





'''simple docstring'''

import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("""ignore""")

lowercase_         =			"""K-MEANS-CLUST/ """





def lowerCamelCase						(					__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   Union[str, Any]=None   )						->List[str]:
    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      data.shape[0]  # number of data points

    # Pick K indices from range [0, N).
    _SCREAMING_SNAKE_CASE     =      np.random.randint(0 ,	__lowerCamelCase ,	__lowerCamelCase   )

    # Keep centroids as dense format, as many entries will be nonzero due to averaging.
    # As long as at least one document in a cluster contains a word,
    # it will carry a nonzero weight in the TF-IDF vector of the centroid.
    _SCREAMING_SNAKE_CASE     =      data[rand_indices, :]

    return centroids





def lowerCamelCase						(					__lowerCamelCase			:   Any ,	__lowerCamelCase			:   Optional[Any]   )						->int:
    return pairwise_distances(__lowerCamelCase ,	__lowerCamelCase ,	metric="""euclidean"""   )





def lowerCamelCase						(					__lowerCamelCase			:   Dict ,	__lowerCamelCase			:   Any   )						->Optional[Any]:
    # Compute distances between each data point and the set of centroids:
    # Fill in the blank (RHS only)
    _SCREAMING_SNAKE_CASE     =      centroid_pairwise_dist(__lowerCamelCase ,	__lowerCamelCase   )

    # Compute cluster assignments for each data point:
    # Fill in the blank (RHS only)
    _SCREAMING_SNAKE_CASE     =      np.argmin(__lowerCamelCase ,	axis=1   )

    return cluster_assignment





def lowerCamelCase						(					__lowerCamelCase			:   Optional[int] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Union[str, Any]   )						->List[str]:
    _SCREAMING_SNAKE_CASE     =      []
    for i in range(__lowerCamelCase   ):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      member_data_points.mean(axis=0   )
        new_centroids.append(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      np.array(__lowerCamelCase   )

    return new_centroids





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int   )						->List[Any]:
    _SCREAMING_SNAKE_CASE     =      0.0
    for i in range(__lowerCamelCase   ):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      data[cluster_assignment == i, :]

        if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
            # Compute distances from centroid to data points (RHS only)
            _SCREAMING_SNAKE_CASE     =      pairwise_distances(
                __lowerCamelCase ,	[centroids[i]] ,	metric="""euclidean"""   )
            _SCREAMING_SNAKE_CASE     =      distances**2
            heterogeneity += np.sum(__lowerCamelCase   )

    return heterogeneity





def lowerCamelCase						(					__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   int   )						->Any:
    plt.figure(figsize=(7, 4)   )
    plt.plot(__lowerCamelCase ,	linewidth=4   )
    plt.xlabel("""# Iterations"""   )
    plt.ylabel("""Heterogeneity"""   )
    plt.title(F'Heterogeneity of clustering over time, K={k:d}'   )
    plt.rcParams.update({"""font.size""": 16}   )
    plt.show()





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   str=500 ,	__lowerCamelCase			:   Any=None ,	__lowerCamelCase			:   Any=False   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      initial_centroids[:]
    _SCREAMING_SNAKE_CASE     =      None

    for itr in range(__lowerCamelCase   ):
        if verbose:
            print(__lowerCamelCase ,	end=""""""   )

        # 1. Make cluster assignments using nearest centroids
        _SCREAMING_SNAKE_CASE     =      assign_clusters(__lowerCamelCase ,	__lowerCamelCase   )

        # 2. Compute a new centroid for each of the k clusters, averaging all data
        #    points assigned to that cluster.
        _SCREAMING_SNAKE_CASE     =      revise_centroids(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )

        # Check for convergence: if none of the assignments changed, stop
        if (
            prev_cluster_assignment is not None
            and (prev_cluster_assignment == cluster_assignment).all()
        ):
            break

        # Print number of new assignments
        if prev_cluster_assignment is not None:
            _SCREAMING_SNAKE_CASE     =      np.sum(prev_cluster_assignment != cluster_assignment   )
            if verbose:
                print(
                    F'    {num_changed:5d} elements changed their cluster assignment.'   )

        # Record heterogeneity convergence metric
        if record_heterogeneity is not None:
            # YOUR CODE HERE
            _SCREAMING_SNAKE_CASE     =      compute_heterogeneity(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
            record_heterogeneity.append(__lowerCamelCase   )

        _SCREAMING_SNAKE_CASE     =      cluster_assignment[:]

    return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
      from sklearn import datasets as ds

      lowercase_         =			ds.load_iris()
      lowercase_         =			3
      lowercase_         =			[]
      lowercase_         =			get_initial_centroids(dataset["""data"""], k, seed=0)
      lowercase_    ,      lowercase_         =			kmeans(
          dataset["""data"""],
          k,
          initial_centroids,
          maxiter=400,
          record_heterogeneity=heterogeneity,
          verbose=True,
      )
      plot_heterogeneity(heterogeneity, k)





def lowerCamelCase						(					__lowerCamelCase			:   pd.DataFrame ,	__lowerCamelCase			:   np.ndarray ,	__lowerCamelCase			:   Union[str, Any]=None   )						->pd.DataFrame:
    # Fill missing values with given rules
    if fill_missing_report:
        _SCREAMING_SNAKE_CASE     =      df.fillna(value=__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      1
    _SCREAMING_SNAKE_CASE     =      df.select_dtypes(np.number   ).columns
    _SCREAMING_SNAKE_CASE     =      (
        df.groupby(["""Cluster"""]   )[  # construct report dataframe
            numeric_cols
        ]  # group by cluster number
        .agg(
            [
                ("""sum""", np.sum),
                ("""mean_with_zeros""", lambda __lowerCamelCase   : np.mean(np.nan_to_num(__lowerCamelCase   )   )),
                ("""mean_without_zeros""", lambda __lowerCamelCase   : x.replace(0 ,	np.NaN   ).mean()),
                (
                    """mean_25-75""",
                    lambda __lowerCamelCase   : np.mean(
                        np.nan_to_num(
                            sorted(__lowerCamelCase   )[
                                round(len(__lowerCamelCase   ) * 25 / 100   ) : round(len(__lowerCamelCase   ) * 75 / 100   )
                            ]   )   ),
                ),
                ("""mean_with_na""", np.mean),
                ("""min""", lambda __lowerCamelCase   : x.min()),
                ("""5%""", lambda __lowerCamelCase   : x.quantile(0.05   )),
                ("""25%""", lambda __lowerCamelCase   : x.quantile(0.25   )),
                ("""50%""", lambda __lowerCamelCase   : x.quantile(0.50   )),
                ("""75%""", lambda __lowerCamelCase   : x.quantile(0.75   )),
                ("""95%""", lambda __lowerCamelCase   : x.quantile(0.95   )),
                ("""max""", lambda __lowerCamelCase   : x.max()),
                ("""count""", lambda __lowerCamelCase   : x.count()),
                ("""stdev""", lambda __lowerCamelCase   : x.std()),
                ("""mode""", lambda __lowerCamelCase   : x.mode()[0]),
                ("""median""", lambda __lowerCamelCase   : x.median()),
                ("""# > 0""", lambda __lowerCamelCase   : (x > 0).sum()),
            ]   )
        .T.reset_index()
        .rename(index=__lowerCamelCase ,	columns={"""level_0""": """Features""", """level_1""": """Type"""}   )
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    _SCREAMING_SNAKE_CASE     =      report[
        (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
    ].copy()  # avoid SettingWithCopyWarning
    _SCREAMING_SNAKE_CASE     =      (
        """ClusterSize"""  # rename created cluster df to match report column names
    )
    _SCREAMING_SNAKE_CASE     =      """# of Customers"""
    _SCREAMING_SNAKE_CASE     =      pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum()   )
    _SCREAMING_SNAKE_CASE     =      """% of Customers"""  # rename created cluster df to match report column names
    _SCREAMING_SNAKE_CASE     =      """ClusterProportion"""
    _SCREAMING_SNAKE_CASE     =      clusterproportion.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[-2:] + cols[:-2]
    _SCREAMING_SNAKE_CASE     =      clusterproportion[cols]  # rearrange columns to match report
    _SCREAMING_SNAKE_CASE     =      report.columns
    _SCREAMING_SNAKE_CASE     =      pd.DataFrame(
        abs(
            report[report["""Type"""] == """count"""].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values   )   )  # generating df with count of nan values
    _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      """# of nan"""
    _SCREAMING_SNAKE_CASE     =      report[
        report["""Type"""] == """count"""
    ].Features.tolist()  # filling values in order to match report
    _SCREAMING_SNAKE_CASE     =      a.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[-2:] + cols[:-2]
    _SCREAMING_SNAKE_CASE     =      a[cols]  # rearrange columns to match report
    _SCREAMING_SNAKE_CASE     =      report.columns  # rename columns to match report
    _SCREAMING_SNAKE_CASE     =      report.drop(
        report[report.Type == """count"""].index   )  # drop count values except cluster size
    _SCREAMING_SNAKE_CASE     =      pd.concat(
        [report, a, clustersize, clusterproportion] ,	axis=0   )  # concat report with clustert size and nan values
    _SCREAMING_SNAKE_CASE     =      report["""Features"""].isin(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      report.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[0:2] + cols[-1:] + cols[2:-1]
    _SCREAMING_SNAKE_CASE     =      report[cols]
    _SCREAMING_SNAKE_CASE     =      {
        """ClusterSize""": 9,
        """ClusterProportion""": 8,
        """mean_with_zeros""": 7,
        """mean_with_na""": 6,
        """max""": 5,
        """50%""": 4,
        """min""": 3,
        """25%""": 2,
        """75%""": 1,
        """# of nan""": 0,
        """# > 0""": -1,
        """sum_with_na""": -2,
    }
    _SCREAMING_SNAKE_CASE     =      (
        report.assign(
            Sortera=lambda __lowerCamelCase   : x.Type.map(__lowerCamelCase   ) ,	Sortera=lambda __lowerCamelCase   : list(reversed(range(len(__lowerCamelCase   )   )   )   ) ,	)
        .sort_values(["""Sorter1""", """Mark""", """Sorter2"""] ,	ascending=__lowerCamelCase   )
        .drop(["""Sorter1""", """Sorter2"""] ,	axis=1   )
    )
    _SCREAMING_SNAKE_CASE     =      """"""
    _SCREAMING_SNAKE_CASE     =      report.reset_index()
    _SCREAMING_SNAKE_CASE     =      report.drop(columns=["""index"""]   )
    return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase_         =			"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION	)
class  a_					(    datasets.Metric	):

    '''simple docstring'''


    def       snake_case_(		self	)		->       Union[str, Any]:
        if version.parse(scb.__version__	) < version.parse("""1.4.12"""	):
            raise ImportWarning(
                """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""	)
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://github.com/mjpost/sacreBLEU"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]			,		reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]			,		)





    def       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      len(references[0]	)
        if any(len(A	) != references_per_prediction for refs in references	):
            raise ValueError("""Sacrebleu requires the same number of references for each prediction"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict



