Issue
from sklearn import ensemble
from sklearn import linear_model
def build_model(hp):
model_type = hp.Choice('model_type', ['random_forest', 'ridge'])
if model_type == 'random_forest':
with hp.conditional_scope('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))
elif model_type == 'ridge':
with hp.conditional_scope('model_type', 'ridge'):
model = linear_model.RidgeClassifier(
alpha=hp.Float('alpha', 1e-3, 1, sampling='log'))
else:
raise ValueError('Unrecognized model_type')
return model
tuner = kt.tuners.Sklearn(
oracle=kt.oracles.BayesianOptimization(
objective=kt.Objective('score', 'max'),
max_trials=10),
hypermodel=build_model,
directory=".")
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]
on execution of this code from the samples on keras-tuner https://keras.io/api/keras_tuner/tuners/sklearn/
I am getting the error shown below. What should be the fix?
c:\users\99ans\appdata\local\programs\python\python39\lib\site-packages\keras_tuner\tuners\sklearn_tuner.py in run_trial(self, trial, X, y, sample_weight, groups)
161 sample_weight[train_indices] if sample_weight is not None else None
162 )
--> 163
164 model = self.hypermodel.build(trial.hyperparameters)
165 #if isinstance(model, Pipeline):
AttributeError: module 'sklearn' has no attribute 'pipeline'
Solution
Adding
import sklearn.pipeline
would temporarily fix the problem.
This is a very recent issue and will be fixed in the next release.
You can find more about it here https://github.com/keras-team/keras-tuner/issues/600
Answered By - Ansh Mehta
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