
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")

__lowerCAmelCase   :     Union[str, Any]      =						"K-MEANS-CLUST/ "


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase=None		)	->  List[str]:
    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(__lowerCAmelCase		)
    __lowercase						:     Dict		      =    data.shape[0]  # number of data points

    # Pick K indices from range [0, N).
    __lowercase						:     Optional[int]		      =    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.
    __lowercase						:     Union[str, Any]		      =    data[rand_indices, :]

    return centroids


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Union[str, Any]:
    return pairwise_distances(__lowerCAmelCase		,   __lowerCAmelCase		,   metric='''euclidean'''		)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Tuple:
    # Compute distances between each data point and the set of centroids:
    # Fill in the blank (RHS only)
    __lowercase						:     str		      =    centroid_pairwise_dist(__lowerCAmelCase		,   __lowerCAmelCase		)

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

    return cluster_assignment


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

    return new_centroids


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  List[str]:
    __lowercase						:     int		      =    0.0
    for i in range(__lowerCAmelCase		):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        __lowercase						:     List[Any]		      =    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)
            __lowercase						:     Optional[int]		      =    pairwise_distances(
                __lowerCAmelCase		,   [centroids[i]]		,   metric='''euclidean'''		)
            __lowercase						:     int		      =    distances**2
            heterogeneity += np.sum(__lowerCAmelCase		)

    return heterogeneity


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Tuple:
    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       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase=500		,   __lowerCAmelCase=None		,   __lowerCAmelCase=False		)	->  Tuple:
    __lowercase						:     str		      =    initial_centroids[:]
    __lowercase						:     str		      =    None

    for itr in range(__lowerCAmelCase		):
        if verbose:
            print(__lowerCAmelCase		,   end=''''''		)

        # 1. Make cluster assignments using nearest centroids
        __lowercase						:     Dict		      =    assign_clusters(__lowerCAmelCase		,   __lowerCAmelCase		)

        # 2. Compute a new centroid for each of the k clusters, averaging all data
        #    points assigned to that cluster.
        __lowercase						:     Dict		      =    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:
            __lowercase						:     Tuple		      =    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
            __lowercase						:     Optional[int]		      =    compute_heterogeneity(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)
            record_heterogeneity.append(__lowerCAmelCase		)

        __lowercase						:     int		      =    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

 __lowerCAmelCase   :     Union[str, Any]      =						ds.load_iris()
 __lowerCAmelCase   :     Any      =						3
 __lowerCAmelCase   :     Tuple      =						[]
 __lowerCAmelCase   :     Any      =						get_initial_centroids(dataset["data"], k, seed=0)
 __lowerCAmelCase ,				__lowerCAmelCase   :     int      =						kmeans(
     dataset["data"],
     k,
     initial_centroids,
     maxiter=400,
     record_heterogeneity=heterogeneity,
     verbose=True,
 )
 plot_heterogeneity(heterogeneity, k)


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase=None		)	->  pd.DataFrame:
    # Fill missing values with given rules
    if fill_missing_report:
        __lowercase						:     Optional[int]		      =    df.fillna(value=__lowerCAmelCase		)
    __lowercase						:     str		      =    1
    __lowercase						:     Optional[int]		      =    df.select_dtypes(np.number		).columns
    __lowercase						:     Tuple		      =    (
        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)
    __lowercase						:     Any		      =    report[
        (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
    ].copy()  # avoid SettingWithCopyWarning
    __lowercase						:     Dict		      =    (
        '''ClusterSize'''  # rename created cluster df to match report column names
    )
    __lowercase						:     Tuple		      =    '''# of Customers'''
    __lowercase						:     int		      =    pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum()		)
    __lowercase						:     int		      =    '''% of Customers'''  # rename created cluster df to match report column names
    __lowercase						:     List[Any]		      =    '''ClusterProportion'''
    __lowercase						:     List[Any]		      =    clusterproportion.columns.tolist()
    __lowercase						:     Optional[int]		      =    cols[-2:] + cols[:-2]
    __lowercase						:     str		      =    clusterproportion[cols]  # rearrange columns to match report
    __lowercase						:     str		      =    report.columns
    __lowercase						:     List[str]		      =    pd.DataFrame(
        abs(
            report[report['''Type'''] == '''count'''].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values		)		)  # generating df with count of nan values
    __lowercase						:     List[str]		      =    0
    __lowercase						:     List[str]		      =    '''# of nan'''
    __lowercase						:     Any		      =    report[
        report['''Type'''] == '''count'''
    ].Features.tolist()  # filling values in order to match report
    __lowercase						:     Optional[Any]		      =    a.columns.tolist()
    __lowercase						:     Tuple		      =    cols[-2:] + cols[:-2]
    __lowercase						:     Dict		      =    a[cols]  # rearrange columns to match report
    __lowercase						:     str		      =    report.columns  # rename columns to match report
    __lowercase						:     Optional[int]		      =    report.drop(
        report[report.Type == '''count'''].index		)  # drop count values except cluster size
    __lowercase						:     Union[str, Any]		      =    pd.concat(
        [report, a, clustersize, clusterproportion]		,   axis=0		)  # concat report with clustert size and nan values
    __lowercase						:     str		      =    report['''Features'''].isin(__lowerCAmelCase		)
    __lowercase						:     int		      =    report.columns.tolist()
    __lowercase						:     List[Any]		      =    cols[0:2] + cols[-1:] + cols[2:-1]
    __lowercase						:     Optional[int]		      =    report[cols]
    __lowercase						:     List[Any]		      =    {
        '''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,
    }
    __lowercase						:     int		      =    (
        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		)
    )
    __lowercase						:     Any		      =    ''''''
    __lowercase						:     int		      =    report.reset_index()
    __lowercase						:     Optional[Any]		      =    report.drop(columns=['''index''']		)
    return report


if __name__ == "__main__":
 import doctest

 doctest.testmod()




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__lowerCAmelCase   :     Optional[Any]      =						importlib.metadata.version("numpy")
__lowerCAmelCase   :     Any      =						".".join([str(x) for x in sys.version_info[:3]])


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      def 						snake_case_     ( self						:    List[Any]			):
          # lt + different version strings
          require_version_core('''numpy<1000.4.5'''			)
          require_version_core('''numpy<1000.4'''			)
          require_version_core('''numpy<1000'''			)

          # le
          require_version_core('''numpy<=1000.4.5'''			)
          require_version_core(F'numpy<={numpy_ver}'			)

          # eq
          require_version_core(F'numpy=={numpy_ver}'			)

          # ne
          require_version_core('''numpy!=1000.4.5'''			)

          # ge
          require_version_core('''numpy>=1.0'''			)
          require_version_core('''numpy>=1.0.0'''			)
          require_version_core(F'numpy>={numpy_ver}'			)

          # gt
          require_version_core('''numpy>1.0.0'''			)

          # mix
          require_version_core('''numpy>1.0.0,<1000'''			)

          # requirement w/o version
          require_version_core('''numpy'''			)

          # unmet requirements due to version conflict
          for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'numpy<{numpy_ver}']:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn('''but found'''			,    str(_snake_case			)			)

        # unmet requirements due to missing module
          for req in ["numpipypie>1", "numpipypie2"]:
              try:
                  require_version_core(_snake_case			)
              except importlib.metadata.PackageNotFoundError as e:
                  self.assertIn(F'The \'{req}\' distribution was not found and is required by this application'			,    str(_snake_case			)			)
                  self.assertIn('''Try: pip install transformers -U'''			,    str(_snake_case			)			)

        # bogus requirements formats:
        # 1. whole thing
          for req in ["numpy??1.0.0", "numpy1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''requirement needs to be in the pip package format'''			,    str(_snake_case			)			)
        # 2. only operators
          for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''need one of '''			,    str(_snake_case			)			)




      def 						snake_case_     ( self						:    List[str]			):
          # matching requirement
          require_version('''python>=3.6.0'''			)

          # not matching requirements
          for req in ["python>9.9.9", "python<3.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn(F'but found python=={python_ver}'			,    str(_snake_case			)			)


