






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""")

SCREAMING_SNAKE_CASE__		:       str   							=							"""K-MEANS-CLUST/ """





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> List[str]:

     '''simple docstring'''
     if seed is not None:  # useful for obtaining consistent results
          np.random.seed(__lowerCamelCase					)
     UpperCAmelCase__			:     int										= data.shape[0]  # number of data points

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

     return centroids





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

     '''simple docstring'''
     return pairwise_distances(__lowerCamelCase   ,       __lowerCamelCase   ,       metric="""euclidean"""					)





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> str:

     '''simple docstring'''
     # Compute distances between each data point and the set of centroids:
     # Fill in the blank (RHS only)
     UpperCAmelCase__			:     Optional[Any]										= centroid_pairwise_dist(__lowerCamelCase   ,       __lowerCamelCase					)

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

     return cluster_assignment





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Any										= []
     for i in range(__lowerCamelCase					):
          # Select all data points that belong to cluster i. Fill in the blank (RHS only)
          UpperCAmelCase__			:     Any										= data[cluster_assignment == i]
          # Compute the mean of the data points. Fill in the blank (RHS only)
          UpperCAmelCase__			:     List[str]										= member_data_points.mean(axis=0					)
          new_centroids.append(__lowerCamelCase					)
     UpperCAmelCase__			:     str										= np.array(__lowerCamelCase					)

     return new_centroids





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> List[str]:

     '''simple docstring'''
     UpperCAmelCase__			:     Tuple										= 0.0
     for i in range(__lowerCamelCase					):
          # Select all data points that belong to cluster i. Fill in the blank (RHS only)
          UpperCAmelCase__			:     int										= 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)
               UpperCAmelCase__			:     Dict										= pairwise_distances(
                   __lowerCamelCase   ,       [centroids[i]]   ,       metric="""euclidean"""					)
               UpperCAmelCase__			:     Optional[Any]										= distances**2
               heterogeneity += np.sum(__lowerCamelCase					)

     return heterogeneity





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:

     '''simple docstring'''
     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   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=500   ,       __lowerCamelCase=None   ,       __lowerCamelCase=False					)   -> Union[str, Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Tuple										= initial_centroids[:]
     UpperCAmelCase__			:     Union[str, Any]										= None

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

          # 1. Make cluster assignments using nearest centroids
          UpperCAmelCase__			:     Optional[int]										= assign_clusters(__lowerCamelCase   ,       __lowerCamelCase					)

          # 2. Compute a new centroid for each of the k clusters, averaging all data
          #    points assigned to that cluster.
          UpperCAmelCase__			:     Any										= 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:
               UpperCAmelCase__			:     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
               UpperCAmelCase__			:     str										= compute_heterogeneity(__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)
               record_heterogeneity.append(__lowerCamelCase					)

          UpperCAmelCase__			:     str										= 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

      SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							ds.load_iris()
      SCREAMING_SNAKE_CASE__		:       Tuple   							=							3
      SCREAMING_SNAKE_CASE__		:       List[str]   							=							[]
      SCREAMING_SNAKE_CASE__		:       List[Any]   							=							get_initial_centroids(dataset["""data"""], k, seed=0)
      SCREAMING_SNAKE_CASE__							, SCREAMING_SNAKE_CASE__		:       Dict   							=							kmeans(
          dataset["""data"""],
          k,
          initial_centroids,
          maxiter=4_00,
          record_heterogeneity=heterogeneity,
          verbose=True,
      )
      plot_heterogeneity(heterogeneity, k)





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None					)   -> pd.DataFrame:

     '''simple docstring'''
     # Fill missing values with given rules
     if fill_missing_report:
          UpperCAmelCase__			:     Union[str, Any]										= df.fillna(value=__lowerCamelCase					)
     UpperCAmelCase__			:     Union[str, Any]										= 1
     UpperCAmelCase__			:     str										= df.select_dtypes(np.number					).columns
     UpperCAmelCase__			:     Optional[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(__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)
     UpperCAmelCase__			:     Dict										= report[
         (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
     ].copy()  # avoid SettingWithCopyWarning
     UpperCAmelCase__			:     Dict										= (
         """ClusterSize"""  # rename created cluster df to match report column names
     )
     UpperCAmelCase__			:     Optional[Any]										= """# of Customers"""
     UpperCAmelCase__			:     Any										= pd.DataFrame(
         clustersize.iloc[:, 2:].values
         / clustersize.iloc[:, 2:].values.sum()					)
     UpperCAmelCase__			:     List[Any]										= """% of Customers"""  # rename created cluster df to match report column names
     UpperCAmelCase__			:     str										= """ClusterProportion"""
     UpperCAmelCase__			:     List[Any]										= clusterproportion.columns.tolist()
     UpperCAmelCase__			:     Optional[int]										= cols[-2:] + cols[:-2]
     UpperCAmelCase__			:     List[Any]										= clusterproportion[cols]  # rearrange columns to match report
     UpperCAmelCase__			:     Dict										= report.columns
     UpperCAmelCase__			:     str										= pd.DataFrame(
         abs(
             report[report["""Type"""] == """count"""].iloc[:, 2:].values
             - clustersize.iloc[:, 2:].values					)					)  # generating df with count of nan values
     UpperCAmelCase__			:     List[str]										= 0
     UpperCAmelCase__			:     Dict										= """# of nan"""
     UpperCAmelCase__			:     Tuple										= report[
         report["""Type"""] == """count"""
     ].Features.tolist()  # filling values in order to match report
     UpperCAmelCase__			:     Tuple										= a.columns.tolist()
     UpperCAmelCase__			:     str										= cols[-2:] + cols[:-2]
     UpperCAmelCase__			:     Optional[int]										= a[cols]  # rearrange columns to match report
     UpperCAmelCase__			:     Optional[Any]										= report.columns  # rename columns to match report
     UpperCAmelCase__			:     Optional[Any]										= report.drop(
         report[report.Type == """count"""].index					)  # drop count values except cluster size
     UpperCAmelCase__			:     str										= pd.concat(
         [report, a, clustersize, clusterproportion]   ,       axis=0					)  # concat report with clustert size and nan values
     UpperCAmelCase__			:     int										= report["""Features"""].isin(__lowerCamelCase					)
     UpperCAmelCase__			:     Optional[int]										= report.columns.tolist()
     UpperCAmelCase__			:     Optional[Any]										= cols[0:2] + cols[-1:] + cols[2:-1]
     UpperCAmelCase__			:     Any										= report[cols]
     UpperCAmelCase__			:     Optional[int]										= {
         """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,
     }
     UpperCAmelCase__			:     List[Any]										= (
         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					)
     )
     UpperCAmelCase__			:     str										= """"""
     UpperCAmelCase__			:     int										= report.reset_index()
     UpperCAmelCase__			:     Any										= report.drop(columns=["""index"""]					)
     return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()














from ..utils import requires_backends


SCREAMING_SNAKE_CASE__		:       int   							=							None







class 							UpperCAmelCase_     :




     def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(self  ,	["""detectron2"""]		)




     @classmethod
     def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(cls  ,	["""detectron2"""]		)






