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Merge pull request #68 from brian-team/update_nevergrad
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Update nevergrad version
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@mstimberg
mstimberg committed Jun 20, 2022
2 parents a74df9d + 650e85a commit e988e6d
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Showing 14 changed files with 151 additions and 138 deletions.
2 changes: 1 addition & 1 deletion .github/workflows/tests.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,6 @@ jobs:
- name: Upload coverage to coveralls
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
if: ${{ matrix.python-version == '3.6' }}
if: ${{ matrix.python-version == '3.7' }}
run: coveralls --service=github

122 changes: 63 additions & 59 deletions brian2modelfitting/fitter.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
import abc
from collections import defaultdict
import numbers
from distutils.version import LooseVersion
from typing import Sequence, Mapping

import sympy
from numpy import (ones, array, arange, concatenate, mean, argmin, nanargmin,
reshape, zeros, sqrt, ndarray, broadcast_to, sum, cumsum,
hstack)
hstack, tile, repeat)

from brian2.parsing.sympytools import sympy_to_str, str_to_sympy
from brian2.units.fundamentalunits import DIMENSIONLESS, get_dimensions, fail_for_dimension_mismatch
Expand All @@ -30,13 +31,17 @@


def get_param_dic(params, param_names, n_traces, n_samples):
"""Transform parameters into a dictionary of appropiate size"""
params = array(params)
"""Transform parameters into a dictionary of appropriate size
From list of dictionaries to dictionary of lists, with variables
repeated for each trace
"""
assert len(params) == n_samples
d = defaultdict(list)

d = dict()
for param_combination in params:
for param_name, param_value in param_combination.items():
d[param_name].extend(repeat(param_value, n_traces))

for name, value in zip(param_names, params.T):
d[name] = (ones((n_traces, n_samples)) * value).T.flatten()
return d


Expand All @@ -63,7 +68,8 @@ def get_full_namespace(additional_namespace, level=0):
# in principle -- by filtering things out, we avoid circular loops
namespace = {key: value
for key, value in get_local_namespace(level=level + 1).items()
if isinstance(value, (Quantity, numbers.Number, Function))}
if (not key.startswith('_') and
isinstance(value, (Quantity, numbers.Number, Function)))}
namespace.update(additional_namespace)

return namespace
Expand Down Expand Up @@ -752,7 +758,7 @@ def fit(self, optimizer, metric=None, n_rounds=1, callback='text',
self._best_objective_errors_normed = tuple(self._objective_errors_normed[best_idx])
self._best_objective_errors = tuple(self._objective_errors[best_idx])
# create output variables
self._best_params = make_dic(self.parameter_names, best_params)
self._best_params = dict(best_params)
if self.use_units:
error_dim = self.metric[0].get_normalized_dimensions(self.output_dim[0])
for metric, output_dim in zip(self.metric[1:], self.output_dim[1:]):
Expand All @@ -763,9 +769,8 @@ def fit(self, optimizer, metric=None, n_rounds=1, callback='text',
'units.')
best_error = Quantity(float(self.best_error), dim=error_dim)
errors = Quantity(errors, dim=error_dim)
param_dicts = [{p: Quantity(v, dim=self.model[p].dim)
for p, v in zip(self.parameter_names,
one_param_set)}
param_dicts = [{p_name: Quantity(one_param_set[p_name], dim=self.model[p_name].dim)
for p_name in self.parameter_names}
for one_param_set in parameters]
best_raw_error_normed = tuple([Quantity(raw_error_normed,
dim=metric.get_normalized_dimensions(output_dim))
Expand Down Expand Up @@ -916,9 +921,7 @@ def results(self, format='list', use_units=None):
use_units = self.use_units
names = list(self.parameter_names)

params = array(self.optimizer.tested_parameters)
params = params.reshape(-1, params.shape[-1])

params = self.optimizer.tested_parameters
if use_units:
error_dim = self.metric[0].get_dimensions(self.output_dim[0])
errors = Quantity(array(self.optimizer.errors).flatten(),
Expand Down Expand Up @@ -946,13 +949,10 @@ def results(self, format='list', use_units=None):
res_list = []
for j in arange(0, len(params)):
temp_data = params[j]
res_dict = dict()

for i, n in enumerate(names):
if use_units:
res_dict[n] = Quantity(temp_data[i], dim=dim[n])
else:
res_dict[n] = float(temp_data[i])
if use_units:
res_dict = {n: Quantity(temp_data[n], dim=dim[n]) for n in names}
else:
res_dict = dict(temp_data)
res_dict['error'] = errors[j]
if len(self.output_var) > 1:
if use_units:
Expand All @@ -969,46 +969,49 @@ def results(self, format='list', use_units=None):

return res_list

elif format == 'dict':
res_dict = dict()
for i, n in enumerate(names):
if use_units:
res_dict[n] = Quantity(params[:, i], dim=dim[n])
else:
res_dict[n] = array(params[:, i])
elif format in ('dict', 'dataframe'):
temp_dict = defaultdict(list)
for param in params:
for param_name, param_value in param.items():
temp_dict[param_name].append(param_value)

if use_units:
res_dict = {param_name: Quantity(param_value, dim=dim[param_name])
for param_name, param_value in temp_dict.items()}
else:
res_dict = {param_name: array(param_value)
for param_name, param_value in temp_dict.items()}

res_dict['error'] = errors
if len(self.output_var) > 1:
if use_units:
res_dict['objective_errors_normalized'] = {output_var: raw_errors_normed[output_var]
for output_var in self.output_var}
res_dict['objective_errors'] = {output_var: raw_errors[output_var]
for output_var in self.output_var}
if format == 'dict':
if use_units:
res_dict['objective_errors_normalized'] = {output_var: raw_errors_normed[output_var]
for output_var in self.output_var}
res_dict['objective_errors'] = {output_var: raw_errors[output_var]
for output_var in self.output_var}
else:
res_dict['objective_errors_normalized'] = {output_var: array([raw_error_normed[idx]
for raw_error_normed in self._objective_errors_normed])
for idx, output_var in enumerate(self.output_var)}
res_dict['objective_errors'] = {output_var: array([raw_error[idx]
for raw_error in self._objective_errors])
for idx, output_var in enumerate(self.output_var)}
else:
res_dict['objective_errors_normalized'] = {output_var: array([raw_error_normed[idx]
for raw_error_normed in self._objective_errors_normed])
for idx, output_var in enumerate(self.output_var)}
res_dict['objective_errors'] = {output_var: array([raw_error[idx]
for raw_error in self._objective_errors])
for idx, output_var in enumerate(self.output_var)}
return res_dict

elif format == 'dataframe':
from pandas import DataFrame
if use_units:
logger.warn('Results in dataframes do not support units. '
'Specify "use_units=False" to avoid this warning.',
name_suffix='dataframe_units')
data = concatenate((params, array(errors)[None, :].transpose()), axis=1)
columns = names + ['error']
if len(self.output_var) > 1:
data = concatenate((data, self._objective_errors_normed), axis=1)
columns += [f'normalized_error_{output_var}'
for output_var in self.output_var]
data = concatenate((data, self._objective_errors), axis=1)
columns += [f'error_{output_var}'
for output_var in self.output_var]
return DataFrame(data=data, columns=columns)
for idx, output_var in enumerate(self.output_var):
res_dict['normalized_error_' + output_var] = array([raw_error_normed[idx]
for raw_error_normed in self._objective_errors_normed])
res_dict['error_' + output_var] = array([raw_error[idx]
for raw_error in self._objective_errors])
if format == 'dict':
return res_dict
else: # dataframe
from pandas import DataFrame
if use_units:
logger.warn('Results in dataframes do not support units. '
'Specify "use_units=False" to avoid this warning.',
name_suffix='dataframe_units')
return DataFrame(data=res_dict)

def generate(self, output_var=None, params=None, param_init=None,
iteration=1e9, level=0):
Expand Down Expand Up @@ -1061,7 +1064,7 @@ def generate(self, output_var=None, params=None, param_init=None,
output_var=output_var,
param_init=param_init,
level=level+1)
param_dic = get_param_dic([params[p] for p in self.parameter_names],
param_dic = get_param_dic([params],
self.parameter_names, self.n_traces, 1)
self.simulator.run(self.duration, param_dic, self.parameter_names,
iteration=iteration, name='generate')
Expand Down Expand Up @@ -1306,7 +1309,8 @@ def refine(self, params=None, metric=None,
for t_s, t_w in zip(t_start, t_weights)]

def _calc_error(params):
param_dic = get_param_dic([params[p] for p in self.parameter_names],
param_dic = get_param_dic([{p: params[p].value
for p in self.parameter_names}],
self.parameter_names, self.n_traces, 1)
self.simulator.run(self.duration, param_dic,
self.parameter_names, iteration=iteration,
Expand Down
26 changes: 12 additions & 14 deletions brian2modelfitting/optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
from sklearn.base import RegressorMixin
warnings.filterwarnings = _filterwarnings

from nevergrad import instrumentation as inst
import nevergrad
from nevergrad.optimization import optimizerlib, registry

logger = get_logger(__name__)
Expand Down Expand Up @@ -168,14 +168,13 @@ def initialize(self, parameter_names, popsize, rounds, **params):

bounds = calc_bounds(parameter_names, **params)

instruments = []
for i, name in enumerate(parameter_names):
assert len(bounds[i]) == 2
instrumentation = inst.var.Array(1).asscalar().bounded(np.array([bounds[i][0]]),
np.array([bounds[i][1]]))
instruments.append(instrumentation)
parameters = {}
for name, bounds in zip(parameter_names, bounds):
assert len(bounds) == 2
p = nevergrad.p.Scalar(lower=float(bounds[0]), upper=float(bounds[1]))
parameters[name] = p

instrum = inst.Instrumentation(*instruments)
parametrization = nevergrad.p.Dict(**parameters)
nevergrad_method = optimizerlib.registry[self.method]
if nevergrad_method.no_parallelization and popsize > 1:
logger.warn(f'Sample size {popsize} requested, but Nevergrad\'s '
Expand All @@ -185,10 +184,9 @@ def initialize(self, parameter_names, popsize, rounds, **params):
name_suffix='no_parallelization')
popsize = 1

budget = rounds*popsize
self.optim = nevergrad_method(instrumentation=instrum,
self.kwds['budget'] = self.kwds.get('budget', rounds*popsize)
self.optim = nevergrad_method(parametrization=parametrization,
num_workers=popsize,
budget=budget,
**self.kwds)
if hasattr(self.optim, 'llambda'):
optimizer_pop_size = self.optim.llambda
Expand All @@ -210,12 +208,12 @@ def ask(self, n_samples):
for _ in range(n_samples):
cand = self.optim.ask()
self.candidates.append(cand)
parameters.append(list(cand.args))
parameters.append(cand.value)

return parameters

def tell(self, parameters, errors):
if not(np.all(parameters == [list(v.args) for v in self.candidates])):
if not(np.all(parameters == [v.value for v in self.candidates])):
raise AssertionError("Parameters and Candidates don't have "
"identical values")

Expand All @@ -227,7 +225,7 @@ def tell(self, parameters, errors):
def recommend(self):
if self.use_nevergrad_recommendation:
res = self.optim.provide_recommendation()
return res.args
return res.value
else:
best = np.argmin(self.errors)
return self.tested_parameters[best]
Expand Down
43 changes: 21 additions & 22 deletions brian2modelfitting/tests/test_modelfitting_tracefitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,10 +64,8 @@ def setup(request):
dt = 0.01 * ms
tf = TraceFitter(dt=dt,
model=model,
input_var='v',
output_var='I',
input=input_traces,
output=output_traces,
input={'v': input_traces},
output={'I': output_traces},
n_samples=30)

def fin():
Expand Down Expand Up @@ -254,17 +252,14 @@ def fin():


def test_get_param_dic():
d = get_param_dic([1, 2], ['a', 'b'], 2, 2)
d = get_param_dic([{'a': 1, 'b': 2}], ['a', 'b'], 4, 1)
assert isinstance(d, dict)
assert_equal(d, {'a': [1, 1, 1, 1], 'b': [2, 2, 2, 2]})

d = get_param_dic([[1, 3], [2, 4]], ['a', 'b'], 1, 1)
d = get_param_dic([{'a': 1, 'b': 3}, {'a': 2, 'b': 4}], ['a', 'b'], 1, 2)
assert_equal(d, {'a': [1, 2], 'b': [3, 4]})

d = get_param_dic([[1, 3], [2, 4]], ['a', 'b'], 1, 2)
assert_equal(d, {'a': [1, 2], 'b': [3, 4]})

d = get_param_dic([[1, 3], [2, 4]], ['a', 'b'], 2, 1)
d = get_param_dic([{'a': 1, 'b': 3}, {'a': 2, 'b': 4}], ['a', 'b'], 2, 2)
assert_equal(d, {'a': [1, 1, 2, 2], 'b': [3, 3, 4, 4]})


Expand Down Expand Up @@ -362,17 +357,22 @@ def test_fitter_fit_methods(method):
input=input_traces,
output=output_traces,
n_samples=30)
# Skip all BO methods for now (TODO: check what is going on)
if 'BO' in method:
# Skip all BO methods for now, also skip ParaPortfolio (TODO: check what is going on)
if 'BO' in method or 'ParaPortfolio' in method:
pytest.skip(f'Skipping method {method}')
optimizer = NevergradOptimizer(method)
# Just make sure that it can run at all
tf.fit(n_rounds=2,
optimizer=optimizer,
metric=metric,
g=[1*nS, 30*nS],
E=[-60*mV, -20*mV],
callback=None)

try:
optimizer = NevergradOptimizer(method) # set high budget to avoid problems for some methods
# Just make sure that it can run at all
tf.fit(n_rounds=2,
optimizer=optimizer,
metric=metric,
g=[1*nS, 30*nS],
E=[-60*mV, -20*mV],
callback=None)
except ImportError as ex:
# Skip methods that need additional packages
pytest.skip(f"Could not test method '{method}', raised '{str(ex)}'.")



Expand Down Expand Up @@ -923,12 +923,11 @@ def test_fitter_results(setup, caplog):
optimizer=n_opt,
metric=metric,
g=[1*nS, 30*nS],
restart=False,)
restart=False)

params_list = tf.results(format='list')
assert isinstance(params_list, list)
assert isinstance(params_list[0], dict)
print(params_list)
assert isinstance(params_list[0]['g'], Quantity)
assert 'g' in params_list[0].keys()
assert 'error' in params_list[0].keys()
Expand Down

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