Skip to content

Reduce memory used by network optimization objectives #100

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
iraikov merged 2 commits into from
Oct 14, 2025
Merged

Reduce memory used by network optimization objectives #100

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2caa5d0
network optimization: ensure objective values are float32
iraikov Oct 5, 2025
9636276
refactoring to reduce memory use during optimization with a large num…
iraikov Oct 11, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 8 additions & 3 deletions src/miv_simulator/optimization.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -370,15 +370,20 @@ def update_run_params(env, param_tuples):
def network_features(env, t_start, t_stop, target_populations):
features_dict = dict()

temporal_resolution = float(env.stimulus_config["Temporal Resolution"])
time_bins = np.arange(t_start, t_stop, temporal_resolution)
analysis_config = env.analysis_config
if analysis_config is None:
analysis_config = {}
fr_inference_config = analysis_config.get("Firing Rate Inference", {})

temporal_resolution = float(fr_inference_config.get("Temporal Resolution", 2.0))
time_bins = np.arange(t_start, t_stop, temporal_resolution).astype(np.float32)

pop_spike_dict = spikedata.get_env_spike_dict(env, include_artificial=False)

for pop_name in target_populations:
n_active = 0
spike_density_dict = spikedata.spike_density_estimate(
pop_name, pop_spike_dict[pop_name], time_bins
pop_name, pop_spike_dict[pop_name], time_bins, return_time_bins=False
)
for gid, dens_dict in spike_density_dict.items():
mean_rate = np.mean(dens_dict["rate"])
Expand Down
18 changes: 10 additions & 8 deletions src/miv_simulator/optimize_network.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"""
Network model optimization script for optimization with dmosopt
"""

import gc
import os
import sys
import datetime
Expand Down Expand Up @@ -410,9 +410,9 @@ def compute_objectives(local_features, operational_config, opt_targets):
sum_mean_rate_local += mean_rate
ip_rate = np.interp(
fr_time_centers,
dens_dict["time"].astype(np.float32),
time_bins_ref,
dens_dict["rate"].astype(np.float32),
)
).astype(np.float32)
active_per_bin = ip_rate > active_threshold
sum_active_per_bin += active_per_bin

Expand All @@ -439,17 +439,19 @@ def compute_objectives(local_features, operational_config, opt_targets):
)

all_features_dict[f"{pop_name} mean fraction active per time bin"] = (
mean_fraction_active_per_bin
float(mean_fraction_active_per_bin)
)
all_features_dict[f"{pop_name} std fraction active per time bin"] = (
std_fraction_active_per_bin
float(std_fraction_active_per_bin)
)
all_features_dict[f"{pop_name} fraction active"] = fraction_active
all_features_dict[f"{pop_name} firing rate"] = mean_rate
all_features_dict[f"{pop_name} fraction active"] = float(fraction_active)
all_features_dict[f"{pop_name} firing rate"] = float(mean_rate)

rate_constr = mean_rate if mean_rate > 0.0 else -1.0
constraints.append(rate_constr)

gc.collect()

objective_names = operational_config["objective_names"]
feature_dtypes = [(feature_name, np.float32) for feature_name in objective_names]

Expand All @@ -470,7 +472,7 @@ def compute_objectives(local_features, operational_config, opt_targets):
features.append(feature_val)

result = (
np.asarray(objectives),
np.asarray(objectives, dtype=np.float32),
np.array([tuple(features)], dtype=np.dtype(feature_dtypes)),
np.asarray(constraints, dtype=np.float32),
)
Expand Down
18 changes: 10 additions & 8 deletions src/miv_simulator/spikedata.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -295,6 +295,7 @@ def spike_density_estimate(
trajectory_id=None,
output_file_path=None,
progress=False,
return_time_bins=True,
inferred_rate_attr_name="Inferred Rate Map",
**kwargs,
):
Expand Down Expand Up @@ -339,7 +340,7 @@ def make_spktrain(lst, t_start, t_stop):
spk_rate_dict = {
ind: baks(spkts / 1000.0, time_bins / 1000.0, **baks_args)[0].reshape((-1,))
if len(spkts) > 1
else np.zeros(time_bins.shape)
else np.zeros(time_bins.shape, dtype=np.float32)
for ind, spkts in seq
}

Expand All @@ -360,13 +361,14 @@ def make_spktrain(lst, t_start, t_stop):
output_file_path, population, attr_dict, namespace=namespace
)

result = {
ind: {"rate": rate, "time": time_bins} for ind, rate in spk_rate_dict.items()
}

result = {
ind: {"rate": rate, "time": time_bins} for ind, rate in spk_rate_dict.items()
}
if return_time_bins:
result = {
ind: {"rate": rate, "time": time_bins} for ind, rate in spk_rate_dict.items()
}
else:
result = {
ind: {"rate": rate} for ind, rate in spk_rate_dict.items()
}

return result

Expand Down
2 changes: 1 addition & 1 deletion src/miv_simulator/utils/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -984,7 +984,7 @@ def baks(spktimes, time, a=1.5, b=None):

threshold = 6.0
h = (gamma(a) / gamma(a + 0.5)) * (sumnum / sumdenom)
rate = np.zeros((len(time),))
rate = np.zeros((len(time),), dtype=np.float32)
for j in range(n):
time_diff = time - spktimes[j]
abs_diff = np.abs(time_diff)
Expand Down
Loading