test_models.py

"""
    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
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
# --------------------------------------------------------------------------------------------------------

# Reading test data
# --------------------------------------------------------------------------------------------------------
def read_test_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)

    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,
    }

    return data_dic
# --------------------------------------------------------------------------------------------------------

# Returning tuned models for each situation
# --------------------------------------------------------------------------------------------------------
def get_tuned_models(group_id, method_id):
    # 1. PRE
    if group_id == 0:
        # 1.1) Trained with original dataset
        if method_id == 0:
            tuned_models = {
            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 117}), 
            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 23, 'warm_start': True}),
            "AB" : AdaBoostClassifier(**{'learning_rate': 1.9189147333140566, 'n_estimators': 131, 'algorithm': 'SAMME'}), 
            "XGB": XGBClassifier(**{'learning_rate': 0.22870029177880222, 'max_depth': 8, 'n_estimators': 909}),
            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
            "SVM" : SVC(**{'C': 0.9872682949695772, 'kernel': 'linear', 'max_iter':1000}), 
            "MLP" : MLPClassifier(**{'activation': 'identity', 'hidden_layer_sizes': 122, 'learning_rate': 'invscaling', 'max_iter':500})
            }
        # 1.2) Trained with original dataset and cost-sensitive learning
        elif method_id == 1:
            tuned_models = {
            "DT": DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'entropy', 'class_weight': 'balanced'}),
            "RF": RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 118, 'class_weight': 'balanced'}),
            "Bagging": BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'warm_start': False, 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
            "AB": AdaBoostClassifier(**{'learning_rate': 0.8159074545140872, 'n_estimators': 121, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
            "LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'none', 'max_iter': 1000, 'class_weight': 'balanced'}),
            "SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced'}),
            }

        # 1.3) Trained with oversampled training dataset
        elif method_id == 2:
            tuned_models = {
            "DT" : DecisionTreeClassifier(**{'splitter': 'random', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 135}), 
            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 26, 'warm_start': True}),
            "AB" : AdaBoostClassifier(**{'learning_rate': 1.6590924545876917, 'n_estimators': 141, 'algorithm': 'SAMME'}), 
            "XGB": XGBClassifier(**{'learning_rate': 0.26946295284728783, 'max_depth': 7, 'n_estimators': 893}),
            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
            "SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000}), 
            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
            }
        # 1.4) Trained with undersampled training dataset
        elif method_id == 3:
            tuned_models = {
            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 104}), 
            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 38, 'warm_start': True}),
            "AB" : AdaBoostClassifier(**{'learning_rate': 1.6996764264041269, 'n_estimators': 93, 'algorithm': 'SAMME'}), 
            "XGB": XGBClassifier(**{'learning_rate': 0.26480707899668926, 'max_depth': 7, 'n_estimators': 959}),
            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
            "SVM" : SVC(**{'C': 1.1996501173654208, 'kernel': 'poly', 'max_iter':1000}), 
            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 131, 'learning_rate': 'constant', 'max_iter':500})
            }
    # 2. POST
    else:
        # 2.1) Trained with original dataset
        if method_id == 0:
            tuned_models = {
            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'gini'}), 
            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 213}), 
            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 32, 'warm_start': True}),
            "AB" : AdaBoostClassifier(**{'learning_rate': 1.7806904141367559, 'n_estimators': 66, 'algorithm': 'SAMME'}), 
            "XGB": XGBClassifier(**{'learning_rate': 0.21889089898592098, 'max_depth': 6, 'n_estimators': 856}),
            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
            "SVM" : SVC(**{'C': 1.9890638540240584, 'kernel': 'linear', 'max_iter':1000}), 
            "MLP" : MLPClassifier(**{'activation': 'logistic', 'hidden_layer_sizes': 112, 'learning_rate': 'constant', 'max_iter':500})
            }
        # 2.2) Trained with original dataset and cost-sensitive learning
        elif method_id == 1:
            ...
        # 2.3) Trained with oversampled training dataset
        elif method_id == 2:
            ...
        # 2.4) Trained with undersampled training dataset
        elif method_id == 3:
            ...
    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 testing data
    data_dic = read_test_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 and AUPRC (plot?)
    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 = data_dic['X_test' + group]
        y = data_dic['y_test' + group]
        for j, method in enumerate(['', '', 'over_', 'under_']):
            # Get tuned models for this group and method
            models = get_tuned_models(group_id=i, method_id=j)
            # Scores df
            scores_df = pd.DataFrame(index=models.keys(), columns=scorings.keys())
            # Evaluate each model
            for model_name, model in models.items():
                # At each of the scores of interest
                for score_name, scorer in scorings.items():
                    score_value = scorer(model, X, y)
                    scores_df.at[model_name, score_name] = score_value
            # 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('./training_models/output/testing_tuned_models.xlsx') as writer:
        for sheet_name, data in scores_sheets.items():
            data.to_excel(writer, sheet_name=sheet_name)
    # --------------------------------------------------------------------------------------------------------