best val_rmse 0.0004

config.json

@@ -1,5 +1,5 @@
 {
-  "best_val_loss": 0.0003486917121335864,
+  "best_val_loss": 0.0003782084968406707,
   "comet_ai_key": null,
   "context_observations": {
     "add_rem": true,
@@ -12,12 +12,29 @@
     "obs_type": "observations_pk_peak_halflife",
     "past_time_ratio": 0.1
   },
+  "debug_test": true,
   "dosing": {
-    "dose": 1.0,
-    "route": "oral",
+    "logdose_mean_range": [
+      -2.0,
+      2.0
+    ],
+    "logdose_std_range": [
+      0.1,
+      0.5
+    ],
+    "num_individuals": 10,
+    "route_options": [
+      "oral",
+      "iv"
+    ],
+    "route_weights": [
+      0.8,
+      0.2
+    ],
+    "same_route": true,
     "time": 0.0
   },
-  "experiment_dir": "/home/cesarali/Pharma/generative_pk/results/comet/node_pk_compartments/0f03bcacc1c340a4b079188c7f28bf94",
+  "experiment_dir": "/home/cesarali/Pharma/generative_pk/results/comet/node_pk_compartments/fb0874f7e0e7492393bb882525283b3a",
   "experiment_indentifier": null,
   "experiment_name": "node_pk_compartments",
   "hf_model_card_path": [
@@ -28,8 +45,12 @@
   "hugging_face_token": null,
   "meta_study": {
     "V_tmag_range": [
-      0.01,
-      0.1
+      0.001,
+      0.001
+    ],
+    "V_tscl_range": [
+      1,
+      5
     ],
     "drug_id_options": [
       "Drug_A",
@@ -38,75 +59,96 @@
     ],
     "k_1p_tmag_range": [
       0.01,
-      0.1
+      0.02
+    ],
+    "k_1p_tscl_range": [
+      1,
+      5
     ],
     "k_a_tmag_range": [
       0.01,
-      0.1
+      0.02
+    ],
+    "k_a_tscl_range": [
+      1,
+      5
     ],
     "k_e_tmag_range": [
       0.01,
-      0.1
+      0.02
+    ],
+    "k_e_tscl_range": [
+      1,
+      5
     ],
     "k_p1_tmag_range": [
       0.01,
-      0.1
+      0.02
+    ],
+    "k_p1_tscl_range": [
+      1,
+      5
     ],
     "log_V_mean_range": [
-      -1.5,
-      1.5
+      2,
+      8
     ],
     "log_V_std_range": [
-      0.1,
-      0.5
+      0.2,
+      0.6
     ],
     "log_k_1p_mean_range": [
-      -1.5,
-      1.5
+      -4,
+      0
     ],
     "log_k_1p_std_range": [
-      0.1,
-      0.5
+      0.2,
+      0.6
     ],
     "log_k_a_mean_range": [
-      -1.5,
-      1.5
+      -1,
+      2
     ],
     "log_k_a_std_range": [
-      0.1,
-      0.5
+      0.2,
+      0.6
     ],
     "log_k_e_mean_range": [
-      -1.5,
-      1.5
+      -5,
+      0
     ],
     "log_k_e_std_range": [
-      0.1,
-      0.5
+      0.2,
+      0.6
     ],
     "log_k_p1_mean_range": [
-      -1.5,
-      1.5
+      -4,
+      -1
     ],
     "log_k_p1_std_range": [
-      0.1,
-      0.5
+      0.2,
+      0.6
     ],
     "num_individuals_range": [
-      10,
+      5,
       10
     ],
     "num_peripherals_range": [
       1,
       3
     ],
+    "rel_ruv_range": [
+      0.001,
+      0.01
+    ],
     "solver_method": "rk4",
     "time_num_steps": 100,
     "time_start": 0.0,
-    "time_stop": 10.0
+    "time_stop": 24.0
   },
   "mix_data": {
     "evaluate_prediction_steps_past": 3,
+    "keep_tempfile": false,
     "log_transform": false,
     "n_of_databatches": 1,
     "n_of_target_individuals": 0,
@@ -119,13 +161,20 @@
     ],
     "pretraining_epochs": 90,
     "pretraining_protocol": "none",
+    "recreate_tempfile": false,
     "return_split_versions": false,
     "split_seed": 42,
     "split_strategy": "subject",
-    "test_protocol": "empirical",
+    "store_in_tempfile": false,
+    "tempfile_path": [
+      "preprocessed",
+      "simulated_ou_as_rates.tr"
+    ],
+    "test_protocol": "node",
     "test_size": 5,
+    "tqdm_progress": false,
     "train_size": 20,
-    "val_protocol": "empirical",
+    "val_protocol": "node",
     "val_size": 5,
     "z_score_normalization": false
   },
@@ -136,6 +185,7 @@
     "activation": "ReLU",
     "aggregator_num_heads": 8,
     "aggregator_type": "attention",
+    "combine_latent_mode": "mlp",
     "cov_proj_dim": 16,
     "decoder_attention_layers": 2,
     "decoder_hidden_dim": 32,
@@ -155,12 +205,17 @@
     "node_step": true,
     "norm": "layer",
     "output_head_num_layers": 2,
+    "prediction_latent_deterministic": false,
+    "prediction_only": false,
     "rnn_decoder_number_of_layers": 1,
     "rnn_individual_encoder_number_of_layers": 1,
+    "study_latent_deterministic": false,
     "time_obs_encoder_hidden_dim": 32,
     "time_obs_encoder_output_dim": 32,
     "use_attention": true,
+    "use_invariance_loss": true,
     "use_kl_i": true,
+    "use_kl_i_np": true,
     "use_kl_init": true,
     "use_kl_s": true,
     "zi_latent_dim": 20
@@ -183,27 +238,31 @@
   },
   "train": {
     "amsgrad": false,
-    "batch_size": 8,
+    "batch_size": 128,
     "betas": [
       0.9,
       0.999
     ],
-    "epochs": 3,
+    "epochs": 50,
     "eps": 1e-08,
+    "eval_empirical_every_pct": 0.25,
     "gradient_clip_val": 1.0,
-    "learning_rate": 0.0001,
+    "learning_rate": 0.00023,
     "log_image_every_epoch": 2,
     "log_interval": 1,
-    "log_vcp": true,
+    "log_vcp": false,
     "num_batch_plot": 1,
-    "num_workers": 3,
+    "num_workers": 8,
     "optimizer_name": "AdamW",
+    "persistent_workers": true,
     "scheduler_name": "CosineAnnealingLR",
     "scheduler_params": {
       "T_max": 1000,
       "eta_min": 5e-05,
       "last_epoch": -1
     },
+    "shuffle_val": true,
+    "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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-size 86545
+oid sha256:787f42a203b6312d5a4a9ac9f5ec93a44ffbadf319314eba9c258dfc476ff220
+size 87185