Note
Click here to download the full example code or to run this example in your browser via Binder
Sequential Usage¶
By default, auto-sklearn fits the machine learning models and build their ensembles in parallel. However, it is also possible to run the two processes sequentially. The example below shows how to first fit the models and build the ensembles afterwards.
from pprint import pprint
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
import autosklearn.classification
Data Loading¶
from autosklearn.ensembles.ensemble_selection import EnsembleSelection
X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
X, y, random_state=1
)
Build and fit the classifier¶
automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=60,
tmp_folder="/tmp/autosklearn_sequential_example_tmp",
# Do not construct ensembles in parallel to avoid using more than one
# core at a time. The ensemble will be constructed after auto-sklearn
# finished fitting all machine learning models.
ensemble_class=None,
delete_tmp_folder_after_terminate=False,
)
automl.fit(X_train, y_train, dataset_name="breast_cancer")
# This call to fit_ensemble uses all models trained in the previous call
# to fit to build an ensemble which can be used with automl.predict()
automl.fit_ensemble(y_train, ensemble_class=EnsembleSelection)
RunKey(config_id=1, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=2.138148307800293, status=<StatusType.SUCCESS: 1>, starttime=1663665046.729183, endtime=1663665048.8936107, additional_info={'duration': 2.025052070617676, 'num_run': 2, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=2, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=1.1886756420135498, status=<StatusType.SUCCESS: 1>, starttime=1663665048.8985617, endtime=1663665050.113383, additional_info={'duration': 1.0999813079833984, 'num_run': 3, 'train_loss': 0.01754385964912286, 'configuration_origin': 'Initial design'})
RunKey(config_id=3, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.05673758865248224, time=1.8330206871032715, status=<StatusType.SUCCESS: 1>, starttime=1663665050.1179628, endtime=1663665051.9798045, additional_info={'duration': 1.750197410583496, 'num_run': 4, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=4, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.03546099290780147, time=2.5171611309051514, status=<StatusType.SUCCESS: 1>, starttime=1663665051.984322, endtime=1663665054.5282989, additional_info={'duration': 2.3936502933502197, 'num_run': 5, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=5, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=1.2775936126708984, status=<StatusType.SUCCESS: 1>, starttime=1663665054.5333724, endtime=1663665055.84306, additional_info={'duration': 1.198927640914917, 'num_run': 6, 'train_loss': 0.024561403508771895, 'configuration_origin': 'Initial design'})
RunKey(config_id=6, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.014184397163120588, time=1.840346336364746, status=<StatusType.SUCCESS: 1>, starttime=1663665055.8481805, endtime=1663665057.716139, additional_info={'duration': 1.7313201427459717, 'num_run': 7, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=7, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.03546099290780147, time=2.512295961380005, status=<StatusType.SUCCESS: 1>, starttime=1663665057.7238793, endtime=1663665060.265423, additional_info={'duration': 2.364440441131592, 'num_run': 8, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=8, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.04255319148936165, time=2.1315276622772217, status=<StatusType.SUCCESS: 1>, starttime=1663665060.271228, endtime=1663665062.429951, additional_info={'duration': 2.0131988525390625, 'num_run': 9, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=9, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=2.4673690795898438, status=<StatusType.SUCCESS: 1>, starttime=1663665062.4357092, endtime=1663665064.9292157, additional_info={'duration': 2.3498237133026123, 'num_run': 10, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=10, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=2.5794708728790283, status=<StatusType.SUCCESS: 1>, starttime=1663665064.934585, endtime=1663665067.5437174, additional_info={'duration': 2.4640066623687744, 'num_run': 11, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=11, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.03546099290780147, time=1.5726971626281738, status=<StatusType.SUCCESS: 1>, starttime=1663665067.5497928, endtime=1663665069.1497045, additional_info={'duration': 1.4734737873077393, 'num_run': 12, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=12, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=1.610743522644043, status=<StatusType.SUCCESS: 1>, starttime=1663665069.156014, endtime=1663665070.7938027, additional_info={'duration': 1.5205156803131104, 'num_run': 13, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=13, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=2.3221628665924072, status=<StatusType.SUCCESS: 1>, starttime=1663665070.8001842, endtime=1663665073.1480403, additional_info={'duration': 2.2304294109344482, 'num_run': 14, 'train_loss': 0.010526315789473717, 'configuration_origin': 'Initial design'})
RunKey(config_id=14, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.049645390070921946, time=5.315098762512207, status=<StatusType.SUCCESS: 1>, starttime=1663665073.1545255, endtime=1663665078.4978535, additional_info={'duration': 5.217244863510132, 'num_run': 15, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=15, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.021276595744680882, time=1.2758090496063232, status=<StatusType.SUCCESS: 1>, starttime=1663665078.505013, endtime=1663665079.8135793, additional_info={'duration': 1.1897251605987549, 'num_run': 16, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=16, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.03546099290780147, time=2.031766176223755, status=<StatusType.SUCCESS: 1>, starttime=1663665079.820808, endtime=1663665081.878261, additional_info={'duration': 1.929640769958496, 'num_run': 17, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=17, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.03546099290780147, time=2.4669880867004395, status=<StatusType.SUCCESS: 1>, starttime=1663665081.8850935, endtime=1663665084.380212, additional_info={'duration': 2.343514919281006, 'num_run': 18, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
RunKey(config_id=18, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=3.1608927249908447, status=<StatusType.SUCCESS: 1>, starttime=1663665084.3869042, endtime=1663665087.5789628, additional_info={'duration': 3.045881748199463, 'num_run': 19, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=19, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.07801418439716312, time=0.8530073165893555, status=<StatusType.SUCCESS: 1>, starttime=1663665087.5859113, endtime=1663665088.4659152, additional_info={'duration': 0.7710719108581543, 'num_run': 20, 'train_loss': 0.10526315789473684, 'configuration_origin': 'Initial design'})
RunKey(config_id=20, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.021276595744680882, time=1.751319169998169, status=<StatusType.SUCCESS: 1>, starttime=1663665088.4732761, endtime=1663665090.2543724, additional_info={'duration': 1.6337840557098389, 'num_run': 21, 'train_loss': 0.007017543859649145, 'configuration_origin': 'Initial design'})
RunKey(config_id=21, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=0.028368794326241176, time=1.2900927066802979, status=<StatusType.SUCCESS: 1>, starttime=1663665090.261764, endtime=1663665091.5791755, additional_info={'duration': 1.198190450668335, 'num_run': 22, 'train_loss': 0.0035087719298245723, 'configuration_origin': 'Initial design'})
RunKey(config_id=22, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=1.0, time=2.0078036785125732, status=<StatusType.TIMEOUT: 2>, starttime=1663665091.5878708, endtime=1663665094.6180243, additional_info={'error': 'Timeout', 'configuration_origin': 'Initial design'})
RunKey(config_id=23, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.0, status=<StatusType.STOP: 8>, starttime=1663665094.6260393, endtime=1663665094.6260395, additional_info={})
AutoSklearnClassifier(delete_tmp_folder_after_terminate=False,
ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
per_run_time_limit=6, time_left_for_this_task=60,
tmp_folder='/tmp/autosklearn_sequential_example_tmp')
Print the final ensemble constructed by auto-sklearn¶
pprint(automl.show_models(), indent=4)
{ 2: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05ca44a700>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05cb155220>,
'ensemble_weight': 0.1,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05ca44ac40>,
'model_id': 2,
'rank': 1,
'sklearn_classifier': RandomForestClassifier(max_features=5, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
3: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d62efdf0>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1a8ddf0>,
'ensemble_weight': 0.08,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05cb9ac4c0>,
'model_id': 3,
'rank': 2,
'sklearn_classifier': MLPClassifier(activation='tanh', alpha=0.0001363185819149026, beta_1=0.999,
beta_2=0.9, early_stopping=True,
hidden_layer_sizes=(115, 115, 115),
learning_rate_init=0.00018009776276177523, max_iter=32,
n_iter_no_change=32, random_state=1, verbose=0, warm_start=True)},
4: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d237e820>,
'cost': 0.05673758865248224,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05ccc37160>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d237ed60>,
'model_id': 4,
'rank': 3,
'sklearn_classifier': MLPClassifier(activation='tanh', alpha=0.00021148999718383549, beta_1=0.999,
beta_2=0.9, hidden_layer_sizes=(113, 113, 113),
learning_rate_init=0.0007452270241186694, max_iter=64,
n_iter_no_change=32, random_state=1, validation_fraction=0.0,
verbose=0, warm_start=True)},
5: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d248cca0>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05cac0c130>,
'ensemble_weight': 0.1,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d62b91c0>,
'model_id': 5,
'rank': 4,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=3, min_samples_leaf=2,
n_estimators=512, n_jobs=1, random_state=1,
warm_start=True)},
6: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d3c54be0>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d61c9460>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d3c54340>,
'model_id': 6,
'rank': 5,
'sklearn_classifier': MLPClassifier(alpha=0.0017940473175767063, beta_1=0.999, beta_2=0.9,
early_stopping=True, hidden_layer_sizes=(101, 101),
learning_rate_init=0.0004684917334431039, max_iter=32,
n_iter_no_change=32, random_state=1, verbose=0, warm_start=True)},
7: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05ccb9f0a0>,
'cost': 0.014184397163120588,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d611d7c0>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05ccb9f220>,
'model_id': 7,
'rank': 6,
'sklearn_classifier': ExtraTreesClassifier(max_features=34, min_samples_leaf=3, min_samples_split=11,
n_estimators=512, n_jobs=1, random_state=1,
warm_start=True)},
8: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05ce5b4910>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d19ad430>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1c36700>,
'model_id': 8,
'rank': 7,
'sklearn_classifier': RandomForestClassifier(max_features=2, min_samples_leaf=2, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
9: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05ca268e20>,
'cost': 0.04255319148936165,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1c0ef10>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d41a25e0>,
'model_id': 9,
'rank': 8,
'sklearn_classifier': ExtraTreesClassifier(max_features=9, min_samples_split=10, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
10: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05cc79aa60>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d3b377f0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05cc79ab80>,
'model_id': 10,
'rank': 9,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=4, min_samples_split=6,
n_estimators=512, n_jobs=1, random_state=1,
warm_start=True)},
11: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d3d35640>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05ca2753a0>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d3d351f0>,
'model_id': 11,
'rank': 10,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=23, min_samples_leaf=7,
n_estimators=512, n_jobs=1, random_state=1,
warm_start=True)},
12: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d25bf4c0>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05cd0af160>,
'ensemble_weight': 0.1,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d25bffa0>,
'model_id': 12,
'rank': 11,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
l2_regularization=0.005326508887463406,
learning_rate=0.060800813211425456, max_iter=512,
max_leaf_nodes=6, min_samples_leaf=5,
n_iter_no_change=5, random_state=1,
validation_fraction=None, warm_start=True)},
13: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d245a3a0>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05ca5617c0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d3dfde50>,
'model_id': 13,
'rank': 12,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=False,
l2_regularization=1.0647401999412075e-10,
learning_rate=0.08291320147381159, max_iter=512,
max_leaf_nodes=39, n_iter_no_change=0,
random_state=1, validation_fraction=None,
warm_start=True)},
14: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1c1d640>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d626b100>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05cdc3a790>,
'model_id': 14,
'rank': 13,
'sklearn_classifier': MLPClassifier(activation='tanh', alpha=2.5550223982458062e-06, beta_1=0.999,
beta_2=0.9, hidden_layer_sizes=(54, 54, 54),
learning_rate_init=0.00027271287919467994, max_iter=256,
n_iter_no_change=32, random_state=1, validation_fraction=0.0,
verbose=0, warm_start=True)},
15: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05cb9ac5b0>,
'cost': 0.049645390070921946,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d684e130>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d47b13d0>,
'model_id': 15,
'rank': 14,
'sklearn_classifier': MLPClassifier(alpha=4.2841884333778574e-06, beta_1=0.999, beta_2=0.9,
hidden_layer_sizes=(263, 263, 263),
learning_rate_init=0.0011804284312897009, max_iter=128,
n_iter_no_change=32, random_state=1, validation_fraction=0.0,
verbose=0, warm_start=True)},
16: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d3e68760>,
'cost': 0.021276595744680882,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05ca44a310>,
'ensemble_weight': 0.08,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d3e68d30>,
'model_id': 16,
'rank': 15,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
l2_regularization=3.387912939529945e-10,
learning_rate=0.30755227194768237, max_iter=128,
max_leaf_nodes=60, min_samples_leaf=39,
n_iter_no_change=18, random_state=1,
validation_fraction=None, warm_start=True)},
17: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d3b7c880>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05cdc31c10>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d6403670>,
'model_id': 17,
'rank': 16,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
l2_regularization=0.4635442279519353,
learning_rate=0.09809681787962342, max_iter=512,
max_leaf_nodes=328, min_samples_leaf=2,
n_iter_no_change=2, random_state=1,
validation_fraction=None, warm_start=True)},
18: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05cc61fdc0>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d62ef2b0>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1035610>,
'model_id': 18,
'rank': 17,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=3, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
19: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d179c040>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d4831760>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05cc636d60>,
'model_id': 19,
'rank': 18,
'sklearn_classifier': ExtraTreesClassifier(criterion='entropy', max_features=448, min_samples_leaf=2,
min_samples_split=20, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
20: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d474c9d0>,
'cost': 0.07801418439716312,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d0f2eb80>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d474c250>,
'model_id': 20,
'rank': 19,
'sklearn_classifier': PassiveAggressiveClassifier(C=0.14268277711454813, max_iter=32, random_state=1,
tol=0.0002600768160857831, warm_start=True)},
21: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05cd03d2e0>,
'cost': 0.021276595744680882,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d103b250>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05cd040e50>,
'model_id': 21,
'rank': 20,
'sklearn_classifier': ExtraTreesClassifier(criterion='entropy', max_features=4, min_samples_leaf=2,
min_samples_split=15, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
22: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d400fa30>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1fa2190>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1fbf820>,
'model_id': 22,
'rank': 21,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
l2_regularization=8.057778875694463e-05,
learning_rate=0.09179220974965213, max_iter=256,
max_leaf_nodes=200, n_iter_no_change=18,
random_state=1,
validation_fraction=0.14295295806077554,
warm_start=True)}}
Get the Score of the final ensemble¶
predictions = automl.predict(X_test)
print(automl.sprint_statistics())
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
auto-sklearn results:
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.985816
Number of target algorithm runs: 22
Number of successful target algorithm runs: 21
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.9440559440559441
Total running time of the script: ( 0 minutes 58.297 seconds)
