def __init__(self, regression_procedure="opus_core.linear_regression",
submodel_string=None,
run_config=None, estimate_config=None, debuglevel=0, dataset_pool=None):
self.debug = DebugPrinter(debuglevel)
self.dataset_pool = self.create_dataset_pool(dataset_pool)
self.regression = RegressionModelFactory().get_model(name=regression_procedure)
if self.regression == None:
raise StandardError, "No regression procedure given."
self.submodel_string = submodel_string
self.run_config = run_config
if self.run_config == None:
self.run_config = Resources()
if not isinstance(self.run_config,Resources) and isinstance(self.run_config, dict):
self.run_config = Resources(self.run_config)
self.estimate_config = estimate_config
if self.estimate_config == None:
self.estimate_config = Resources()
if not isinstance(self.estimate_config,Resources) and isinstance(self.estimate_config, dict):
self.estimate_config = Resources(self.estimate_config)
self.data = {}
self.coefficient_names = {}
ChunkModel.__init__(self)
self.get_status_for_gui().initialize_pieces(3, pieces_description = array(['initialization', 'computing variables', 'submodel: 1']))
def run(self,
specification,
coefficients,
dataset,
index=None,
chunk_specification=None,
data_objects=None,
run_config=None,
initial_values=None,
procedure=None,
debuglevel=0):
"""'specification' is of type EquationSpecification,
'coefficients' is of type Coefficients,
'dataset' is of type Dataset,
'index' are indices of individuals in dataset for which
the model runs. If it is None, the whole dataset is considered.
'chunk_specification' determines number of chunks in which the simulation is processed.
'data_objects' is a dictionary where each key is the name of an data object
('zone', ...) and its value is an object of class Dataset.
'run_config' is of type Resources, it gives additional arguments for the run.
If 'procedure' is given, it overwrites the regression_procedure of the constructor.
'initial_values' is an array of the initial values of the results. It will be overwritten
by the results for those elements that are handled by the model (defined by submodels in the specification).
By default the results are initialized with 0.
'debuglevel' overwrites the constructor 'debuglevel'.
"""
self.debug.flag = debuglevel
if run_config == None:
run_config = Resources()
if not isinstance(run_config, Resources) and isinstance(
run_config, dict):
run_config = Resources(run_config)
self.run_config = run_config.merge_with_defaults(self.run_config)
self.run_config.merge({"debug": self.debug})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
self.dataset_pool.replace_dataset(dataset.get_dataset_name(), dataset)
if procedure is not None:
self.regression = RegressionModelFactory().get_model(
name=procedure)
if initial_values is None:
self.initial_values = zeros((dataset.size(), ), dtype=float32)
else:
self.initial_values = zeros((dataset.size(), ),
dtype=initial_values.dtype)
self.initial_values[index] = initial_values
if dataset.size() <= 0: # no data loaded yet
dataset.get_id_attribute()
if index == None:
index = arange(dataset.size())
result = ChunkModel.run(self,
chunk_specification,
dataset,
index,
float32,
specification=specification,
coefficients=coefficients)
return result
def __init__(self,
regression_procedure="opus_core.linear_regression",
submodel_string=None,
run_config=None,
estimate_config=None,
debuglevel=0,
dataset_pool=None):
self.debug = DebugPrinter(debuglevel)
self.dataset_pool = self.create_dataset_pool(dataset_pool)
self.regression = RegressionModelFactory().get_model(
name=regression_procedure)
if self.regression == None:
raise StandardError, "No regression procedure given."
self.submodel_string = submodel_string
self.run_config = run_config
if self.run_config == None:
self.run_config = Resources()
if not isinstance(self.run_config, Resources) and isinstance(
self.run_config, dict):
self.run_config = Resources(self.run_config)
self.estimate_config = estimate_config
if self.estimate_config == None:
self.estimate_config = Resources()
if not isinstance(self.estimate_config, Resources) and isinstance(
self.estimate_config, dict):
self.estimate_config = Resources(self.estimate_config)
self.data = {}
self.coefficient_names = {}
ChunkModel.__init__(self)
self.get_status_for_gui().initialize_pieces(3,
pieces_description=array([
'initialization',
'computing variables',
'submodel: 1'
]))
def run(self, specification, coefficients, dataset, index=None, chunk_specification=None,
data_objects=None, run_config=None, initial_values=None, procedure=None, debuglevel=0):
"""'specification' is of type EquationSpecification,
'coefficients' is of type Coefficients,
'dataset' is of type Dataset,
'index' are indices of individuals in dataset for which
the model runs. If it is None, the whole dataset is considered.
'chunk_specification' determines number of chunks in which the simulation is processed.
'data_objects' is a dictionary where each key is the name of an data object
('zone', ...) and its value is an object of class Dataset.
'run_config' is of type Resources, it gives additional arguments for the run.
If 'procedure' is given, it overwrites the regression_procedure of the constructor.
'initial_values' is an array of the initial values of the results. It will be overwritten
by the results for those elements that are handled by the model (defined by submodels in the specification).
By default the results are initialized with 0.
'debuglevel' overwrites the constructor 'debuglevel'.
"""
self.debug.flag = debuglevel
if run_config == None:
run_config = Resources()
if not isinstance(run_config,Resources) and isinstance(run_config, dict):
run_config = Resources(run_config)
self.run_config = run_config.merge_with_defaults(self.run_config)
self.run_config.merge({"debug":self.debug})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
self.dataset_name = dataset.get_dataset_name()
self.dataset_pool.replace_dataset(self.dataset_name, dataset)
if procedure is not None:
self.regression = RegressionModelFactory().get_model(name=procedure)
if initial_values is None:
self.initial_values = zeros((dataset.size(),), dtype=float32)
else:
self.initial_values = zeros((dataset.size(),), dtype=initial_values.dtype)
self.initial_values[index] = initial_values
if dataset.size()<=0: # no data loaded yet
dataset.get_id_attribute()
if index == None:
index = arange(dataset.size())
result = ChunkModel.run(self, chunk_specification, dataset, index, float32,
specification=specification, coefficients=coefficients)
return result
def _get_status_piece_description(self):
return "%s %s" % (ChunkModel._get_status_piece_description(
self), self.get_status_for_gui().get_current_piece_description())
def _get_status_current_piece(self):
return ChunkModel._get_status_current_piece(
self) * self.get_status_for_gui().get_total_number_of_pieces(
) + self.get_status_for_gui().get_current_piece()
def _get_status_piece_description(self):
return "%s %s" % (ChunkModel._get_status_piece_description(self), self.get_status_for_gui().get_current_piece_description())
def _get_status_current_piece(self):
return ChunkModel._get_status_current_piece(self)*self.get_status_for_gui().get_total_number_of_pieces() + self.get_status_for_gui().get_current_piece()
