best val_rmse 0.0002

config.json

@@ -1,10 +1,10 @@
 {
-  "best_val_loss": 0.01571166329085827,
+  "best_val_loss": 0.00024314314941875637,
   "comet_ai_key": null,
   "context_observations": {
     "add_rem": true,
     "divide_in_past_and_future": false,
-    "empirical_number_of_obs": false,
+    "empirical_number_of_obs": true,
     "max_num_obs": 15,
     "min_num_of_past_context": 3,
     "num_of_past_context": 5,
@@ -12,7 +12,7 @@
     "obs_type": "observations_pk_peak_halflife",
     "past_time_ratio": 0.1
   },
-  "debug_test": "",
+  "debug_test": false,
   "dosing": {
     "logdose_mean_range": [
       -2.0,
@@ -25,12 +25,12 @@
     "num_individuals": 10,
     "route_options": [
       "oral",
-      "iv"
+      "oral"
     ],
     "same_route": true,
     "time": 0.0
   },
-  "experiment_dir": "/work/ojedamarin/Projects/Pharma/Results/comet/node_pk_compartments/edf4c9ff9b6f4e7dbf2a67afd2d52fa9",
+  "experiment_dir": "/home/cesarali/Pharma/generative_pk/results/comet/node_pk_compartments/50ccc961a6a3454785a4d5e9e54100c0",
   "experiment_indentifier": null,
   "experiment_name": "node_pk_compartments",
   "hf_model_card_path": [
@@ -42,7 +42,7 @@
   "meta_study": {
     "V_tmag_range": [
       0.001,
-      0.0099
+      0.001
     ],
     "V_tscl_range": [
       1,
@@ -55,7 +55,7 @@
     ],
     "k_1p_tmag_range": [
       0.01,
-      0.2
+      0.02
     ],
     "k_1p_tscl_range": [
       1,
@@ -63,7 +63,7 @@
     ],
     "k_a_tmag_range": [
       0.01,
-      0.2
+      0.02
     ],
     "k_a_tscl_range": [
       1,
@@ -71,7 +71,7 @@
     ],
     "k_e_tmag_range": [
       0.01,
-      0.2
+      0.02
     ],
     "k_e_tscl_range": [
       1,
@@ -79,7 +79,7 @@
     ],
     "k_p1_tmag_range": [
       0.01,
-      0.2
+      0.02
     ],
     "k_p1_tscl_range": [
       1,
@@ -126,7 +126,7 @@
       0.6
     ],
     "num_individuals_range": [
-      6,
+      5,
       10
     ],
     "num_peripherals_range": [
@@ -134,8 +134,8 @@
       3
     ],
     "rel_ruv_range": [
-      0.05,
-      0.3
+      0.001,
+      0.01
     ],
     "solver_method": "rk4",
     "time_num_steps": 100,
@@ -155,7 +155,7 @@
       "lenuzza",
       "Lenuzza2016.csv"
     ],
-    "pretraining_epochs": "",
+    "pretraining_epochs": 800,
     "pretraining_protocol": "none",
     "recreate_tempfile": false,
     "return_split_versions": true,
@@ -167,15 +167,15 @@
       "simulated_ou_as_rates"
     ],
     "test_protocol": "simulated",
-    "test_size": 128,
+    "test_size": 5,
     "tqdm_progress": false,
     "train_size": 1000,
     "val_protocol": "simulated",
-    "val_size": 1000,
+    "val_size": 5,
     "z_score_normalization": false
   },
   "model_type": "node_pk",
-  "my_results_path": "/work/ojedamarin/Projects/Pharma/Results/",
+  "my_results_path": null,
   "name_str": "NeuralProcessPK",
   "network": {
     "activation": "ReLU",
@@ -218,8 +218,8 @@
   },
   "run_index": 0,
   "tags": [
-    "META-STUDY",
-    "YOLO-0",
+    "NP",
+    "OU",
     "N-0"
   ],
   "target_observations": {
@@ -240,12 +240,12 @@
       0.9,
       0.999
     ],
-    "epochs": 500,
+    "epochs": 50,
     "eps": 1e-08,
-    "eval_empirical_every_pct": 0.3,
+    "eval_empirical_every_pct": 0.75,
     "gradient_clip_val": 1.0,
-    "learning_rate": 0.0001,
-    "log_image_every_epoch": 10,
+    "learning_rate": 0.00023,
+    "log_image_every_epoch": 25,
     "log_interval": 1,
     "log_vcp": false,
     "num_batch_plot": 1,
@@ -259,7 +259,7 @@
       "last_epoch": -1
     },
     "shuffle_val": true,
-    "val_empirical_every_pct_epochs": 0.1,
+    "val_empirical_every_pct_epochs": 0.5,
     "weight_decay": 0.0001
   },
   "transformers_version": "4.52.4",

pytorch_model.bin

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