best val_rmse 0.0292

config.json

@@ -1,14 +1,14 @@
 {
-  "best_val_loss": 0.007497346960008144,
+  "best_val_loss": 0.029227718710899353,
   "comet_ai_key": null,
   "context_observations": {
     "add_rem": true,
     "divide_in_past_and_future": false,
-    "empirical_number_of_obs": true,
+    "empirical_number_of_obs": false,
     "max_num_obs": 15,
     "min_num_of_past_context": 3,
     "num_of_past_context": 5,
-    "obs_dataset": "/home/ojedamarin/Projects/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
+    "obs_dataset": "/home/cesarali/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
     "obs_type": "observations_pk_peak_halflife",
     "past_time_ratio": 0.1
   },
@@ -117,8 +117,9 @@
       "lenuzza",
       "Lenuzza2016.csv"
     ],
-    "pretraining_epochs": 760,
+    "pretraining_epochs": 90,
     "pretraining_protocol": "none",
+    "return_split_versions": false,
     "split_seed": 42,
     "split_strategy": "study",
     "test_protocol": "simulated",
@@ -129,22 +130,22 @@
     "z_score_normalization": false
   },
   "model_type": "node_pk",
-  "my_results_path": "/work/ojedamarin/Projects/Pharma/Results/",
+  "my_results_path": null,
   "name_str": "SNodePK",
   "network": {
     "activation": "ReLU",
     "aggregator_num_heads": 8,
-    "aggregator_type": "mean",
+    "aggregator_type": "attention",
     "cov_proj_dim": 16,
-    "decoder_hidden_dim": 512,
-    "decoder_name": "RNNDecoder",
-    "decoder_num_layers": 4,
-    "decoder_rnn_hidden_dim": 256,
+    "decoder_hidden_dim": 128,
+    "decoder_name": "TransformerDecoder",
+    "decoder_num_layers": 2,
+    "decoder_rnn_hidden_dim": 200,
     "drift_activation": "Tanh",
     "drift_num_layers": 3,
     "dropout": 0.1,
-    "encoder_rnn_hidden_dim": 256,
-    "exclusive_node_step": true,
+    "encoder_rnn_hidden_dim": 200,
+    "exclusive_node_step": false,
     "individual_encoder_name": "RNNContextEncoder",
     "individual_encoder_number_of_heads": 4,
     "init_hidden_num_layers": 2,
@@ -152,21 +153,17 @@
     "loss_name": "nll",
     "node_step": true,
     "norm": "layer",
-    "output_head_num_layers": 3,
+    "output_head_num_layers": 2,
     "rnn_decoder_number_of_layers": 4,
-    "rnn_individual_encoder_number_of_layers": 4,
-    "time_obs_encoder_hidden_dim": 256,
-    "time_obs_encoder_output_dim": 256,
+    "rnn_individual_encoder_number_of_layers": 2,
+    "time_obs_encoder_hidden_dim": 128,
+    "time_obs_encoder_output_dim": 128,
     "use_attention": true,
-    "use_covariance": false,
-    "zi_latent_dim": 128
+    "zi_latent_dim": 200
   },
-  "run_index": 41,
+  "run_index": 0,
   "tags": [
-    "FAttention",
-    "Long",
-    "ZS",
-    "init_recon",
+    "snode-pk",
     "S-0"
   ],
   "target_observations": {
@@ -176,26 +173,26 @@
     "max_num_obs": 14,
     "min_num_of_past_context": 3,
     "num_of_past_context": 4,
-    "obs_dataset": "/home/ojedamarin/Projects/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
+    "obs_dataset": "/home/cesarali/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
     "obs_type": "observations_pk_peak_halflife",
     "past_time_ratio": 0.1
   },
   "train": {
     "amsgrad": false,
-    "batch_size": 16,
+    "batch_size": 8,
     "betas": [
       0.9,
       0.999
     ],
-    "epochs": 950,
+    "epochs": 15,
     "eps": 1e-08,
     "gradient_clip_val": 1.0,
     "learning_rate": 0.0001,
-    "log_image_every_epoch": 5,
+    "log_image_every_epoch": 2,
     "log_interval": 1,
     "log_vcp": true,
     "num_batch_plot": 1,
-    "num_workers": 8,
+    "num_workers": 3,
     "optimizer_name": "AdamW",
     "scheduler_name": "CosineAnnealingLR",
     "scheduler_params": {

pytorch_model.bin

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