'C': 0.001, 'verbose': 0 }) glm_anova.fit(X_train_anova_p, y_train_p) glm_anova_raw_preds, glm_anova_preds, glm_anova_score = glm_anova.predict( X_test_anova_p, y_test_p) glm_anova_fig = Classifiers.metrics(y_test_p, glm_anova_raw_preds[:, 1], glm_anova_preds) ################################ ######## XGB ROC Curves ######## xg_total = Classifiers.XGBoostModel(params={ 'booster': 'gbtree', 'eta': 0.3, 'max_depth': 6 }) xg_total.fit(X_train_p, y_train_p) xg_total_raw_preds, xg_total_preds, xg_total_score = xg_total.predict( X_test_p, y_test_p) xg_total_fig = Classifiers.metrics(y_test_p, xg_total_raw_preds[:, 1], xg_total_preds) xg_vt = Classifiers.XGBoostModel(params={ 'booster': 'gbtree', 'eta': 0.3, 'max_depth': 6 })
# 'C' : 0.001, "verbose": 0, } ) glm_anova.fit(X_train_p, y_train_p) glm_anova_raw_preds, glm_anova_preds, glm_anova_score = glm_anova.predict( X_test_p, y_test_p ) auc_glm, pr_auc_glm, fpr_glm, tpr_glm, roc_thresholds_glm, recalls_glm, precisions_glm = get_auc_pr( y_test_p, glm_anova_raw_preds ) xgb = Classifiers.XGBoostModel(params={**best_params_xg}) xgb.fit(X_train_p, y_train_p) xgb_raw_preds, xgb_preds, xgb_score = xgb.predict(X_test_p, y_test_p) auc_xgb, pr_auc_xgb, fpr_xgb, tpr_xgb, roc_thresholds_xgb, recalls_xgb, precisions_xgb = get_auc_pr( y_test_p, xgb_raw_preds ) recall_no_skill = y_train_p.sum() / len(y_train_p) optimal_preds, threshold = optimal_points(fpr, tpr, rf_anova_raw_preds, roc_thresholds) optimal_preds_glm, threshold_glm = optimal_points( fpr_glm, tpr_glm, glm_anova_raw_preds, roc_thresholds_glm
# Save model to disk filename = 'S:/Dehydration_stroke/Team Emerald/Working GitHub Directories/'\ 'Michael/stroke-hemodynamics/Aim 2/Models/FullModelResults/UpdatedResults/TEST24hr_model_rf.sav' with open(filename, 'rb') as f: rf = pickle.load(f) rf_raw_preds, rf_preds, rf_score = rf.predict(X_test_p, y_test_p) auc_rf, pr_auc_rf, fpr, tpr, roc_thresholds, recalls, precisions = get_auc_pr( y_test_p, rf_raw_preds ) xgb = Classifiers.XGBoostModel( params={"booster": "gblinear", "eta": 0.1, "max_depth": 8} ) xgb.fit(X_train_p, y_train_p) # Save model to disk filename = 'S:/Dehydration_stroke/Team Emerald/Working GitHub Directories/'\ 'Michael/stroke-hemodynamics/Aim 2/Models/FullModelResults/UpdatedResults/TEST24hr_model_xgb.sav' with open(filename, 'rb') as f: xgb = pickle.load(f) xgb_raw_preds, xgb_preds, xgb_score = xgb.predict(X_test_p, y_test_p) auc_xgb, pr_auc_xgb, fpr_xgb, tpr_xgb, roc_thresholds_xgb, recalls_xgb, precisions_xgb = get_auc_pr( y_test_p, xgb_raw_preds )