hypernets.core package

Submodules

hypernets.core.callbacks module

class hypernets.core.callbacks.Callback

Bases: object

on_build_estimator(hyper_model, space, estimator, trial_no)

on_search_end(hyper_model)

on_search_error(hyper_model)

on_search_start(hyper_model, X, y, X_eval, y_eval, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

on_skip_trial(hyper_model, space, trial_no, reason, reward, improved, elapsed)

on_trial_begin(hyper_model, space, trial_no)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

on_trial_error(hyper_model, space, trial_no)

class hypernets.core.callbacks.EarlyStoppingCallback(max_no_improvement_trials=0, mode='min', min_delta=0, time_limit=None, expected_reward=None)

Bases: hypernets.core.callbacks.Callback

REASON_EXPECTED_REWARD = 'expected_reward'

REASON_TIME_LIMIT = 'time_limit'

REASON_TRIAL_LIMIT = 'max_no_improvement_trials'

on_search_start(hyper_model, X, y, X_eval, y_eval, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

on_trial_begin(hyper_model, space, trial_no)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

exception hypernets.core.callbacks.EarlyStoppingError(*arg)

Bases: RuntimeError

class hypernets.core.callbacks.FileLoggingCallback(searcher, output_dir=None)

Bases: hypernets.core.callbacks.Callback

static mkdirs(dir_path, exist_ok)

on_build_estimator(hyper_model, space, estimator, trial_no)

on_skip_trial(hyper_model, space, trial_no, reason, reward, improved, elapsed)

on_trial_begin(hyper_model, space, trial_no)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

static open(file_path, mode)

class hypernets.core.callbacks.FileStorageLoggingCallback(searcher, output_dir=None)

Bases: hypernets.core.callbacks.FileLoggingCallback

static mkdirs(dir_path, exist_ok)

static open(file_path, mode)

class hypernets.core.callbacks.NotebookCallback

Bases: hypernets.core.callbacks.Callback

on_search_end(hyper_model)

on_search_start(hyper_model, X, y, X_eval, y_eval, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

on_trial_begin(hyper_model, space, trial_no)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

on_trial_error(hyper_model, space, trial_no)

class hypernets.core.callbacks.ProgressiveCallback

Bases: hypernets.core.callbacks.Callback

on_search_end(hyper_model)

on_search_error(hyper_model)

on_search_start(hyper_model, X, y, X_eval, y_eval, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

on_trial_error(hyper_model, space, trial_no)

class hypernets.core.callbacks.SummaryCallback

Bases: hypernets.core.callbacks.Callback

on_build_estimator(hyper_model, space, estimator, trial_no)

on_search_start(hyper_model, X, y, X_eval, y_eval, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

on_skip_trial(hyper_model, space, trial_no, reason, reward, improved, elapsed)

on_trial_begin(hyper_model, space, trial_no)

on_trial_end(hyper_model, space, trial_no, reward, improved, elapsed)

hypernets.core.config module

hypernets.core.context module

class hypernets.core.context.Context

Bases: object

get(key)

put(key, value)

class hypernets.core.context.DefaultContext

Bases: hypernets.core.context.Context

get(key)

put(key, value)

hypernets.core.dispatcher module

class hypernets.core.dispatcher.Dispatcher

Bases: object

dispatch(hyper_model, X, y, X_val, y_val, X_test, cv, num_folds, max_trials, dataset_id, trial_store, **fit_kwargs)

hypernets.core.meta_learner module

class hypernets.core.meta_learner.MetaLearner(history, dataset_id, trial_store)

Bases: object

extract_features_and_labels(signature)

fit(space_signature)

new_sample(space_sample)

predict(space_sample, default_value=inf)

hypernets.core.mutables module

class hypernets.core.mutables.Mutable(scope, name=None)

Bases: object

attach_to_scope(scope, name=None)

id

type

update()

class hypernets.core.mutables.MutableScope

Bases: object

assign_id(mutable)

current_path

entry(name)

exit()

get_mutable(id)

get_mutable_by_name(name)

register(mutable)

reset()

hypernets.core.objective module

class hypernets.core.objective.Objective(name, direction, need_train_data=False, need_val_data=True, need_test_data=False)

Bases: object

Objective = Indicator metric + Direction

evaluate(trial, estimator, X_train, y_train, X_val, y_val, X_test=None, **kwargs) → float

evaluate_cv(trial, estimator, X_trains, y_trains, X_vals, y_vals, X_test=None, **kwargs) → float

hypernets.core.ops module

class hypernets.core.ops.ConnectLooseEnd(inputs, keep_link=False, space=None, name=None)

Bases: hypernets.core.ops.ConnectionSpace

class hypernets.core.ops.ConnectionSpace(dynamic_fn, keep_link=False, space=None, name=None, **hyperparams)

Bases: hypernets.core.search_space.ModuleSpace

connect_module_or_subgraph(from_module, to_module, support_subgraph=True)

class hypernets.core.ops.HyperInput(space=None, name=None, **hyperparams)

Bases: hypernets.core.search_space.ModuleSpace

class hypernets.core.ops.Identity(space=None, name=None, **hyperparams)

Bases: hypernets.core.search_space.ModuleSpace

class hypernets.core.ops.InputChoice(inputs, num_chosen_most=0, num_chosen_least=1, keep_link=False, space=None, name=None, hp_choice=None)

Bases: hypernets.core.ops.ConnectionSpace

class hypernets.core.ops.ModuleChoice(module_list, keep_link=False, space=None, name=None, hp_or=None)

Bases: hypernets.core.ops.ConnectionSpace

or_fn(m)

class hypernets.core.ops.Optional(module, keep_link=False, space=None, name=None, hp_opt=None)

Bases: hypernets.core.ops.ConnectionSpace

optional_fn(m)

class hypernets.core.ops.Permutation(module_list, keep_link=False, space=None, name=None, hp_seq=None)

Bases: hypernets.core.ops.ConnectionSpace

permutation_fn(m)

class hypernets.core.ops.Reduction(reduction_op, space=None, name=None, **hyperparams)

Bases: hypernets.core.search_space.ModuleSpace

class hypernets.core.ops.Repeat(module_fn, keep_link=False, space=None, name=None, repeat_times=[1])

Bases: hypernets.core.ops.ConnectionSpace

repeat_fn(m)

class hypernets.core.ops.Sequential(module_list, keep_link=False, space=None, name=None)

Bases: hypernets.core.ops.ConnectionSpace

sequential_fn(m)

hypernets.core.pareto module

hypernets.core.pareto.calc_nondominated_set(solutions: numpy.ndarray, dominate_func=None, directions=None)

hypernets.core.pareto.pareto_dominate(x1, x2, directions=None)

dominance in pareto scene, if x1 dominate x2 return True.

hypernets.core.random_state module

hypernets.core.random_state.get_random_state()

hypernets.core.random_state.randint()

hypernets.core.random_state.set_random_state(seed)

hypernets.core.search_space module

class hypernets.core.search_space.Bool(random_state=None, space=None, name=None)

Bases: hypernets.core.search_space.Choice

class hypernets.core.search_space.Cascade(lambda_fn, space=None, name=None, **param_dict)

Bases: hypernets.core.search_space.ParameterSpace

assigned

is_mutable

param_dict

update()

class hypernets.core.search_space.Choice(options, random_state=None, space=None, name=None)

Bases: hypernets.core.search_space.ParameterSpace

config_keys

expansion(sample_num=0)

is_mutable

numeric2value(numeric)

value2numeric(value)

class hypernets.core.search_space.Constant(value, space=None, name=None)

Bases: hypernets.core.search_space.ParameterSpace

config_keys

is_mutable

class hypernets.core.search_space.DefaultSpaceStack

Bases: hypernets.core.search_space.DefaultStack

get_default()

Override that returns a global default if the stack is empty.

reset()

class hypernets.core.search_space.DefaultStack

Bases: _thread._local

get_controller(default)

get_default()

is_cleared()

reset()

class hypernets.core.search_space.Dynamic(lambda_fn, space=None, name=None, **param_dict)

Bases: hypernets.core.search_space.ParameterSpace

is_mutable

param_dict

update()

class hypernets.core.search_space.HyperNode(space=None, name=None)

Bases: hypernets.core.mutables.Mutable

attach_to_space(space=None, name=None)

space

class hypernets.core.search_space.HyperSpace(scope=None, name=None)

Bases: hypernets.core.mutables.Mutable

add_node(node)

all_assigned

as_default()

assign_by_vectors(vectors)

assigned_params_stack

combinations

compile(deepcopy=True)

compile_and_forward(inputs=None, deepcopy=True)

connect(from_module, to_module)

disconnect(from_module, to_module)

disconnect_all(module)

forward(inputs=None)

get_all_params()

get_assigned_param_values(traverse_direction='forward')

get_assigned_params()

get_inputs(module=None, discard_isolated_node=True)

get_outputs(module=None, discard_isolated_node=True)

get_sub_graph_end_modules(module_in_subgraph, direction='forward')

get_sub_graph_inputs(module_in_subgraph)

get_sub_graph_outputs(module_in_subgraph)

get_unassigned_params(traverse_direction='forward')

is_isolated_module(module)

params_iterator

params_summary(only_assignable=True, line_width=60, LR='\n')

push_assigned_param(param)

random_sample()

replace_route(old_module, new_module)

reroute_from(old_module, new_module)

reroute_to(old_module, new_module)

set_inputs(modules)

set_outputs(modules)

signature

traverse(fn, direction='forward', start_modules=[], discard_isolated_node=True)

type

vectors

class hypernets.core.search_space.Int(low, high, step=1, random_state=None, space=None, name=None)

Bases: hypernets.core.search_space.ParameterSpace

config_keys

expansion(sample_num)

numeric2value(numeric)

value2numeric(value)

class hypernets.core.search_space.ModuleSpace(space=None, name=None, **hyperparams)

Bases: hypernets.core.search_space.HyperNode

add_parameters(**hyperparameters)

all_assigned

compile()

compile_and_forward(inputs=None)

connect(module_or_list)

forward(inputs=None)

get_all_params()

get_assignable_params()

hyper_params

is_compiled

is_params_ready

output

param_values

type

update()

class hypernets.core.search_space.MultipleChoice(options, num_chosen_most=0, num_chosen_least=1, random_state=None, space=None, name=None)

Bases: hypernets.core.search_space.ParameterSpace

config_keys

expansion(sample_num)

numeric2value(numeric)

value2numeric(value)

class hypernets.core.search_space.ParameterSpace(space=None, name=None, random_state=None)

Bases: hypernets.core.search_space.HyperNode

assign(value)

assigned

attach(mutable, alias=None)

choice_num

config_keys

detach(mutable)

expansion(sample_num)

is_mutable

label

numeric2value(numeric)

random_sample(assign=True)

same_config(other)

type

value

value2numeric(value)

class hypernets.core.search_space.Real(low, high, q=None, prior='uniform', step=0.01, max_expansion=100, random_state=None, space=None, name=None)

Bases: hypernets.core.search_space.ParameterSpace

config_keys

expansion(sample_num)

numeric2value(numeric)

value2numeric(value)

hypernets.core.search_space.get_default_space()

hypernets.core.searcher module

class hypernets.core.searcher.OptimizeDirection

Bases: enum.Enum

An enumeration.

Maximize = 'max'

Minimize = 'min'

class hypernets.core.searcher.Searcher(space_fn, optimize_direction=<OptimizeDirection.Minimize: 'min'>, use_meta_learner=True, space_sample_validation_fn=None)

Bases: hypernets.core.stateful.Stateful

export()

get_best()

kind()

Type of the Searcher, should be one of soo, moo. This property used to avoid having to import MOOSearcher when detecting Searcher type.

parallelizable

reset()

sample(space_options=None)

set_meta_learner(meta_learner)

summary()

update_result(space, result)

hypernets.core.stateful module

class hypernets.core.stateful.Stateful

Bases: object

get_state_path()

load_state()

save_state()

hypernets.core.trial module

class hypernets.core.trial.DiskTrialStore(home_dir=None)

Bases: hypernets.core.trial.TrialStore

clear_history()

get_all(dataset_id, space_signature)

get_trial_path(dataset_id, space_sample)

load()

persist()

prepare_home_dir(home_dir)

reset()

class hypernets.core.trial.DominateBasedTrialHistory(directions, objective_names)

Bases: hypernets.core.trial.TrialHistory

append(trial)

get_best()

plot_best_trials(index=True, figsize=(5, 5), loc=None, bbox_to_anchor=None, xlim=None, ylim=None)

to_df(include_params=False)

class hypernets.core.trial.Trial(space_sample, trial_no, reward=None, elapsed=None, model_file=None, succeeded=True)

Bases: object

get_model()

to_df(include_params=False)

class hypernets.core.trial.TrialHistory(optimize_direction)

Bases: object

append(trial)

diff(trials)

get_best()

get_space_signatures()

get_top(n=None)

get_trajectories()

get_trial(space_sample)

get_worst()

is_existed(space_sample)

static load_history(space_fn, filepath)

plot_hyperparams(destination='notebook', output='hyperparams.html')

Plot hyperparams in a parallel line chart

Parameters:

• destination – one of notebook, html
• output – the html file path

Returns:

save(filepath)

to_df(include_params=False)

class hypernets.core.trial.TrialStore

Bases: object

check_trial(trial)

get(dataset_id, space_sample)

get_all(dataset_id, space_signature)

get_from_cache(dataset_id, space_sample)

load()

persist()

put(dataset_id, trial)

put_to_cache(dataset_id, trial)

reset()

sample2key(space_sample)

hypernets.core.trial.get_default_trial_store()

hypernets.core.trial.set_default_trial_store(trial_store)

Module contents