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''')

UpperCamelCase    				=		'K-MEANS-CLUST/ '

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Dict=None):



    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(__A)
    lowercase__					:  Any          =							data.shape[0]  # number of data points

    # Pick K indices from range [0, N).
    lowercase__					:  List[str]          =							np.random.randint(0						,      __A						,      __A)

    # 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__					:  Tuple          =							data[rand_indices, :]

    return centroids

def      lowercase_							(     _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			List[Any]):
    return pairwise_distances(__A						,      __A						,      metric="euclidean")

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Optional[int]):
    lowercase__					:  Tuple          =							centroid_pairwise_dist(__A						,      __A)

    # Compute cluster assignments for each data point:
    # Fill in the blank (RHS only)
    lowercase__					:  Optional[int]          =							np.argmin(__A						,      axis=1)

    return cluster_assignment

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  Union[str, Any]          =							[]
    for i in range(__A):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        lowercase__					:  Union[str, Any]          =							data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        lowercase__					:  str          =							member_data_points.mean(axis=0)
        new_centroids.append(__A)
    lowercase__					:  Tuple          =							np.array(__A)

    return new_centroids

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Optional[Any]						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  Optional[int]          =							0.0
    for i in range(__A):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        lowercase__					:  str          =							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__					:  List[Any]          =							pairwise_distances(
                __A						,      [centroids[i]]						,      metric="euclidean")
            lowercase__					:  Dict          =							distances**2
            heterogeneity += np.sum(__A)

    return heterogeneity

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[Any]):
    plt.figure(figsize=(7, 4))
    plt.plot(__A						,      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      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Tuple=500						,      _lowerCamelCase				:			Any=None						,      _lowerCamelCase				:			Dict=False):
    lowercase__					:  Tuple          =							initial_centroids[:]
    lowercase__					:  List[Any]          =							None

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

        # 1. Make cluster assignments using nearest centroids
        lowercase__					:  Any          =							assign_clusters(__A						,      __A)

        # 2. Compute a new centroid for each of the k clusters, averaging all data
        #    points assigned to that cluster.
        lowercase__					:  List[Any]          =							revise_centroids(__A						,      __A						,      __A)

        # 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__					:  Union[str, Any]          =							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__					:  Any          =							compute_heterogeneity(__A						,      __A						,      __A						,      __A)
            record_heterogeneity.append(__A)

        lowercase__					:  Optional[Any]          =							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

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

def      lowercase_							(     _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			List[str]=None):



    if fill_missing_report:
        lowercase__					:  Optional[Any]          =							df.fillna(value=__A)
    lowercase__					:  Optional[int]          =							1
    lowercase__					:  List[Any]          =							df.select_dtypes(np.number).columns
    lowercase__					:  List[Any]          =							(
        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(__A))),
                ("mean_without_zeros", lambda _lowerCamelCase: x.replace(0						,      np.NaN).mean()),
                (
                    "mean_25-75",
                    lambda _lowerCamelCase: np.mean(
                        np.nan_to_num(
                            sorted(__A)[
                                round(len(__A) * 25 / 100) : round(len(__A) * 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=__A						,      columns={"level_0": "Features", "level_1": "Type"})
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    lowercase__					:  int          =							report[
        (report["Features"] == "dummy") & (report["Type"] == "count")
    ].copy()  # avoid SettingWithCopyWarning
    lowercase__					:  Union[str, Any]          =							(
        "ClusterSize"  # rename created cluster df to match report column names
    )
    lowercase__					:  int          =							"# of Customers"
    lowercase__					:  List[Any]          =							pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum())
    lowercase__					:  Optional[Any]          =							"% of Customers"  # rename created cluster df to match report column names
    lowercase__					:  Optional[int]          =							"ClusterProportion"
    lowercase__					:  List[str]          =							clusterproportion.columns.tolist()
    lowercase__					:  List[Any]          =							cols[-2:] + cols[:-2]
    lowercase__					:  Any          =							clusterproportion[cols]  # rearrange columns to match report
    lowercase__					:  List[Any]          =							report.columns
    lowercase__					:  Optional[Any]          =							pd.DataFrame(
        abs(
            report[report["Type"] == "count"].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values))  # generating df with count of nan values
    lowercase__					:  int          =							0
    lowercase__					:  str          =							"# of nan"
    lowercase__					:  Any          =							report[
        report["Type"] == "count"
    ].Features.tolist()  # filling values in order to match report
    lowercase__					:  List[Any]          =							a.columns.tolist()
    lowercase__					:  int          =							cols[-2:] + cols[:-2]
    lowercase__					:  Optional[int]          =							a[cols]  # rearrange columns to match report
    lowercase__					:  Optional[Any]          =							report.columns  # rename columns to match report
    lowercase__					:  Union[str, Any]          =							report.drop(
        report[report.Type == "count"].index)  # drop count values except cluster size
    lowercase__					:  List[str]          =							pd.concat(
        [report, a, clustersize, clusterproportion]						,      axis=0)  # concat report with clustert size and nan values
    lowercase__					:  Tuple          =							report["Features"].isin(__A)
    lowercase__					:  Dict          =							report.columns.tolist()
    lowercase__					:  Tuple          =							cols[0:2] + cols[-1:] + cols[2:-1]
    lowercase__					:  Union[str, Any]          =							report[cols]
    lowercase__					:  Dict          =							{
        "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__					:  Union[str, Any]          =							(
        report.assign(
            Sortera=lambda _lowerCamelCase: x.Type.map(__A)						,      Sortera=lambda _lowerCamelCase: list(reversed(range(len(__A))))						,      )
        .sort_values(["Sorter1", "Mark", "Sorter2"]						,      ascending=__A)
        .drop(["Sorter1", "Sorter2"]						,      axis=1)
    )
    lowercase__					:  Optional[Any]          =							""
    lowercase__					:  int          =							report.reset_index()
    lowercase__					:  Union[str, Any]          =							report.drop(columns=["index"])
    return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()







'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a__    :   Optional[Any]								=			TypeVar('T', bound='Enum')
@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      80
    __UpperCAmelCase       =      70
    __UpperCAmelCase       =      60
    __UpperCAmelCase       =      50
    __UpperCAmelCase       =      40
    __UpperCAmelCase       =      30
    __UpperCAmelCase       =      20
    __UpperCAmelCase       =      10

@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      24
    __UpperCAmelCase       =      21
    __UpperCAmelCase       =      18
    __UpperCAmelCase       =      15
    __UpperCAmelCase       =      12
    __UpperCAmelCase       =      9
    __UpperCAmelCase       =      6
    __UpperCAmelCase       =      3
    __UpperCAmelCase       =      2
    __UpperCAmelCase       =      1
    __UpperCAmelCase       =      -1
    __UpperCAmelCase       =      -2
    __UpperCAmelCase       =      -3
    __UpperCAmelCase       =      -6
    __UpperCAmelCase       =      -9
    __UpperCAmelCase       =      -12
    __UpperCAmelCase       =      -15
    __UpperCAmelCase       =      -18
    __UpperCAmelCase       =      -21
    __UpperCAmelCase       =      -24


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value > 0}


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value < 0}







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
      for name_prefix, value_prefix in prefixes.items():
            UpperCamelCase__      =							value / (10**value_prefix)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {name_prefix}'''
      return str(__A       )







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      for prefix in BinaryUnit:
            UpperCamelCase__      =							value / (2**prefix.value)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {prefix.name}'''
      return str(__A       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





