cv_metric_gen.py

"""
    Metric generation for each tuned model.
    Done in a different script for perfomance and clarity purposes.
"""

# 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.model_selection import StratifiedKFold, cross_validate
from sklearn.metrics import RocCurveDisplay, auc
from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve
import matplotlib.pyplot as plt
import ast # String to dictionary
# --------------------------------------------------------------------------------------------------------

# Function to read training datasets
# --------------------------------------------------------------------------------------------------------
def read_data():

    # 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_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 _, row in tuned_models_df.iterrows():
        model_name = row.iloc[0]
        # Read dictionary
        parameters = ast.literal_eval(row['Best 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__":

    # Setup
    # --------------------------------------------------------------------------------------------------------
    # Reading training data
    data_dic = read_data()
    # Scorings to use for cv metric generation
    scorings = {
        'F1':make_scorer(f1_score), 
        'PREC':make_scorer(precision_score), 
        'REC':make_scorer(recall_score), 
        'ACC': make_scorer(accuracy_score),
        'NREC': negative_recall_scorer, 
        'TN':TN_scorer, 
        'FN':FN_scorer, 
        'FP':FP_scorer, 
        'TP':TP_scorer,
        'AUROC': make_scorer(roc_auc_score), 
        'AUPRC': make_scorer(average_precision_score)
        } 
    method_names = {
        0: "ORIG",
        1: "ORIG_CW",
        2: "OVER",
        3: "UNDER"
    }
    # Defining cross-validation protocol
    cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=1) 
    # --------------------------------------------------------------------------------------------------------

    # Metric generation through cv for tuned models3
    # --------------------------------------------------------------------------------------------------------
    scores_sheets = {} # To store score dfs as sheets in the same excel file
    for i, group in enumerate(['pre']): # 'post'
        for j, method in enumerate(['']): # '', 'over_', 'under_'
            # 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 -> one column per cv split, one row for each model-metric
            scores_df = pd.DataFrame(columns=range(1,11), index=[f"{model_name}_{metric_name}" for model_name in models.keys() for metric_name in scorings.keys()])
            # Create a figure for all models in this group-method
            fig, axes = plt.subplots(len(models), 2, figsize=(10, 8 * len(models)))
            if len(models) == 1:  # Adjustment if there's only one model (axes indexing issue)
                axes = [axes]
            # Metric generation for each model
            for model_idx, (model_name, model) in enumerate(models.items()):
                if model_name == 'XGB':
                    print(f"{group}-{method_names[j]}-{model_name}")
                    # Retrieve cv scores for our metrics of interest
                    scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
                    # Save results of each fold
                    for metric_name in scorings.keys():
                        scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
                    # ---------------------------------------- Generate curves ----------------------------------------
                    mean_fpr = np.linspace(0, 1, 100)
                    tprs, aucs = [], []
                    mean_recall = np.linspace(0, 1, 100)
                    precisions, pr_aucs = [], []
                    cmap = plt.get_cmap('tab10')  # Colormap
                    # Loop through each fold in the cross-validation
                    for fold_idx, (train, test) in enumerate(cv.split(X_train, y_train)):
                        # Fit the model on the training data
                        model.fit(X_train[train], y_train[train])
                        # Generate ROC curve for the fold
                        roc_display = RocCurveDisplay.from_estimator(model, X_train[test], y_train[test],
                                                                    name=f"ROC fold {fold_idx}", alpha=0.6, lw=2,
                                                                    ax=axes[model_idx][0], color=cmap(fold_idx % 10))
                        interp_tpr = np.interp(mean_fpr, roc_display.fpr, roc_display.tpr)
                        interp_tpr[0] = 0.0
                        tprs.append(interp_tpr)
                        aucs.append(roc_display.roc_auc)
                        # Generate Precision-Recall curve for the fold
                        pr_display = PrecisionRecallDisplay.from_estimator(model, X_train[test], y_train[test],
                                                                        name=f"PR fold {fold_idx}", alpha=0.6, lw=2,
                                                                        ax=axes[model_idx][1], color=cmap(fold_idx % 10))
                        interp_precision = np.interp(mean_recall, pr_display.recall[::-1], pr_display.precision[::-1])
                        precisions.append(interp_precision)
                        pr_aucs.append(pr_display.average_precision)
                    # Plot diagonal line for random guessing in ROC curve
                    axes[model_idx][0].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8, label='Random guessing')
                    # Compute mean ROC curve
                    mean_tpr = np.mean(tprs, axis=0)
                    mean_tpr[-1] = 1.0
                    mean_auc = auc(mean_fpr, mean_tpr)
                    axes[model_idx][0].plot(mean_fpr, mean_tpr, color='b', lw=4, label=r'Mean ROC (AUC = %0.2f)' % mean_auc, alpha=.8)
                    # Set ROC plot limits and title
                    axes[model_idx][0].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"ROC Curve - {model_name} ({group}-{method_names[j]})")
                    axes[model_idx][0].legend(loc="lower right")
                    # Compute mean Precision-Recall curve
                    mean_precision = np.mean(precisions, axis=0)
                    mean_pr_auc = np.mean(pr_aucs)
                    axes[model_idx][1].plot(mean_recall, mean_precision, color='b', lw=4, label=r'Mean PR (AUC = %0.2f)' % mean_pr_auc, alpha=.8)
                    # Plot baseline precision (proportion of positive samples)
                    baseline = np.sum(y_train) / len(y_train)
                    axes[model_idx][1].plot([0, 1], [baseline, baseline], linestyle='--', lw=2, color='r', alpha=.8, label='Baseline')
                    # Set Precision-Recall plot limits and title
                    axes[model_idx][1].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"Precision-Recall Curve - {model_name} ({group}-{method_names[j]})")
                    axes[model_idx][1].legend(loc="lower right")
                    # ---------------------------------------- End Generate Curves  ----------------------------------------
            # 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
            # Adjust layout and save figure
            plt.tight_layout()
            plt.savefig(f'./output_cv_metrics/curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
            plt.close(fig)
    # Write results to Excel file
    with pd.ExcelWriter('./output_cv_metrics/metrics.xlsx') as writer:
        for sheet_name, data in scores_sheets.items():
            data.to_excel(writer, sheet_name=sheet_name)
    print("Successful cv metric generation for tuned models")