from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report

import warnings

import pandas as pd

import joblib

import os

warnings.filterwarnings('ignore')

# Load trained model & holdout test set

try:

    model = joblib.load(open(os.path.join(os.path.dirname(__file__), '../../models/gradient_boosting.joblib'), 'rb'))

    data = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/input/test.csv'), 'r'))

except FileNotFoundError as err:

    print(f'Ann error occoured: {err}')

# Predict holdout test set

def _predict(data):

    return model.predict(data.drop('LUNG_CANCER', axis='columns'))

# Get the score metrices

def score_metrics(data, metrics):

    return metrics(data['LUNG_CANCER'], _predict(data))

# Get the class matrix

def matrix(data, matrix):

    classes = matrix(data['LUNG_CANCER'], _predict(data))

    return f'TP: {classes[1][1]} - TN: {classes[0][0]} - FP: {classes[0][1]} - FN: {classes[1][0]}'

# Get the auc (TPR/FPR)

def auc(data):

    probs = model.predict_proba(data.drop('LUNG_CANCER', axis='columns'))[:, 1] 

    return roc_auc_score(data['LUNG_CANCER'], probs)

# Get the class reports

def report(data, class_metric):

    return class_metric(data['LUNG_CANCER'], _predict(data))

if __name__ == '__main__':

   print(f'Accuracy: {score_metrics(data, accuracy_score)}')

   print(f'Precision: {score_metrics(data, precision_score)}')

   print(f'Recall: {score_metrics(data, recall_score)}')

   print(f'Measure: {score_metrics(data, f1_score)}')

   print(f'\n{matrix(data, confusion_matrix)}')

   print(f'\nAUC: {auc(data)}')

   print(f'\n{report(data, classification_report)}')