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

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=120,
    per_run_time_limit=30,
    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_size=0,
    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_size=50)

Out:

/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:68: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  self.metafeatures = self.metafeatures.append(metafeatures)
/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:72: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  self.algorithm_runs[metric].append(runs)

AutoSklearnClassifier(delete_tmp_folder_after_terminate=False, ensemble_size=0,
                      per_run_time_limit=30, time_left_for_this_task=120,
                      tmp_folder='/tmp/autosklearn_sequential_example_tmp')

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))

Out:

auto-sklearn results:
  Dataset name: breast_cancer
  Metric: accuracy
  Best validation score: 0.985816
  Number of target algorithm runs: 34
  Number of successful target algorithm runs: 33
  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 0.958041958041958

Total running time of the script: ( 2 minutes 3.305 seconds)

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