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full benchmark
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@anujanegi
anujanegi committed Feb 16, 2024
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Showing 1 changed file with 106 additions and 98 deletions.
204 changes: 106 additions & 98 deletions bsi_zoo/run_benchmark.py
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Expand Up @@ -18,11 +18,11 @@
from bsi_zoo.metrics import euclidean_distance, mse, emd, f1, reconstructed_noise
from bsi_zoo.config import get_leadfield_path

n_jobs = 30
n_jobs = 20
nruns = 10
spatial_cv = [False, True]
subjects = ["CC120264", "CC120313", "CC120309"]
# "CC120166", "CC120313",
#
subjects = ["CC120166", "CC120264", "CC120313", "CC120309"]
metrics = [
euclidean_distance,
mse,
Expand All @@ -32,17 +32,8 @@
] # list of metric functions here
nnzs = [1, 2, 3, 5]
alpha_SNR = [0.99, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.01]
# estimator_alphas = [
# 0.01,
# 0.01544452,
# 0.02385332,
# 0.03684031,
# 0.0568981,
# 0.08787639,
# 0.13572088,
# 0.2096144,
# ] # logspaced
estimator_alphas = np.logspace(0, -2, 20)[1:]
estimator_alphas_I = np.logspace(0, -2, 20)[1:]
estimator_alphas_II = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
memory = Memory(".")

for do_spatial_cv in spatial_cv:
Expand Down Expand Up @@ -73,16 +64,16 @@
}

estimators = [
(fake_solver, data_args_I, {"alpha": estimator_alphas}, {}),
# (eloreta, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L1, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L2, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_sqrt, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
# (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 1}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 2}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 3}),
(fake_solver, data_args_I, {"alpha": estimator_alphas_I}, {}),
(eloreta, data_args_I, {"alpha": estimator_alphas_II}, {}),
(iterative_L1, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L2, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_sqrt, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
(iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 1}),
(gamma_map, data_args_II, {"alpha": estimator_alphas_II}, {"update_mode": 2}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 3}),
]

df_results = []
Expand All @@ -102,15 +93,103 @@
results = benchmark.run(nruns=nruns)
df_results.append(results)
# save results
data_path = Path("bsi_zoo/data")
data_path = Path("bsi_zoo/data/updated_alpha_grid")
data_path.mkdir(exist_ok=True)
FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
if do_spatial_cv:
FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
else:
FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
results.to_pickle(data_path / FILE_NAME)


df_results = pd.concat(df_results, axis=0)

data_path = Path("bsi_zoo/data")
data_path = Path("bsi_zoo/data/ramen")
data_path.mkdir(exist_ok=True)
if do_spatial_cv:
FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
else:
FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
df_results.to_pickle(data_path / FILE_NAME)

print(df_results)

""" Free orientation parameters for the benchmark """

orientation_type = "free"
data_args_I = {
"n_sensors": [50],
"n_times": [10],
"n_sources": [200],
"nnz": nnzs,
"cov_type": ["diag"],
"path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
"orientation_type": [orientation_type],
"alpha": alpha_SNR, # this is actually SNR
}

data_args_II = {
"n_sensors": [50],
"n_times": [10],
"n_sources": [200],
"nnz": nnzs,
"cov_type": ["full"],
"path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
"orientation_type": [orientation_type],
"alpha": alpha_SNR, # this is actually SNR
}

if spatial_cv:
# currently no support for type II methods
estimators = [
(fake_solver, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L1, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L2, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_sqrt, data_args_I, {"alpha": estimator_alphas_I}, {}),
]
else:
estimators = [
(fake_solver, data_args_I, {"alpha": estimator_alphas_I}, {}),
(eloreta, data_args_I, {"alpha": estimator_alphas_II}, {}),
(iterative_L1, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L2, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_sqrt, data_args_I, {"alpha": estimator_alphas_I}, {}),
(iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
(iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas_I}, {}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 1}),
(gamma_map, data_args_II, {"alpha": estimator_alphas_II}, {"update_mode": 2}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas_I}, {"update_mode": 3}),
]

df_results = []
for estimator, data_args, estimator_args, estimator_extra_params in estimators:
benchmark = Benchmark(
estimator,
subject,
metrics,
data_args,
estimator_args,
random_state=42,
memory=memory,
n_jobs=n_jobs,
do_spatial_cv=do_spatial_cv,
estimator_extra_params=estimator_extra_params,
)
results = benchmark.run(nruns=nruns)
df_results.append(results)
# save results
data_path = Path("bsi_zoo/data/free2")
data_path.mkdir(exist_ok=True)

if do_spatial_cv:
FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
else:
FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
results.to_pickle(data_path / FILE_NAME)

df_results = pd.concat(df_results, axis=0)

data_path = Path("bsi_zoo/data/free2")
data_path.mkdir(exist_ok=True)
if do_spatial_cv:
FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
Expand All @@ -120,74 +199,3 @@

print(df_results)

# """ Free orientation parameters for the benchmark """

# orientation_type = "free"
# data_args_I = {
# "n_sensors": [50],
# "n_times": [10],
# "n_sources": [200],
# "nnz": nnzs,
# "cov_type": ["diag"],
# "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
# "orientation_type": [orientation_type],
# "alpha": alpha_SNR, # this is actually SNR
# }

# data_args_II = {
# "n_sensors": [50],
# "n_times": [10],
# "n_sources": [200],
# "nnz": nnzs,
# "cov_type": ["full"],
# "path_to_leadfield": [get_leadfield_path(subject, type=orientation_type)],
# "orientation_type": [orientation_type],
# "alpha": alpha_SNR, # this is actually SNR
# }

# estimators = [
# (fake_solver, data_args_I, {"alpha": estimator_alphas}, {}),
# (eloreta, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L1, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L2, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_sqrt, data_args_I, {"alpha": estimator_alphas}, {}),
# (iterative_L1_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
# (iterative_L2_typeII, data_args_II, {"alpha": estimator_alphas}, {}),
# # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 1}),
# (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 2}),
# # (gamma_map, data_args_II, {"alpha": estimator_alphas}, {"update_mode": 3}),
# ]

# df_results = []
# for estimator, data_args, estimator_args, estimator_extra_params in estimators:
# benchmark = Benchmark(
# estimator,
# subject,
# metrics,
# data_args,
# estimator_args,
# random_state=42,
# memory=memory,
# n_jobs=n_jobs,
# do_spatial_cv=do_spatial_cv,
# estimator_extra_params=estimator_extra_params,
# )
# results = benchmark.run(nruns=nruns)
# df_results.append(results)
# # save results
# data_path = Path("bsi_zoo/data")
# data_path.mkdir(exist_ok=True)
# FILE_NAME = f"{estimator}_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
# results.to_pickle(data_path / FILE_NAME)

# df_results = pd.concat(df_results, axis=0)

# data_path = Path("bsi_zoo/data")
# data_path.mkdir(exist_ok=True)
# if do_spatial_cv:
# FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_spatialCV_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
# else:
# FILE_NAME = f"benchmark_data_{subject}_{data_args['orientation_type'][0]}_{time.strftime('%b-%d-%Y_%H%M', time.localtime())}.pkl"
# df_results.to_pickle(data_path / FILE_NAME)

# print(df_results)

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