# Copyright 2019-2026 The AmpliGraph Authors. All Rights Reserved.
#
# This file is Licensed under the Apache License, Version 2.0.
# A copy of the Licence is available in LICENCE, or at:
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
import json
import logging
import os
from collections import namedtuple

from ampligraph.utils.file_utils import _get_data_home, _fetch_file

import numpy as np
import pandas as pd

AMPLIGRAPH_ENV_NAME = "AMPLIGRAPH_DATA_HOME"

DatasetMetadata = namedtuple(
    "DatasetMetadata",
    [
        "dataset_name",
        "filename",
        "url",
        "train_name",
        "valid_name",
        "test_name",
        "train_checksum",
        "valid_checksum",
        "test_checksum",
        "test_human_name",
        "test_human_checksum",
        "test_human_ids_name",
        "test_human_ids_checksum",
        "mapper_name",
        "mapper_checksum",
        "valid_negatives_name",
        "valid_negatives_checksum",
        "test_negatives_name",
        "test_negatives_checksum",
    ],
    defaults=(None, None, None, None, None, None, None, None, None, None),
)

logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)


def _clean_data(X, return_idx=False):
    """
    Clean dataset **X** by removing unseen entities and relations from valid and test sets.

    Parameters
    ----------
    X: dict
        Dictionary containing the following keys: `train`, `valid`, `test`.
        Each key should contain a ndarray of shape [n, m] with m >= 3
        (> if weights are included in the array).

    return_idx: bool
        Whether to return the indices of the remaining rows in valid and test respectively.

    Returns
    -------
    filtered_X: dict
        Dictionary containing the following keys: `train`, `valid`, `test`.
        Each key contains a ndarray of shape [n, m].
        Valid and test do not contain entities or relations that are not present in train.

    valid_idx: ndarray
        Indices of the remaining rows of the `valid` dataset (with respect to the original `valid` ndarray).

    test_idx: ndarray
        Indices of the remaining rows of the `test` dataset (with respect to the original `test` ndarray).

    """
    filtered_X = {}
    train = pd.DataFrame(X["train"][:,:3], columns=["s", "p", "o"])
    filtered_X["train"] = X["train"]

    valid = pd.DataFrame(X["valid"][:,:3], columns=["s", "p", "o"])
    test = pd.DataFrame(X["test"][:,:3], columns=["s", "p", "o"])

    train_ent = np.unique(np.concatenate((train.s, train.o)))
    train_rel = train.p.unique()

    if "valid_negatives" in X:
        valid_negatives = pd.DataFrame(
            X["valid_negatives"][:,:3], columns=["s", "p", "o"]
        )
        valid_negatives_idx = (
            valid_negatives.s.isin(train_ent)
            & valid_negatives.o.isin(train_ent)
            & valid_negatives.p.isin(train_rel)
        )
        filtered_valid_negatives = valid_negatives[valid_negatives_idx].values
        filtered_X["valid_negatives"] = filtered_valid_negatives
    if "test_negatives" in X:
        test_negatives = pd.DataFrame(
            X["test_negatives"][:,:3], columns=["s", "p", "o"]
        )
        test_negatives_idx = (
            test_negatives.s.isin(train_ent)
            & test_negatives.o.isin(train_ent)
            & test_negatives.p.isin(train_rel)
        )
        filtered_test_negatives = test[test_negatives_idx].values
        filtered_X["test_negatives"] = filtered_test_negatives

    valid_idx = (
        valid.s.isin(train_ent)
        & valid.o.isin(train_ent)
        & valid.p.isin(train_rel)
    )
    test_idx = (
        test.s.isin(train_ent)
        & test.o.isin(train_ent)
        & test.p.isin(train_rel)
    )

    # filtered_valid = valid[valid_idx].values
    # filtered_test = test[test_idx].values
    filtered_valid = X["valid"][valid_idx]
    filtered_test = X["test"][test_idx]

    filtered_X["valid"] = filtered_valid
    filtered_X["test"] = filtered_test

    if "mapper" in X:
        filtered_X["mapper"] = X["mapper"]
    if "test-human" in X and "test-human-ids" in X:
        filtered_X["test-human"] = X["test-human"]
        filtered_X["test-human-ids"] = X["test-human-ids"]

    if return_idx:
        return filtered_X, valid_idx, test_idx
    else:
        return filtered_X


def _add_reciprocal_relations(triples_df):
    """Add reciprocal relations to the triples

    Parameters
    ----------

    triples_df: Dataframe
        Dataframe of triples.

    Returns
    -------
    triples_df: Dataframe
        Dataframe of triples and their reciprocals.
    """
    # create a copy of the original triples to add reciprocal relations
    df_reciprocal = triples_df.copy()

    # swap subjects and objects
    cols = list(df_reciprocal.columns)
    cols[0], cols[2] = cols[2], cols[0]
    df_reciprocal.columns = cols

    # add reciprocal relations
    df_reciprocal.iloc[:, 1] = df_reciprocal.iloc[:, 1] + "_reciprocal"

    # append to original triples
    triples_df = pd.concat([triples_df, df_reciprocal])

    return triples_df


def load_from_csv(
    directory_path, file_name, sep="\t", header=None, add_reciprocal_rels=False
):
    """Load a knowledge graph from a .csv file.

    Loads a knowledge graph serialized in a .csv file filtering duplicated statements. In the .csv file, each line
    has to represent a triple, and entities and relations are separated by ``sep``.
    For instance, if ``sep="\\t"``, the .csv file look like:

    .. code-block:: text

       subj1    relationX   obj1
       subj1    relationY   obj2
       subj3    relationZ   obj2
       subj4    relationY   obj2
                  ...

    .. hint::
        To split a generic knowledge graphs into **training**, **validation**, and **test** sets do not use the above
        function, but rather :meth:`~ampligraph.evaluation.protocol.train_test_split_no_unseen`: this will return
        validation and test sets not including triples with entities not present in the training set.


    Parameters
    ----------

    directory_path: str
        Folder where the input file is stored.
    file_name: str
        File name.
    sep: str
        The subject-predicate-object separator (default: ``"\\t"``).
    header: int or None
        The row of the header of the csv file. Same as pandas.read_csv header param.
    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).


    Returns
    -------
    triples: ndarray, shape (n, 3)
        The actual triples of the file.

    Example
    -------
    >>> PATH_TO_FOLDER = 'your/path/to/folder/'
    >>> from ampligraph.datasets import load_from_csv
    >>> X = load_from_csv(PATH_TO_FOLDER, 'dataset.csv', sep=',')
    >>> X[:3]
    array([['a', 'y', 'b'],
           ['b', 'y', 'a'],
           ['a', 'y', 'c']],
          dtype='<U1')

    """

    logger.debug("Loading data from {}.".format(file_name))
    df = pd.read_csv(
        os.path.join(directory_path, file_name),
        sep=sep,
        header=header,
        names=None,
        dtype=str,
    )
    logger.debug("Dropping duplicates.")
    df = df.drop_duplicates()
    if add_reciprocal_rels:
        df = _add_reciprocal_relations(df)

    return df.values


def _load_dataset(
    dataset_metadata,
    data_home=None,
    check_md5hash=False,
    add_reciprocal_rels=False,
):
    """Load a dataset from the details provided.

    DatasetMetadata = namedtuple('DatasetMetadata', ['dataset_name', 'filename', 'url', 'train_name', 'valid_name',
                                                     'test_name', 'train_checksum', 'valid_checksum', 'test_checksum'])

    Parameters
    ----------
    dataset_metadata: DatasetMetadata
        Named tuple containing remote datasets meta information: dataset name, dataset filename,
        url, train filename, validation filename, test filename, train checksum, valid checksum, test checksum.

    data_home: str
        The location to save the dataset to (default: `None`).

    check_md5hash: bool
        If True, check the md5hash of the files after they are downloaded (default: `False`).

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).
    """
    dataset = {}
    if dataset_metadata.dataset_name is None:
        if dataset_metadata.url is None:
            raise ValueError(
                "The dataset name or url must be provided to load a dataset."
            )
        dataset_metadata.dataset_name = dataset_metadata.url[
            dataset_metadata.url.rfind("/")
            + 1: dataset_metadata.url.rfind(".")
        ]
    dataset_path = _fetch_file(dataset_metadata, data_home, check_md5hash, file_type='datasets')
    train = load_from_csv(
        dataset_path,
        dataset_metadata.train_name,
        add_reciprocal_rels=add_reciprocal_rels,
    )
    dataset["train"] = train
    valid = load_from_csv(
        dataset_path,
        dataset_metadata.valid_name,
        add_reciprocal_rels=add_reciprocal_rels,
    )
    dataset["valid"] = valid
    test = load_from_csv(
        dataset_path,
        dataset_metadata.test_name,
        add_reciprocal_rels=add_reciprocal_rels,
    )
    dataset["test"] = test
    if dataset_metadata.valid_negatives_name is not None:
        valid_negatives = load_from_csv(
            dataset_path,
            dataset_metadata.valid_negatives_name,
            add_reciprocal_rels=add_reciprocal_rels,
        )
        dataset["valid_negatives"] = valid_negatives
    if dataset_metadata.test_negatives_name is not None:
        test_negatives = load_from_csv(
            dataset_path,
            dataset_metadata.test_negatives_name,
            add_reciprocal_rels=add_reciprocal_rels,
        )
        dataset["test_negatives"] = test_negatives

    if (
        dataset_metadata.test_human_checksum is not None
        and dataset_metadata.test_human_ids_checksum is not None
    ):
        test_human = load_from_csv(
            dataset_path, dataset_metadata.test_human_name
        )
        dataset["test-human"] = test_human
        test_human_ids = load_from_csv(
            dataset_path, dataset_metadata.test_human_ids_name
        )
        dataset["test-human-ids"] = test_human_ids
    if dataset_metadata.mapper_checksum is not None:
        mapper = load_mapper_from_json(
            dataset_path, dataset_metadata.mapper_name
        )
        dataset["mapper"] = mapper
    return dataset


def load_mapper_from_json(directory_path, file_name):
    """Load a mapper from a .json file.

    Loads a mapper for a graph serialized in a .json file as:

    .. code-block:: text

       subj1: human_labeled_subj1
       relationX: human_labeled_relationX
       obj1: human_labeled_obj1
       human_labeled_relationX: description_of_relationX

       ...

    Parameters
    ----------

    directory_path: str
        Folder where the input file is stored.
    file_name: str
        File name.

    Returns
    -------

    mapper: dict
        Dictionary of mappings between graph entities and predicates and human-readable version of them.

    Example
    -------

    >>> from ampligraph.datasets import load_mapper_from_json
    >>> mapper = load_mapper_from_json('folder', 'mapper.json')
    >>> mapper['/m/234fsd/']
    'Dog'
    """

    logger.debug("Loading mapper from {}.".format(file_name))
    with open(os.path.join(directory_path, file_name)) as f:
        mapper = json.loads(f.read())
    return mapper


def load_wn18(check_md5hash=False, add_reciprocal_rels=False):
    """Load the WN18 dataset.

    WN18 is a subset of Wordnet. It was first presented by :cite:`bordes2013translating`.

    .. warning::
        The dataset includes a large number of inverse relations that spilled to the test set, and its use in
        experiments has been deprecated. **Use WN18RR instead**.

    The WN18 dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    The dataset is divided in three splits:

    - `train`: 141,442 triples
    - `valid` 5,000 triples
    - `test` 5,000 triples

    ========= ========= ======= ======= ============ ===========
     Dataset  Train     Valid   Test    Entities     Relations
    ========= ========= ======= ======= ============ ===========
    WN18      141,442   5,000   5,000   40,943        18
    ========= ========= ======= ======= ============ ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    Returns
    -------

    splits: dict
        The dataset splits `{'train': train, 'valid': valid, 'test': test}`. Each split is a ndarray of shape (n, 3).

    Example
    -------
    >>> from ampligraph.datasets import load_wn18
    >>> X = load_wn18()
    >>> X['test'][:3]
    array([['06845599', '_member_of_domain_usage', '03754979'],
           ['00789448', '_verb_group', '01062739'],
           ['10217831', '_hyponym', '10682169']], dtype=object)

    """

    wn18 = DatasetMetadata(
        dataset_name="wn18",
        filename="wn18.zip",
        url="https://ndownloader.figshare.com/files/66192227",
        train_name="train.txt",
        valid_name="valid.txt",
        test_name="test.txt",
        train_checksum="7d68324d293837ac165c3441a6c8b0eb",
        valid_checksum="f4f66fec0ca83b5ebe7ad7003404e61d",
        test_checksum="b035247a8916c7ec3443fa949e1ff02c",
    )

    return _load_dataset(
        wn18,
        data_home=None,
        check_md5hash=check_md5hash,
        add_reciprocal_rels=add_reciprocal_rels,
    )


def load_wn18rr(
    check_md5hash=False, clean_unseen=True, add_reciprocal_rels=False
):
    """Load the WN18RR dataset.

    The dataset is described in :cite:`DettmersMS018`.

     .. warning:: *WN18RR*'s validation set contains 198 unseen entities over 210 triples. The test set
        has 209 unseen entities, distributed over 210 triples.

    The WN18RR dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.


    This dataset is divided in three splits:

    - `train`: 86,835 triples
    - `valid`: 3,034 triples
    - `test`: 3,134 triples

    ========= ========= ======= ======= ============ ===========
     Dataset  Train     Valid   Test    Entities     Relations
    ========= ========= ======= ======= ============ ===========
    WN18RR    86,835    3,034   3,134   40,943        11
    ========= ========= ======= ======= ============ ===========

    Parameters
    ----------
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    check_md5hash: bool
        If `True` check the md5hash of the datset files (default: `False`).

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'test': test}`. Each split is a ndarray of shape (n, 3).

    Example
    -------

    >>> from ampligraph.datasets import load_wn18rr
    >>> X = load_wn18rr()
    >>> X["valid"][0]
    array(['02174461', '_hypernym', '02176268'], dtype=object)

    """

    wn18rr = DatasetMetadata(
        dataset_name="wn18RR",
        filename="wn18RR.zip",
        url="https://ndownloader.figshare.com/files/66192221",
        train_name="train.txt",
        valid_name="valid.txt",
        test_name="test.txt",
        train_checksum="35e81af3ae233327c52a87f23b30ad3c",
        valid_checksum="74a2ee9eca9a8d31f1a7d4d95b5e0887",
        test_checksum="2b45ba1ba436b9d4ff27f1d3511224c9",
    )

    if clean_unseen:
        return _clean_data(
            _load_dataset(
                wn18rr,
                data_home=None,
                check_md5hash=check_md5hash,
                add_reciprocal_rels=add_reciprocal_rels,
            )
        )
    else:
        return _load_dataset(
            wn18rr,
            data_home=None,
            check_md5hash=check_md5hash,
            add_reciprocal_rels=add_reciprocal_rels,
        )


def load_fb15k(check_md5hash=False, add_reciprocal_rels=False):
    """Load the FB15k dataset.

    FB15k is a split of Freebase, first proposed by :cite:`bordes2013translating`.

    .. warning::
        The dataset includes a large number of inverse relations that spilled to the test set, and its use in
        experiments has been deprecated. **Use FB15k-237 instead**.

    The FB15k dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    The dataset is divided in three splits:

    - `train`: 483,142 triples
    - `valid`: 50,000 triples
    - `test`: 59,071 triples

    ========= ========= ======= ======= ============ ===========
     Dataset  Train     Valid   Test    Entities     Relations
    ========= ========= ======= ======= ============ ===========
    FB15K     483,142   50,000  59,071  14,951        1,345
    ========= ========= ======= ======= ============ ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).


    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'test': test}`. Each split is a ndarray of shape (n, 3).

    Example
    -------

    >>> from ampligraph.datasets import load_fb15k
    >>> X = load_fb15k()
    >>> X['test'][:3]
    array([['/m/01qscs',
            '/award/award_nominee/award_nominations./award/award_nomination/award',
            '/m/02x8n1n'],
           ['/m/040db', '/base/activism/activist/area_of_activism', '/m/0148d'],
           ['/m/08966',
            '/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month',
            '/m/05lf_']], dtype=object)

    """

    FB15K = DatasetMetadata(
        dataset_name="fb15k",
        filename="fb15k.zip",
        url="https://ndownloader.figshare.com/files/66192239",
        train_name="train.txt",
        valid_name="valid.txt",
        test_name="test.txt",
        train_checksum="5a87195e68d7797af00e137a7f6929f2",
        valid_checksum="275835062bb86a86477a3c402d20b814",
        test_checksum="71098693b0efcfb8ac6cd61cf3a3b505",
    )

    return _load_dataset(
        FB15K,
        data_home=None,
        check_md5hash=check_md5hash,
        add_reciprocal_rels=add_reciprocal_rels,
    )


def load_fb15k_237(
    check_md5hash=False,
    clean_unseen=True,
    add_reciprocal_rels=False,
    return_mapper=False,
):
    """Load the FB15k-237 dataset (with option to load human labeled test subset).

    FB15k-237 is a reduced version of FB15K. It was first proposed by :cite:`toutanova2015representing`.

    .. warning:: *FB15K-237*'s validation set contains 8 unseen entities over 9 triples. The test set has 29 unseen entities,
         distributed over 28 triples.

    The FB15k-237 dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    The dataset is divided in three splits:

    - `train`: 272,115 triples
    - `valid`: 17,535 triples
    - `test`: 20,466 triples

    It also contains a subset of the test set with human-readable labels, available here:

    - `test-human`
    - `test-human-ids`

    ========= ========= ======= ======= ==========  ========  =========
     Dataset  Train     Valid   Test    Test-Human  Entities  Relations
    ========= ========= ======= ======= ==========  ========  =========
    FB15K-237 272,115   17,535  20,466   273        14,541    237
    ========= ========= ======= ======= ==========  ========  =========


    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    add_reciprocal_rels: bool
         Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
         this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    return_mapper: bool
         Whether to return human-readable labels in a form of dictionary in ``X["mapper"]`` field (default: `False`).

    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'test': test, 'test-human':test_human, 'test-human-ids': test_human_ids}`.
        Each split is a ndarray of shape (n, 3).

    Example
    -------

    >>> from ampligraph.datasets import load_fb15k_237
    >>> X = load_fb15k_237()
    >>> X["train"][2]
    array(['/m/07s9rl0', '/media_common/netflix_genre/titles', '/m/0170z3'],
      dtype=object)

    """

    if return_mapper:
        fb15k_237 = DatasetMetadata(
            dataset_name="fb15k-237",
            filename="fb15k-237_human_interpretability.zip",
            url="https://ndownloader.figshare.com/files/66193391",
            train_name="train.txt",
            valid_name="valid.txt",
            test_name="test.txt",
            train_checksum="c05b87b9ac00f41901e016a2092d7837",
            valid_checksum="6a94efd530e5f43fcf84f50bc6d37b69",
            test_checksum="f5bdf63db39f455dec0ed259bb6f8628",
            test_human_name="test_human.txt",
            test_human_ids_name="test_human_ids.txt",
            mapper_name="mapper.json",
            test_human_checksum="5f43e8e2fb07846ffaf80877b0734744",
            test_human_ids_checksum="e731d027b3bf9d4914393d75dae77dda",
            mapper_checksum="b4dbdfaf1faf075746d2c32946be0234",
        )
    else:
        fb15k_237 = DatasetMetadata(
            dataset_name="fb15k-237",
            filename="fb15k-237.zip",
            url="https://ndownloader.figshare.com/files/66193226",
            train_name="train.txt",
            valid_name="valid.txt",
            test_name="test.txt",
            train_checksum="c05b87b9ac00f41901e016a2092d7837",
            valid_checksum="6a94efd530e5f43fcf84f50bc6d37b69",
            test_checksum="f5bdf63db39f455dec0ed259bb6f8628",
        )

    if clean_unseen:
        return _clean_data(
            _load_dataset(
                fb15k_237,
                data_home=None,
                check_md5hash=check_md5hash,
                add_reciprocal_rels=add_reciprocal_rels,
            )
        )
    else:
        return _load_dataset(
            fb15k_237,
            data_home=None,
            check_md5hash=check_md5hash,
            add_reciprocal_rels=add_reciprocal_rels,
        )


def load_yago3_10(
    check_md5hash=False, clean_unseen=True, add_reciprocal_rels=False
):
    """Load the YAGO3-10 dataset.

    The dataset is a split of YAGO3 :cite:`mahdisoltani2013yago3`,
    and has been first presented in :cite:`DettmersMS018`.

    The YAGO3-10 dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    This dataset is divided in three splits:

    - `train`: 1,079,040 triples
    - `valid`: 5,000 triples
    - `test`: 5,000 triples

    ========= ========= ======= ======= ============ ===========
     Dataset  Train     Valid   Test    Entities     Relations
    ========= ========= ======= ======= ============ ===========
    YAGO3-10  1,079,040 5,000   5,000   123,182       37
    ========= ========= ======= ======= ============ ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default:`False`).

    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'test': test}`. Each split is a ndarray of shape (n, 3).

    Example
    -------

    >>> from ampligraph.datasets import load_yago3_10
    >>> X = load_yago3_10()
    >>> X["valid"][0]
    array(['Mikheil_Khutsishvili', 'playsFor', 'FC_Merani_Tbilisi'], dtype=object)

    """
    yago3_10 = DatasetMetadata(
        dataset_name="YAGO3-10",
        filename="YAGO3-10.zip",
        url="https://ndownloader.figshare.com/files/66192236",
        train_name="train.txt",
        valid_name="valid.txt",
        test_name="test.txt",
        train_checksum="a9da8f583ec3920570eeccf07199229a",
        valid_checksum="2d679a906f2b1ac29d74d5c948c1ad09",
        test_checksum="14bf97890b2fee774dbce5f326acd189",
    )

    if clean_unseen:
        return _clean_data(
            _load_dataset(
                yago3_10,
                data_home=None,
                check_md5hash=check_md5hash,
                add_reciprocal_rels=add_reciprocal_rels,
            )
        )
    else:
        return _load_dataset(
            yago3_10,
            data_home=None,
            check_md5hash=check_md5hash,
            add_reciprocal_rels=add_reciprocal_rels,
        )


def load_wn11(
    check_md5hash=False, clean_unseen=True, add_reciprocal_rels=False
):
    """Load the WordNet11 (WN11) dataset.

    WordNet was originally proposed in `WordNet: a lexical database for English` :cite:`miller1995wordnet`.

    .. note::
        WN11 also provide true and negative labels for the triples in the validation and tests sets.
        The positive base rate is close to 50%.

    WN11 dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    This dataset is divided in three splits:

    - `train`: 110361 triples
    - `valid`: 5215 triples
    - `test`: 21035 triples

    Both the validation and test splits are associated with labels (binary ndarrays),
    with `True` for positive statements and `False` for  negatives:

    - `valid_labels`
    - `test_labels`

    ========= ========= ========== ========== ======== ======== ============ ===========
     Dataset  Train     Valid Pos  Valid Neg  Test Pos Test Neg Entities     Relations
    ========= ========= ========== ========== ======== ======== ============ ===========
    WN11      110361    2606       2609       10493    10542    38588        11
    ========= ========= ========== ========== ======== ======== ============ ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'valid_labels': valid_labels,
        'test': test, 'test_labels': test_labels}`.
        Each split containing a dataset is a ndarray of shape (n, 3).
        The labels are a ndarray of shape (n).

    Example
    -------

    >>> from ampligraph.datasets import load_wn11
    >>> X = load_wn11()
    >>> X["valid"][0]
    array(['__genus_xylomelum_1', '_type_of', '__dicot_genus_1'], dtype=object)
    >>> X["valid_labels"][0:3]
    array([ True, False,  True])

    """
    wn11 = DatasetMetadata(
        dataset_name="wordnet11",
        filename="wordnet11.zip",
        url="https://ndownloader.figshare.com/files/66192215",
        train_name="train.txt",
        valid_name="dev.txt",
        test_name="test.txt",
        train_checksum="2429c672c89e33ad4fa8e1a3ade416e4",
        valid_checksum="87bf86e225e79294a2524089614b96aa",
        test_checksum="24113b464f8042c339e3e6833c1cebdf",
    )

    dataset = _load_dataset(
        wn11,
        data_home=None,
        check_md5hash=check_md5hash,
        add_reciprocal_rels=add_reciprocal_rels,
    )

    valid_labels = dataset["valid"][:, 3]
    test_labels = dataset["test"][:, 3]

    dataset["valid"] = dataset["valid"][:, 0:3]
    dataset["test"] = dataset["test"][:, 0:3]

    dataset["valid_labels"] = valid_labels == "1"
    dataset["test_labels"] = test_labels == "1"

    if clean_unseen:
        clean_dataset, valid_idx, test_idx = _clean_data(
            dataset, return_idx=True
        )
        clean_dataset["valid_labels"] = dataset["valid_labels"][valid_idx]
        clean_dataset["test_labels"] = dataset["test_labels"][test_idx]
        return clean_dataset
    else:
        return dataset


def load_fb13(
    check_md5hash=False, clean_unseen=True, add_reciprocal_rels=False
):
    """Load the Freebase13 (FB13) dataset.

    FB13 is a subset of Freebase :cite:`bollacker2008freebase`
    and was initially presented in
    `Reasoning With Neural Tensor Networks for Knowledge Base Completion` :cite:`socher2013reasoning`.

    .. note::
        FB13 also provide true and negative labels for the triples in the validation and tests sets.
        The positive base rate is close to 50%.

    FB13 dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    This dataset is divided in three splits:

    - `train`: 316232 triples
    - `valid`: 11816 triples
    - `test`: 47464 triples

    Both the validation and test splits are associated with labels (binary ndarrays),
    with `True` for positive statements and `False` for  negatives:

    - `valid_labels`
    - `test_labels`

    ========= ========= ========== ========== ======== ======== ============ ===========
     Dataset  Train     Valid Pos  Valid Neg  Test Pos Test Neg Entities     Relations
    ========= ========= ========== ========== ======== ======== ============ ===========
    FB13      316232    5908       5908       23733    23731    75043        13
    ========= ========= ========== ========== ======== ======== ============ ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).

    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: False).

    Returns
    -------

    splits: dict
        The dataset splits: {'train': train, 'valid': valid, 'valid_labels': valid_labels,
        'test': test, 'test_labels': test_labels}.
        Each split containing a dataset is a ndarray of shape (n, 3).
        The labels are ndarray of shape (n).

    Example
    -------

    >>> from ampligraph.datasets import load_fb13
    >>> X = load_fb13()
    >>> X["valid"][0]
    array(['cornelie_van_zanten', 'gender', 'female'], dtype=object)
    >>> X["valid_labels"][0:3]
    array([True, False, True], dtype=object)

    """
    fb13 = DatasetMetadata(
        dataset_name="freebase13",
        filename="freebase13.zip",
        url="https://ndownloader.figshare.com/files/66192218",
        train_name="train.txt",
        valid_name="dev.txt",
        test_name="test.txt",
        train_checksum="9099ebcd85ab3ce723cfaaf34f74dceb",
        valid_checksum="c4ef7b244baa436a97c2a5e57d4ba7ed",
        test_checksum="f9af2eac7c5a86996c909bdffd295528",
    )

    dataset = _load_dataset(
        fb13,
        data_home=None,
        check_md5hash=check_md5hash,
        add_reciprocal_rels=add_reciprocal_rels,
    )

    valid_labels = dataset["valid"][:, 3]
    test_labels = dataset["test"][:, 3]

    dataset["valid"] = dataset["valid"][:, 0:3]
    dataset["test"] = dataset["test"][:, 0:3]

    dataset["valid_labels"] = valid_labels == "1"
    dataset["test_labels"] = test_labels == "1"

    if clean_unseen:
        clean_dataset, valid_idx, test_idx = _clean_data(
            dataset, return_idx=True
        )
        clean_dataset["valid_labels"] = dataset["valid_labels"][valid_idx]
        clean_dataset["test_labels"] = dataset["test_labels"][test_idx]
        return clean_dataset
    else:
        return dataset


def load_all_datasets(check_md5hash=False):
    load_wn18(check_md5hash)
    load_wn18rr(check_md5hash)
    load_fb15k(check_md5hash)
    load_fb15k_237(check_md5hash)
    load_yago3_10(check_md5hash)
    load_wn11(check_md5hash)
    load_fb13(check_md5hash)


def load_from_rdf(
    folder_name,
    file_name,
    rdf_format="nt",
    data_home=None,
    add_reciprocal_rels=False,
):
    """Load an RDF file.

    Loads an RDF knowledge graph using rdflib_ APIs.
    Multiple RDF serialization formats are supported (`nt`, `ttl`, `rdf`/`xml`, etc).
    The entire graph will be loaded in memory, and converted into an rdflib `Graph` object.

    .. _rdflib: https://rdflib.readthedocs.io/

    .. warning::
        Large RDF graphs should be serialized to ntriples beforehand and loaded with ``load_from_ntriples()`` instead.
        This function, indeed, is faster by orders of magnitude.

    .. hint::
        To split a generic knowledge graphs into **training**, **validation**, and **test** sets do not use the above
        function, but rather :meth:`~ampligraph.evaluation.protocol.train_test_split_no_unseen`: this will return
        validation and test sets not including triples with entities not present in the training set.


    Parameters
    ----------
    folder_name: str
        Base folder where the file is stored.
    file_name: str
        File name.
    rdf_format: str
        The RDF serialization format (`nt`, `ttl`, `rdf`/`xml` - see rdflib documentation).
    data_home: str
       The path to the folder that contains the datasets.
    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    Returns
    -------
        triples: ndarray, shape (n, 3)
            The actual triples of the file.
    """

    logger.debug("Loading rdf data from {}.".format(file_name))
    data_home = _get_data_home(data_home, file_type="datasets")
    from rdflib import Graph

    g = Graph()
    g.parse(
        os.path.join(data_home, folder_name, file_name),
        format=rdf_format,
        publicID="http://test#",
    )
    triples = pd.DataFrame(np.array(g))
    triples = triples.drop_duplicates()
    if add_reciprocal_rels:
        triples = _add_reciprocal_relations(triples)

    return triples.values


def load_from_ntriples(
    folder_name, file_name, data_home=None, add_reciprocal_rels=False
):
    """Load a dataset of RDF ntriples.

    Loads an RDF knowledge graph serialized as ntriples, without building an RDF graph in memory.
    This function should be preferred over ``load_from_rdf()``, since it does not load the graph into an rdflib
    model (and it is therefore faster by order of magnitudes).
    Nevertheless, it requires a ntriples_ serialization as in the example below:

    .. _ntriples: https://www.w3.org/TR/n-triples/.

    .. code-block:: text

        _:alice <http://xmlns.com/foaf/0.1/knows> _:bob .
        _:bob <http://xmlns.com/foaf/0.1/knows> _:alice .

    .. hint::
        To split a generic knowledge graphs into **training**, **validation**, and **test** sets do not use the above
        function, but rather :meth:`~ampligraph.evaluation.protocol.train_test_split_no_unseen`: this will return
        validation and test sets not including triples with entities not present in the training set.


    Parameters
    ----------
    folder_name: str
        Base folder where the file is stored.
    file_name: str
        File name.
    data_home: str
       The path to the folder that contains the datasets.
    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).

    Returns
    -------
        triples: ndarray, shape (n, 3)
            The actual triples of the file.
    """

    logger.debug("Loading rdf ntriples from {}.".format(file_name))
    data_home = _get_data_home(data_home, file_type="datasets")
    df = pd.read_csv(
        os.path.join(data_home, folder_name, file_name),
        sep=" ",
        header=None,
        names=None,
        dtype=str,
        usecols=[0, 1, 2],
    )

    # Remove trailing full stop (if present)
    df[2] = df[2].apply(lambda x: x.rsplit(".", 1)[0])

    if add_reciprocal_rels:
        df = _add_reciprocal_relations(df)

    return df.values


# FocusE
def generate_focusE_dataset_splits(
    dataset, split_test_into_top_bottom=True, split_threshold=0.1
):
    """Creates the dataset splits for training models with FocusE layers

    Parameters
    ----------
    dataset: dict
        Dictionary of train, test, valid datasets of size (n,m) - where m>3. The first 3 cols are `subject`,
        `predicate`, and `object`. Afterwards, is the numeric values (potentially multiple) associated with each triple.

    split_test_into_top_bottom: bool
        Splits the test set by numeric values and returns `test_top_split` and `test_bottom_split` by splitting
        based on sorted numeric values and returning top and bottom k*100% triples, where `k` is specified by
        `split_threshold` argument.

    split_threshold: float
        Specifies the top and bottom percentage of triples to return.

    Returns
    -------
    splits: dict
        The dataset splits: `{'train': train,
                             'train_numeric_values': train_numeric_values,
                             'valid': valid,
                             'valid_numeric_values': valid_numeric_values,
                             'test': test,
                             'test_numeric_values': test_numeric_values,
                             'test_topk': test_topk,
                             'test_topk_numeric_values': test_topk_numeric_values,
                             'test_bottomk': test_bottomk,
                             'test_bottomk_numeric_values': test_bottomk_numeric_values}`.
        Each numeric value split contains numeric values associated with the corresponding dataset split and
        is a ndarray of shape (n, 1).
        Each dataset split is a ndarray of shape (n,3).
        The `topk` and `bottomk` splits are only returned when `split_test_into_top_bottom` is set to `True` and contain
        the triples ordered by highest/lowest numeric edge value associated. These are typically used at evaluation time
        aiming at observing a model that assigns the highest rank possible to the `_topk` and the lowest possible to
        the `_bottomk`.
    """
    dataset["train_numeric_values"] = dataset["train"][:, 3:].astype(
        np.float32
    )
    dataset["valid_numeric_values"] = dataset["valid"][:, 3:].astype(
        np.float32
    )
    dataset["test_numeric_values"] = dataset["test"][:, 3:].astype(np.float32)

    dataset["train"] = dataset["train"][:, 0:3]
    dataset["valid"] = dataset["valid"][:, 0:3]
    dataset["test"] = dataset["test"][:, 0:3]

    sorted_indices = np.squeeze(
        np.argsort(dataset["test_numeric_values"], axis=0)
    )
    dataset["test"] = dataset["test"][sorted_indices]
    dataset["test_numeric_values"] = dataset["test_numeric_values"][
        sorted_indices
    ]

    if split_test_into_top_bottom:
        split_threshold = int(split_threshold * dataset["test"].shape[0])

        dataset["test_bottomk"] = dataset["test"][:split_threshold]
        dataset["test_bottomk_numeric_values"] = dataset[
            "test_numeric_values"
        ][:split_threshold]

        dataset["test_topk"] = dataset["test"][-split_threshold:]
        dataset["test_topk_numeric_values"] = dataset["test_numeric_values"][
            -split_threshold:
        ]

    return dataset


def load_onet20k(
    check_md5hash=False,
    clean_unseen=True,
    split_test_into_top_bottom=True,
    split_threshold=0.1,
):
    """Load the O*NET20K dataset.

    O*NET20K was originally proposed in :cite:`pai2021learning`.
    It is a subset  of `O*NET <https://www.onetonline.org/>`_, a dataset that includes job descriptions, skills
    and labeled, binary relations between such concepts. Each triple is labeled with a numeric value that
    indicates the importance of that link.

    O*NET20K dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    This dataset is divided in three splits:

    - `train`: 461,932 triples
    - `valid`: 850 triples
    - `test`: 2,000 triples

    Each triple in these splits is associated to a numeric value which represents the importance/relevance of
    the link.

    ========= ========= ======== =========== ========== ===========
    Dataset   Train     Valid    Test        Entities   Relations
    ========= ========= ======== =========== ========== ===========
    ONET*20K  461,932    850     2,000       20,643     19
    ========= ========= ======== =========== ========== ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training
        set.

    split_test_into_top_bottom: bool
        Splits the test set by numeric values and returns `test_top_split` and `test_bottom_split` by splitting based
        on sorted numeric values and returning top and bottom k% triples, where `k` is specified by `split_threshold`
        argument.

    split_threshold: float
        Specifies the top and bottom percentage of triples to return.

    Returns
    -------
    splits: dict
        The dataset splits: `{'train': train,
        'valid': valid,
        'test': test,
        'test_topk': test_topk,
        'test_bottomk': test_bottomk,
        'train_numeric_values': train_numeric_values,
        'valid_numeric_values':valid_numeric_values,
        'test_numeric_values': test_numeric_values,
        'test_topk_numeric_values': test_topk_numeric_values,
        'test_bottomk_numeric_values': test_bottomk_numeric_values}`.
        Each ``*_numeric_values`` split contains numeric values associated to the corresponding dataset split and
        is a ndarray of shape (n).
        Each dataset split is a ndarray of shape (n,3).
        The ``*_topk`` and ``*_bottomk`` splits are only returned when ``split_test_into_top_bottom=True`` and contain
        the triples ordered by highest/lowest numeric edge value associated. These are typically used at evaluation time
        aiming at observing a model that assigns the highest rank possible to the `_topk` and the lowest possible to
        the `_bottomk`.

    Example
    -------
    >>> from ampligraph.datasets import load_onet20k
    >>> X = load_onet20k()
    >>> X["train"][0]
    ['Job_27-1021.00' 'has_ability_LV' '1.A.1.b.2']
    >>> X['train_numeric_values'][0]
    [0.6257143]
    """
    onet20k = DatasetMetadata(
        dataset_name="onet20k",
        filename="onet20k.zip",
        url="https://ndownloader.figshare.com/files/66192209",
        train_name="train.tsv",
        valid_name="valid.tsv",
        test_name="test.tsv",
        train_checksum="516220427a9a18516fd7a804a6944d64",
        valid_checksum="d7806951ac3d916c5c5a0304eea064d2",
        test_checksum="e5baec19037cb0bddc5a2fe3c0f4445a",
    )

    dataset = _load_dataset(
        onet20k, data_home=None, check_md5hash=check_md5hash
    )

    if clean_unseen:
        dataset = _clean_data(dataset)

    return generate_focusE_dataset_splits(
        dataset, split_test_into_top_bottom, split_threshold
    )


def load_ppi5k(
    check_md5hash=False,
    clean_unseen=True,
    split_test_into_top_bottom=True,
    split_threshold=0.1,
):
    """Load the PPI5K dataset.

    Originally proposed in :cite:`chen2019embedding`, PPI5K is a subset of the protein-protein
    interactions (PPI) knowledge graph :cite:`PPI`. Numeric values represent the confidence of the link
    based on existing scientific literature evidence.

    PPI5K is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    It is divided into three splits:

    - `train`: 230,929 triples
    - `valid`: 19,017 triples
    - `test`: 21,720 triples

    Each triple in these splits is associated to a numeric value which models additional information on the
    fact (importance, relevance of the link).

    ========= ========= ======== =========== ========== ===========
    Dataset   Train     Valid    Test        Entities   Relations
    ========= ========= ======== =========== ========== ===========
    PPI5K     230929    19017    21720       4999       7
    ========= ========= ======== =========== ========== ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training
        set.

    split_test_into_top_bottom: bool
        When set to `True`, the function also returns subsets of the test set that includes only the top-k or
        bottom-k numeric-enriched triples. Splits `test_topk`, `test_bottomk` and their
        numeric values. Such splits are generated by sorting Splits the test set by numeric values and returns
        `test_top_split` and `test_bottom_split` by splitting based on sorted numeric values and returning top
        and bottom k% triples, where `k` is specified by the ``split_threshold`` argument.

    split_threshold: float
        Specifies the top and bottom percentage of triples to return.

    Returns
    -------
    splits: dict
        The dataset splits: `{'train': train,
        'valid': valid,
        'test': test,
        'test_topk': test_topk,
        'test_bottomk': test_bottomk,
        'train_numeric_values': train_numeric_values,
        'valid_numeric_values':valid_numeric_values,
        'test_numeric_values': test_numeric_values,
        'test_topk_numeric_values': test_topk_numeric_values,
        'test_bottomk_numeric_values': test_bottomk_numeric_values}`.
        Each ``*_numeric_values`` split contains numeric values associated to the corresponding dataset split and
        is a ndarray of shape (n).
        Each dataset split is a ndarray of shape (n,3).
        The ``*_topk`` and ``*_bottomk`` splits are only returned when ``split_test_into_top_bottom=True`` and contain
        the triples ordered by highest/lowest numeric edge value associated. These are typically used at evaluation time
        aiming at observing a model that assigns the highest rank possible to the `_topk` and the lowest possible to
        the `_bottomk`.

    Example
    -------
    >>> from ampligraph.datasets import load_ppi5k
    >>> X = load_ppi5k()
    >>> X["train"][0]
    ['4001' '5' '4176']
    >>> X['train_numeric_values'][0]
    [0.329]
    """
    ppi5k = DatasetMetadata(
        dataset_name="ppi5k",
        filename="ppi5k.zip",
        url="https://ndownloader.figshare.com/files/66192224",
        train_name="train.tsv",
        valid_name="valid.tsv",
        test_name="test.tsv",
        train_checksum="d8b54de3482c0d043118cbd05f2666cf",
        valid_checksum="2bd094118f4be1f4f6d6a1d4707271c1",
        test_checksum="7e6e345f496ed9a0cc58b91d4877ddd6",
    )

    dataset = _load_dataset(ppi5k, data_home=None, check_md5hash=check_md5hash)

    if clean_unseen:
        dataset = _clean_data(dataset)

    return generate_focusE_dataset_splits(
        dataset, split_test_into_top_bottom, split_threshold
    )


def load_nl27k(
    check_md5hash=False,
    clean_unseen=True,
    split_test_into_top_bottom=True,
    split_threshold=0.1,
):
    """Load the NL27K dataset.

    NL27K was originally proposed in :cite:`chen2019embedding`. It is a subset of the Never Ending Language
    Learning (NELL) dataset :cite:`mitchell2018never`, which collects data from web pages.
    Numeric values on triples represent link uncertainty.

    NL27K is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    It is divided into three splits:

    - `train`: 149,100 triples
    - `valid`: 12,274 triples
    - `test`: 14,026 triples

    Each triple in these splits is associated to a numeric value which represents the importance/relevance of
    the link.

    ========= ========= ======== =========== ========== ===========
    Dataset   Train     Valid    Test        Entities   Relations
    ========= ========= ======== =========== ========== ===========
    NL27K     149,100    12,274    14,026       27,221      405
    ========= ========= ======== =========== ========== ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True` check the md5hash of the files (default: `False`).
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training
        set.

    split_test_into_top_bottom: bool
        Splits the test set by numeric values and returns `test_top_split` and `test_bottom_split` by splitting based
        on sorted numeric values and returning top and bottom k% triples, where `k` is specified by `split_threshold`
        argument.

    split_threshold: float
        Specifies the top and bottom percentage of triples to return.

    Returns
    -------
    splits: dict
        The dataset splits: `{'train': train,
        'valid': valid,
        'test': test,
        'test_topk': test_topk,
        'test_bottomk': test_bottomk,
        'train_numeric_values': train_numeric_values,
        'valid_numeric_values':valid_numeric_values,
        'test_numeric_values': test_numeric_values,
        'test_topk_numeric_values': test_topk_numeric_values,
        'test_bottomk_numeric_values': test_bottomk_numeric_values}`.
        Each ``*_numeric_values`` split contains numeric values associated to the corresponding dataset split and
        is a ndarray of shape (n).
        Each dataset split is a ndarray of shape (n,3).
        The ``*_topk`` and ``*_bottomk`` splits are only returned when ``split_test_into_top_bottom=True`` and contain
        the triples ordered by highest/lowest numeric edge value associated. These are typically used at evaluation time
        aiming at observing a model that assigns the highest rank possible to the `_topk` and the lowest possible to
        the `_bottomk`.

    Example
    -------
    >>> from ampligraph.datasets import load_nl27k
    >>> X = load_nl27k()
    >>> X["train"][0]
    ['concept:company:business_review' 'concept:competeswith' 'concept:company:miami_herald001']
    >>> X['train_numeric_values'][0]
    [0.859375]
    """
    nl27k = DatasetMetadata(
        dataset_name="nl27k",
        filename="nl27k.zip",
        url="https://ndownloader.figshare.com/files/66192212",
        train_name="train.tsv",
        valid_name="valid.tsv",
        test_name="test.tsv",
        train_checksum="d4ce775401d299074d98e046f13e7283",
        valid_checksum="00177fa6b9f5cec18814ee599c02eae3",
        test_checksum="2ba17f29119688d93c9d29ab40f63b3e",
    )

    dataset = _load_dataset(nl27k, data_home=None, check_md5hash=check_md5hash)

    if clean_unseen:
        dataset = _clean_data(dataset)

    return generate_focusE_dataset_splits(
        dataset, split_test_into_top_bottom, split_threshold
    )


def load_cn15k(
    check_md5hash=False,
    clean_unseen=True,
    split_test_into_top_bottom=True,
    split_threshold=0.1,
):
    """Load the CN15K dataset.

    CN15K was originally proposed in :cite:`chen2019embedding`, it is a subset of ConceptNet :cite:`CN`,
    a common-sense knowledge graph built to represent general human knowledge.
    Numeric values on triples represent uncertainty.

    CN15k dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    It is divided into three splits:

    - `train`: 199,417 triples
    - `valid`: 16,829 triples
    - `test`: 19,224 triples

    Each triple in these splits is associated to a numeric value which represents the importance/relevance of
    the link.

    ========= ========= ======== =========== ========== ===========
    Dataset   Train     Valid    Test        Entities   Relations
    ========= ========= ======== =========== ========== ===========
    CN15K     199,417    16,829    19,224       15,000      36
    ========= ========= ======== =========== ========== ===========

    Parameters
    ----------
    check_md5hash: bool
        If `True`, check the md5hash of the files (default: `False`).
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training
        set.

    split_test_into_top_bottom: bool
        Splits the test set by numeric values and returns `test_top_split` and `test_bottom_split` by splitting based
        on sorted numeric values and returning top and bottom k% triples, where `k` is specified by `split_threshold`
        argument.

    split_threshold: float
        Specifies the top and bottom percentage of triples to return.

    Returns
    -------
    splits: dict
        The dataset splits: `{'train': train,
        'valid': valid,
        'test': test,
        'test_topk': test_topk,
        'test_bottomk': test_bottomk,
        'train_numeric_values': train_numeric_values,
        'valid_numeric_values':valid_numeric_values,
        'test_numeric_values': test_numeric_values,
        'test_topk_numeric_values': test_topk_numeric_values,
        'test_bottomk_numeric_values': test_bottomk_numeric_values}`.
        Each ``*_numeric_values`` split contains numeric values associated to the corresponding dataset split and
        is a ndarray of shape (n).
        Each dataset split is a ndarray of shape (n,3).
        The ``*_topk`` and ``*_bottomk`` splits are only returned when ``split_test_into_top_bottom=True`` and contain
        the triples ordered by highest/lowest numeric edge value associated. These are typically used at evaluation time
        aiming at observing a model that assigns the highest rank possible to the `_topk` and the lowest possible to
        the `_bottomk`.

    Example
    -------
    >>> from ampligraph.datasets import load_cn15k
    >>> X = load_cn15k()
    >>> X["train"][0]
    ['260' '2' '13895']
    >>> X['train_numeric_values'][0]
    [0.8927088]
    """
    cn15k = DatasetMetadata(
        dataset_name="cn15k",
        filename="cn15k.zip",
        url="https://ndownloader.figshare.com/files/66192206",
        train_name="train.tsv",
        valid_name="valid.tsv",
        test_name="test.tsv",
        train_checksum="8bf2ecc8f34e7b3b544afc30abaac478",
        valid_checksum="15b63ebd7428a262ad5fe869cc944208",
        test_checksum="29df4b8d24a3d89fc7c1032b9c508112",
    )

    dataset = _load_dataset(cn15k, data_home=None, check_md5hash=check_md5hash)

    if clean_unseen:
        dataset = _clean_data(dataset)

    return generate_focusE_dataset_splits(
        dataset, split_test_into_top_bottom, split_threshold
    )


def _load_xai_fb15k_237_experiment_log(full=False, subset="all"):
    """Load the XAI FB15k-237 experiment log

        XAI-FB15k-237 is a reduced version of FB15K-237 containing human-readable triples.

        The dataset contains several fields, by default the returned data frame contains only triples, when
        option full is equal to True (``full=True``) the full data is returned (it reflects filtering protocol).

        Fields:

        - predicate,
        - predicate label,
        - predicates_description,
        - subject,
        - subject_label,
        - object_label,
        - object.

        All triples are returned 273 x 7.

        Full Fields:

        *note: some field can have 3 forms, these are marked with X, X = {1,2,3} for 3 triples,
               that were displayed to the annotators with a given predicate.

        - predicate: evaluated predicate,
        - predicate label: human label for predicate,
        - predicates_description: human description of what the predicate means,
        - question triple X: textual form of triple 1 containing predicate,
        - subject_tripleX: subject of triple X,
        - object_tripleX: object of triple X,
        - subject_label_tripleX: human label of subject of triple X,
        - object_label_tripleX: human label of object of triple X,
        - avg rank triple X: avergae rank that the triple obtain among models,
        - std rank triple X: standard deviation of rank that the triple obtain among models,
        - avg O rank triple X: average object rank that the triple obtain among models,
        - std O rank triple X: standard deviation of object rank that the triple obtain among models,
        - avg S rank triple X: average subject rank that the triple obtain among models,
        - std S rank triple X: standard deviation of subject rank that the triple obtain among models,
        - evaluated: summed score of 3 evaluators for a predicate (when each evaluator gave score 0 - not understandable or 1- understandable):
                 0 - triples with this predicate are not understandable - full agreement between annotators.
                 1 - triples with this predicate are mostly understandable - partial agreement between annotators.
                 2 - triples with this predicate are mostly not understandable - partial agreement between annotators.
                 3 - triples with this predicate are clearly understandable - full agreement between annotators.

       All predicates are returned 91 x 37 records each containing 3 triples.

        ============= ========= ==========
        Dataset       Entities  Relations
        ============= ========= ==========
        XAI-FB15K-237  446       91
        ============= ========= ==========


        Parameters
        ----------
        full [False]: wether to return full dataset or reduced view with triples.
        subset ["all"]: subset of records to be returned:
             - "all" - returns all records,
             - "clear" - returns only triples which all annotators marked as understandable,
             - "not clear" - not understandable triples,
             - "confusing+" - mostly understandable triples,
             - "confusing-" - mostly not understandable.


        X: pandas data frame containing triples (full=False), records with predicates (full=True).

        Example
        -------

        >>> from ampligraph.datasets import _load_xai_fb15k_237_experiment_log
        >>> X = _load_xai_fb15k_237_experiment_log()
        >>> X.head(2)

        predicate 	                        predicate label 	predicates_description 	                        subject 	subject_label 	object_label 	object
    0 	/media_common/netflix_genre/titles 	Titles 	                Titles that have this Genre in Netflix@en 	/m/07c52 	Television 	Friends 	/m/030cx
    1 	/film/film/edited_by 	                Edited by 	        NaN 	                                        /m/0cc5qkt 	War Horse 	Michael Kahn 	/m/03q8ch

    """
    import requests

    url = "https://ampgraphenc.s3-eu-west-1.amazonaws.com/datasets/xai_fb15k_237.csv"

    r = requests.get(url, allow_redirects=True)
    open("xai_fb15k_237.csv", "wb").write(r.content)

    mapper = {
        "all": "all",
        "clear": 3,
        "not clear": 0,
        "confusing+": 2,
        "confusing-": 1,
    }
    if subset != "all":
        if subset in mapper:
            X = pd.read_csv("xai_fb15k_237.csv", sep=",")
            X = X[X["evaluated"] == mapper[subset]]
        else:
            print("No such option!")
    else:
        X = pd.read_csv("xai_fb15k_237.csv", sep=",")

    if full:
        return X
    else:
        t1 = X[
            [
                "predicate",
                "predicate label",
                "predicates_description",
                "subject_triple1",
                "subject_label_triple1",
                "object_label_triple1",
                "object_triple1",
            ]
        ]
        t2 = X[
            [
                "predicate",
                "predicate label",
                "predicates_description",
                "subject_triple2",
                "subject_label_triple2",
                "object_label_triple2",
                "object_triple2",
            ]
        ]
        t3 = X[
            [
                "predicate",
                "predicate label",
                "predicates_description",
                "subject_triple3",
                "subject_label_triple3",
                "object_label_triple3",
                "object_triple3",
            ]
        ]
        mapper1 = {
            "subject_triple1": "subject",
            "subject_label_triple1": "subject_label",
            "object_label_triple1": "object_label",
            "object_triple1": "object",
        }
        t1 = t1.rename(columns=mapper1)
        mapper2 = {
            "subject_triple2": "subject",
            "subject_label_triple2": "subject_label",
            "object_label_triple2": "object_label",
            "object_triple2": "object",
        }
        t2 = t2.rename(columns=mapper2)
        mapper3 = {
            "subject_triple3": "subject",
            "subject_label_triple3": "subject_label",
            "object_label_triple3": "object_label",
            "object_triple3": "object",
        }
        t3 = t3.rename(columns=mapper3)
        t1 = t1.append(t2, ignore_index=True)
        t1 = t1.append(t3, ignore_index=True)
        return t1


def load_codex(
    check_md5hash=False,
    clean_unseen=True,
    add_reciprocal_rels=False,
    return_mapper=False,
):
    """Load the CoDEx-M dataset.

    The dataset is described in :cite:`safavi_codex_2020`.

    .. note::
        CODEX-M contains also ground truths negative triples for test and validation sets. For more information, see
        the above reference to the original paper.

    The CodDEx dataset is loaded from file if it exists at the ``AMPLIGRAPH_DATA_HOME`` location.
    If ``AMPLIGRAPH_DATA_HOME`` is not set, the default  ``~/ampligraph_datasets`` is checked.
    If the dataset is not found at either location, it is downloaded and placed in ``AMPLIGRAPH_DATA_HOME``
    or ``~/ampligraph_datasets``.

    This dataset is divided in three splits:

    - `train`: 185,584 triples
    - `valid`: 10,310 triples
    - `test`: 10,310 triples

    Both the validation and test splits are associated with labels (binary ndarrays),
    with `True` for positive statements and `False` for  negatives:

    - `valid_labels`
    - `test_labels`

    ========= ========= ======= ================ ======= =============== ============ ===========
    Dataset   Train      Valid  Valid-negatives  Test    Test-negatives  Entities     Relations
    ========= ========= ======= ================ ======= =============== ============ ===========
    CoDEx-M   185,584   10,310  10,310           10311   10311           17,050       51
    ========= ========= ======= ================ ======= =============== ============ ===========


    Parameters
    ----------
    clean_unseen: bool
        If `True`, filters triples in validation and test sets that include entities not present in the training set.

    check_md5hash: bool
        If `True`, check the `md5hash` of the datset files (default: `False`).

    add_reciprocal_rels: bool
        Flag which specifies whether to add reciprocal relations. For every <s, p, o> in the dataset
        this creates a corresponding triple with reciprocal relation <o, p_reciprocal, s> (default: `False`).
    return_mapper: bool
        Whether to return human-readable labels in a form of dictionary in ``X["mapper"]`` field (default: `False`).

    Returns
    -------

    splits: dict
        The dataset splits: `{'train': train, 'valid': valid, 'valid_negatives': valid_negatives', 'test': test, 'test_negatives': test_negatives}`.
        Each split is a ndarray of shape (n, 3).

    Example
    -------

    >>> from ampligraph.datasets import load_codex
    >>> X = load_codex()
    >>> X["valid"][0]
    array(['Q60684', 'P106', 'Q4964182'], dtype=object)
    >>> X = load_codex(return_mapper=True)
    >>> [X['mapper'][elem]['label'] for elem in X['valid'][0]]
    ['Novalis', 'occupation', 'philosopher']

    """

    codex = DatasetMetadata(
        dataset_name="codex",
        filename="codex.zip",
        url="https://ndownloader.figshare.com/files/66192203",
        train_name="train.txt",
        valid_name="valid.txt",
        test_name="test.txt",
        valid_negatives_name="valid_negatives.txt",
        test_negatives_name="test_negatives.txt",
        mapper_name="mapper.json" if return_mapper else None,
        train_checksum="d507616dd7b9f6ddbacf83766efaa1dd",
        valid_checksum="0fd5e85f41e0ba3ef6c10093cbe2a435",
        test_checksum="7186374c5ca7075d268ccf316927041d",
        mapper_checksum="9cf7209df69562dff36ae94f95f67e82"
        if return_mapper
        else None,
        test_negatives_checksum="2dc6755e9cc54145e782480c5bb2ef44",
        valid_negatives_checksum="381300fbd297df9db2fd05bb6cfc1f2d",
    )

    if clean_unseen:
        return _clean_data(
            _load_dataset(
                codex,
                data_home=None,
                check_md5hash=check_md5hash,
                add_reciprocal_rels=add_reciprocal_rels,
            )
        )
    else:
        return _load_dataset(
            codex,
            data_home=None,
            check_md5hash=check_md5hash,
            add_reciprocal_rels=add_reciprocal_rels,
        )
