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()

###Reload the model
from h2o import load_model
restored_model1 = h2o.load_model("./product/top_model_1/StackedEnsemble_AllModels_1_AutoML_1_20250401_220205")
restored_model2 = h2o.load_model("./product/top_model_2/StackedEnsemble_BestOfFamily_1_AutoML_1_20250401_220205")
restored_model3 = h2o.load_model("./product/top_model_3/GBM_4_AutoML_1_20250401_220205")

###Test the model
perf1=restored_model1.model_performance(test_h2o)
perf2=restored_model2.model_performance(test_h2o)
perf3=restored_model3.model_performance(test_h2o)

###Get the result with different threshold

threshold = 0.5
acc = perf1.accuracy(thresholds=[threshold])[0][1]
f1 = perf1.F1(thresholds=[threshold])[0][1]
prec = perf1.precision(thresholds=[threshold])[0][1]
rec = perf1.recall(thresholds=[threshold])[0][1]
spec = perf1.specificity(thresholds=[threshold])[0][1]
print(f"Threshold = {threshold}")
print(f"Accuracy     = {acc:.4f}")
print(f"F1 Score     = {f1:.4f}")
print(f"Precision    = {prec:.4f}")
print(f"Recall       = {rec:.4f}")
print(f"Specificity  = {spec:.4f}")

threshold = 0.5
acc = perf2.accuracy(thresholds=[threshold])[0][1]
f1 = perf2.F1(thresholds=[threshold])[0][1]
prec = perf2.precision(thresholds=[threshold])[0][1]
rec = perf2.recall(thresholds=[threshold])[0][1]
spec = perf2.specificity(thresholds=[threshold])[0][1]
print(f"Threshold = {threshold}")
print(f"Accuracy     = {acc:.4f}")
print(f"F1 Score     = {f1:.4f}")
print(f"Precision    = {prec:.4f}")
print(f"Recall       = {rec:.4f}")
print(f"Specificity  = {spec:.4f}")

threshold = 0.5
acc = perf3.accuracy(thresholds=[threshold])[0][1]
f1 = perf3.F1(thresholds=[threshold])[0][1]
prec = perf3.precision(thresholds=[threshold])[0][1]
rec = perf3.recall(thresholds=[threshold])[0][1]
spec = perf3.specificity(thresholds=[threshold])[0][1]
print(f"Threshold = {threshold}")
print(f"Accuracy     = {acc:.4f}")
print(f"F1 Score     = {f1:.4f}")
print(f"Precision    = {prec:.4f}")
print(f"Recall       = {rec:.4f}")
print(f"Specificity  = {spec:.4f}")

metrics1 = {
    "AUC": perf1.auc(),
    "LogLoss": perf1.logloss(),
    "Accuracy": perf1.accuracy(),        
    "F1": perf1.F1(),
    "Precision": perf1.precision(),
    "Recall": perf1.recall(),
    "Specificity": perf1.specificity()    
}

metrics2 = {
    "AUC": perf2.auc(),
    "LogLoss": perf2.logloss(),
    "Accuracy": perf2.accuracy(),
    "F1": perf2.F1(),
    "Precision": perf2.precision(),
    "Recall": perf2.recall(),
    "Specificity": perf2.specificity()   
}

metrics3 = {
    "AUC": perf3.auc(),
    "LogLoss": perf3.logloss(),
    "Accuracy": perf3.accuracy(),
    "F1": perf3.F1(),
    "Precision": perf3.precision(),
    "Recall": perf3.recall(),
    "Specificity": perf3.specificity()   
}
###Get the figure for roc and pr
perf1.plot(type="roc")
perf1.plot(type="pr")

perf2.plot(type="roc")
perf2.plot(type="pr")

perf3.plot(type="roc")
perf3.plot(type="pr")

### SHAP analysis
restored_model3.shap_summary_plot(test_h2o[:,:-1])
fig = plt.gcf()
fig.savefig("./product/3shap_summary_plot.png", dpi=300, bbox_inches="tight")