import copy
import numpy as np
from nems.registry import KeywordRegistry
from nems.plugins import default_keywords
from nems.utils import find_module
from nems.analysis.api import fit_basic
from nems.fitters.api import scipy_minimize
import nems.priors as priors
import nems.modelspec as ms
import nems.metrics.api as metrics
from nems import get_setting
log = logging.getLogger(__name__)
default_kws = KeywordRegistry()
default_kws.register_module(default_keywords)
default_kws.register_plugins(get_setting('KEYWORD_PLUGINS'))
def from_keywords(keyword_string, registry=None, rec=None, meta={}):
'''
Returns a modelspec created by splitting keyword_string on underscores
and replacing each keyword with what is found in the nems.keywords.defaults
registry. You may provide your own keyword registry using the
registry={...} argument.
'''
if registry is None:
registry = default_kws
keywords = keyword_string.split('-')
# Lookup the modelspec fragments in the registry
'githash': os.environ.get('CODEHASH', ''),
'recording': loadkey}
load_keywords, model_keywords, fit_keywords = modelname.split("_")
# xforms_kwargs = {'cellid': cellid, 'batch': int(batch)}
xforms_kwargs = {}
xforms_init_context = {'cellid': cellid, 'batch': int(batch),
'meta': meta, 'keywordstring': model_keywords}
xforms_lib = KeywordRegistry(**xforms_kwargs)
xforms_lib.register_modules([default_loaders, default_fitters,
default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 0) set up initial context
xfspec.append(['nems.xforms.init_context', xforms_init_context])
# 1) Load the data
xfspec.extend(xhelp._parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append(['nems.xforms.init_from_keywords', {'registry': keyword_lib}])
#xfspec.append(['nems.xforms.init_from_keywords', {}])
def loader_registry():
loaders = KeywordRegistry(recording_uri='dummy_recording_uri')
loaders.register_module(default_loaders)
return loaders
def fit_xforms_model(batch, cellid, modelname, save_analysis=False):
# parse modelname into loaders, modelspecs, and fit keys
load_keywords, model_keywords, fit_keywords = modelname.split("_")
# construct the meta data dict
meta = {
'batch': batch,
'cellid': cellid,
'modelname': modelname,
'loader': load_keywords,
'fitkey': fit_keywords,
'modelspecname': model_keywords,
'username': '******',
'labgroup': 'lbhb',
'public': 1,
'githash': os.environ.get('CODEHASH', ''),
'recording': load_keywords
}
xforms_kwargs = {}
xforms_init_context = {'cellid': cellid, 'batch': int(batch)}
recording_uri = None
kw_kwargs = {}
xforms_lib = KeywordRegistry(**xforms_kwargs)
xforms_lib.register_modules(
[default_loaders, default_fitters, default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 0) set up initial context
if xforms_init_context is None:
xforms_init_context = {}
if kw_kwargs is not None:
xforms_init_context['kw_kwargs'] = kw_kwargs
xforms_init_context['keywordstring'] = model_keywords
xforms_init_context['meta'] = meta
xfspec.append(['nems.xforms.init_context', xforms_init_context])
# 1) Load the data
xfspec.extend(xhelp._parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append(
['nems.xforms.init_from_keywords', {
'registry': keyword_lib
}])
# 3) fit the data
xfspec.extend(xhelp._parse_kw_string(fit_keywords, xforms_lib))
# Generate a prediction
xfspec.append(['nems.xforms.predict', {}])
# 4) add some performance statistics
xfspec.append(['nems.xforms.add_summary_statistics', {}])
# 5) plot
#xfspec.append(['nems_lbhb.lv_helpers.add_summary_statistics', {}])
# Create a log stream set to the debug level; add it as a root log handler
log_stream = io.StringIO()
ch = logging.StreamHandler(log_stream)
ch.setLevel(logging.DEBUG)
fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
formatter = logging.Formatter(fmt)
ch.setFormatter(formatter)
rootlogger = logging.getLogger()
rootlogger.addHandler(ch)
ctx = {}
for xfa in xfspec:
ctx = xforms.evaluate_step(xfa, ctx)
# Close the log, remove the handler, and add the 'log' string to context
log.info('Done (re-)evaluating xforms.')
ch.close()
rootlogger.removeFilter(ch)
log_xf = log_stream.getvalue()
modelspec = ctx['modelspec']
if save_analysis:
# save results
if get_setting('USE_NEMS_BAPHY_API'):
prefix = 'http://' + get_setting(
'NEMS_BAPHY_API_HOST') + ":" + str(
get_setting('NEMS_BAPHY_API_PORT')) + '/results/'
else:
prefix = get_setting('NEMS_RESULTS_DIR')
if type(cellid) is list:
cell_name = cellid[0].split("-")[0]
else:
cell_name = cellid
destination = os.path.join(prefix, str(batch), cell_name,
modelspec.get_longname())
modelspec.meta['modelpath'] = destination
modelspec.meta.update(meta)
log.info('Saving modelspec(s) to {0} ...'.format(destination))
xforms.save_analysis(destination,
recording=ctx['rec'],
modelspec=modelspec,
xfspec=xfspec,
figures=[],
log=log_xf)
# save performance and some other metadata in database Results table
nd.update_results_table(modelspec)
return xfspec, ctx
def model_registry():
models = KeywordRegistry()
models.register_module(default_keywords)
models.register_plugins(['tests/resources/plugin1.py'])
return models
def fitter_registry():
fitters = KeywordRegistry()
fitters.register_module(default_fitters)
return fitters
def model_registry():
models = KeywordRegistry()
models.register_module(default_keywords)
return models
def generate_xforms_spec(recording_uri=None,
modelname=None,
meta={},
xforms_kwargs={},
kw_kwargs={},
autoPred=True,
autoStats=True,
autoPlot=True):
"""
Generate an xforms spec based on a modelname, which can then be evaluated
in order to process and fit a model.
Parameter
---------
recording_uri : str
Location to load recording from, e.g. a filepath or URL.
modelname : str
NEMS-formatted modelname, e.g. 'ld-sev_wc.18x2-fir.2x15-dexp.1_basic'
The modelname will be parsed into a series of xforms functions using
xforms and keyword registries.
meta : dict
Additional keyword arguments for nems.initializers.init_from_keywords
xforms_kwargs : dict
Additional keyword arguments for the xforms registry
kw_kwargs : dict
Additional keyword arguments for the keyword registry
autoPred : boolean
If true, will automatically append nems.xforms.predict to the xfspec
if it is not already present.
autoStats : boolean
If true, will automatically append nems.xforms.add_summary_statistics
to the xfspec if it is not already present.
autoPlot : boolean
If true, will automatically append nems.xforms.plot_summary to the
xfspec if it is not already present.
Returns
-------
xfspec : list of 2- or 4- tuples
"""
log.info('Initializing modelspec(s) for recording/model {0}/{1}...'.format(
recording_uri, modelname))
# parse modelname and assemble xfspecs for loader and fitter
# TODO: naming scheme change: pre_modules, modules, post_modules?
# or something along those lines... since they aren't really
# just loaders and fitters
load_keywords, model_keywords, fit_keywords = escaped_split(modelname, '_')
if recording_uri is not None:
xforms_lib = KeywordRegistry(recording_uri=recording_uri,
**xforms_kwargs)
else:
xforms_lib = KeywordRegistry(**xforms_kwargs)
xforms_lib.register_modules(
[default_loaders, default_fitters, default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry(**kw_kwargs)
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 1) Load the data
xfspec.extend(_parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append([
'nems.xforms.init_from_keywords', {
'keywordstring': model_keywords,
'meta': meta,
'registry': keyword_lib
}
])
# 3) fit the data
xfspec.extend(_parse_kw_string(fit_keywords, xforms_lib))
# TODO: need to make this smarter about how to handle the ordering
# of pred/stats when only stats is overridden.
# For now just have to manually include pred if you want to
# do your own stats or plot xform (like using stats.pm)
# 4) generate a prediction (optional)
if autoPred:
if not _xform_exists(xfspec, 'nems.xforms.predict'):
xfspec.append(['nems.xforms.predict', {}])
# 5) add some performance statistics (optional)
if autoStats:
if not _xform_exists(xfspec, 'nems.xforms.add_summary_statistics'):
xfspec.append(['nems.xforms.add_summary_statistics', {}])
# 6) generate plots (optional)
if autoPlot:
if not _xform_exists(xfspec, 'nems.xforms.plot_summary'):
# log.info('Adding summary plot to xfspec...')
xfspec.append(['nems.xforms.plot_summary', {}])
return xfspec
def init_pop_pca(est, modelspec, flip_pcs=False, IsReload=False, **context):
""" fit up through the fir module of a population model using the pca
signal"""
if IsReload:
return {}
# preserve input modelspec. necessary?
modelspec = copy.deepcopy(modelspec)
ifir = find_module('filter_bank', modelspec)
iwc = find_module('weight_channels', modelspec)
chan_count = modelspec[ifir]['fn_kwargs']['bank_count']
chan_per_bank = int(modelspec[iwc]['prior']['mean'][1]['mean'].shape[0] /
chan_count)
rec = est.copy()
tmodelspec = copy.deepcopy(modelspec)
kw = [m['id'] for m in modelspec[:iwc]]
wc = modelspec[iwc]['id'].split(".")
wcs = wc[1].split("x")
wcs[1] = str(chan_per_bank)
wc[1] = "x".join(wcs)
wc = ".".join(wc)
fir = modelspec[ifir]['id'].split(".")
fircore = fir[1].split("x")
fir[1] = "x".join(fircore[:-1])
fir = ".".join(fir)
kw.append(wc)
kw.append(fir)
kw.append("lvl.1")
keywordstring = "-".join(kw)
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
if flip_pcs:
pc_fit_count = int(np.ceil(chan_count / 2))
else:
pc_fit_count = chan_count
for pc_idx in range(pc_fit_count):
r = rec['pca'].extract_channels([rec['pca'].chans[pc_idx]])
m = np.nanmean(r.as_continuous())
d = np.nanstd(r.as_continuous())
rec['resp'] = r._modified_copy((r._data - m) / d)
tmodelspec = init.from_keywords(keyword_string=keywordstring,
meta={},
registry=keyword_lib,
rec=rec)
tolerance = 1e-4
tmodelspec = init.prefit_LN(rec,
tmodelspec,
tolerance=tolerance,
max_iter=700)
# save results back into main modelspec
itfir = find_module('fir', tmodelspec)
itwc = find_module('weight_channels', tmodelspec)
if pc_idx == 0:
for tm, m in zip(tmodelspec[:(iwc + 1)], modelspec[:(iwc + 1)]):
m['phi'] = tm['phi'].copy()
modelspec[ifir]['phi'] = tmodelspec[itfir]['phi'].copy()
else:
for k, v in tmodelspec[iwc]['phi'].items():
modelspec[iwc]['phi'][k] = np.concatenate(
(modelspec[iwc]['phi'][k], v))
for k, v in tmodelspec[itfir]['phi'].items():
#if k=='coefficients':
# v/=100 # kludge
modelspec[ifir]['phi'][k] = np.concatenate(
(modelspec[ifir]['phi'][k], v))
if flip_pcs and (pc_idx * 2 < chan_count):
# add negative flipped version of fit
for k, v in tmodelspec[iwc]['phi'].items():
modelspec[iwc]['phi'][k] = np.concatenate(
(modelspec[iwc]['phi'][k], v))
for k, v in tmodelspec[itfir]['phi'].items():
#if k=='coefficients':
# v/=100 # kludge
modelspec[ifir]['phi'][k] = np.concatenate(
(-modelspec[ifir]['phi'][k], v))
respcount = est['resp'].shape[0]
fit_set_all, fit_set_slice = _figure_out_mod_split(modelspec)
cd_kwargs = {}
cd_kwargs.update({
'tolerance': tolerance,
'max_iter': 100,
'step_size': 0.1
})
for s in range(respcount):
log.info('Pre-fit slice %d', s)
modelspec = fit_population_slice(est,
modelspec,
slice=s,
fit_set=fit_set_slice,
analysis_function=analysis.fit_basic,
metric=metrics.nmse,
fitter=coordinate_descent,
fit_kwargs=cd_kwargs)
return {'modelspec': modelspec}
def generate_xforms_spec(recording_uri,
modelname,
meta={},
xforms_kwargs={},
kw_kwargs={},
autoPred=True,
autoStats=True,
autoPlot=True):
"""
TODO: Update this doc
OUTDATED
Fits a single NEMS model
eg, 'ozgf100ch18_wc18x1_lvl1_fir15x1_dexp1_fit01'
generates modelspec with 'wc18x1_lvl1_fir1x15_dexp1'
based on fit_model function in nems/scripts/fit_model.py
example xfspec:
xfspec = [
['nems.xforms.load_recordings', {'recording_uri_list': recordings}],
['nems.xforms.add_average_sig', {'signal_to_average': 'resp',
'new_signalname': 'resp',
'epoch_regex': '^STIM_'}],
['nems.xforms.split_by_occurrence_counts', {'epoch_regex': '^STIM_'}],
['nems.xforms.init_from_keywords', {'keywordstring': modelspecname}],
['nems.xforms.set_random_phi', {}],
['nems.xforms.fit_basic', {}],
# ['nems.xforms.add_summary_statistics', {}],
['nems.xforms.plot_summary', {}],
# ['nems.xforms.save_recordings', {'recordings': ['est', 'val']}],
['nems.xforms.fill_in_default_metadata', {}],
]
"""
log.info('Initializing modelspec(s) for recording/model {0}/{1}...'.format(
recording_uri, modelname))
# parse modelname and assemble xfspecs for loader and fitter
# TODO: naming scheme change: pre_modules, modules, post_modules?
# or something along those lines... since they aren't really
# just loaders and fitters
load_keywords, model_keywords, fit_keywords = escaped_split(modelname, '_')
xforms_lib = KeywordRegistry(recording_uri=recording_uri, **xforms_kwargs)
xforms_lib.register_modules(
[default_loaders, default_fitters, default_initializers])
xforms_lib.register_plugins(get_setting('XFORMS_PLUGINS'))
keyword_lib = KeywordRegistry(**kw_kwargs)
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(get_setting('KEYWORD_PLUGINS'))
# Generate the xfspec, which defines the sequence of events
# to run through (like a packaged-up script)
xfspec = []
# 1) Load the data
xfspec.extend(_parse_kw_string(load_keywords, xforms_lib))
# 2) generate a modelspec
xfspec.append([
'nems.xforms.init_from_keywords', {
'keywordstring': model_keywords,
'meta': meta,
'registry': keyword_lib
}
])
# 3) fit the data
xfspec.extend(_parse_kw_string(fit_keywords, xforms_lib))
# TODO: need to make this smarter about how to handle the ordering
# of pred/stats when only stats is overridden.
# For now just have to manually include pred if you want to
# do your own stats or plot xform (like using stats.pm)
# 4) generate a prediction (optional)
if autoPred:
if not _xform_exists(xfspec, 'nems.xforms.predict'):
xfspec.append(['nems.xforms.predict', {}])
# 5) add some performance statistics (optional)
if autoStats:
if not _xform_exists(xfspec, 'nems.xforms.add_summary_statistics'):
xfspec.append(['nems.xforms.add_summary_statistics', {}])
# 6) generate plots (optional)
if autoPlot:
if not _xform_exists(xfspec, 'nems.xforms.plot_summary'):
log.info('Adding summary plot to xfspec...')
xfspec.append(['nems.xforms.plot_summary', {}])
return xfspec
def keyword_registry():
keyword_lib = KeywordRegistry()
keyword_lib.register_module(default_keywords)
keyword_lib.register_plugins(ngs('XF_LOADER_PLUGINS'))
return keyword_lib
def fitter_registry():
fitter_lib = KeywordRegistry()
fitter_lib.register_module(default_fitters)
fitter_lib.register_plugins(ngs('XF_FITTER_PLUGINS'))
return fitter_lib
def loader_registry():
loader_lib = KeywordRegistry('dummy_recording_uri')
loader_lib.register_module(default_loaders)
loader_lib.register_plugins(ngs('XF_LOADER_PLUGINS'))
return loader_lib