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from sklearn.model_selection import train_test_split, StratifiedKFold |
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from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score, roc_auc_score, average_precision_score |
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from fuson_plm.utils.logging import log_update |
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import time |
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import xgboost as xgb |
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import numpy as np |
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import pandas as pd |
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def train_final_predictor(X_train, y_train, n_estimators=50,tree_method="hist"): |
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clf = xgb.XGBClassifier(n_estimators=n_estimators, tree_method=tree_method) |
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clf.fit(X_train, y_train) |
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return clf |
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def evaluate_predictor(clf,X_test,y_test,class1_thresh=None): |
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y_pred_test = clf.predict(X_test) |
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y_pred_prob_test = clf.predict_proba(X_test)[:, 1] |
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if class1_thresh is not None: y_pred_customthresh_test = np.where(np.array(y_pred_prob_test) >= class1_thresh, 1, 0) |
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accuracy = accuracy_score(y_test, y_pred_test) |
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precision = precision_score(y_test, y_pred_test) |
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recall = recall_score(y_test, y_pred_test) |
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f1 = f1_score(y_test, y_pred_test) |
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auroc_prob = roc_auc_score(y_test, y_pred_prob_test) |
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auprc_prob = average_precision_score(y_test, y_pred_prob_test) |
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auroc_label = roc_auc_score(y_test, y_pred_test) |
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auprc_label = average_precision_score(y_test, y_pred_test) |
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automatic_stats_df = pd.DataFrame(data={ |
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'Accuracy': [accuracy], |
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'Precision': [precision], |
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'Recall': [recall], |
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'F1 Score': [f1], |
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'AUROC': [auroc_prob], |
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'AUROC Label': [auroc_label], |
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'AUPRC': [auprc_prob], |
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'AUPRC Label': [auprc_label] |
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}) |
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if class1_thresh is not None: |
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accuracy_custom = accuracy_score(y_test, y_pred_customthresh_test) |
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precision_custom = precision_score(y_test, y_pred_customthresh_test) |
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recall_custom = recall_score(y_test, y_pred_customthresh_test) |
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f1_custom = f1_score(y_test, y_pred_customthresh_test) |
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auroc_prob_custom = roc_auc_score(y_test, y_pred_prob_test) |
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auprc_prob_custom = average_precision_score(y_test, y_pred_prob_test) |
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auroc_label_custom = roc_auc_score(y_test, y_pred_customthresh_test) |
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auprc_label_custom = average_precision_score(y_test, y_pred_customthresh_test) |
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custom_stats_df = pd.DataFrame(data={ |
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'Accuracy': [accuracy_custom], |
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'Precision': [precision_custom], |
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'Recall': [recall_custom], |
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'F1 Score': [f1_custom], |
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'AUROC': [auroc_prob_custom], |
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'AUROC Label': [auroc_label_custom], |
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'AUPRC': [auprc_prob_custom], |
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'AUPRC Label': [auprc_label_custom] |
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}) |
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else: |
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custom_stats_df = None |
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return automatic_stats_df, custom_stats_df |
