"""KDDCUP 99 dataset.

A classic dataset for anomaly detection.

The dataset page is available from UCI Machine Learning Repository

https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz

"""

import errno
import logging
import os
from gzip import GzipFile
from os.path import exists, join

import joblib
import numpy as np

from ..utils import Bunch, check_random_state
from ..utils import shuffle as shuffle_method
from ..utils._param_validation import StrOptions, validate_params
from . import get_data_home
from ._base import (
    RemoteFileMetadata,
    _convert_data_dataframe,
    _fetch_remote,
    load_descr,
)

# The original data can be found at:
# https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data.gz
ARCHIVE = RemoteFileMetadata(
    filename="kddcup99_data",
    url="https://ndownloader.figshare.com/files/5976045",
    checksum="3b6c942aa0356c0ca35b7b595a26c89d343652c9db428893e7494f837b274292",
)

# The original data can be found at:
# https://archive.ics.uci.edu/ml/machine-learning-databases/kddcup99-mld/kddcup.data_10_percent.gz
ARCHIVE_10_PERCENT = RemoteFileMetadata(
    filename="kddcup99_10_data",
    url="https://ndownloader.figshare.com/files/5976042",
    checksum="8045aca0d84e70e622d1148d7df782496f6333bf6eb979a1b0837c42a9fd9561",
)

logger = logging.getLogger(__name__)


@validate_params(
    {
        "subset": [StrOptions({"SA", "SF", "http", "smtp"}), None],
        "data_home": [str, os.PathLike, None],
        "shuffle": ["boolean"],
        "random_state": ["random_state"],
        "percent10": ["boolean"],
        "download_if_missing": ["boolean"],
        "return_X_y": ["boolean"],
        "as_frame": ["boolean"],
    },
    prefer_skip_nested_validation=True,
)
def fetch_kddcup99(
    *,
    subset=None,
    data_home=None,
    shuffle=False,
    random_state=None,
    percent10=True,
    download_if_missing=True,
    return_X_y=False,
    as_frame=False,
):
    """Load the kddcup99 dataset (classification).

    Download it if necessary.

    =================   ====================================
    Classes                                               23
    Samples total                                    4898431
    Dimensionality                                        41
    Features            discrete (int) or continuous (float)
    =================   ====================================

    Read more in the :ref:`User Guide <kddcup99_dataset>`.

    .. versionadded:: 0.18

    Parameters
    ----------
    subset : {'SA', 'SF', 'http', 'smtp'}, default=None
        To return the corresponding classical subsets of kddcup 99.
        If None, return the entire kddcup 99 dataset.

    data_home : str or path-like, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

        .. versionadded:: 0.19

    shuffle : bool, default=False
        Whether to shuffle dataset.

    random_state : int, RandomState instance or None, default=None
        Determines random number generation for dataset shuffling and for
        selection of abnormal samples if `subset='SA'`. Pass an int for
        reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    percent10 : bool, default=True
        Whether to load only 10 percent of the data.

    download_if_missing : bool, default=True
        If False, raise an OSError if the data is not locally available
        instead of trying to download the data from the source site.

    return_X_y : bool, default=False
        If True, returns ``(data, target)`` instead of a Bunch object. See
        below for more information about the `data` and `target` object.

        .. versionadded:: 0.20

    as_frame : bool, default=False
        If `True`, returns a pandas Dataframe for the ``data`` and ``target``
        objects in the `Bunch` returned object; `Bunch` return object will also
        have a ``frame`` member.

        .. versionadded:: 0.24

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : {ndarray, dataframe} of shape (494021, 41)
            The data matrix to learn. If `as_frame=True`, `data` will be a
            pandas DataFrame.
        target : {ndarray, series} of shape (494021,)
            The regression target for each sample. If `as_frame=True`, `target`
            will be a pandas Series.
        frame : dataframe of shape (494021, 42)
            Only present when `as_frame=True`. Contains `data` and `target`.
        DESCR : str
            The full description of the dataset.
        feature_names : list
            The names of the dataset columns
        target_names: list
            The names of the target columns

    (data, target) : tuple if ``return_X_y`` is True
        A tuple of two ndarray. The first containing a 2D array of
        shape (n_samples, n_features) with each row representing one
        sample and each column representing the features. The second
        ndarray of shape (n_samples,) containing the target samples.

        .. versionadded:: 0.20
    """
    data_home = get_data_home(data_home=data_home)
    kddcup99 = _fetch_brute_kddcup99(
        data_home=data_home,
        percent10=percent10,
        download_if_missing=download_if_missing,
    )

    data = kddcup99.data
    target = kddcup99.target
    feature_names = kddcup99.feature_names
    target_names = kddcup99.target_names

    if subset == "SA":
        s = target == b"normal."
        t = np.logical_not(s)
        normal_samples = data[s, :]
        normal_targets = target[s]
        abnormal_samples = data[t, :]
        abnormal_targets = target[t]

        n_samples_abnormal = abnormal_samples.shape[0]
        # selected abnormal samples:
        random_state = check_random_state(random_state)
        r = random_state.randint(0, n_samples_abnormal, 3377)
        abnormal_samples = abnormal_samples[r]
        abnormal_targets = abnormal_targets[r]

        data = np.r_[normal_samples, abnormal_samples]
        target = np.r_[normal_targets, abnormal_targets]

    if subset == "SF" or subset == "http" or subset == "smtp":
        # select all samples with positive logged_in attribute:
        s = data[:, 11] == 1
        data = np.c_[data[s, :11], data[s, 12:]]
        feature_names = feature_names[:11] + feature_names[12:]
        target = target[s]

        data[:, 0] = np.log((data[:, 0] + 0.1).astype(float, copy=False))
        data[:, 4] = np.log((data[:, 4] + 0.1).astype(float, copy=False))
        data[:, 5] = np.log((data[:, 5] + 0.1).astype(float, copy=False))

        if subset == "http":
            s = data[:, 2] == b"http"
            data = data[s]
            target = target[s]
            data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
            feature_names = [feature_names[0], feature_names[4], feature_names[5]]

        if subset == "smtp":
            s = data[:, 2] == b"smtp"
            data = data[s]
            target = target[s]
            data = np.c_[data[:, 0], data[:, 4], data[:, 5]]
            feature_names = [feature_names[0], feature_names[4], feature_names[5]]

        if subset == "SF":
            data = np.c_[data[:, 0], data[:, 2], data[:, 4], data[:, 5]]
            feature_names = [
                feature_names[0],
                feature_names[2],
                feature_names[4],
                feature_names[5],
            ]

    if shuffle:
        data, target = shuffle_method(data, target, random_state=random_state)

    fdescr = load_descr("kddcup99.rst")

    frame = None
    if as_frame:
        frame, data, target = _convert_data_dataframe(
            "fetch_kddcup99", data, target, feature_names, target_names
        )

    if return_X_y:
        return data, target

    return Bunch(
        data=data,
        target=target,
        frame=frame,
        target_names=target_names,
        feature_names=feature_names,
        DESCR=fdescr,
    )


def _fetch_brute_kddcup99(data_home=None, download_if_missing=True, percent10=True):
    """Load the kddcup99 dataset, downloading it if necessary.

    Parameters
    ----------
    data_home : str, default=None
        Specify another download and cache folder for the datasets. By default
        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

    download_if_missing : bool, default=True
        If False, raise an OSError if the data is not locally available
        instead of trying to download the data from the source site.

    percent10 : bool, default=True
        Whether to load only 10 percent of the data.

    Returns
    -------
    dataset : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data : ndarray of shape (494021, 41)
            Each row corresponds to the 41 features in the dataset.
        target : ndarray of shape (494021,)
            Each value corresponds to one of the 21 attack types or to the
            label 'normal.'.
        feature_names : list
            The names of the dataset columns
        target_names: list
            The names of the target columns
        DESCR : str
            Description of the kddcup99 dataset.

    """

    data_home = get_data_home(data_home=data_home)
    dir_suffix = "-py3"

    if percent10:
        kddcup_dir = join(data_home, "kddcup99_10" + dir_suffix)
        archive = ARCHIVE_10_PERCENT
    else:
        kddcup_dir = join(data_home, "kddcup99" + dir_suffix)
        archive = ARCHIVE

    samples_path = join(kddcup_dir, "samples")
    targets_path = join(kddcup_dir, "targets")
    available = exists(samples_path)

    dt = [
        ("duration", int),
        ("protocol_type", "S4"),
        ("service", "S11"),
        ("flag", "S6"),
        ("src_bytes", int),
        ("dst_bytes", int),
        ("land", int),
        ("wrong_fragment", int),
        ("urgent", int),
        ("hot", int),
        ("num_failed_logins", int),
        ("logged_in", int),
        ("num_compromised", int),
        ("root_shell", int),
        ("su_attempted", int),
        ("num_root", int),
        ("num_file_creations", int),
        ("num_shells", int),
        ("num_access_files", int),
        ("num_outbound_cmds", int),
        ("is_host_login", int),
        ("is_guest_login", int),
        ("count", int),
        ("srv_count", int),
        ("serror_rate", float),
        ("srv_serror_rate", float),
        ("rerror_rate", float),
        ("srv_rerror_rate", float),
        ("same_srv_rate", float),
        ("diff_srv_rate", float),
        ("srv_diff_host_rate", float),
        ("dst_host_count", int),
        ("dst_host_srv_count", int),
        ("dst_host_same_srv_rate", float),
        ("dst_host_diff_srv_rate", float),
        ("dst_host_same_src_port_rate", float),
        ("dst_host_srv_diff_host_rate", float),
        ("dst_host_serror_rate", float),
        ("dst_host_srv_serror_rate", float),
        ("dst_host_rerror_rate", float),
        ("dst_host_srv_rerror_rate", float),
        ("labels", "S16"),
    ]

    column_names = [c[0] for c in dt]
    target_names = column_names[-1]
    feature_names = column_names[:-1]

    if available:
        try:
            X = joblib.load(samples_path)
            y = joblib.load(targets_path)
        except Exception as e:
            raise OSError(
                "The cache for fetch_kddcup99 is invalid, please delete "
                f"{str(kddcup_dir)} and run the fetch_kddcup99 again"
            ) from e

    elif download_if_missing:
        _mkdirp(kddcup_dir)
        logger.info("Downloading %s" % archive.url)
        _fetch_remote(archive, dirname=kddcup_dir)
        DT = np.dtype(dt)
        logger.debug("extracting archive")
        archive_path = join(kddcup_dir, archive.filename)
        file_ = GzipFile(filename=archive_path, mode="r")
        Xy = []
        for line in file_.readlines():
            line = line.decode()
            Xy.append(line.replace("\n", "").split(","))
        file_.close()
        logger.debug("extraction done")
        os.remove(archive_path)

        Xy = np.asarray(Xy, dtype=object)
        for j in range(42):
            Xy[:, j] = Xy[:, j].astype(DT[j])

        X = Xy[:, :-1]
        y = Xy[:, -1]
        # XXX bug when compress!=0:
        # (error: 'Incorrect data length while decompressing[...] the file
        #  could be corrupted.')

        joblib.dump(X, samples_path, compress=0)
        joblib.dump(y, targets_path, compress=0)
    else:
        raise OSError("Data not found and `download_if_missing` is False")

    return Bunch(
        data=X,
        target=y,
        feature_names=feature_names,
        target_names=[target_names],
    )


def _mkdirp(d):
    """Ensure directory d exists (like mkdir -p on Unix)
    No guarantee that the directory is writable.
    """
    try:
        os.makedirs(d)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise