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README.md

@@ -12,7 +12,7 @@ datasets:
 
 # Predictor of _Francisella tularensis_ MICs 
 
-_Updated:_ cd ..Tue Apr  1 03:31:39 BST 2025
+_Updated:_ Tue  1 Apr 08:02:46 BST 2025
 
 Trained on the _Francisella tularensis_, WT accumulator phenotype subset of the [human-curated SPARK dataset](https://doi.org/10.1021/acsinfecdis.8b00193) (9671 rows in total for _Francisella tularensis_).
 
@@ -26,7 +26,7 @@ Duvida also saves the training data in this checkpoint to allows the calculation
 based on that training data.
 
 This model is the best regression model from a hyperparameter search, determined
-by Spearman's $\rho$ on a held-out test set not used in training or early stopping. 
+by Pearson's $$r$$ on a held-out test set not used in training or early stopping. 
 
 ### Model architecture
 
@@ -35,14 +35,14 @@ by Spearman's $\rho$ on a held-out test set not used in training or early stoppi
 ```json
 
 {
-    "dropout": 0.2,
+    "dropout": 0.0,
     "ensemble_size": 3,
     "extra_featurizers": null,
     "learning_rate": 1e-05,
-    "model_class": "FPMLPModelBox",
-    "n_hidden": 5,
-    "n_units": 64,
-    "use_2d": true,
+    "model_class": "ChempropModelBox",
+    "n_hidden": 3,
+    "n_units": 8,
+    "use_2d": false,
     "use_fp": true
 }
 ```
@@ -79,39 +79,39 @@ Train (6770 rows):
 ```json
 
 {
-    "Pearson r": 0.42962190847203624,
-    "RMSE": 0.11870407313108444,
-    "Spearman rho": 0.6795582112917522
+    "Pearson r": 0.516938771017399,
+    "RMSE": 0.12588317692279816,
+    "Spearman rho": 0.6954019742202038
 }
 ```
 
-<img src="predictions-train.png" width=450>
+<img src="predictions_train.png" width=450>
 
 Validation (1450 rows):
 
 ```json
 
 {
-    "Pearson r": 0.31501651354350624,
-    "RMSE": 0.12135504186153412,
-    "Spearman rho": 0.6161830171198623
+    "Pearson r": 0.2963203237123644,
+    "RMSE": 0.13911816477775574,
+    "Spearman rho": 0.5852028507710829
 }
 ```
 
-<img src="predictions-validation.png" width=450>
+<img src="predictions_validation.png" width=450>
 
 Test (1451 rows):
 
 ```json
 
 {
-    "Pearson r": 0.3534956375117685,
-    "RMSE": 0.10038620978593826,
-    "Spearman rho": 0.6542369423525334
+    "Pearson r": 0.3693269511911911,
+    "RMSE": 0.12272950261831284,
+    "Spearman rho": 0.6347207507195295
 }
 ```
 
-<img src="predictions-test.png" width=450>
+<img src="predictions_test.png" width=450>
 
 ## Training data details
 

data-config.json

@@ -9,7 +9,7 @@
     ],
     "_out_key": "labels",
     "input_shape": [
-        2248
+        2048
     ],
     "output_shape": [
         1

data-load-args.json

@@ -1,5 +1,5 @@
 {
-    "cache": "/nemo/lab/johnsone/home/users/johnsoe/projects/abx-discovery-strategy/models/spark-dv-2503/Francisella-tularensis/117/cache",
+    "cache": "/nemo/lab/johnsone/home/users/johnsoe/projects/abx-discovery-strategy/models/spark-dv-2503/Francisella-tularensis/32/cache",
     "features": [
         "smiles"
     ],

eval-metrics_test.json

@@ -1,5 +1,5 @@
 {
-    "Pearson r": 0.3534956375117685,
-    "RMSE": 0.10038620978593826,
-    "Spearman rho": 0.6542369423525334
+    "Pearson r": 0.3693269511911911,
+    "RMSE": 0.12272950261831284,
+    "Spearman rho": 0.6347207507195295
 }

eval-metrics_train.json

@@ -1,5 +1,5 @@
 {
-    "Pearson r": 0.42962190847203624,
-    "RMSE": 0.11870407313108444,
-    "Spearman rho": 0.6795582112917522
+    "Pearson r": 0.516938771017399,
+    "RMSE": 0.12588317692279816,
+    "Spearman rho": 0.6954019742202038
 }

eval-metrics_validation.json

@@ -1,5 +1,5 @@
 {
-    "Pearson r": 0.31501651354350624,
-    "RMSE": 0.12135504186153412,
-    "Spearman rho": 0.6161830171198623
+    "Pearson r": 0.2963203237123644,
+    "RMSE": 0.13911816477775574,
+    "Spearman rho": 0.5852028507710829
 }

logs-csv/lightning_logs/version_0/hparams.yaml

@@ -1,13 +1 @@
-dropout: 0.2
-ensemble_size: 3
-extra_featurizers: null
-learning_rate: 1.0e-05
-n_hidden: 5
-n_input: 2248
-n_out: 1
-n_units: 64
-optimizer: !!python/name:torch.optim.adam.Adam ''
-reduce_lr_on_plateau: true
-reduce_lr_patience: 10
-use_2d: true
-use_fp: true
+{}

logs-csv/lightning_logs/version_0/metrics.csv

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