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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
import functools
from enum import Enum, unique
import json_tricks
from schema import And
from . import parameter_expressions
to_json = functools.partial(json_tricks.dumps, allow_nan=True)
@unique
class OptimizeMode(Enum):
"""Optimize Mode class
if OptimizeMode is 'minimize', it means the tuner need to minimize the reward
that received from Trial.
if OptimizeMode is 'maximize', it means the tuner need to maximize the reward
that received from Trial.
"""
Minimize = 'minimize'
Maximize = 'maximize'
class NodeType:
"""Node Type class
"""
ROOT = 'root'
TYPE = '_type'
VALUE = '_value'
INDEX = '_index'
NAME = '_name'
class MetricType:
"""The types of metric data
"""
FINAL = 'FINAL'
PERIODICAL = 'PERIODICAL'
REQUEST_PARAMETER = 'REQUEST_PARAMETER'
def split_index(params):
"""
Delete index infromation from params
"""
if isinstance(params, dict):
if NodeType.INDEX in params.keys():
return split_index(params[NodeType.VALUE])
result = {}
for key in params:
result[key] = split_index(params[key])
return result
else:
return params
def extract_scalar_reward(value, scalar_key='default'):
"""
Extract scalar reward from trial result.
Parameters
----------
value : int, float, dict
the reported final metric data
scalar_key : str
the key name that indicates the numeric number
Raises
------
RuntimeError
Incorrect final result: the final result should be float/int,
or a dict which has a key named "default" whose value is float/int.
"""
if isinstance(value, (float, int)):
reward = value
elif isinstance(value, dict) and scalar_key in value and isinstance(value[scalar_key], (float, int)):
reward = value[scalar_key]
else:
raise RuntimeError('Incorrect final result: the final result should be float/int, ' \
'or a dict which has a key named "default" whose value is float/int.')
return reward
def extract_scalar_history(trial_history, scalar_key='default'):
"""
Extract scalar value from a list of intermediate results.
Parameters
----------
trial_history : list
accumulated intermediate results of a trial
scalar_key : str
the key name that indicates the numeric number
Raises
------
RuntimeError
Incorrect final result: the final result should be float/int,
or a dict which has a key named "default" whose value is float/int.
"""
return [extract_scalar_reward(ele, scalar_key) for ele in trial_history]
def convert_dict2tuple(value):
"""
convert dict type to tuple to solve unhashable problem.
NOTE: this function will change original data.
"""
if isinstance(value, dict):
for _keys in value:
value[_keys] = convert_dict2tuple(value[_keys])
return tuple(sorted(value.items()))
return value
def json2space(x, oldy=None, name=NodeType.ROOT):
"""
Change search space from json format to hyperopt format
"""
y = list()
if isinstance(x, dict):
if NodeType.TYPE in x.keys():
_type = x[NodeType.TYPE]
name = name + '-' + _type
if _type == 'choice':
if oldy is not None:
_index = oldy[NodeType.INDEX]
y += json2space(x[NodeType.VALUE][_index],
oldy[NodeType.VALUE], name=name+'[%d]' % _index)
else:
y += json2space(x[NodeType.VALUE], None, name=name)
y.append(name)
else:
for key in x.keys():
y += json2space(x[key], oldy[key] if oldy else None, name+"[%s]" % str(key))
elif isinstance(x, list):
for i, x_i in enumerate(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.')
y += json2space(x_i, oldy[i] if oldy else None, name + "[%d]" % i)
return y
def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
"""
Json to pramaters.
"""
if isinstance(x, dict):
if NodeType.TYPE in x.keys():
_type = x[NodeType.TYPE]
_value = x[NodeType.VALUE]
name = name + '-' + _type
Rand |= is_rand[name]
if Rand is True:
if _type == 'choice':
_index = random_state.randint(len(_value))
y = {
NodeType.INDEX: _index,
NodeType.VALUE: json2parameter(
x[NodeType.VALUE][_index],
is_rand,
random_state,
None,
Rand,
name=name+"[%d]" % _index
)
}
else:
y = getattr(parameter_expressions, _type)(*(_value + [random_state]))
else:
y = copy.deepcopy(oldy)
else:
y = dict()
for key in x.keys():
y[key] = json2parameter(
x[key],
is_rand,
random_state,
oldy[key] if oldy else None,
Rand,
name + "[%s]" % str(key)
)
elif isinstance(x, list):
y = list()
for i, x_i in enumerate(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.')
y.append(json2parameter(
x_i,
is_rand,
random_state,
oldy[i] if oldy else None,
Rand,
name + "[%d]" % i
))
else:
y = copy.deepcopy(x)
return y
def merge_parameter(base_params, override_params):
"""
Update the parameters in ``base_params`` with ``override_params``.
Can be useful to override parsed command line arguments.
Parameters
----------
base_params : namespace or dict
Base parameters. A key-value mapping.
override_params : dict or None
Parameters to override. Usually the parameters got from ``get_next_parameters()``.
When it is none, nothing will happen.
Returns
-------
namespace or dict
The updated ``base_params``. Note that ``base_params`` will be updated inplace. The return value is
only for convenience.
"""
if override_params is None:
return base_params
is_dict = isinstance(base_params, dict)
for k, v in override_params.items():
if is_dict:
if k not in base_params:
raise ValueError('Key \'%s\' not found in base parameters.' % k)
if type(base_params[k]) != type(v) and base_params[k] is not None:
raise TypeError('Expected \'%s\' in override parameters to have type \'%s\', but found \'%s\'.' %
(k, type(base_params[k]), type(v)))
base_params[k] = v
else:
if not hasattr(base_params, k):
raise ValueError('Key \'%s\' not found in base parameters.' % k)
if type(getattr(base_params, k)) != type(v) and getattr(base_params, k) is not None:
raise TypeError('Expected \'%s\' in override parameters to have type \'%s\', but found \'%s\'.' %
(k, type(getattr(base_params, k)), type(v)))
setattr(base_params, k, v)
return base_params
class ClassArgsValidator(object):
"""
NNI tuners/assessors/adivisors accept a `classArgs` parameter in experiment configuration file.
This ClassArgsValidator interface is used to validate the classArgs section in exeperiment
configuration file.
"""
def validate_class_args(self, **kwargs):
"""
Validate the classArgs configuration in experiment configuration file.
Parameters
----------
kwargs: dict
kwargs passed to tuner/assessor/advisor constructor
Raises:
Raise an execption if the kwargs is invalid.
"""
pass
def choices(self, key, *args):
"""
Utility method to create a scheme to check whether the `key` is one of the `args`.
Parameters:
----------
key: str
key name of the data to be validated
args: list of str
list of the choices
Returns: Schema
--------
A scheme to check whether the `key` is one of the `args`.
"""
return And(lambda n: n in args, error='%s should be in [%s]!' % (key, str(args)))
def range(self, key, keyType, start, end):
"""
Utility method to create a schema to check whether the `key` is in the range of [start, end].
Parameters:
----------
key: str
key name of the data to be validated
keyType: type
python data type, such as int, float
start: type is specified by keyType
start of the range
end: type is specified by keyType
end of the range
Returns: Schema
--------
A scheme to check whether the `key` is in the range of [start, end].
"""
return And(
And(keyType, error='%s should be %s type!' % (key, keyType.__name__)),
And(lambda n: start <= n <= end, error='%s should be in range of (%s, %s)!' % (key, start, end))
)
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