Note
Click here to download the full example code or to run this example in your browser via Binder
Classification¶
The following example shows how to fit a simple classification model with auto-sklearn.
from pprint import pprint
import sklearn.datasets
import sklearn.metrics
import autosklearn.classification
Data Loading¶
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 a classifier¶
automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=120,
per_run_time_limit=30,
tmp_folder="/tmp/autosklearn_classification_example_tmp",
)
automl.fit(X_train, y_train, dataset_name="breast_cancer")
Fitting to the training data: 0%| | 0/120 [00:00<?, ?it/s, The total time budget for this task is 0:02:00]
Fitting to the training data: 1%| | 1/120 [00:01<02:00, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 2%|1 | 2/120 [00:02<01:58, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 2%|2 | 3/120 [00:03<01:57, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 3%|3 | 4/120 [00:04<01:56, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 4%|4 | 5/120 [00:05<01:55, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 5%|5 | 6/120 [00:06<01:54, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 6%|5 | 7/120 [00:07<01:53, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 7%|6 | 8/120 [00:08<01:52, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 8%|7 | 9/120 [00:09<01:51, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 8%|8 | 10/120 [00:10<01:50, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 9%|9 | 11/120 [00:11<01:49, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 10%|# | 12/120 [00:12<01:48, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 11%|# | 13/120 [00:13<01:47, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 12%|#1 | 14/120 [00:14<01:46, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 12%|#2 | 15/120 [00:15<01:45, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 13%|#3 | 16/120 [00:16<01:44, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 14%|#4 | 17/120 [00:17<01:43, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 15%|#5 | 18/120 [00:18<01:42, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 16%|#5 | 19/120 [00:19<01:41, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 17%|#6 | 20/120 [00:20<01:40, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 18%|#7 | 21/120 [00:21<01:39, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 18%|#8 | 22/120 [00:22<01:38, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 19%|#9 | 23/120 [00:23<01:37, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 20%|## | 24/120 [00:24<01:36, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 21%|## | 25/120 [00:25<01:35, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 22%|##1 | 26/120 [00:26<01:34, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 22%|##2 | 27/120 [00:27<01:33, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 23%|##3 | 28/120 [00:28<01:32, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 24%|##4 | 29/120 [00:29<01:31, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 25%|##5 | 30/120 [00:30<01:30, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 26%|##5 | 31/120 [00:31<01:29, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 27%|##6 | 32/120 [00:32<01:28, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 28%|##7 | 33/120 [00:33<01:27, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 28%|##8 | 34/120 [00:34<01:26, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 29%|##9 | 35/120 [00:35<01:25, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 30%|### | 36/120 [00:36<01:24, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 31%|### | 37/120 [00:37<01:23, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 32%|###1 | 38/120 [00:38<01:22, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 32%|###2 | 39/120 [00:39<01:21, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 33%|###3 | 40/120 [00:40<01:20, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 34%|###4 | 41/120 [00:41<01:19, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 35%|###5 | 42/120 [00:42<01:18, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 36%|###5 | 43/120 [00:43<01:17, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 37%|###6 | 44/120 [00:44<01:16, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 38%|###7 | 45/120 [00:45<01:15, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 38%|###8 | 46/120 [00:46<01:14, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 39%|###9 | 47/120 [00:47<01:13, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 40%|#### | 48/120 [00:48<01:12, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 41%|#### | 49/120 [00:49<01:11, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 42%|####1 | 50/120 [00:50<01:10, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 42%|####2 | 51/120 [00:51<01:09, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 43%|####3 | 52/120 [00:52<01:08, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 44%|####4 | 53/120 [00:53<01:07, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 45%|####5 | 54/120 [00:54<01:06, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 46%|####5 | 55/120 [00:55<01:05, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 47%|####6 | 56/120 [00:56<01:04, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 48%|####7 | 57/120 [00:57<01:03, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 48%|####8 | 58/120 [00:58<01:02, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 49%|####9 | 59/120 [00:59<01:01, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 50%|##### | 60/120 [01:00<01:00, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 51%|##### | 61/120 [01:01<00:59, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 52%|#####1 | 62/120 [01:02<00:58, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 52%|#####2 | 63/120 [01:03<00:57, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 53%|#####3 | 64/120 [01:04<00:56, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 54%|#####4 | 65/120 [01:05<00:55, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 55%|#####5 | 66/120 [01:06<00:54, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 56%|#####5 | 67/120 [01:07<00:53, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 57%|#####6 | 68/120 [01:08<00:52, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 57%|#####7 | 69/120 [01:09<00:51, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 58%|#####8 | 70/120 [01:10<00:50, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 59%|#####9 | 71/120 [01:11<00:49, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 60%|###### | 72/120 [01:12<00:48, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 61%|###### | 73/120 [01:13<00:47, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 62%|######1 | 74/120 [01:14<00:46, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 62%|######2 | 75/120 [01:15<00:45, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 63%|######3 | 76/120 [01:16<00:44, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 64%|######4 | 77/120 [01:17<00:43, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 65%|######5 | 78/120 [01:18<00:42, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 66%|######5 | 79/120 [01:19<00:41, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 67%|######6 | 80/120 [01:20<00:40, 1.01s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 68%|######7 | 81/120 [01:21<00:39, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 68%|######8 | 82/120 [01:22<00:38, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 69%|######9 | 83/120 [01:23<00:37, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 70%|####### | 84/120 [01:24<00:36, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 71%|####### | 85/120 [01:25<00:35, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 72%|#######1 | 86/120 [01:26<00:34, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 72%|#######2 | 87/120 [01:27<00:33, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 73%|#######3 | 88/120 [01:28<00:32, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 74%|#######4 | 89/120 [01:29<00:31, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 75%|#######5 | 90/120 [01:30<00:30, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 76%|#######5 | 91/120 [01:31<00:29, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 77%|#######6 | 92/120 [01:32<00:28, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 78%|#######7 | 93/120 [01:33<00:27, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 78%|#######8 | 94/120 [01:34<00:26, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 79%|#######9 | 95/120 [01:35<00:25, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 80%|######## | 96/120 [01:36<00:24, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 81%|######## | 97/120 [01:37<00:23, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 82%|########1 | 98/120 [01:38<00:22, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 82%|########2 | 99/120 [01:39<00:21, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 83%|########3 | 100/120 [01:40<00:20, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 84%|########4 | 101/120 [01:41<00:19, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 85%|########5 | 102/120 [01:42<00:18, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 86%|########5 | 103/120 [01:43<00:17, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 87%|########6 | 104/120 [01:44<00:16, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 88%|########7 | 105/120 [01:45<00:15, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 88%|########8 | 106/120 [01:46<00:14, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 89%|########9 | 107/120 [01:47<00:13, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 90%|######### | 108/120 [01:48<00:12, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 91%|######### | 109/120 [01:49<00:11, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 92%|#########1| 110/120 [01:50<00:10, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 92%|#########2| 111/120 [01:51<00:09, 1.00s/it, The total time budget for this task is 0:02:00]
Fitting to the training data: 100%|##########| 120/120 [01:51<00:00, 1.08it/s, The total time budget for this task is 0:02:00]
AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
per_run_time_limit=30, time_left_for_this_task=120,
tmp_folder='/tmp/autosklearn_classification_example_tmp')
View the models found by auto-sklearn¶
print(automl.leaderboard())
rank ensemble_weight type cost duration
model_id
7 1 0.16 extra_trees 0.014184 1.783823
27 2 0.12 extra_trees 0.014184 2.412009
16 5 0.04 gradient_boosting 0.021277 1.184460
21 4 0.06 extra_trees 0.021277 1.684483
30 3 0.04 extra_trees 0.021277 12.855899
2 6 0.04 random_forest 0.028369 2.076491
3 7 0.10 mlp 0.028369 1.128072
6 8 0.02 mlp 0.028369 1.241316
10 10 0.02 random_forest 0.028369 2.286348
11 11 0.04 random_forest 0.028369 2.388377
14 12 0.02 mlp 0.028369 2.163702
22 9 0.06 gradient_boosting 0.028369 1.293493
5 16 0.02 random_forest 0.035461 2.427761
8 15 0.02 random_forest 0.035461 2.451311
12 14 0.02 gradient_boosting 0.035461 1.499043
18 17 0.02 random_forest 0.035461 2.374192
31 13 0.04 random_forest 0.035461 1.992918
9 18 0.02 extra_trees 0.042553 2.113635
4 19 0.02 mlp 0.056738 1.822940
33 20 0.06 decision_tree 0.070922 9.993254
32 21 0.02 gradient_boosting 0.078014 1.103364
29 22 0.04 mlp 0.134752 2.343633
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 0x7f2afcd18a60>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd570250>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afcd18f10>,
'model_id': 2,
'rank': 6,
'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 0x7f2afd4e3700>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd562e80>,
'ensemble_weight': 0.1,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afd4e35b0>,
'model_id': 3,
'rank': 7,
'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 0x7f2afd2798e0>,
'cost': 0.05673758865248224,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd562460>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afd279790>,
'model_id': 4,
'rank': 26,
'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 0x7f2afcf3a7c0>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd4e69a0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afd1ac5e0>,
'model_id': 5,
'rank': 20,
'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 0x7f2afcef2160>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afcd89e20>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afceccf70>,
'model_id': 6,
'rank': 8,
'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 0x7f2afc15d8b0>,
'cost': 0.014184397163120588,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd2892b0>,
'ensemble_weight': 0.16,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afc15d5b0>,
'model_id': 7,
'rank': 1,
'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 0x7f2af7694940>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afcf57550>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af7684cd0>,
'model_id': 8,
'rank': 19,
'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 0x7f2afd48d850>,
'cost': 0.04255319148936165,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afcea7e50>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afc04cca0>,
'model_id': 9,
'rank': 23,
'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 0x7f2af736f820>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af763d910>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af7351160>,
'model_id': 10,
'rank': 10,
'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 0x7f2af6fe4310>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af7663160>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af70158b0>,
'model_id': 11,
'rank': 11,
'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 0x7f2af6e32dc0>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afc0113d0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af6e32190>,
'model_id': 12,
'rank': 18,
'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)},
14: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af54c9220>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af7235430>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af5565d00>,
'model_id': 14,
'rank': 14,
'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)},
16: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af548a4c0>,
'cost': 0.021276595744680882,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af6fb43d0>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af548a340>,
'model_id': 16,
'rank': 5,
'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)},
18: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af7351580>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af554adc0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af713ed00>,
'model_id': 18,
'rank': 21,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=3, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
21: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2afd47ca90>,
'cost': 0.021276595744680882,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af5503970>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2afd4a1d30>,
'model_id': 21,
'rank': 4,
'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 0x7f2af4e63520>,
'cost': 0.028368794326241176,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af5396280>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4f4e640>,
'model_id': 22,
'rank': 12,
'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)},
27: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af4c8a160>,
'cost': 0.014184397163120588,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af71ebc70>,
'ensemble_weight': 0.12,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4cfafa0>,
'model_id': 27,
'rank': 2,
'sklearn_classifier': ExtraTreesClassifier(max_features=134, min_samples_leaf=12, min_samples_split=4,
n_estimators=512, n_jobs=1, random_state=1,
warm_start=True)},
29: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af4bc2f10>,
'cost': 0.13475177304964536,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2afd3bd2b0>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4bc2940>,
'model_id': 29,
'rank': 32,
'sklearn_classifier': MLPClassifier(alpha=0.0007119897774330087, beta_1=0.999, beta_2=0.9,
hidden_layer_sizes=(51, 51, 51),
learning_rate_init=0.00028079049815589414, max_iter=256,
n_iter_no_change=32, random_state=1, validation_fraction=0.0,
verbose=0, warm_start=True)},
30: { 'balancing': Balancing(random_state=1, strategy='weighting'),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af4af0be0>,
'cost': 0.021276595744680882,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af4f2d580>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4af0940>,
'model_id': 30,
'rank': 3,
'sklearn_classifier': ExtraTreesClassifier(max_features=5390, min_samples_leaf=7, n_estimators=512,
n_jobs=1, random_state=1, warm_start=True)},
31: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af4934a00>,
'cost': 0.03546099290780147,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af4c94250>,
'ensemble_weight': 0.04,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4934670>,
'model_id': 31,
'rank': 16,
'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=2, min_samples_leaf=10,
min_samples_split=7, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)},
32: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af471cf10>,
'cost': 0.07801418439716312,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af4b3d1f0>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af471c6d0>,
'model_id': 32,
'rank': 30,
'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
l2_regularization=6.118619248990061e-06,
learning_rate=0.9217672331809242, max_iter=512,
max_leaf_nodes=223, min_samples_leaf=122,
n_iter_no_change=6, random_state=1,
validation_fraction=None, warm_start=True)},
33: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f2af4274d00>,
'cost': 0.07092198581560283,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f2af49838b0>,
'ensemble_weight': 0.06,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f2af4274c40>,
'model_id': 33,
'rank': 29,
'sklearn_classifier': DecisionTreeClassifier(criterion='entropy', max_depth=30, min_samples_leaf=9,
min_samples_split=14, random_state=1)}}
Get the Score of the final ensemble¶
predictions = automl.predict(X_test)
print("Accuracy score:", sklearn.metrics.accuracy_score(y_test, predictions))
Accuracy score: 0.9440559440559441
Total running time of the script: ( 2 minutes 7.096 seconds)
