zoo.automl.config package

Submodules

zoo.automl.config.recipe module

class zoo.automl.config.recipe.BayesRecipe(num_samples=1, look_back=2, epochs=5, reward_metric=-0.05, training_iteration=5)

Bases: zoo.automl.config.recipe.Recipe

A Bayes search Recipe. (Experimental)

tsp = TimeSequencePredictor(…,recipe = BayesRecipe(5))

fixed_params()

search_algorithm()

search_algorithm_params()

search_space(all_available_features)

class zoo.automl.config.recipe.GridRandomRecipe(num_rand_samples=1, look_back=2, epochs=5, training_iteration=10)

Bases: zoo.automl.config.recipe.Recipe

A recipe involves both grid search and random search.

tsp = TimeSequencePredictor(…,recipe = GridRandomRecipe(1))

search_space(all_available_features)

class zoo.automl.config.recipe.LSTMGridRandomRecipe(num_rand_samples=1, epochs=5, training_iteration=10, look_back=2, lstm_1_units=[16, 32, 64, 128], lstm_2_units=[16, 32, 64], batch_size=[32, 64])

Bases: zoo.automl.config.recipe.Recipe

A recipe involves both grid search and random search, only for LSTM.

tsp = TimeSequencePredictor(…,recipe = LSTMGridRandomRecipe(1))

search_space(all_available_features)

class zoo.automl.config.recipe.MTNetGridRandomRecipe(num_rand_samples=1, epochs=5, training_iteration=10, time_step=[3, 4], long_num=[3, 4], cnn_height=[2, 3], cnn_hid_size=[32, 50, 100], ar_size=[2, 3], batch_size=[32, 64])

Bases: zoo.automl.config.recipe.Recipe

Grid+Random Recipe for MTNet

search_space(all_available_features)

class zoo.automl.config.recipe.MTNetSmokeRecipe

Bases: zoo.automl.config.recipe.Recipe

A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.

search_space(all_available_features)

class zoo.automl.config.recipe.PastSeqParamHandler

Bases: object

Utility to handle PastSeq Param

static get_past_seq_config(look_back)

generate pass sequence config based on look_back :param look_back: look_back configuration :return: search configuration for past sequence

class zoo.automl.config.recipe.RandomRecipe(num_rand_samples=1, look_back=2, epochs=5, reward_metric=-0.05, training_iteration=10)

Bases: zoo.automl.config.recipe.Recipe

Pure random sample Recipe. Often used as baseline.

tsp = TimeSequencePredictor(…,recipe = RandomRecipe(5))

search_space(all_available_features)

class zoo.automl.config.recipe.Recipe

Bases: object

fixed_params()

runtime_params()

scheduler_algorithm()

search_algorithm()

search_algorithm_params()

search_space(all_available_features)

class zoo.automl.config.recipe.SmokeRecipe

Bases: zoo.automl.config.recipe.Recipe

A very simple Recipe for smoke test that runs one epoch and one iteration with only 1 random sample.

search_space(all_available_features)

class zoo.automl.config.recipe.XgbRegressorGridRandomRecipe(num_rand_samples=1, n_estimators=[8, 15], max_depth=[10, 15], n_jobs=-1, tree_method='hist', random_state=2, seed=0, lr=(0.0001, 0.1), subsample=0.8, colsample_bytree=0.8, min_child_weight=[1, 2, 3], gamma=0, reg_alpha=0, reg_lambda=1)

Bases: zoo.automl.config.recipe.Recipe

search_space(all_available_features)

class zoo.automl.config.recipe.XgbRegressorSkOptRecipe(num_rand_samples=10, n_estimators_range=(50, 1000), max_depth_range=(2, 15))

Bases: zoo.automl.config.recipe.Recipe

fixed_params()

opt_params()

scheduler_algorithm()

search_algorithm()

search_space(all_available_features)

Module contents