# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
hyperopt_tuner.py
"""
import copy
import logging
import hyperopt as hp
import numpy as np
from schema import Optional, Schema
from nni import ClassArgsValidator
from nni.tuner import Tuner
from nni.utils import NodeType, OptimizeMode, extract_scalar_reward, split_index
logger = logging.getLogger('hyperopt_AutoML')
def json2space(in_x, name=NodeType.ROOT):
"""
Change json to search space in hyperopt.
Parameters
----------
in_x : dict/list/str/int/float
The part of json.
name : str
name could be NodeType.ROOT, NodeType.TYPE, NodeType.VALUE or NodeType.INDEX, NodeType.NAME.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if NodeType.TYPE in in_x.keys():
_type = in_x[NodeType.TYPE]
name = name + '-' + _type
_value = json2space(in_x[NodeType.VALUE], name=name)
if _type == 'choice':
out_y = hp.hp.choice(name, _value)
elif _type == 'randint':
out_y = hp.hp.randint(name, _value[1] - _value[0])
else:
if _type in ['loguniform', 'qloguniform']:
_value[:2] = np.log(_value[:2])
out_y = getattr(hp.hp, _type)(name, *_value)
else:
out_y = dict()
for key in in_x.keys():
out_y[key] = json2space(in_x[key], name + '[%s]' % str(key))
elif isinstance(in_x, list):
out_y = list()
for i, x_i in enumerate(in_x):
if isinstance(x_i, dict):
if NodeType.NAME not in x_i.keys():
raise RuntimeError(
'\'_name\' key is not found in this nested search space.'
)
out_y.append(json2space(x_i, name + '[%d]' % i))
return out_y
def json2parameter(in_x, parameter, name=NodeType.ROOT):
"""
Change json to parameters.
"""
out_y = copy.deepcopy(in_x)
if isinstance(in_x, dict):
if NodeType.TYPE in in_x.keys():
_type = in_x[NodeType.TYPE]
name = name + '-' + _type
if _type == 'choice':
_index = parameter[name]
out_y = {
NodeType.INDEX:
_index,
NodeType.VALUE:
json2parameter(in_x[NodeType.VALUE][_index],
parameter,
name=name + '[%d]' % _index)
}
else:
if _type in ['quniform', 'qloguniform']:
out_y = np.clip(parameter[name], in_x[NodeType.VALUE][0], in_x[NodeType.VALUE][1])
elif _type == 'randint':
out_y = parameter[name] + in_x[NodeType.VALUE][0]
else:
out_y = parameter[name]
else:
out_y = dict()
for key in in_x.keys():
out_y[key] = json2parameter(in_x[key], parameter,
name + '[%s]' % str(key))
elif isinstance(in_x, list):
out_y = list()
for i, x_i in enumerate(in_x):
if isinstance(x_i, dict):
if NodeType.NAME not in x_i.keys():
raise RuntimeError(
'\'_name\' key is not found in this nested search space.'
)
out_y.append(json2parameter(x_i, parameter, name + '[%d]' % i))
return out_y
def json2vals(in_x, vals, out_y, name=NodeType.ROOT):
if isinstance(in_x, dict):
if NodeType.TYPE in in_x.keys():
_type = in_x[NodeType.TYPE]
name = name + '-' + _type
try:
out_y[name] = vals[NodeType.INDEX]
# TODO - catch exact Exception
except Exception:
out_y[name] = vals
if _type == 'choice':
_index = vals[NodeType.INDEX]
json2vals(in_x[NodeType.VALUE][_index],
vals[NodeType.VALUE],
out_y,
name=name + '[%d]' % _index)
if _type == 'randint':
out_y[name] -= in_x[NodeType.VALUE][0]
else:
for key in in_x.keys():
json2vals(in_x[key], vals[key], out_y,
name + '[%s]' % str(key))
elif isinstance(in_x, list):
for i, temp in enumerate(in_x):
# nested json
if isinstance(temp, dict):
if NodeType.NAME not in temp.keys():
raise RuntimeError(
'\'_name\' key is not found in this nested search space.'
)
else:
json2vals(temp, vals[i], out_y, name + '[%d]' % i)
else:
json2vals(temp, vals[i], out_y, name + '[%d]' % i)
def _add_index(in_x, parameter):
"""
change parameters in NNI format to parameters in hyperopt format(This function also support nested dict.).
For example, receive parameters like:
{'dropout_rate': 0.8, 'conv_size': 3, 'hidden_size': 512}
Will change to format in hyperopt, like:
{'dropout_rate': 0.8, 'conv_size': {'_index': 1, '_value': 3}, 'hidden_size': {'_index': 1, '_value': 512}}
"""
if NodeType.TYPE not in in_x: # if at the top level
out_y = dict()
for key, value in parameter.items():
out_y[key] = _add_index(in_x[key], value)
return out_y
elif isinstance(in_x, dict):
value_type = in_x[NodeType.TYPE]
value_format = in_x[NodeType.VALUE]
if value_type == "choice":
choice_name = parameter[0] if isinstance(parameter,
list) else parameter
for pos, item in enumerate(
value_format): # here value_format is a list
if isinstance(
item,
list): # this format is ["choice_key", format_dict]
choice_key = item[0]
choice_value_format = item[1]
if choice_key == choice_name:
return {
NodeType.INDEX: pos,
NodeType.VALUE: [
choice_name,
_add_index(choice_value_format, parameter[1])
]
}
elif choice_name == item:
return {NodeType.INDEX: pos, NodeType.VALUE: item}
else:
return parameter
return None # note: this is not written by original author, feel free to modify if you think it's incorrect
class HyperoptClassArgsValidator(ClassArgsValidator):
def validate_class_args(self, **kwargs):
Schema({
Optional('optimize_mode'): self.choices('optimize_mode', 'maximize', 'minimize'),
Optional('parallel_optimize'): bool,
Optional('constant_liar_type'): self.choices('constant_liar_type', 'min', 'max', 'mean')
}).validate(kwargs)
class HyperoptTuner(Tuner):
"""
HyperoptTuner is a tuner which using hyperopt algorithm.
"""
def __init__(self, algorithm_name, optimize_mode='minimize',
parallel_optimize=False, constant_liar_type='min'):
"""
Parameters
----------
algorithm_name : str
algorithm_name includes "tpe", "random_search" and anneal".
optimize_mode : str
parallel_optimize : bool
More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
constant_liar_type : str
constant_liar_type including "min", "max" and "mean"
More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
"""
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.rval = None
self.supplement_data_num = 0
self.parallel = parallel_optimize
if self.parallel:
self.CL_rval = None
self.constant_liar_type = constant_liar_type
self.running_data = []
self.optimal_y = None
def _choose_tuner(self, algorithm_name):
"""
Parameters
----------
algorithm_name : str
algorithm_name includes "tpe", "random_search" and anneal"
"""
if algorithm_name == 'tpe':
return hp.tpe.suggest
if algorithm_name == 'random_search':
return hp.rand.suggest
if algorithm_name == 'anneal':
return hp.anneal.suggest
raise RuntimeError('Not support tuner algorithm in hyperopt.')
def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in parameters.
Will called when first setup experiemnt or update search space in WebUI.
Parameters
----------
search_space : dict
"""
self.json = search_space
search_space_instance = json2space(self.json)
rstate = np.random.RandomState()
trials = hp.Trials()
domain = hp.Domain(None,
search_space_instance,
pass_expr_memo_ctrl=None)
algorithm = self._choose_tuner(self.algorithm_name)
self.rval = hp.FMinIter(algorithm,
domain,
trials,
max_evals=-1,
rstate=rstate,
verbose=0)
self.rval.catch_eval_exceptions = False
def generate_parameters(self, parameter_id, **kwargs):
"""
Returns a set of trial (hyper-)parameters, as a serializable object.
Parameters
----------
parameter_id : int
Returns
-------
params : dict
"""
total_params = self.get_suggestion(random_search=False)
# avoid generating same parameter with concurrent trials because hyperopt doesn't support parallel mode
if total_params in self.total_data.values():
# but it can cause duplicate parameter rarely
total_params = self.get_suggestion(random_search=True)
self.total_data[parameter_id] = total_params
if self.parallel:
self.running_data.append(parameter_id)
params = split_index(total_params)
return params
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""
Record an observation of the objective function
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
value is final metrics of the trial.
"""
reward = extract_scalar_reward(value)
# restore the paramsters contains '_index'
if parameter_id not in self.total_data:
raise RuntimeError('Received parameter_id not in total_data.')
params = self.total_data[parameter_id]
# code for parallel
if self.parallel:
constant_liar = kwargs.get('constant_liar', False)
if constant_liar:
rval = self.CL_rval
else:
rval = self.rval
# ignore duplicated reported final result (due to aware of intermedate result)
if parameter_id not in self.running_data:
logger.info("Received duplicated final result with parameter id: %s", parameter_id)
return
self.running_data.remove(parameter_id)
# update the reward of optimal_y
if self.optimal_y is None:
if self.constant_liar_type == 'mean':
self.optimal_y = [reward, 1]
else:
self.optimal_y = reward
else:
if self.constant_liar_type == 'mean':
_sum = self.optimal_y[0] + reward
_number = self.optimal_y[1] + 1
self.optimal_y = [_sum, _number]
elif self.constant_liar_type == 'min':
self.optimal_y = min(self.optimal_y, reward)
elif self.constant_liar_type == 'max':
self.optimal_y = max(self.optimal_y, reward)
logger.debug("Update optimal_y with reward, optimal_y = %s", self.optimal_y)
else:
rval = self.rval
if self.optimize_mode is OptimizeMode.Maximize:
reward = -reward
domain = rval.domain
trials = rval.trials
new_id = len(trials)
rval_specs = [None]
rval_results = [domain.new_result()]
rval_miscs = [dict(tid=new_id, cmd=domain.cmd, workdir=domain.workdir)]
vals = params
idxs = dict()
out_y = dict()
json2vals(self.json, vals, out_y)
vals = out_y
for key in domain.params:
if key in [NodeType.VALUE, NodeType.INDEX]:
continue
if key not in vals or vals[key] is None or vals[key] == []:
idxs[key] = vals[key] = []
else:
idxs[key] = [new_id]
vals[key] = [vals[key]]
self.miscs_update_idxs_vals(rval_miscs,
idxs,
vals,
idxs_map={new_id: new_id},
assert_all_vals_used=False)
trial = trials.new_trial_docs([new_id], rval_specs, rval_results,
rval_miscs)[0]
trial['result'] = {'loss': reward, 'status': 'ok'}
trial['state'] = hp.JOB_STATE_DONE
trials.insert_trial_docs([trial])
trials.refresh()
def miscs_update_idxs_vals(self,
miscs,
idxs,
vals,
assert_all_vals_used=True,
idxs_map=None):
"""
Unpack the idxs-vals format into the list of dictionaries that is
`misc`.
Parameters
----------
idxs_map : dict
idxs_map is a dictionary of id->id mappings so that the misc['idxs'] can
contain different numbers than the idxs argument.
"""
if idxs_map is None:
idxs_map = {}
assert set(idxs.keys()) == set(vals.keys())
misc_by_id = {m['tid']: m for m in miscs}
for m in miscs:
m['idxs'] = {key: [] for key in idxs}
m['vals'] = {key: [] for key in idxs}
for key in idxs:
assert len(idxs[key]) == len(vals[key])
for tid, val in zip(idxs[key], vals[key]):
tid = idxs_map.get(tid, tid)
if assert_all_vals_used or tid in misc_by_id:
misc_by_id[tid]['idxs'][key] = [tid]
misc_by_id[tid]['vals'][key] = [val]
def get_suggestion(self, random_search=False):
"""
get suggestion from hyperopt
Parameters
----------
random_search : bool
flag to indicate random search or not (default: {False})
Returns
----------
total_params : dict
parameter suggestion
"""
if self.parallel and len(self.total_data) > 20 and self.running_data and self.optimal_y is not None:
self.CL_rval = copy.deepcopy(self.rval)
if self.constant_liar_type == 'mean':
_constant_liar_y = self.optimal_y[0] / self.optimal_y[1]
else:
_constant_liar_y = self.optimal_y
for _parameter_id in self.running_data:
self.receive_trial_result(parameter_id=_parameter_id, parameters=None, value=_constant_liar_y, constant_liar=True)
rval = self.CL_rval
random_state = np.random.randint(2**31 - 1)
else:
rval = self.rval
random_state = rval.rstate.randint(2**31 - 1)
trials = rval.trials
algorithm = rval.algo
new_ids = rval.trials.new_trial_ids(1)
rval.trials.refresh()
if random_search:
new_trials = hp.rand.suggest(new_ids, rval.domain, trials,
random_state)
else:
new_trials = algorithm(new_ids, rval.domain, trials, random_state)
rval.trials.refresh()
vals = new_trials[0]['misc']['vals']
parameter = dict()
for key in vals:
try:
parameter[key] = vals[key][0].item()
except (KeyError, IndexError):
parameter[key] = None
# remove '_index' from json2parameter and save params-id
total_params = json2parameter(self.json, parameter)
return total_params
def import_data(self, data):
"""
Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
_completed_num += 1
if self.algorithm_name == 'random_search':
return
assert "parameter" in trial_info
_params = trial_info["parameter"]
assert "value" in trial_info
_value = trial_info['value']
if not _value:
logger.info("Useless trial data, value is %s, skip this trial data.", _value)
continue
self.supplement_data_num += 1
_parameter_id = '_'.join(
["ImportData", str(self.supplement_data_num)])
self.total_data[_parameter_id] = _add_index(in_x=self.json,
parameter=_params)
self.receive_trial_result(parameter_id=_parameter_id,
parameters=_params,
value=_value)
logger.info("Successfully import data to TPE/Anneal tuner.")
