hypernets.examples package

Submodules

hypernets.examples.plain_model module

class hypernets.examples.plain_model.PlainEstimator(space_sample, task='binary', transformer=None)[source]

Bases: hypernets.model.estimator.Estimator

evaluate(X, y, metrics=None, **kwargs)[source]
fit(X, y, **kwargs)[source]
fit_cross_validation(X, y, stratified=True, num_folds=3, shuffle=False, random_state=9527, metrics=None, **kwargs)[source]
get_iteration_scores()[source]
static load(model_file)[source]
predict(X, **kwargs)[source]
predict_proba(X, *, ingore_transformer=False, **kwargs)[source]
proba2predict(proba, proba_threshold=0.5)[source]
save(model_file)[source]
summary()[source]
class hypernets.examples.plain_model.PlainModel(searcher, dispatcher=None, callbacks=None, reward_metric=None, task=None, discriminator=None, transformer=None)[source]

Bases: hypernets.model.hyper_model.HyperModel

load_estimator(model_file)[source]
class hypernets.examples.plain_model.PlainSearchSpace(enable_dt=True, enable_lr=True, enable_nn=True, enable_dtr=False)[source]

Bases: object

create_feature_selection(hyper_input, importances, seq_no=0)[source]
dt
dtr
lr
nn
hypernets.examples.plain_model.train(X_train, y_train, X_eval, y_eval, task=None, reward_metric=None, optimize_direction='max', **kwargs)[source]
hypernets.examples.plain_model.train_heart_disease(**kwargs)[source]

hypernets.examples.smoke_testing module

hypernets.examples.smoke_testing.get_space()[source]

Module contents