


"""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')

__magic_name__	:   Optional[Any]          =							'K-MEANS-CLUST/ '





def 			a_   (  lowercase__   :Optional[Any], lowercase__   :Optional[Any], lowercase__   :Union[str, Any]=None    ):
			if seed is not None:  # useful for obtaining consistent results
						np.random.seed(lowercase__    )
			__lowerCamelCase        =			data.shape[0]  # number of data points

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

			# 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        =			data[rand_indices, :]

			return centroids





def 			a_   (  lowercase__   :Dict, lowercase__   :List[Any]    ):
			return pairwise_distances(lowercase__, lowercase__, metric="""euclidean"""    )





def 			a_   (  lowercase__   :Dict, lowercase__   :Optional[Any]    ):
			# Compute distances between each data point and the set of centroids:
			# Fill in the blank (RHS only)
			__lowerCamelCase        =			centroid_pairwise_dist(lowercase__, lowercase__    )

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

			return cluster_assignment





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

			return new_centroids





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

			return heterogeneity





def 			a_   (  lowercase__   :Optional[int], lowercase__   :Optional[int]    ):
			plt.figure(figsize=(7, 4)    )
			plt.plot(lowercase__, 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_   (  lowercase__   :Dict, lowercase__   :Optional[Any], lowercase__   :List[Any], lowercase__   :int=500, lowercase__   :List[str]=None, lowercase__   :Any=False    ):
			__lowerCamelCase        =			initial_centroids[:]
			__lowerCamelCase        =			None

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

						# 1. Make cluster assignments using nearest centroids
						__lowerCamelCase        =			assign_clusters(lowercase__, lowercase__    )

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

						# 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        =			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        =			compute_heterogeneity(lowercase__, lowercase__, lowercase__, lowercase__    )
									record_heterogeneity.append(lowercase__    )

						__lowerCamelCase        =			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

					__magic_name__	:   str          =							ds.load_iris()
					__magic_name__	:   Optional[Any]          =							3
					__magic_name__	:   Tuple          =							[]
					__magic_name__	:   Any          =							get_initial_centroids(dataset['data'], k, seed=0)
					__magic_name__     ,   __magic_name__	:   Any          =							kmeans(
					    dataset['data'],
					    k,
					    initial_centroids,
					    maxiter=4_0_0,
					    record_heterogeneity=heterogeneity,
					    verbose=True,
					)
					plot_heterogeneity(heterogeneity, k)





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


if __name__ == "__main__":
					import doctest

					doctest.testmod()








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__	:   Optional[Any]          =							{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   List[Any]          =							[
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__	:   Tuple          =							[
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




