
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""")

A__       :   Optional[Any]   						=					"""K-MEANS-CLUST/ """



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  List[Any]=None    ):
   if seed is not None:  # useful for obtaining consistent results
      np.random.seed(__UpperCamelCase    )
   lowerCAmelCase__ :    List[Any]				      =			data.shape[0]  # number of data points

   # Pick K indices from range [0, N).
   lowerCAmelCase__ :    int				      =			np.random.randint(0	,__UpperCamelCase	,__UpperCamelCase    )

   # 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.
   lowerCAmelCase__ :    List[str]				      =			data[rand_indices, :]

   return centroids



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Any    ):
   return pairwise_distances(__UpperCamelCase	,__UpperCamelCase	,metric='''euclidean'''    )



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Tuple    ):
   # Compute distances between each data point and the set of centroids:
   # Fill in the blank (RHS only)
   lowerCAmelCase__ :    Tuple				      =			centroid_pairwise_dist(__UpperCamelCase	,__UpperCamelCase    )

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

   return cluster_assignment



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

   return new_centroids



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

   return heterogeneity



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Optional[Any]    ):
   plt.figure(figsize=(7, 4)    )
   plt.plot(__UpperCamelCase	,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      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[str]=500	,__UpperCamelCase       :  int=None	,__UpperCamelCase       :  Union[str, Any]=False    ):
   lowerCAmelCase__ :    Optional[int]				      =			initial_centroids[:]
   lowerCAmelCase__ :    Union[str, Any]				      =			None

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

      # 1. Make cluster assignments using nearest centroids
      lowerCAmelCase__ :    Tuple				      =			assign_clusters(__UpperCamelCase	,__UpperCamelCase    )

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

      # 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:
         lowerCAmelCase__ :    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
         lowerCAmelCase__ :    Optional[int]				      =			compute_heterogeneity(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase    )
         record_heterogeneity.append(__UpperCamelCase    )

      lowerCAmelCase__ :    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

       A__       :   str   						=					ds.load_iris()
       A__       :   List[str]   						=					3
       A__       :   str   						=					[]
       A__       :   Dict   						=					get_initial_centroids(dataset["""data"""], k, seed=0)
       A__       :   List[Any]   						=					kmeans(
           dataset["""data"""],
           k,
           initial_centroids,
           maxiter=4_0_0,
           record_heterogeneity=heterogeneity,
           verbose=True,
       )
       plot_heterogeneity(heterogeneity, k)



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


if __name__ == "__main__":
       import doctest

       doctest.testmod()





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



