def node_from_yaml(node_spec):
""" Create the node based on the node_spec """
node_spec = copy.deepcopy(node_spec)
# call parent class method for most of the work
node_spec["parameters"], flow_template = \
ParameterOptimizationBase.check_parameters(node_spec["parameters"])
# check grid search specific params
optimization = node_spec["parameters"].pop("optimization")
assert("ranges" in optimization), "Grid Search needs *ranges* parameter"
BaseNode.eval_dict(optimization)
node_obj = GridSearchNode(ranges=optimization["ranges"],
flow_template=flow_template,
**node_spec["parameters"])
return node_obj
def node_from_yaml(node_spec):
""" Create the node based on the node_spec """
node_spec = copy.deepcopy(node_spec)
# call parent class method for most of the work
node_spec["parameters"], flow_template = \
ParameterOptimizationBase.check_parameters(node_spec["parameters"])
if node_spec["parameters"].has_key("optimization"):
BaseNode.eval_dict(node_spec["parameters"]["optimization"])
# since pattern search specific params are all optional, add them to
# **kwargs and let the __init__ do the default assignments
for key, value in node_spec["parameters"].pop("optimization").iteritems():
node_spec["parameters"][key] = value
node_obj = PatternSearchNode(flow_template=flow_template,
**node_spec["parameters"])
return node_obj
def node_from_yaml(node_spec):
""" Create the node based on the node_spec """
node_spec = copy.deepcopy(node_spec)
# call parent class method for most of the work
node_spec["parameters"], flow_template = \
ParameterOptimizationBase.check_parameters(node_spec["parameters"])
# check grid search specific params
optimization = node_spec["parameters"].pop("optimization")
assert ("ranges"
in optimization), "Grid Search needs *ranges* parameter"
BaseNode.eval_dict(optimization)
node_obj = GridSearchNode(ranges=optimization["ranges"],
flow_template=flow_template,
**node_spec["parameters"])
return node_obj
def node_from_yaml(node_spec):
""" Create the node based on the node_spec """
node_spec = copy.deepcopy(node_spec)
# call parent class method for most of the work
node_spec["parameters"], flow_template = \
ParameterOptimizationBase.check_parameters(node_spec["parameters"])
if node_spec["parameters"].has_key("optimization"):
BaseNode.eval_dict(node_spec["parameters"]["optimization"])
# since pattern search specific params are all optional, add them to
# **kwargs and let the __init__ do the default assignments
for key, value in node_spec["parameters"].pop(
"optimization").iteritems():
node_spec["parameters"][key] = value
node_obj = PatternSearchNode(flow_template=flow_template,
**node_spec["parameters"])
return node_obj
def check_parameters(param_spec):
""" Check input parameters of existence and appropriateness """
assert("nodes" in param_spec and "optimization" in param_spec),\
"Parameter Optimization node requires specification of a " \
"list of nodes and optimization algorithm!"
validation_set = param_spec.get("validation_set", {})
validation_set["splits"] = validation_set.get("splits", 5)
validation_set["split_node"] = validation_set.get(
"split_node", {
'node': 'CV_Splitter',
'parameters': {
'splits': validation_set["splits"]
}
})
evaluation = param_spec.get("evaluation", {})
evaluation["ir_class"] = evaluation.get("ir_class", "Target")
evaluation["performance_sink_node"] = evaluation.get(
"performance_sink_node", {
'node': 'Classification_Performance_Sink',
'parameters': {
'ir_class': evaluation["ir_class"]
}
})
# build flow template
nodes_spec = param_spec.pop("nodes")
flow_template = [{
'node': 'External_Generator_Source_Node'
}, validation_set["split_node"]]
flow_template.extend(nodes_spec)
flow_template.append(evaluation["performance_sink_node"])
# Evaluate all remaining parameters
BaseNode.eval_dict(param_spec)
# params with defaults in __init__ have to be added to param_spec dict
if validation_set.has_key("runs"):
param_spec["runs"] = validation_set["runs"]
if evaluation.has_key("metric"):
param_spec["metric"] = evaluation["metric"]
if evaluation.has_key("std_weight"):
param_spec["std_weight"] = evaluation['std_weight']
return param_spec, flow_template
def check_parameters(param_spec):
""" Check input parameters of existence and appropriateness """
assert("nodes" in param_spec and "optimization" in param_spec), \
"Parameter Optimization node requires specification of a " \
"list of nodes and optimization algorithm!"
validation_set = param_spec.pop("validation_set", {})
validation_set["splits"] = validation_set.get("splits",5)
validation_set["split_node"] = validation_set.get("split_node",
{'node': 'CV_Splitter',
'parameters': {'splits': validation_set["splits"]}})
evaluation = param_spec.pop("evaluation", {})
evaluation["ir_class"] = evaluation.get("ir_class", "Target")
evaluation["performance_sink_node"] = \
evaluation.get("performance_sink_node",
{'node': 'Classification_Performance_Sink',
'parameters': {'ir_class': evaluation["ir_class"]}})
# build flow template
nodes_spec = param_spec.pop("nodes")
flow_template = [{'node': 'External_Generator_Source_Node'},
validation_set["split_node"]]
flow_template.extend(nodes_spec)
flow_template.append(evaluation["performance_sink_node"])
# Evaluate all remaining parameters
BaseNode.eval_dict(param_spec)
# params with defaults in __init__ have to be added to param_spec dict
if validation_set.has_key("runs"):
param_spec["runs"] = validation_set["runs"]
if evaluation.has_key("metric"):
param_spec["metric"] = evaluation["metric"]
if evaluation.has_key("inverse_metric"):
param_spec["inverse_metric"] = evaluation["inverse_metric"]
if evaluation.has_key("std_weight"):
param_spec["std_weight"] = evaluation['std_weight']
return param_spec, flow_template