Add H2O AutoML training script

train_automl_h2o.py

@@ -0,0 +1,51 @@
+"""
+This script loads sanitized molecular fingerprint data, runs H2O AutoML
+to predict Ki values (nM) for serotonin receptor ligands, and saves the
+test set predictions.
+
+Expected input file: fingerprints_with_ki.csv
+Output: h2o_test_predictions.csv
+"""
+
+import h2o
+import pandas as pd
+from h2o.automl import H2OAutoML
+
+# Initialize H2O cluster
+h2o.init(max_mem_size="8G")
+
+# Load fingerprinted dataset (must be in same directory or provide full path)
+df = pd.read_csv("fingerprints_with_ki.csv")
+
+# Convert to H2OFrame
+df_h2o = h2o.H2OFrame(df)
+
+# Ensure target column is numeric
+df_h2o["Ki_nM"] = df_h2o["Ki_nM"].asnumeric()
+
+# Split into training and test sets
+train, test = df_h2o.split_frame(ratios=[0.8], seed=42)
+
+# Define predictors and response
+x = df_h2o.columns[:-1]  # all fingerprint columns
+y = "Ki_nM"
+
+# Run AutoML
+aml = H2OAutoML(
+    max_models=20,
+    max_runtime_secs=600,
+    seed=1,
+    sort_metric="RMSE"
+)
+aml.train(x=x, y=y, training_frame=train)
+
+# Evaluate model
+perf = aml.leader.model_performance(test_data=test)
+print("\n✅ Test Set Performance:")
+print("R² Score:", perf.r2())
+print("RMSE:", perf.rmse())
+
+# Save predictions
+preds = aml.leader.predict(test)
+h2o.export_file(preds, path="h2o_test_predictions.csv", force=True)
+