""" Evaluating optimized models with test data """ # Libraries # -------------------------------------------------------------------------------------------------------- import pandas as pd import numpy as np from xgboost import XGBClassifier from sklearn.metrics import confusion_matrix from sklearn.metrics import f1_score, make_scorer, precision_score, recall_score, accuracy_score, roc_auc_score, average_precision_score from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier from sklearn.neural_network import MLPClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import RocCurveDisplay, roc_curve from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay import ast # String to dictionary # -------------------------------------------------------------------------------------------------------- # Reading data # -------------------------------------------------------------------------------------------------------- def read_data(): # Load test data X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True) y_test_pre = np.load('../gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True) X_test_post = np.load('../gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True) y_test_post = np.load('../gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True) # Load ORIGINAL training data X_train_pre = np.load('../gen_train_data/data/output/pre/X_train_pre.npy', allow_pickle=True) y_train_pre = np.load('../gen_train_data/data/output/pre/y_train_pre.npy', allow_pickle=True) X_train_post = np.load('../gen_train_data/data/output/post/X_train_post.npy', allow_pickle=True) y_train_post = np.load('../gen_train_data/data/output/post/y_train_post.npy', allow_pickle=True) # Load oversampled training data X_train_over_pre = np.load('../gen_train_data/data/output/pre/X_train_over_pre.npy', allow_pickle=True) y_train_over_pre = np.load('../gen_train_data/data/output/pre/y_train_over_pre.npy', allow_pickle=True) X_train_over_post = np.load('../gen_train_data/data/output/post/X_train_over_post.npy', allow_pickle=True) y_train_over_post = np.load('../gen_train_data/data/output/post/y_train_over_post.npy', allow_pickle=True) # Load undersampled training data X_train_under_pre = np.load('../gen_train_data/data/output/pre/X_train_under_pre.npy', allow_pickle=True) y_train_under_pre = np.load('../gen_train_data/data/output/pre/y_train_under_pre.npy', allow_pickle=True) X_train_under_post = np.load('../gen_train_data/data/output/post/X_train_under_post.npy', allow_pickle=True) y_train_under_post = np.load('../gen_train_data/data/output/post/y_train_under_post.npy', allow_pickle=True) data_dic = { "X_test_pre": X_test_pre, "y_test_pre": y_test_pre, "X_test_post": X_test_post, "y_test_post": y_test_post, "X_train_pre": X_train_pre, "y_train_pre": y_train_pre, "X_train_post": X_train_post, "y_train_post": y_train_post, "X_train_over_pre": X_train_over_pre, "y_train_over_pre": y_train_over_pre, "X_train_over_post": X_train_over_post, "y_train_over_post": y_train_over_post, "X_train_under_pre": X_train_under_pre, "y_train_under_pre": y_train_under_pre, "X_train_under_post": X_train_under_post, "y_train_under_post": y_train_under_post, } return data_dic # -------------------------------------------------------------------------------------------------------- # Returning tuned models for each situation # -------------------------------------------------------------------------------------------------------- def get_tuned_models(group_str, method_str): # Read sheet corresponding to group and method with tuned models and their hyperparam tuned_models_df = pd.read_excel("./output_hyperparam/hyperparamers.xlsx",sheet_name=f"{group_str}_{method_str}") # Mapping from model abbreviations to sklearn model classes model_mapping = { 'DT': DecisionTreeClassifier, 'RF': RandomForestClassifier, 'Bagging': BaggingClassifier, 'AB': AdaBoostClassifier, 'XGB': XGBClassifier, 'LR': LogisticRegression, 'SVM': SVC, 'MLP': MLPClassifier } tuned_models = {} # Iterate through each row of the DataFrame for index, row in tuned_models_df.iterrows(): model_name = row[0] # Read dictionary parameters = ast.literal_eval(row['Parameters']) # Add extra parameters if model_name == 'AB': parameters['algorithm'] = 'SAMME' elif model_name == 'LR': parameters['max_iter'] = 1000 elif model_name == 'SVM': parameters['max_iter'] = 1000 parameters['probability'] = True elif model_name == "MLP": parameters['max_iter'] = 500 # Add class_weight argument for cost-sensitive learning method if 'CW' in method_str: if model_name == 'Bagging' or model_name == 'AB': parameters['estimator'] = DecisionTreeClassifier(class_weight='balanced') else: parameters['class_weight'] = 'balanced' # Fetch class model_class = model_mapping[model_name] # Initialize model tuned_models[model_name] = model_class(**parameters) return tuned_models # -------------------------------------------------------------------------------------------------------- # Scorers # -------------------------------------------------------------------------------------------------------- def TN_scorer(clf, X, y): """Gives the number of samples predicted as true negatives""" y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) TN = cm[0,0] return TN def FN_scorer(clf, X, y): """Gives the number of samples predicted as false negatives""" y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) FN = cm[0,1] return FN def FP_scorer(clf, X, y): """Gives the number of samples predicted as false positive""" y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) FP = cm[1,0] return FP def TP_scorer(clf, X, y): """Gives the number of samples predicted as true positive""" y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) TP = cm[1,1] return TP def negative_recall_scorer(clf, X, y): """Gives the negative recall defined as the (number of true_negative_samples)/(total number of negative samples)""" y_pred = clf.predict(X) cm = confusion_matrix(y, y_pred) TN_prop = cm[0,0]/(cm[0,1]+cm[0,0]) return TN_prop # -------------------------------------------------------------------------------------------------------- if __name__ == "__main__": # Reading data data_dic = read_data() # Setup # -------------------------------------------------------------------------------------------------------- # Scorings to use for model evaluation scorings = { 'F1':make_scorer(f1_score), 'NREC': negative_recall_scorer, 'REC':make_scorer(recall_score), 'PREC':make_scorer(precision_score), 'ACC': make_scorer(accuracy_score), 'TN':TN_scorer, 'FN':FN_scorer, 'FP':FP_scorer, 'TP':TP_scorer, 'AUROC': make_scorer(roc_auc_score), # AUROC requires decision function or probability outputs 'AUPRC': make_scorer(average_precision_score) # AUPRC requires probability outputs } method_names = { 0: "ORIG", 1: "ORIG_CW", 2: "OVER", 3: "UNDER" } # -------------------------------------------------------------------------------------------------------- # Evaluating performance using test dataset # -------------------------------------------------------------------------------------------------------- scores_sheets = {} # To store score dfs as sheets in the same excel file for i, group in enumerate(['pre', 'post']): # Get test dataset based on group X_test = data_dic['X_test_' + group] y_test = data_dic['y_test_' + group] for j, method in enumerate(['', '', 'over_', 'under_']): print(f"{group}-{method_names[j]}") # Get train dataset based on group and method X_train = data_dic['X_train_' + method + group] y_train = data_dic['y_train_' + method + group] # Get tuned models for this group and method models = get_tuned_models(group, method_names[j]) # Scores df scores_df = pd.DataFrame(index=models.keys(), columns=scorings.keys()) # Create a figure for all models in this group-method fig, axes = plt.subplots(len(models), 3, figsize=(10, 8 * len(models))) if len(models) == 1: # Adjustment if there's only one model (axes indexing issue) axes = [axes] # Evaluate each model for model_idx, (model_name, model) in enumerate(models.items()): # ----------- TEMPORAL ------------- # Train the model (it was just initialized above) model.fit(X_train, y_train) if hasattr(model, "decision_function"): y_score = model.decision_function(X_test) else: y_score = model.predict_proba(X_test)[:, 1] # Use probability of positive class # Calculate ROC curve and ROC area for each class fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=model.classes_[1]) roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot(ax=axes[model_idx][0]) # Calculate precision-recall curve precision, recall, _ = precision_recall_curve(y_test, y_score, pos_label=model.classes_[1]) pr_display = PrecisionRecallDisplay(precision=precision, recall=recall).plot(ax=axes[model_idx][1]) # Get confusion matrix plot y_pred = model.predict(X_test) cm = confusion_matrix(y_test, y_pred) ConfusionMatrixDisplay(cm).plot(ax=axes[model_idx][2]) # Give name to plots axes[model_idx][0].set_title(f'ROC Curve for {model_name}') axes[model_idx][1].set_title(f'PR Curve for {model_name}') axes[model_idx][2].set_title(f'CM for {model_name}') # Evaluate at each of the scores of interest for score_name, scorer in scorings.items(): score_value = scorer(model, X_test, y_test) scores_df.at[model_name, score_name] = score_value # Adjust layout and save/show figure plt.tight_layout() plt.savefig(f'./test_results/aux_plots/{group}_{method_names[j]}.svg', format='svg', dpi=500) plt.close(fig) # Store the DataFrame in the dictionary with a unique key for each sheet sheet_name = f"{group}_{method_names[j]}" scores_sheets[sheet_name] = scores_df # Write results to Excel file with pd.ExcelWriter('./test_results/testing_tuned_models.xlsx') as writer: for sheet_name, data in scores_sheets.items(): data.to_excel(writer, sheet_name=sheet_name) # --------------------------------------------------------------------------------------------------------