def __init__(self, goal: str, parameters: Dict[str, Any], num_samples=10,
**kwargs) -> None:
HyperoptSampler.__init__(self, goal, parameters)
params_for_join_space = copy.deepcopy(parameters)
cat_params_values_types = {}
for param_name, param_values in params_for_join_space.items():
if param_values[TYPE] == CATEGORY:
param_values[TYPE] = 'cat'
values_str = []
values_types = {}
for value in param_values['values']:
value_str = str(value)
values_str.append(value_str)
value_type = type(value)
if value_type == bool:
value_type = str2bool
values_types[value_str] = value_type
param_values['values'] = values_str
cat_params_values_types[param_name] = values_types
if param_values[TYPE] == FLOAT:
param_values[TYPE] = 'real'
if param_values[TYPE] == INT or param_values[TYPE] == 'real':
if SPACE not in param_values:
param_values[SPACE] = 'linear'
param_values['range'] = (param_values['low'],
param_values['high'])
del param_values['low']
del param_values['high']
self.cat_params_values_types = cat_params_values_types
self.pysot_optimizer = PySOTOptimizer(params_for_join_space)
self.sampled_so_far = 0
self.num_samples = num_samples
class PySOTSampler(HyperoptSampler):
"""pySOT: Surrogate optimization in Python.
This is a wrapper around the pySOT package (https://github.com/dme65/pySOT):
David Eriksson, David Bindel, Christine Shoemaker
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
"""
def __init__(self,
goal: str,
parameters: Dict[str, Any],
num_samples=10,
**kwargs) -> None:
HyperoptSampler.__init__(self, goal, parameters)
params_for_join_space = copy.deepcopy(parameters)
for param_values in params_for_join_space.values():
if param_values[TYPE] == CATEGORY:
param_values[TYPE] = 'cat'
if param_values[TYPE] == FLOAT:
param_values[TYPE] = 'real'
if param_values[TYPE] == INT or param_values[TYPE] == 'real':
if SPACE not in param_values:
param_values[SPACE] = 'linear'
param_values['range'] = (param_values['low'],
param_values['high'])
del param_values['low']
del param_values['high']
self.pysot_optimizer = PySOTOptimizer(params_for_join_space)
self.sampled_so_far = 0
self.num_samples = num_samples
def sample(self) -> Dict[str, Any]:
"""Suggest one new point to be evaluated."""
if self.sampled_so_far >= self.num_samples:
raise IndexError()
sample = self.pysot_optimizer.suggest(n_suggestions=1)[0]
self.sampled_so_far += 1
return sample
def update(self, sampled_parameters: Dict[str, Any], metric_score: float):
self.pysot_optimizer.observe([sampled_parameters], [metric_score])
def finished(self) -> bool:
return self.sampled_so_far >= self.num_samples
def __init__(self,
goal: str,
parameters: Dict[str, Any],
num_samples=10,
**kwargs) -> None:
HyperoptSampler.__init__(self, goal, parameters)
params_for_join_space = copy.deepcopy(parameters)
cat_params_values_types = {}
for param_name, param_values in params_for_join_space.items():
if param_values[TYPE] == CATEGORY:
param_values[TYPE] = "cat"
values_str = []
values_types = {}
for value in param_values["values"]:
value_type = type(value)
if value_type == bool:
value_str = str(value)
value_type = str2bool
elif value_type == str or value_type == int or value_type == float:
value_str = str(value)
else:
value_str = json.dumps(value)
value_type = json.loads
values_str.append(value_str)
values_types[value_str] = value_type
param_values["values"] = values_str
cat_params_values_types[param_name] = values_types
if param_values[TYPE] == FLOAT:
param_values[TYPE] = "real"
if param_values[TYPE] == INT or param_values[TYPE] == "real":
if SPACE not in param_values:
param_values[SPACE] = "linear"
param_values["range"] = (param_values["low"],
param_values["high"])
del param_values["low"]
del param_values["high"]
self.cat_params_values_types = cat_params_values_types
self.pysot_optimizer = PySOTOptimizer(params_for_join_space)
self.sampled_so_far = 0
self.num_samples = num_samples
def __init__(self,
goal: str,
parameters: Dict[str, Any],
num_samples=10,
**kwargs) -> None:
HyperoptSampler.__init__(self, goal, parameters)
params_for_join_space = copy.deepcopy(parameters)
for param_values in params_for_join_space.values():
if param_values[TYPE] == CATEGORY:
param_values[TYPE] = 'cat'
if param_values[TYPE] == FLOAT:
param_values[TYPE] = 'real'
if param_values[TYPE] == INT or param_values[TYPE] == 'real':
if SPACE not in param_values:
param_values[SPACE] = 'linear'
param_values['range'] = (param_values['low'],
param_values['high'])
del param_values['low']
del param_values['high']
self.pysot_optimizer = PySOTOptimizer(params_for_join_space)
self.sampled_so_far = 0
self.num_samples = num_samples
class PySOTSampler(HyperoptSampler):
"""pySOT: Surrogate optimization in Python.
This is a wrapper around the pySOT package (https://github.com/dme65/pySOT):
David Eriksson, David Bindel, Christine Shoemaker
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
"""
def __init__(self,
goal: str,
parameters: Dict[str, Any],
num_samples=10,
**kwargs) -> None:
HyperoptSampler.__init__(self, goal, parameters)
params_for_join_space = copy.deepcopy(parameters)
cat_params_values_types = {}
for param_name, param_values in params_for_join_space.items():
if param_values[TYPE] == CATEGORY:
param_values[TYPE] = 'cat'
values_str = []
values_types = {}
for value in param_values['values']:
value_type = type(value)
if value_type == bool:
value_str = str(value)
value_type = str2bool
elif value_type == str or value_type == int or \
value_type == float:
value_str = str(value)
else:
value_str = json.dumps(value)
value_type = json.loads
values_str.append(value_str)
values_types[value_str] = value_type
param_values['values'] = values_str
cat_params_values_types[param_name] = values_types
if param_values[TYPE] == FLOAT:
param_values[TYPE] = 'real'
if param_values[TYPE] == INT or param_values[TYPE] == 'real':
if SPACE not in param_values:
param_values[SPACE] = 'linear'
param_values['range'] = (param_values['low'],
param_values['high'])
del param_values['low']
del param_values['high']
self.cat_params_values_types = cat_params_values_types
self.pysot_optimizer = PySOTOptimizer(params_for_join_space)
self.sampled_so_far = 0
self.num_samples = num_samples
def sample(self) -> Dict[str, Any]:
"""Suggest one new point to be evaluated."""
if self.sampled_so_far >= self.num_samples:
raise IndexError()
sample = self.pysot_optimizer.suggest(n_suggestions=1)[0]
for key in sample:
if key in self.cat_params_values_types:
values_types = self.cat_params_values_types[key]
sample[key] = values_types[sample[key]](sample[key])
self.sampled_so_far += 1
return sample
def update(self, sampled_parameters: Dict[str, Any], metric_score: float):
for key in sampled_parameters:
if key in self.cat_params_values_types:
if type(sampled_parameters[key]) not in {
bool, int, float, str
}:
sampled_parameters[key] = json.dumps(
sampled_parameters[key])
else:
sampled_parameters[key] = str(sampled_parameters[key])
self.pysot_optimizer.observe([sampled_parameters], [metric_score])
def finished(self) -> bool:
return self.sampled_so_far >= self.num_samples