def __init__(self, fname, i_options={}, dptype=None, debug=False):
txt = read_file_or_url(fname)
try:
model_data, hmodel_data = yaml.compose_all(txt,
Loader=yaml.BaseLoader)
except Exception as ex:
print(
"Error while parsing YAML file. Probable YAML syntax error in file : ",
fname,
)
raise ex
self.data = hmodel_data
self.__model__ = Model(model_data)
self.discretization_options = i_options
# cache for functions
self.__symbols__ = None
self.__transition__ = None
self.__equilibrium__ = None
self.__projection__ = None
self.__features__ = None
self.debug = debug
self.__set_changed__()
from dolo.numeric.processes import IIDProcess, ProductProcess
self.check()
self.dptype = dptype
def yaml_import(fname, check=True, check_only=False):
txt = read_file_or_url(fname)
txt = txt.replace('^', '**')
try:
data = ry.load(txt, ry.RoundTripLoader)
except Exception as ex:
print(
"Error while parsing YAML file. Probable syntax error in your model file : ",
fname)
raise ex
if check:
from dolo.linter import lint
output = lint(data, source=fname)
if len(output) > 0:
print(output)
if check_only:
return output
data['filename'] = fname
from dolo.compiler.model import Model
return Model(data)
def yaml_import(fname, check=True, check_only=False):
txt = read_file_or_url(fname)
try:
data = yaml.compose(txt)
# print(data)
# return data
except Exception as ex:
print(
"Error while parsing YAML file. Probable YAML syntax error in file : ",
fname,
)
raise ex
# if check:
# from dolo.linter import lint
# data = ry.load(txt, ry.RoundTripLoader)
# output = lint(data, source=fname)
# if len(output) > 0:
# print(output)
# if check_only:
# return output
data["filename"] = fname
from dolo.compiler.model import Model
return Model(data, check=check)
def yaml_import(fname, return_symbolic=False, check=True, check_only=False):
txt = read_file_or_url(fname)
if check:
from dolo.linter import lint
output = lint(txt)
if len(output)>0:
print(output)
if check_only:
return output
txt = txt.replace('^', '**')
return fast_import(txt, return_symbolic=return_symbolic, filename=fname)
def yaml_import(fname, check=True, check_only=False):
txt = read_file_or_url(fname)
if check:
from dolo.linter import lint
output = lint(txt)
if len(output) > 0:
print(output)
if check_only:
return output
txt = txt.replace('^', '**')
data = ry.load(txt, ry.RoundTripLoader)
data['filename'] = fname
from dolo.compiler.model import Model
return Model(data)
def __init__(self, fname, i_options={}, dptype=None, debug=False):
txt = read_file_or_url(fname)
try:
model_data, hmodel_data = ry.load_all(txt, ry.RoundTripLoader)
except Exception as ex:
print(
"Error while parsing YAML file. Probable YAML syntax error in file : ",
fname)
raise ex
model_data, hmodel_data = ry.load_all(txt, Loader=ry.RoundTripLoader)
self.__model__ = Model(model_data)
self.data = hmodel_data
self.discretization_options = i_options
# cache for functions
self.__equilibrium__ = None
self.__projection__ = None
self.__features__ = None
self.debug = debug
self.check()
self.__set_changed__()
from dolo.numeric.processes import IIDProcess, ProductProcess
if dptype is None and isinstance(
self.model.exogenous,
ProductProcess) and (self.model.exogenous.processes[1],
IIDProcess):
dptype = 'iid'
else:
dptype = 'mc'
self.dptype = dptype
def yaml_import(fname, txt=None, return_symbolic=False):
if txt is None:
txt = read_file_or_url(fname)
txt = txt.replace('^', '**')
import yaml
try:
data = yaml.safe_load(txt)
except Exception as e:
raise e
if 'symbols' not in data:
if 'declarations' in data:
data['symbols'] = data['declarations']
# TODO: raise an error/warning here
else:
raise Exception("Missing section: 'symbols'.")
if 'model_type' not in data:
if 'markov_states' in data['symbols']:
model_type = 'dtmscc'
elif 'states' in data['symbols']:
model_type = 'dtcscc'
elif 'variables' in data['symbols']:
model_type = 'dynare'
else:
raise Exception("'model_type' was not defined and couldn't be guessed.")
print("Model type detected as '{}'".format(model_type))
else:
model_type = data['model_type']
if 'name' not in data:
data['name'] = 'anonymous'
print("Missing model name. Set as '{}'".format(data['name']))
if 'auxiliary' in data['symbols']:
aux = data['symbols'].pop('auxiliary')
data['symbols']['auxiliaries'] = aux
# check equations
if 'equations' not in data:
raise Exception("Missing section: 'equations'.")
if 'calibration' not in data:
raise Exception("Missing section: 'calibration'.")
options = data.get('options')
if 'steady_state' in data['calibration']:
oldstyle = data['calibration']
covs = oldstyle['covariances']
steady = oldstyle['steady_state']
params = oldstyle['parameters']
pp = dict()
pp.update(steady)
pp.update(params)
data['calibration'] = pp
data['covariances'] = eval(
"numpy.array({}, dtype='object')".format(covs)
)
# model specific
if model_type in ('dtcscc', 'dynare'):
if 'distribution' not in data:
if 'covariances' in data:
data['distribution'] = {'Normal':data['covariances']}
else:
n_s = len(data['symbols']['shocks'])
zero_matrix = [ [0.0]*n_s ]*n_s
data['distribution'] = {'Normal': [zero_matrix]}
# raise Exception(
# "Missing section (model type {}): 'distribution'.".format(model_type)
# )
if model_type == 'dtmscc':
if 'discrete_transition' not in data:
if 'markov_chain' not in data:
raise Exception(
"Missing section (model {}): 'discrete_transition'.".format(model_type)
)
else:
mc = data['markov_chain']
if isinstance(mc, list):
data['discrete_transition'] = {'MarkovChain': mc}
else:
data['discrete_transition'] = mc
# data['markov_chain'] = data['distribution']['MarkovChain']
model_name = data['name']
symbols = data['symbols']
symbolic_equations = data['equations']
symbolic_calibration = data['calibration']
# all symbols are initialized to nan
# except shocks and markov_states which are initialized to 0
initial_values = {
'shocks': 0,
'markov_states': 0,
'controls': float('nan'),
'states': float('nan')
}
# variables defined by a model equation default to using these definitions
initialized_from_model = {
'auxiliaries': 'auxiliary',
'values': 'value',
'expectations': 'expectation',
'direct_responses': 'direct_response'
}
for symbol_group in symbols:
if symbol_group not in initialized_from_model.keys():
if symbol_group in initial_values:
default = initial_values[symbol_group]
else:
default = float('nan')
for s in symbols[symbol_group]:
if s not in symbolic_calibration:
symbolic_calibration[s] = default
if model_type != 'dynare':
# add the calibration of variables defined in the model not in calibration
all_variables = sum( [symbols[group] for group in symbols if group!= 'parameters'], [])
from dolo.compiler.function_compiler_ast import timeshift
print(all_variables)
for eq_group in symbolic_equations.keys():
if eq_group in initialized_from_model.values():
for eq in symbolic_equations[eq_group]:
lhs, rhs = str.split(eq, '=')
lhs, rhs = [str.strip(e) for e in [lhs, rhs]]
rhs_ss = timeshift(rhs, all_variables, 'S')
if lhs not in symbolic_calibration.keys():
print((lhs, rhs_ss))
symbolic_calibration[lhs] = rhs_ss
# read covariance matrix
symbolic_covariances = data.get('covariances')
if symbolic_covariances is not None:
try:
tl = numpy.array(symbolic_covariances, dtype='object')
except:
msg = "Incorrect covariances matrix: {}.".format(
symbolic_covariances
)
raise Exception(msg)
try:
assert(tl.ndim == 2)
assert(tl.shape[0] == tl.shape[1])
except:
msg = """Covariances matrix should be square.\
Found {} matrix""".format(tl.shape)
raise Exception(msg)
symbolic_covariances = tl
symbolic_distribution = data.get('distribution')
symbolic_discrete_transition = data.get('discrete_transition')
definitions = data.get('definitions', {})
options = data.get('options')
infos = dict()
infos['filename'] = fname
infos['name'] = model_name
infos['type'] = model_type
from dolo.compiler.model_symbolic import SymbolicModel
smodel = SymbolicModel(model_name, model_type, symbols, symbolic_equations,
symbolic_calibration,
discrete_transition=symbolic_discrete_transition,
distribution=symbolic_distribution,
options=options, definitions=definitions)
if return_symbolic:
return smodel
if model_type in ('dtcscc','dtmscc'):
from dolo.compiler.model_numeric import NumericModel
model = NumericModel(smodel, infos=infos)
else:
from dolo.compiler.model_dynare import DynareModel
model = DynareModel(smodel, infos=infos)
return model
def yaml_import(fname, txt=None, return_symbolic=False):
if txt is None:
txt = read_file_or_url(fname)
txt = txt.replace('^', '**')
import yaml
try:
data = yaml.safe_load(txt)
except Exception as e:
raise e
if 'symbols' not in data:
if 'declarations' in data:
data['symbols'] = data['declarations']
# TODO: raise an error/warning here
else:
raise Exception("Missing section: 'symbols'.")
if 'model_type' not in data:
if 'markov_states' in data['symbols']:
model_type = 'dtmscc'
elif 'states' in data['symbols']:
model_type = 'dtcscc'
elif 'variables' in data['symbols']:
model_type = 'dynare'
else:
raise Exception("'model_type' was not defined and couldn't be guessed.")
print("Model type detected as '{}'".format(model_type))
else:
model_type = data['model_type']
if 'name' not in data:
data['name'] = 'anonymous'
print("Missing model name. Set as '{}'".format(data['name']))
if 'auxiliary' in data['symbols']:
aux = data['symbols'].pop('auxiliary')
data['symbols']['auxiliaries'] = aux
# check equations
if 'equations' not in data:
raise Exception("Missing section: 'equations'.")
if 'calibration' not in data:
raise Exception("Missing section: 'calibration'.")
options = data.get('options')
if 'steady_state' in data['calibration']:
oldstyle = data['calibration']
covs = oldstyle['covariances']
steady = oldstyle['steady_state']
params = oldstyle['parameters']
pp = dict()
pp.update(steady)
pp.update(params)
data['calibration'] = pp
data['covariances'] = eval(
"numpy.array({}, dtype='object')".format(covs)
)
# model specific
if model_type in ('dtcscc', 'dynare'):
if 'distribution' not in data:
if 'covariances' in data:
data['distribution'] = {'Normal':data['covariances']}
else:
raise Exception(
"Missing section (model type {}): 'distribution'.".format(model_type)
)
if model_type == 'dtmscc':
if 'discrete_transition' not in data:
if 'markov_chain' not in data:
raise Exception(
"Missing section (model {}): 'discrete_transition'.".format(model_type)
)
else:
mc = data['markov_chain']
if isinstance(mc, list):
data['discrete_transition'] = {'MarkovChain': mc}
else:
data['discrete_transition'] = mc
# data['markov_chain'] = data['distribution']['MarkovChain']
model_name = data['name']
symbols = data['symbols']
symbolic_equations = data['equations']
symbolic_calibration = data['calibration']
# all symbols are initialized to nan
# except shocks and markov_states which are initialized to 0
initial_values = {
'shocks': 0,
'markov_states': 0,
'controls': float('nan'),
'states': float('nan')
}
for symbol_group in symbols:
if symbol_group in initial_values:
default = initial_values[symbol_group]
else:
default = float('nan')
for s in symbols[symbol_group]:
if s not in symbolic_calibration:
symbolic_calibration[s] = default
# read covariance matrix
symbolic_covariances = data.get('covariances')
if symbolic_covariances is not None:
try:
tl = numpy.array(symbolic_covariances, dtype='object')
except:
msg = "Incorrect covariances matrix: {}.".format(
symbolic_covariances
)
raise Exception(msg)
try:
assert(tl.ndim == 2)
assert(tl.shape[0] == tl.shape[1])
except:
msg = """Covariances matrix should be square.\
Found {} matrix""".format(tl.shape)
raise Exception(msg)
symbolic_covariances = tl
symbolic_distribution = data.get('distribution')
symbolic_discrete_transition = data.get('discrete_transition')
definitions = data.get('definitions', {})
options = data.get('options')
infos = dict()
infos['filename'] = fname
infos['name'] = model_name
infos['type'] = model_type
from dolo.compiler.model_symbolic import SymbolicModel
smodel = SymbolicModel(model_name, model_type, symbols, symbolic_equations,
symbolic_calibration,
discrete_transition=symbolic_discrete_transition,
distribution=symbolic_distribution,
options=options, definitions=definitions)
if return_symbolic:
return smodel
if model_type in ('dtcscc','dtmscc'):
from dolo.compiler.model_numeric import NumericModel
model = NumericModel(smodel, infos=infos)
else:
from dolo.compiler.model_dynare import DynareModel
model = DynareModel(smodel, infos=infos)
return model