"""
Base IO code for all datasets
"""

# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
#               2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
#               2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from collections import namedtuple
from os import environ, listdir, makedirs
from os.path import dirname, exists, expanduser, isdir, join, splitext
import hashlib

from ..utils import Bunch
from ..utils import check_random_state
from ..utils import check_pandas_support
from ..utils.validation import _deprecate_positional_args

import numpy as np

from urllib.request import urlretrieve

RemoteFileMetadata = namedtuple('RemoteFileMetadata',
                                ['filename', 'url', 'checksum'])


def get_data_home(data_home=None):
    """Return the path of the scikit-learn data dir.

    This folder is used by some large dataset loaders to avoid downloading the
    data several times.

    By default the data dir is set to a folder named 'scikit_learn_data' in the
    user home folder.

    Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment
    variable or programmatically by giving an explicit folder path. The '~'
    symbol is expanded to the user home folder.

    If the folder does not already exist, it is automatically created.

    Parameters
    ----------
    data_home : str | None
        The path to scikit-learn data dir.
    """
    if data_home is None:
        data_home = environ.get('SCIKIT_LEARN_DATA',
                                join('~', 'scikit_learn_data'))
    data_home = expanduser(data_home)
    if not exists(data_home):
        makedirs(data_home)
    return data_home


def clear_data_home(data_home=None):
    """Delete all the content of the data home cache.

    Parameters
    ----------
    data_home : str | None
        The path to scikit-learn data dir.
    """
    data_home = get_data_home(data_home)
    shutil.rmtree(data_home)


def _convert_data_dataframe(caller_name, data, target,
                            feature_names, target_names):
    pd = check_pandas_support('{} with as_frame=True'.format(caller_name))
    data_df = pd.DataFrame(data, columns=feature_names)
    target_df = pd.DataFrame(target, columns=target_names)
    combined_df = pd.concat([data_df, target_df], axis=1)
    X = combined_df[feature_names]
    y = combined_df[target_names]
    if y.shape[1] == 1:
        y = y.iloc[:, 0]
    return combined_df, X, y


@_deprecate_positional_args
def load_files(container_path, *, description=None, categories=None,
               load_content=True, shuffle=True, encoding=None,
               decode_error='strict', random_state=0):
    """Load text files with categories as subfolder names.

    Individual samples are assumed to be files stored a two levels folder
    structure such as the following:

        container_folder/
            category_1_folder/
                file_1.txt
                file_2.txt
                ...
                file_42.txt
            category_2_folder/
                file_43.txt
                file_44.txt
                ...

    The folder names are used as supervised signal label names. The individual
    file names are not important.

    This function does not try to extract features into a numpy array or scipy
    sparse matrix. In addition, if load_content is false it does not try to
    load the files in memory.

    To use text files in a scikit-learn classification or clustering algorithm,
    you will need to use the :mod`~sklearn.feature_extraction.text` module to
    build a feature extraction transformer that suits your problem.

    If you set load_content=True, you should also specify the encoding of the
    text using the 'encoding' parameter. For many modern text files, 'utf-8'
    will be the correct encoding. If you leave encoding equal to None, then the
    content will be made of bytes instead of Unicode, and you will not be able
    to use most functions in :mod:`~sklearn.feature_extraction.text`.

    Similar feature extractors should be built for other kind of unstructured
    data input such as images, audio, video, ...

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

    Parameters
    ----------
    container_path : string or unicode
        Path to the main folder holding one subfolder per category

    description : string or unicode, optional (default=None)
        A paragraph describing the characteristic of the dataset: its source,
        reference, etc.

    categories : A collection of strings or None, optional (default=None)
        If None (default), load all the categories. If not None, list of
        category names to load (other categories ignored).

    load_content : bool, optional (default=True)
        Whether to load or not the content of the different files. If true a
        'data' attribute containing the text information is present in the data
        structure returned. If not, a filenames attribute gives the path to the
        files.

    shuffle : bool, optional (default=True)
        Whether or not to shuffle the data: might be important for models that
        make the assumption that the samples are independent and identically
        distributed (i.i.d.), such as stochastic gradient descent.

    encoding : string or None (default is None)
        If None, do not try to decode the content of the files (e.g. for images
        or other non-text content). If not None, encoding to use to decode text
        files to Unicode if load_content is True.

    decode_error : {'strict', 'ignore', 'replace'}, optional
        Instruction on what to do if a byte sequence is given to analyze that
        contains characters not of the given `encoding`. Passed as keyword
        argument 'errors' to bytes.decode.

    random_state : int, RandomState instance or None, default=0
        Determines random number generation for dataset shuffling. Pass an int
        for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

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

        data : list of str
            Only present when `load_content=True`.
            The raw text data to learn.
        target : ndarray
            The target labels (integer index).
        target_names : list
            The names of target classes.
        DESCR : str
            The full description of the dataset.
        filenames: ndarray
            The filenames holding the dataset.
    """
    target = []
    target_names = []
    filenames = []

    folders = [f for f in sorted(listdir(container_path))
               if isdir(join(container_path, f))]

    if categories is not None:
        folders = [f for f in folders if f in categories]

    for label, folder in enumerate(folders):
        target_names.append(folder)
        folder_path = join(container_path, folder)
        documents = [join(folder_path, d)
                     for d in sorted(listdir(folder_path))]
        target.extend(len(documents) * [label])
        filenames.extend(documents)

    # convert to array for fancy indexing
    filenames = np.array(filenames)
    target = np.array(target)

    if shuffle:
        random_state = check_random_state(random_state)
        indices = np.arange(filenames.shape[0])
        random_state.shuffle(indices)
        filenames = filenames[indices]
        target = target[indices]

    if load_content:
        data = []
        for filename in filenames:
            with open(filename, 'rb') as f:
                data.append(f.read())
        if encoding is not None:
            data = [d.decode(encoding, decode_error) for d in data]
        return Bunch(data=data,
                     filenames=filenames,
                     target_names=target_names,
                     target=target,
                     DESCR=description)

    return Bunch(filenames=filenames,
                 target_names=target_names,
                 target=target,
                 DESCR=description)


def load_data(module_path, data_file_name):
    """Loads data from module_path/data/data_file_name.

    Parameters
    ----------
    module_path : string
        The module path.

    data_file_name : string
        Name of csv file to be loaded from
        module_path/data/data_file_name. For example 'wine_data.csv'.

    Returns
    -------
    data : Numpy array
        A 2D array with each row representing one sample and each column
        representing the features of a given sample.

    target : Numpy array
        A 1D array holding target variables for all the samples in `data.
        For example target[0] is the target varible for data[0].

    target_names : Numpy array
        A 1D array containing the names of the classifications. For example
        target_names[0] is the name of the target[0] class.
    """
    with open(join(module_path, 'data', data_file_name)) as csv_file:
        data_file = csv.reader(csv_file)
        temp = next(data_file)
        n_samples = int(temp[0])
        n_features = int(temp[1])
        target_names = np.array(temp[2:])
        data = np.empty((n_samples, n_features))
        target = np.empty((n_samples,), dtype=np.int)

        for i, ir in enumerate(data_file):
            data[i] = np.asarray(ir[:-1], dtype=np.float64)
            target[i] = np.asarray(ir[-1], dtype=np.int)

    return data, target, target_names


@_deprecate_positional_args
def load_wine(*, return_X_y=False, as_frame=False):
    """Load and return the wine dataset (classification).

    .. versionadded:: 0.18

    The wine dataset is a classic and very easy multi-class classification
    dataset.

    =================   ==============
    Classes                          3
    Samples per class        [59,71,48]
    Samples total                  178
    Dimensionality                  13
    Features            real, positive
    =================   ==============

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

    Parameters
    ----------
    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.

    as_frame : bool, default=False
        If True, the data is a pandas DataFrame including columns with
        appropriate dtypes (numeric). The target is
        a pandas DataFrame or Series depending on the number of target columns.
        If `return_X_y` is True, then (`data`, `target`) will be pandas
        DataFrames or Series as described below.

        .. versionadded:: 0.23

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

        data : {ndarray, dataframe} of shape (178, 13)
            The data matrix. If `as_frame=True`, `data` will be a pandas
            DataFrame.
        target: {ndarray, Series} of shape (178,)
            The classification target. If `as_frame=True`, `target` will be
            a pandas Series.
        feature_names: list
            The names of the dataset columns.
        target_names: list
            The names of target classes.
        frame: DataFrame of shape (178, 14)
            Only present when `as_frame=True`. DataFrame with `data` and
            `target`.

            .. versionadded:: 0.23
        DESCR: str
            The full description of the dataset.

    (data, target) : tuple if ``return_X_y`` is True

    The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit
    standard format from:
    https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

    Examples
    --------
    Let's say you are interested in the samples 10, 80, and 140, and want to
    know their class name.

    >>> from sklearn.datasets import load_wine
    >>> data = load_wine()
    >>> data.target[[10, 80, 140]]
    array([0, 1, 2])
    >>> list(data.target_names)
    ['class_0', 'class_1', 'class_2']