src/05_predict_outcomes_WEE1.py

import pandas as pd
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import h2o
from h2o.automl import H2OAutoML
from h2o.frame import H2OFrame
import pickle
import os

# Define the target
target = "WEE1"  # Replace with WEE1 or CXCR4 as needed

# Start the H2O cluster
h2o.init()

# Load datasets
data_train = pq.read_table("intermediate_data/data_train_joined.parquet").to_pandas()
data_test = pq.read_table("intermediate_data/data_test_joined.parquet").to_pandas()

# Filter data for the selected target
data_train = data_train[data_train["Target Name"] == target]
data_test = data_test[data_test["Target Name"] == target]

# Define target column
target_column = " RMSD_Energy"

# Identify feature columns: all columns after "LF_score" (inclusive)
start_idx = list(data_train.columns).index("LF_score")
feature_columns = data_train.columns[start_idx:].tolist()
feature_columns = [col for col in feature_columns if col != " RMSD_Energy"]

# Convert to H2OFrame
train_h2o = H2OFrame(data_train)
test_h2o = H2OFrame(data_test)

# train_h2o[target_column] = train_h2o[target_column].asnumeric()
# test_h2o[target_column] = test_h2o[target_column].asnumeric()

# train_h2o[target_column] = train_h2o[target_column].asfactor()
# test_h2o[target_column] = test_h2o[target_column].asfactor()

# Train AutoML model
aml = H2OAutoML(max_models=1, seed=42)
aml.train(x=feature_columns, y=target_column, training_frame=train_h2o)

# Save the trained model
top_model = aml.leader
model_path = h2o.save_model(top_model, path = f"intermediate_data/top_model{target}", force=True)

# Generate predictions
for dataset_name, dataset_h2o, dataset_df in [
    ("train", train_h2o, data_train),
    ("test", test_h2o, data_test),
]:
    predictions = aml.leader.predict(dataset_h2o).as_data_frame()
    dataset_df["predictions"] = predictions["predict"]

    # Save predictions
    output_path = f"intermediate_data/data_{dataset_name}_{target}_pred.parquet"
    pq.write_table(pa.Table.from_pandas(dataset_df), output_path)

# Shutdown H2O cluster
h2o.shutdown(prompt=False)

print(f"Completed training and predictions for {target}")