Datasets:
File size: 3,850 Bytes
edad6ca a61f6bb edad6ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
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")
|