# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.

# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.                                                                              *

"""Evaluation script for measuring mean squared error."""
import json
import logging
import pathlib
import pickle
import tarfile

import numpy as np
import pandas as pd
import os
import xgboost

from sklearn.metrics import mean_squared_error

def is_within_directory(directory, target):         
    abs_directory = os.path.abspath(directory)
    abs_target = os.path.abspath(target)

    prefix = os.path.commonprefix([abs_directory, abs_target])
    
    return prefix == abs_directory

def safe_extract(tar, path="."):
    for member in tar.getmembers():
        member_path = os.path.join(path, member.name)
        if not is_within_directory(path, member_path):
            raise Exception("Attempted Path Traversal in Tar File")
    tar.extractall(path) 

logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())

if __name__ == "__main__":
    logger.debug("Starting evaluation.")
    model_path = "/opt/ml/processing/model/model.tar.gz"
    with tarfile.open(model_path) as tar: 
        safe_extract(tar, path=".")

    logger.debug("Loading xgboost model.")
    model = pickle.load(open("xgboost-model", "rb"))

    logger.debug("Reading test data.")
    test_path = "/opt/ml/processing/test/test.csv"
    df = pd.read_csv(test_path, header=None)

    logger.debug("Reading test data.")
    y_test = df.iloc[:, 0].to_numpy()
    df.drop(df.columns[0], axis=1, inplace=True)
    X_test = xgboost.DMatrix(df.values)

    logger.info("Performing predictions against test data.")
    predictions = model.predict(X_test)

    logger.debug("Calculating mean squared error.")
    mse = mean_squared_error(y_test, predictions)
    std = np.std(y_test - predictions)
    report_dict = {
        "regression_metrics": {
            "mse": {"value": mse, "standard_deviation": std},
        },
    }

    output_dir = "/opt/ml/processing/evaluation"
    pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)

    logger.info("Writing out evaluation report with mse: %f", mse)
    evaluation_path = f"{output_dir}/evaluation.json"
    with open(evaluation_path, "w") as f:
        f.write(json.dumps(report_dict))