




'''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')

lowerCamelCase_		       =						"""K-MEANS-CLUST/ """
def  SCREAMING_SNAKE_CASE_ (							__A				: Union[str, Any]       ,     __A				: List[Any]       ,     __A				: List[Any]=None  ) -> Union[str, Any]:






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

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

   # 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  SCREAMING_SNAKE_CASE_ (							__A				: List[str]       ,     __A				: Optional[int]  ) -> Any:
   return pairwise_distances(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     metric="euclidean"  )
def  SCREAMING_SNAKE_CASE_ (							__A				: int       ,     __A				: List[Any]  ) -> Union[str, Any]:
   _SCREAMING_SNAKE_CASE       =      centroid_pairwise_dist(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_  )

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

   return cluster_assignment
def  SCREAMING_SNAKE_CASE_ (							__A				: Any       ,     __A				: str       ,     __A				: str  ) -> int:
   _SCREAMING_SNAKE_CASE       =      []
   for i in range(SCREAMING_SNAKE_CASE_  ):
      # 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(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       =      np.array(SCREAMING_SNAKE_CASE_  )

   return new_centroids
def  SCREAMING_SNAKE_CASE_ (							__A				: Optional[Any]       ,     __A				: Tuple       ,     __A				: Tuple       ,     __A				: Dict  ) -> Any:
   _SCREAMING_SNAKE_CASE       =      0.0
   for i in range(SCREAMING_SNAKE_CASE_  ):
      # 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(
             SCREAMING_SNAKE_CASE_       ,     [centroids[i]]       ,     metric="euclidean"  )
         _SCREAMING_SNAKE_CASE       =      distances**2
         heterogeneity += np.sum(SCREAMING_SNAKE_CASE_  )

   return heterogeneity
def  SCREAMING_SNAKE_CASE_ (							__A				: Union[str, Any]       ,     __A				: str  ) -> Optional[Any]:
   plt.figure(figsize=(7, 4)  )
   plt.plot(SCREAMING_SNAKE_CASE_       ,     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  SCREAMING_SNAKE_CASE_ (							__A				: Any       ,     __A				: Optional[Any]       ,     __A				: Any       ,     __A				: Optional[int]=5_00       ,     __A				: Union[str, Any]=None       ,     __A				: int=False  ) -> List[str]:
   _SCREAMING_SNAKE_CASE       =      initial_centroids[:]
   _SCREAMING_SNAKE_CASE       =      None

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

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

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

      # 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(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_  )
         record_heterogeneity.append(SCREAMING_SNAKE_CASE_  )

      _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

 lowerCamelCase_		       =						ds.load_iris()
 lowerCamelCase_		       =						3
 lowerCamelCase_		       =						[]
 lowerCamelCase_		       =						get_initial_centroids(dataset['data'], k, seed=0)
 lowerCamelCase_		       =						kmeans(
     dataset['data'],
     k,
     initial_centroids,
     maxiter=4_00,
     record_heterogeneity=heterogeneity,
     verbose=True,
 )
 plot_heterogeneity(heterogeneity, k)
def  SCREAMING_SNAKE_CASE_ (							__A				: Any       ,     __A				: str       ,     __A				: Optional[Any]=None  ) -> pd.DataFrame:






   if fill_missing_report:
      _SCREAMING_SNAKE_CASE       =      df.fillna(value=SCREAMING_SNAKE_CASE_  )
   _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 __A  : np.mean(np.nan_to_num(SCREAMING_SNAKE_CASE_  )  )),
               ("mean_without_zeros", lambda __A  : x.replace(0       ,     np.NaN  ).mean()),
               (
                   "mean_25-75",
                   lambda __A  : np.mean(
                       np.nan_to_num(
                           sorted(SCREAMING_SNAKE_CASE_  )[
                               round(len(SCREAMING_SNAKE_CASE_  ) * 25 / 1_00  ) : round(len(SCREAMING_SNAKE_CASE_  ) * 75 / 1_00  )
                           ]  )  ),
               ),
               ("mean_with_na", np.mean),
               ("min", lambda __A  : x.min()),
               ("5%", lambda __A  : x.quantile(0.0_5  )),
               ("25%", lambda __A  : x.quantile(0.2_5  )),
               ("50%", lambda __A  : x.quantile(0.5_0  )),
               ("75%", lambda __A  : x.quantile(0.7_5  )),
               ("95%", lambda __A  : x.quantile(0.9_5  )),
               ("max", lambda __A  : x.max()),
               ("count", lambda __A  : x.count()),
               ("stdev", lambda __A  : x.std()),
               ("mode", lambda __A  : x.mode()[0]),
               ("median", lambda __A  : x.median()),
               ("# > 0", lambda __A  : (x > 0).sum()),
           ]  )
       .T.reset_index()
       .rename(index=SCREAMING_SNAKE_CASE_       ,     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(SCREAMING_SNAKE_CASE_  )
   _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 __A  : x.Type.map(SCREAMING_SNAKE_CASE_  )       ,     Sortera=lambda __A  : list(reversed(range(len(SCREAMING_SNAKE_CASE_  )  )  )  )       ,     )
       .sort_values(["Sorter1", "Mark", "Sorter2"]       ,     ascending=SCREAMING_SNAKE_CASE_  )
       .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()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						:int      =			{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE						:List[Any]      =			[
     """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """YosoForMaskedLM""",
     """YosoForMultipleChoice""",
     """YosoForQuestionAnswering""",
     """YosoForSequenceClassification""",
     """YosoForTokenClassification""",
     """YosoLayer""",
     """YosoModel""",
     """YosoPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_yoso import (
      YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoLayer,
      YosoModel,
      YosoPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE						:str      =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
