214 # the function to delegate validation to the estimator, but we replace
215 # the name of the estimator by the name of the function in the error
216 # message to avoid confusion.
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:309, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)
308 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 309 results = parallel(
310 delayed(_fit_and_score)(
311 clone(estimator),
312 X,
313 y,
314 scorers,
315 train,
316 test,
317 verbose,
318 None,
319 fit_params,
320 return_train_score=return_train_score,
321 return_times=True,
322 return_estimator=return_estimator,
323 error_score=error_score,
324 )
325 for train, test in indices
326 )
328 _warn_or_raise_about_fit_failures(results, error_score)
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:65, in Parallel.__call__(self, iterable)
61 iterable_with_config = (
62 (_with_config(delayed_func, config), args, kwargs)
63 for delayed_func, args, kwargs in iterable
64 )
---> 65 return super().__call__(iterable_with_config)
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:1085, in Parallel.__call__(self, iterable)
1084 self._iterating = False
-> 1085 if self.dispatch_one_batch(iterator):
1086 self._iterating = self._original_iterator is not None
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
900 else:
--> 901 self._dispatch(tasks)
902 return True
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:819, in Parallel._dispatch(self, batch)
818 job_idx = len(self._jobs)
--> 819 job = self._backend.apply_async(batch, callback=cb)
820 # A job can complete so quickly than its callback is
821 # called before we get here, causing self._jobs to
822 # grow. To ensure correct results ordering, .insert is
823 # used (rather than .append) in the following line
File ~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
File ~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
594 def __init__(self, batch):
595 # Don't delay the application, to avoid keeping the input
596 # arguments in memory
--> 597 self.results = batch()
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:288, in BatchedCalls.__call__(self)
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:288, in <listcomp>(.0)
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
126 with config_context(**config):
--> 127 return self.function(*args, **kwargs)
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:754, 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)
753 fit_time = time.time() - start_time
--> 754 test_scores = _score(estimator, X_test, y_test, scorer, error_score)
755 score_time = time.time() - start_time - fit_time
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:813, in _score(estimator, X_test, y_test, scorer, error_score)
812 else:
--> 813 scores = scorer(estimator, X_test, y_test)
814 except Exception:
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:144, in _MultimetricScorer.__call__(self, estimator, *args, **kwargs)
143 if self._raise_exc:
--> 144 raise e
145 else:
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:136, in _MultimetricScorer.__call__(self, estimator, *args, **kwargs)
135 if isinstance(scorer, _BaseScorer):
--> 136 score = scorer._score(
137 cached_call, estimator, *args, **routed_params.get(name).score
138 )
139 else:
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:353, in _PredictScorer._score(self, method_caller, estimator, X, y_true, **kwargs)
345 self._warn_overlap(
346 message=(
347 "There is an overlap between set kwargs of this scorer instance and"
(...)
351 kwargs=kwargs,
352 )
--> 353 y_pred = method_caller(estimator, "predict", X)
354 scoring_kwargs = {**self._kwargs, **kwargs}
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:86, in _cached_call(cache, estimator, response_method, *args, **kwargs)
84 return cache[response_method]
---> 86 result, _ = _get_response_values(
87 estimator, *args, response_method=response_method, **kwargs
88 )
90 if cache is not None:
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_response.py:74, in _get_response_values(estimator, X, response_method, pos_label)
73 prediction_method = _check_response_method(estimator, response_method)
---> 74 classes = estimator.classes_
75 target_type = "binary" if len(classes) <= 2 else "multiclass"
File ~/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py:758, in Pipeline.classes_(self)
757 """The classes labels. Only exist if the last step is a classifier."""
--> 758 return self.steps[-1][1].classes_
AttributeError: 'PytorchLRClassifier' object has no attribute 'classes_'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[2], line 10
6 X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)
8 clf = TPOTClassifier(config_dict='TPOT NN', template='Selector-Transformer-PytorchLRClassifier',
9 verbosity=2, population_size=10, generations=10)
---> 10 clf.fit(X_train, y_train)
11 print(clf.score(X_test, y_test))
12 clf.export('tpot_nn_demo_pipeline.py')
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:864, in TPOTBase.fit(self, features, target, sample_weight, groups)
861 except (KeyboardInterrupt, SystemExit, Exception) as e:
862 # raise the exception if it's our last attempt
863 if attempt == (attempts - 1):
--> 864 raise e
865 return self
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:855, in TPOTBase.fit(self, features, target, sample_weight, groups)
852 if not isinstance(self._pbar, type(None)):
853 self._pbar.close()
--> 855 self._update_top_pipeline()
856 self._summary_of_best_pipeline(features, target)
857 # Delete the temporary cache before exiting
File ~/anaconda3/lib/python3.11/site-packages/tpot/base.py:925, in TPOTBase._update_top_pipeline(self)
922 sklearn_pipeline = self._toolbox.compile(expr=pipeline)
923 from sklearn.model_selection import cross_val_score
--> 925 cv_scores = cross_val_score(
926 sklearn_pipeline,
927 self.pretest_X,
928 self.pretest_y,
929 cv=self.cv,
930 scoring=self.scoring_function,
931 verbose=0,
932 error_score="raise",
933 )
934 break
935 raise RuntimeError(
936 "There was an error in the TPOT optimization "
937 "process. This could be because the data was "
(...)
945 "https://epistasislab.github.io/tpot/using/"
946 )
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:562, in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
559 # To ensure multimetric format is not supported
560 scorer = check_scoring(estimator, scoring=scoring)
--> 562 cv_results = cross_validate(
563 estimator=estimator,
564 X=X,
565 y=y,
566 groups=groups,
567 scoring={"score": scorer},
568 cv=cv,
569 n_jobs=n_jobs,
570 verbose=verbose,
571 fit_params=fit_params,
572 pre_dispatch=pre_dispatch,
573 error_score=error_score,
574 )
575 return cv_results["test_score"]
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:211, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
205 try:
206 with config_context(
207 skip_parameter_validation=(
208 prefer_skip_nested_validation or global_skip_validation
209 )
210 ):
--> 211 return func(*args, **kwargs)
212 except InvalidParameterError as e:
213 # When the function is just a wrapper around an estimator, we allow
214 # the function to delegate validation to the estimator, but we replace
215 # the name of the estimator by the name of the function in the error
216 # message to avoid confusion.
217 msg = re.sub(
218 r"parameter of \w+ must be",
219 f"parameter of {func.__qualname__} must be",
220 str(e),
221 )
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:309, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)
306 # We clone the estimator to make sure that all the folds are
307 # independent, and that it is pickle-able.
308 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 309 results = parallel(
310 delayed(_fit_and_score)(
311 clone(estimator),
312 X,
313 y,
314 scorers,
315 train,
316 test,
317 verbose,
318 None,
319 fit_params,
320 return_train_score=return_train_score,
321 return_times=True,
322 return_estimator=return_estimator,
323 error_score=error_score,
324 )
325 for train, test in indices
326 )
328 _warn_or_raise_about_fit_failures(results, error_score)
330 # For callable scoring, the return type is only know after calling. If the
331 # return type is a dictionary, the error scores can now be inserted with
332 # the correct key.
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:65, in Parallel.__call__(self, iterable)
60 config = get_config()
61 iterable_with_config = (
62 (_with_config(delayed_func, config), args, kwargs)
63 for delayed_func, args, kwargs in iterable
64 )
---> 65 return super().__call__(iterable_with_config)
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:1085, in Parallel.__call__(self, iterable)
1076 try:
1077 # Only set self._iterating to True if at least a batch
1078 # was dispatched. In particular this covers the edge
(...)
1082 # was very quick and its callback already dispatched all the
1083 # remaining jobs.
1084 self._iterating = False
-> 1085 if self.dispatch_one_batch(iterator):
1086 self._iterating = self._original_iterator is not None
1088 while self.dispatch_one_batch(iterator):
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
899 return False
900 else:
--> 901 self._dispatch(tasks)
902 return True
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:819, in Parallel._dispatch(self, batch)
817 with self._lock:
818 job_idx = len(self._jobs)
--> 819 job = self._backend.apply_async(batch, callback=cb)
820 # A job can complete so quickly than its callback is
821 # called before we get here, causing self._jobs to
822 # grow. To ensure correct results ordering, .insert is
823 # used (rather than .append) in the following line
824 self._jobs.insert(job_idx, job)
File ~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:208, in SequentialBackend.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)
File ~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
594 def __init__(self, batch):
595 # Don't delay the application, to avoid keeping the input
596 # arguments in memory
--> 597 self.results = batch()
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:288, in BatchedCalls.__call__(self)
284 def __call__(self):
285 # Set the default nested backend to self._backend but do not set the
286 # change the default number of processes to -1
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:288, in <listcomp>(.0)
284 def __call__(self):
285 # Set the default nested backend to self._backend but do not set the
286 # change the default number of processes to -1
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
125 config = {}
126 with config_context(**config):
--> 127 return self.function(*args, **kwargs)
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:754, 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)
751 result["fit_error"] = None
753 fit_time = time.time() - start_time
--> 754 test_scores = _score(estimator, X_test, y_test, scorer, error_score)
755 score_time = time.time() - start_time - fit_time
756 if return_train_score:
File ~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:813, in _score(estimator, X_test, y_test, scorer, error_score)
811 scores = scorer(estimator, X_test)
812 else:
--> 813 scores = scorer(estimator, X_test, y_test)
814 except Exception:
815 if isinstance(scorer, _MultimetricScorer):
816 # If `_MultimetricScorer` raises exception, the `error_score`
817 # parameter is equal to "raise".
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:144, in _MultimetricScorer.__call__(self, estimator, *args, **kwargs)
142 except Exception as e:
143 if self._raise_exc:
--> 144 raise e
145 else:
146 scores[name] = format_exc()
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:136, in _MultimetricScorer.__call__(self, estimator, *args, **kwargs)
134 try:
135 if isinstance(scorer, _BaseScorer):
--> 136 score = scorer._score(
137 cached_call, estimator, *args, **routed_params.get(name).score
138 )
139 else:
140 score = scorer(estimator, *args, **routed_params.get(name).score)
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:353, in _PredictScorer._score(self, method_caller, estimator, X, y_true, **kwargs)
316 """Evaluate predicted target values for X relative to y_true.
317
318 Parameters
(...)
343 Score function applied to prediction of estimator on X.
344 """
345 self._warn_overlap(
346 message=(
347 "There is an overlap between set kwargs of this scorer instance and"
(...)
351 kwargs=kwargs,
352 )
--> 353 y_pred = method_caller(estimator, "predict", X)
354 scoring_kwargs = {**self._kwargs, **kwargs}
355 return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_scorer.py:86, in _cached_call(cache, estimator, response_method, *args, **kwargs)
83 if cache is not None and response_method in cache:
84 return cache[response_method]
---> 86 result, _ = _get_response_values(
87 estimator, *args, response_method=response_method, **kwargs
88 )
90 if cache is not None:
91 cache[response_method] = result
File ~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_response.py:74, in _get_response_values(estimator, X, response_method, pos_label)
72 if is_classifier(estimator):
73 prediction_method = _check_response_method(estimator, response_method)
---> 74 classes = estimator.classes_
75 target_type = "binary" if len(classes) <= 2 else "multiclass"
77 if pos_label is not None and pos_label not in classes.tolist():
File ~/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py:758, in Pipeline.classes_(self)
755 @property
756 def classes_(self):
757 """The classes labels. Only exist if the last step is a classifier."""
--> 758 return self.steps[-1][1].classes_
AttributeError: 'PytorchLRClassifier' object has no attribute 'classes_'
Context of the issue
Copied and pasted code into Jupy notebook.
Python 3.11.5
Anaconda
tpot 0.12.1
pytorch 3.11_cpu_0
Process to reproduce the issue
[ordered list the process to finding and recreating the issue, example below]
User creates TPOT instance
User calls TPOT TPOTClassifier(config_dict='TPOT NN', function with sample code from documentation
TPOT crashes with a AttributeError: 'PytorchLRClassifier' object has no attribute 'classes_' at first generation
Expected result
This example is somewhat trivial, but it should result in nearly 100% classification accuracy.
Hi,
I just ran the demo code for the NNs. I am not very familiar with the problem.
https://epistasislab.github.io/tpot/examples/#neural-network-classifier-using-tpot-nn
Error trace:
Context of the issue
Copied and pasted code into Jupy notebook.
Python 3.11.5 Anaconda
tpot 0.12.1 pytorch 3.11_cpu_0
Process to reproduce the issue
[ordered list the process to finding and recreating the issue, example below]
TPOTClassifier(config_dict='TPOT NN',function with sample code from documentationAttributeError: 'PytorchLRClassifier' object has no attribute 'classes_'at first generationExpected result
Current result
Crash
Possible fix
PytorchLRClassifier call probably changed?
Demo code NN issuescreenshot