best val_rmse 0.0675

config.json

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+{
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+  "comet_ai_key": null,
+  "context_observations": {
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+    "divide_in_past_and_future": false,
+    "empirical_number_of_obs": false,
+    "max_num_obs": 15,
+    "min_num_of_past_context": 3,
+    "num_of_past_context": 5,
+    "obs_dataset": "/home/cesarali/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
+    "obs_type": "observations_pk_peak_halflife",
+    "past_time_ratio": 0.1
+  },
+  "dosing": {
+    "dose": 1.0,
+    "route": "oral",
+    "time": 0.0
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+  "experiment_dir": null,
+  "experiment_indentifier": null,
+  "experiment_name": "node_pk_compartments",
+  "hf_model_card_path": [
+    "hf_model_cards",
+    "SNODE-PK_Readme.md"
+  ],
+  "hf_model_name": "NodePK_development",
+  "hugging_face_token": null,
+  "meta_study": {
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+      0.1
+    ],
+    "drug_id_options": [
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+      "Drug_B",
+      "Drug_C"
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+    "k_1p_tmag_range": [
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+      0.1
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+    "k_a_tmag_range": [
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+      0.1
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+    "k_e_tmag_range": [
+      0.01,
+      0.1
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+    "k_p1_tmag_range": [
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+      0.1
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+    "log_V_mean_range": [
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+      1.5
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+      0.5
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+    "log_k_1p_mean_range": [
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+      1.5
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+      0.5
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+      1.5
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+      1.5
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+      0.5
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+    "num_individuals_range": [
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+      10
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+    "num_peripherals_range": [
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+      3
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+    "solver_method": "rk4",
+    "time_num_steps": 100,
+    "time_start": 0.0,
+    "time_stop": 10.0
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+    "log_transform": false,
+    "n_of_databatches": 3,
+    "n_of_target_individuals": 1,
+    "normalize_by_max": true,
+    "normalize_time": true,
+    "pretraining_dataset_path": [
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+      "lenuzza",
+      "Lenuzza2016.csv"
+    ],
+    "pretraining_epochs": 90,
+    "pretraining_protocol": "none",
+    "split_seed": 42,
+    "split_strategy": "study",
+    "test_protocol": "simulated",
+    "test_size": 5,
+    "train_size": 100,
+    "val_protocol": "simulated",
+    "val_size": 5,
+    "z_score_normalization": false
+  },
+  "model_type": "node_pk",
+  "my_results_path": null,
+  "name_str": "SNodePK",
+  "network": {
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+    "aggregator_num_heads": 8,
+    "aggregator_type": "attention",
+    "decoder_hidden_dim": 128,
+    "decoder_name": "RNNDecoder",
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+    "decoder_rnn_hidden_dim": 200,
+    "drift_activation": "Tanh",
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+    "dropout": 0.1,
+    "encoder_rnn_hidden_dim": 200,
+    "exclusive_node_step": false,
+    "individual_encoder_name": "RNNContextEncoder",
+    "individual_encoder_number_of_heads": 4,
+    "init_hidden_num_layers": 2,
+    "input_encoding_hidden_dim": 128,
+    "loss_name": "nll",
+    "node_step": true,
+    "norm": "layer",
+    "output_head_num_layers": 2,
+    "rnn_decoder_number_of_layers": 4,
+    "rnn_individual_encoder_number_of_layers": 2,
+    "time_obs_encoder_hidden_dim": 128,
+    "time_obs_encoder_output_dim": 128,
+    "use_attention": true,
+    "zi_latent_dim": 200
+  },
+  "run_index": 0,
+  "tags": [
+    "snode-pk",
+    "S-0"
+  ],
+  "target_observations": {
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+    "divide_in_past_and_future": true,
+    "empirical_number_of_obs": false,
+    "max_num_obs": 14,
+    "min_num_of_past_context": 3,
+    "num_of_past_context": 4,
+    "obs_dataset": "/home/cesarali/Pharma/generative_pk/data/preprocessed/lenuzza/Lenuzza2016.csv",
+    "obs_type": "observations_pk_peak_halflife",
+    "past_time_ratio": 0.1
+  },
+  "train": {
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+    "batch_size": 8,
+    "betas": [
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+      0.999
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+    "epochs": 5,
+    "eps": 1e-08,
+    "gradient_clip_val": 1.0,
+    "learning_rate": 0.0001,
+    "log_image_every_epoch": 2,
+    "log_interval": 1,
+    "num_batch_plot": 1,
+    "num_workers": 3,
+    "optimizer_name": "AdamW",
+    "scheduler_name": "CosineAnnealingLR",
+    "scheduler_params": {
+      "T_max": 1000,
+      "eta_min": 5e-05,
+      "last_epoch": -1
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+    "weight_decay": 0.0001
+  },
+  "transformers_version": "4.52.4",
+  "upload_to_hf_hub": true,
+  "verbose": false
+}

pytorch_model.bin

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