best val_rmse 0.0198

config.json

@@ -1,5 +1,5 @@
 {
-  "best_val_loss": 0.025066407397389412,
+  "best_val_loss": 0.01979512721300125,
   "comet_ai_key": null,
   "context_observations": {
     "add_rem": true,
@@ -7,12 +7,11 @@
     "max_num_obs": 15,
     "max_past": 5,
     "min_past": 3,
-    "obs_dataset": "/home/ojedamarin/Projects/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
     "past_time_ratio": 0.1,
     "split_past_future": false,
     "type": "pk_peak_half_life"
   },
-  "debug_test": true,
+  "debug_test": false,
   "dosing": {
     "logdose_mean_range": [
       -2.0,
@@ -34,7 +33,7 @@
     "same_route": true,
     "time": 0.0
   },
-  "experiment_dir": "/home/cesarali/Pharma/sim_priors_pk/results/comet/aistats/81d6319dfebd4df781c5b19c2a18d6ad",
+  "experiment_dir": "/work/ojedamarin/Projects/Pharma/Results/comet/aistats/c2b9bc645f434a6dbac001e145eae4e4",
   "experiment_indentifier": null,
   "experiment_name": "aistats",
   "hf_model_card_path": [
@@ -155,29 +154,24 @@
     "n_of_target_individuals": 1,
     "normalize_by_max": true,
     "normalize_time": true,
-    "pretraining_epochs": 800,
-    "pretraining_protocol": "none",
     "recreate_tempfile": false,
-    "split_seed": 42,
-    "split_strategy": "study",
     "store_in_tempfile": true,
     "tempfile_path": [
       "preprocessed",
       "simulated_ou_as_rates"
     ],
     "test_empirical_datasets": [
+      "cesarali/lenuzza-2016",
       "cesarali/Theophylline"
     ],
-    "test_protocol": "simulated",
-    "test_size": 120,
+    "test_size": 64,
     "tqdm_progress": false,
-    "train_size": 1000,
-    "val_protocol": "simulated",
-    "val_size": 120,
+    "train_size": 128,
+    "val_size": 64,
     "z_score_normalization": false
   },
   "model_type": "node_pk",
-  "my_results_path": null,
+  "my_results_path": "/work/ojedamarin/Projects/Pharma/Results/",
   "name_str": "AICMEPK",
   "network": {
     "activation": "ReLU",
@@ -234,32 +228,78 @@
     "max_num_obs": 15,
     "max_past": 5,
     "min_past": 3,
-    "obs_dataset": "/home/ojedamarin/Projects/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
     "past_time_ratio": 0.1,
     "split_past_future": true,
     "type": "pk_peak_half_life"
   },
+  "tracking": {
+    "best": {
+      "val_rmse": {
+        "epoch": null,
+        "step": null,
+        "value": 0.01979512721300125
+      }
+    },
+    "meta": {}
+  },
   "train": {
     "amsgrad": false,
-    "batch_size": 32,
+    "batch_size": 128,
     "betas": [
       0.9,
       0.999
     ],
-    "epochs": 4,
+    "empirical_every_pct": 0.1,
+    "epochs": 10,
     "eps": 1e-08,
+    "generative_image_ids": null,
+    "generative_image_ids_empirical_during": null,
+    "generative_image_ids_empirical_end": [
+      "new_individuals_images"
+    ],
+    "generative_image_ids_val": [
+      "new_individuals_images"
+    ],
+    "generative_metric_ids": null,
+    "generative_metric_ids_empirical_during": null,
+    "generative_metric_ids_empirical_end": [
+      "new_individuals_metrics"
+    ],
+    "generative_metric_ids_val": [],
     "gradient_clip_val": 0.5,
     "learning_rate": 0.0001,
-    "log_empirical_evaluation_pct": 0.5,
-    "log_image_every_epoch_pct": 0.5,
+    "log_generative_empirical_during": false,
+    "log_generative_empirical_end": true,
+    "log_generative_val": true,
+    "log_images_empirical_during": false,
+    "log_images_empirical_end": true,
+    "log_images_val": true,
     "log_interval": 1,
-    "log_prediction_in_val": false,
-    "log_reconstruction_in_val": false,
-    "log_vcp": false,
+    "log_predictive_empirical_during": false,
+    "log_predictive_empirical_end": true,
+    "log_predictive_val": true,
+    "models_end_of_training": [
+      "last",
+      "best"
+    ],
     "num_batch_plot": 1,
-    "num_workers": 4,
+    "num_workers": 8,
     "optimizer_name": "AdamW",
     "persistent_workers": true,
+    "predictive_image_ids": null,
+    "predictive_image_ids_empirical_during": null,
+    "predictive_image_ids_empirical_end": [
+      "prediction_images"
+    ],
+    "predictive_image_ids_val": [
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+    "predictive_metric_ids": null,
+    "predictive_metric_ids_empirical_during": null,
+    "predictive_metric_ids_empirical_end": [
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+    "predictive_metric_ids_val": [],
     "scheduler_name": "CosineAnnealingLR",
     "scheduler_params": {
       "T_max": 1000,
@@ -267,6 +307,7 @@
       "last_epoch": -1
     },
     "shuffle_val": true,
+    "val_every_pct": 0.1,
     "weight_decay": 0.0001
   },
   "transformers_version": "4.52.4",

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@@ -1,3 +1,3 @@
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