update subnet

Author: arrjon

case_study1/load_results_benchmark.py

@@ -17,7 +17,7 @@ def model_sampler_key(_model: str, _sampler: str) -> str:
 
 benchmarks = list(
     (m, mlp, t, s) for s, m, mlp, t in itertools.product(
-    SAMPLER_SETTINGS, MODELS.keys(), ['mlp', 'time_mlp'], sbibm.get_available_tasks()
+    SAMPLER_SETTINGS, MODELS.keys(), ['time_mlp'], sbibm.get_available_tasks()
 ) if is_compatible(m, s)
 )
 BASE = Path(__file__).resolve().parent

case_study1/plots/c2st_benchmark_boxplot_best.pdf

@@ -26,14 +26,16 @@
 partition = os.environ.get("SLURM_JOB_PARTITION", "unknown")
 benchmarks = list(
     (m, mlp, t, s) for s, m, mlp, t in itertools.product(
-    ['ode', 'sde', 'langevin'], MODELS.keys(), ['mlp', 'time_mlp'], sbibm.get_available_tasks()
+    ['ode', 'sde', 'langevin'], MODELS.keys(), ['time_mlp'], sbibm.get_available_tasks()
 ) if is_compatible(m, s)
-) # 640 jobs, first 360 are ODE
+) # 320 jobs, first 180 are ODE
 
 model_name, subnet, task_name, sampler_family = benchmarks[job_id]
 BASE = Path(__file__).resolve().parent
 metrics_dir = BASE / 'metrics'
+models_dir = BASE / 'models'
 #metrics_dir = Path('/lustre/scratch/data/jarruda_hpc-diffusion_experiments/case_study1/metrics')
+#models_dir = Path('/lustre/scratch/data/jarruda_hpc-diffusion_experiments/case_study1/models')
 
 logging.info(f"Running job {job_id} with model {model_name}, subnet {subnet}, task {task_name}, sampler {sampler_family}.")
 task = sbibm.get_task(task_name)
@@ -83,7 +85,7 @@ def sbibm_simulator_bf():
 workflow = load_model(conf_tuple=conf_tuple,
                       training_data=training_data,
                       simulator=simulator,
-                      storage=BASE / "models",
+                      storage=models_dir,
                       problem_name=task_name, model_name=model_name)
 
 #%%

case_study1/plots/c2st_benchmark_boxplot_merge_problems.pdf

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.18.4"
+__generated_with = "0.19.4"
 app = marimo.App(width="full")
 
 
@@ -42,7 +42,6 @@ def _():
 def _(BASE, pd):
     # Load the dataset
     results = pd.read_csv(BASE / 'plots' / 'c2st_benchmark_results.csv')
-    results = results[results.subnet == ['mlp', 'time_mlp'][1]]
     results.reset_index(inplace=True, drop=True)
     results
     return (results,)
@@ -51,7 +50,7 @@ def _(BASE, pd):
 @app.cell
 def _(SAMPLER_SETTINGS):
     all_samplers= ['best', 'merge_problems'] + [k for k in SAMPLER_SETTINGS.keys()]
-    SHOW_SAMPLER = all_samplers[0]
+    SHOW_SAMPLER = all_samplers[1]
     print(SHOW_SAMPLER)
     return (SHOW_SAMPLER,)
 

case_study1/plots/c2st_benchmark_results.csv

@@ -60,7 +60,7 @@
   <!-- Preconnect for performance (local only) -->
   <link rel="preconnect" href="/">
 
-  <title>Diffusion Models In Simulation-Based Inference: A Tutorial Review - Arruda et al. | Academic Research</title>
+  <title>Diffusion Models In Simulation-Based Inference: A Tutorial Review - Arruda et al.</title>
 
   <!-- Critical CSS - Load synchronously -->
   <link rel="stylesheet" href="static/css/bulma.min.css">

case_study1/plots/euler_benchmark_boxplot_fm.pdf

@@ -1,4 +1,5 @@
 from collections import OrderedDict
+import logging
 
 import numpy as np
 import scipy
@@ -18,6 +19,8 @@
     "legend.fontsize": 10,
 }
 
+logging.getLogger("matplotlib").setLevel(logging.ERROR)
+
 
 class InverseKinematicsModel:
     n_parameters = 4