def run(dataName=None, allocModelName=None, obsModelName=None, algName=None, \
doSaveToDisk=True, doWriteStdOut=True,
taskID=None, **kwargs):
''' Fit specified model to data with learning algorithm.
Usage
-------
To fit a Gauss MixModel to a custom dataset defined in matrix X
>> Data = bnpy.data.XData(X)
>> hmodel = run(Data, 'MixModel', 'Gauss', 'EM', K=3, nLap=10)
To load a dataset specified in a specific script
For example, 2D toy data in demodata/AsteriskK8.py
>> hmodel = run('AsteriskK8', 'MixModel', 'Gauss', 'VB', K=3)
To run 5 tasks (separate initializations) and get best of 5 runs:
>> opts = dict(K=8, nLap=100, printEvery=0)
>> hmodel = run('AsteriskK8','MixModel','Gauss','VB', nTask=5, **opts)
Args
-------
dataName : either one of
* bnpy Data object,
* string filesystem path of Data module within BNPYDATADIR
allocModelName : string name of allocation (latent structure) model
{MixModel, DPMixModel, AdmixModel, HMM, etc.}
obsModelName : string name of observation (likelihood) model
{Gauss, ZMGauss, WordCount, etc.}
**kwargs : keyword args defining properties of the model or alg
see Doc for details [TODO]
Returns
-------
hmodel : best model fit to the dataset (across nTask runs)
LP : local parameters of that best model on the dataset
evBound : log evidence (ELBO) for the best model on the dataset
scalar, real value where larger value implies better model
'''
hasReqArgs = dataName is not None
hasReqArgs &= allocModelName is not None
hasReqArgs &= obsModelName is not None
hasReqArgs &= algName is not None
if hasReqArgs:
ReqArgs = dict(dataName=dataName,
allocModelName=allocModelName,
obsModelName=obsModelName,
algName=algName)
else:
ReqArgs = BNPYArgParser.parseRequiredArgs()
dataName = ReqArgs['dataName']
allocModelName = ReqArgs['allocModelName']
obsModelName = ReqArgs['obsModelName']
algName = ReqArgs['algName']
KwArgs, UnkArgs = BNPYArgParser.parseKeywordArgs(ReqArgs, **kwargs)
jobname = KwArgs['OutputPrefs']['jobname']
if taskID is None:
starttaskid = KwArgs['OutputPrefs']['taskid']
else:
starttaskid = taskID
KwArgs['OutputPrefs']['taskid'] = taskID
nTask = KwArgs['OutputPrefs']['nTask']
bestInfo = None
bestEvBound = -np.inf
for taskid in range(starttaskid, starttaskid + nTask):
hmodel, LP, Info = _run_task_internal(jobname, taskid, nTask, ReqArgs,
KwArgs, UnkArgs, dataName,
allocModelName, obsModelName,
algName, doSaveToDisk,
doWriteStdOut)
if (Info['evBound'] > bestEvBound):
bestModel = hmodel
bestLP = LP
bestEvBound = Info['evBound']
bestInfo = Info
return bestModel, bestLP, bestInfo
def run(dataName=None,
allocModelName=None,
obsModelName=None,
algName=None,
doSaveToDisk=True,
doWriteStdOut=True,
taskID=None,
**kwargs):
""" Fit specified model to data with learning algorithm.
Args
-------
dataName : either one of
* bnpy Data object,
* string name of python file within BNPYDATADIR
allocModelName : string name of allocation (latent structure) model
obsModelName : string name of observation (likelihood) model
**kwargs : keyword args defining properties of the model or alg
Returns
-------
hmodel : best model fit to the dataset (across nTask runs)
Info : dict of information about this best model
"""
hasReqArgs = dataName is not None
hasReqArgs &= allocModelName is not None
hasReqArgs &= obsModelName is not None
hasReqArgs &= algName is not None
if hasReqArgs:
ReqArgs = dict(dataName=dataName,
allocModelName=allocModelName,
obsModelName=obsModelName,
algName=algName)
else:
ReqArgs = BNPYArgParser.parseRequiredArgs()
dataName = ReqArgs['dataName']
allocModelName = ReqArgs['allocModelName']
obsModelName = ReqArgs['obsModelName']
algName = ReqArgs['algName']
KwArgs, UnkArgs = BNPYArgParser.parseKeywordArgs(ReqArgs, **kwargs)
KwArgs['OutputPrefs']['doSaveToDisk'] = doSaveToDisk
KwArgs['OutputPrefs']['doWriteStdOut'] = doWriteStdOut
jobname = KwArgs['OutputPrefs']['jobname']
# Update stored numerical options via keyword args
bnpy.util.NumericUtil.UpdateConfig(**UnkArgs)
if taskID is None:
starttaskid = KwArgs['OutputPrefs']['taskid']
else:
starttaskid = taskID
KwArgs['OutputPrefs']['taskid'] = taskID
nTask = KwArgs['OutputPrefs']['nTask']
best_info_dict = None
best_loss = np.inf
for taskid in range(starttaskid, starttaskid + nTask):
hmodel, info_dict = _run_task_internal(jobname, taskid, nTask, ReqArgs,
KwArgs, UnkArgs, dataName,
allocModelName, obsModelName,
algName, doSaveToDisk,
doWriteStdOut)
if (taskid == starttaskid or info_dict['loss'] < best_loss):
bestModel = hmodel
best_loss = info_dict['loss']
best_info_dict = info_dict
return bestModel, best_info_dict
def run(dataName=None, allocModelName=None, obsModelName=None, algName=None, \
doSaveToDisk=True, doWriteStdOut=True,
taskID=None, **kwargs):
''' Fit specified model to data with learning algorithm.
Usage
-------
To fit a Gauss MixModel to a custom dataset defined in matrix X
>> Data = bnpy.data.XData(X)
>> hmodel = run(Data, 'MixModel', 'Gauss', 'EM', K=3, nLap=10)
To load a dataset specified in a specific script
For example, 2D toy data in demodata/AsteriskK8.py
>> hmodel = run('AsteriskK8', 'MixModel', 'Gauss', 'VB', K=3)
To run 5 tasks (separate initializations) and get best of 5 runs:
>> opts = dict(K=8, nLap=100, printEvery=0)
>> hmodel = run('AsteriskK8','MixModel','Gauss','VB', nTask=5, **opts)
Args
-------
dataName : either one of
* bnpy Data object,
* string filesystem path of Data module within BNPYDATADIR
allocModelName : string name of allocation (latent structure) model
{MixModel, DPMixModel, AdmixModel, HMM, etc.}
obsModelName : string name of observation (likelihood) model
{Gauss, ZMGauss, WordCount, etc.}
**kwargs : keyword args defining properties of the model or alg
see Doc for details [TODO]
Returns
-------
hmodel : best model fit to the dataset (across nTask runs)
LP : local parameters of that best model on the dataset
evBound : log evidence (ELBO) for the best model on the dataset
scalar, real value where larger value implies better model
'''
hasReqArgs = dataName is not None
hasReqArgs &= allocModelName is not None
hasReqArgs &= obsModelName is not None
hasReqArgs &= algName is not None
if hasReqArgs:
ReqArgs = dict(dataName=dataName, allocModelName=allocModelName,
obsModelName=obsModelName, algName=algName)
else:
ReqArgs = BNPYArgParser.parseRequiredArgs()
dataName = ReqArgs['dataName']
allocModelName = ReqArgs['allocModelName']
obsModelName = ReqArgs['obsModelName']
algName = ReqArgs['algName']
KwArgs, UnkArgs = BNPYArgParser.parseKeywordArgs(ReqArgs, **kwargs)
jobname = KwArgs['OutputPrefs']['jobname']
if taskID is None:
starttaskid = KwArgs['OutputPrefs']['taskid']
else:
starttaskid = taskID
KwArgs['OutputPrefs']['taskid'] = taskID
nTask = KwArgs['OutputPrefs']['nTask']
bestInfo = None
bestEvBound = -np.inf
for taskid in range(starttaskid, starttaskid + nTask):
hmodel, LP, Info = _run_task_internal(jobname, taskid, nTask,
ReqArgs, KwArgs, UnkArgs,
dataName, allocModelName, obsModelName, algName,
doSaveToDisk, doWriteStdOut)
if (Info['evBound'] > bestEvBound):
bestModel = hmodel
bestLP = LP
bestEvBound = Info['evBound']
bestInfo = Info
return bestModel, bestLP, bestInfo
