import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import h2o
from h2o.automl import H2OAutoML
import pyarrow as pa
import pyarrow.parquet as pq

h2o.init()

###Data preparation
df_train = pd.read_csv("./intermediate/train_rdkit_descriptors.csv")
df_test = pd.read_csv("./intermediate/test_rdkit_descriptors.csv")

x_train = df_train.drop(columns=["label",  "Standardized_SMILES"])
y_train = df_train["label"]
x_test = df_test.drop(columns=["label", "Standardized_SMILES"])
y_test = df_test["label"]

train_h2o = h2o.H2OFrame(pd.concat([x_train, y_train], axis=1))
test_h2o = h2o.H2OFrame(pd.concat([x_test, y_test], axis=1))

train_h2o["label"] = train_h2o["label"].asfactor()
test_h2o["label"] = test_h2o["label"].asfactor()
feature_cols = x_train.columns.tolist()

###Training
aml = H2OAutoML(max_models=20,seed=42,nfolds=10,sort_metric="AUC")
aml.train(x=feature_cols, y="label", training_frame=train_h2o)

###Model Selection
lb = aml.leaderboard
lb.head(rows=lb.nrows)
best_model = aml.leader

###Save the best model
#best_model = h2o.save_model(model = best_model, path ='./product/', force = True)
top_3_models = lb.as_data_frame()["model_id"].head(3).tolist()

for i, model_id in enumerate(top_3_models, start=1):
    model = h2o.get_model(model_id)
    model_path = h2o.save_model(model=model, path=f"./product/top_model_{i}", force=True)

###Save the corss validation result for all models
model_ids = lb.as_data_frame()["model_id"].tolist()
all_model_summaries = []
for model_id in model_ids:
    model = h2o.get_model(model_id)
    cv_summary = model.cross_validation_metrics_summary().as_data_frame()
    cv_summary["model_id"] = model_id
    all_model_summaries.append(cv_summary)

cv_all_models_df = pd.concat(all_model_summaries, ignore_index=True)
cv_all_models_df.to_csv("./intermediate/cross_validation_result.csv",index=False)