Sklearn Tuner

• Original Link : https://keras.io/api/keras_tuner/tuners/sklearn/
• Last Checked at : 2024-11-25

source

SklearnTuner class

keras_tuner.SklearnTuner(
    oracle, hypermodel, scoring=None, metrics=None, cv=None, **kwargs
)

Tuner for Scikit-learn Models.

Performs cross-validated hyperparameter search for Scikit-learn models.

Examples

import keras_tuner
from sklearn import ensemble
from sklearn import datasets
from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
def build_model(hp):
  model_type = hp.Choice('model_type', ['random_forest', 'ridge'])
  if model_type == 'random_forest':
    model = ensemble.RandomForestClassifier(
        n_estimators=hp.Int('n_estimators', 10, 50, step=10),
        max_depth=hp.Int('max_depth', 3, 10))
  else:
    model = linear_model.RidgeClassifier(
        alpha=hp.Float('alpha', 1e-3, 1, sampling='log'))
  return model
tuner = keras_tuner.tuners.SklearnTuner(
    oracle=keras_tuner.oracles.BayesianOptimizationOracle(
        objective=keras_tuner.Objective('score', 'max'),
        max_trials=10),
    hypermodel=build_model,
    scoring=metrics.make_scorer(metrics.accuracy_score),
    cv=model_selection.StratifiedKFold(5),
    directory='.',
    project_name='my_project')
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
    X, y, test_size=0.2)
tuner.search(X_train, y_train)
best_model = tuner.get_best_models(num_models=1)[0]

Arguments

• oracle: A keras_tuner.Oracle instance. Note that for this Tuner, the objective for the Oracle should always be set to Objective('score', direction='max'). Also, Oracles that exploit Neural-Network-specific training (e.g. Hyperband) should not be used with this Tuner.
• hypermodel: A HyperModel instance (or callable that takes hyperparameters and returns a Model instance).
• scoring: An sklearn scoring function. For more information, see sklearn.metrics.make_scorer. If not provided, the Model’s default scoring will be used via model.score. Note that if you are searching across different Model families, the default scoring for these Models will often be different. In this case you should supply scoring here in order to make sure your Models are being scored on the same metric.
• metrics: Additional sklearn.metrics functions to monitor during search. Note that these metrics do not affect the search process.
• cv: An sklearn.model_selection Splitter class. Used to determine how samples are split up into groups for cross-validation.
• **kwargs: Keyword arguments relevant to all Tuner subclasses. Please see the docstring for Tuner.