



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				:							int								="""K-MEANS-CLUST/ """



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

					# Pick K indices from range [0, N).
					UpperCamelCase_											=				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_											=				data[rand_indices, :]

					return centroids



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					return pairwise_distances(_lowerCAmelCase				,		_lowerCAmelCase				,		metric="euclidean")



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					# Compute distances between each data point and the set of centroids:
					# Fill in the blank (RHS only)
					UpperCamelCase_											=				centroid_pairwise_dist(_lowerCAmelCase				,		_lowerCAmelCase)

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

					return cluster_assignment



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				[]
					for i in range(_lowerCAmelCase):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										UpperCamelCase_											=				data[cluster_assignment == i]
										# Compute the mean of the data points. Fill in the blank (RHS only)
										UpperCamelCase_											=				member_data_points.mean(axis=0)
										new_centroids.append(_lowerCAmelCase)
					UpperCamelCase_											=				np.array(_lowerCAmelCase)

					return new_centroids



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				0.0
					for i in range(_lowerCAmelCase):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										UpperCamelCase_											=				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_											=				pairwise_distances(
															    _lowerCAmelCase				,		[centroids[i]]				,		metric="euclidean")
															UpperCamelCase_											=				distances**2
															heterogeneity += np.sum(_lowerCAmelCase)

					return heterogeneity



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					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=5_00				,		_lowerCAmelCase=None				,		_lowerCAmelCase=False):
					UpperCamelCase_											=				initial_centroids[:]
					UpperCamelCase_											=				None

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

										# 1. Make cluster assignments using nearest centroids
										UpperCamelCase_											=				assign_clusters(_lowerCAmelCase				,		_lowerCAmelCase)

										# 2. Compute a new centroid for each of the k clusters, averaging all data
										#    points assigned to that cluster.
										UpperCamelCase_											=				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_											=				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_											=				compute_heterogeneity(_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase)
															record_heterogeneity.append(_lowerCAmelCase)

										UpperCamelCase_											=				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				:							str								=ds.load_iris()
	UpperCAmelCase				:							Dict								=3
	UpperCAmelCase				:							Dict								=[]
	UpperCAmelCase				:							Tuple								=get_initial_centroids(dataset["""data"""], k, seed=0)
	UpperCAmelCase	,	UpperCAmelCase				:							List[str]								=kmeans(
	    dataset["""data"""],
	    k,
	    initial_centroids,
	    maxiter=400,
	    record_heterogeneity=heterogeneity,
	    verbose=True,
	)
	plot_heterogeneity(heterogeneity, k)



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=None):
					# Fill missing values with given rules
					if fill_missing_report:
										UpperCamelCase_											=				df.fillna(value=_lowerCAmelCase)
					UpperCamelCase_											=				1
					UpperCamelCase_											=				df.select_dtypes(np.number).columns
					UpperCamelCase_											=				(
					    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 / 1_00) : round(len(_lowerCAmelCase) * 75 / 1_00)
					                        ])),
					            ),
					            ("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_											=				report[
					    (report["Features"] == "dummy") & (report["Type"] == "count")
					].copy()  # avoid SettingWithCopyWarning
					UpperCamelCase_											=				(
					    "ClusterSize"  # rename created cluster df to match report column names
					)
					UpperCamelCase_											=				"# of Customers"
					UpperCamelCase_											=				pd.DataFrame(
					    clustersize.iloc[:, 2:].values
					    / clustersize.iloc[:, 2:].values.sum())
					UpperCamelCase_											=				"% of Customers"  # rename created cluster df to match report column names
					UpperCamelCase_											=				"ClusterProportion"
					UpperCamelCase_											=				clusterproportion.columns.tolist()
					UpperCamelCase_											=				cols[-2:] + cols[:-2]
					UpperCamelCase_											=				clusterproportion[cols]  # rearrange columns to match report
					UpperCamelCase_											=				report.columns
					UpperCamelCase_											=				pd.DataFrame(
					    abs(
					        report[report["Type"] == "count"].iloc[:, 2:].values
					        - clustersize.iloc[:, 2:].values))  # generating df with count of nan values
					UpperCamelCase_											=				0
					UpperCamelCase_											=				"# of nan"
					UpperCamelCase_											=				report[
					    report["Type"] == "count"
					].Features.tolist()  # filling values in order to match report
					UpperCamelCase_											=				a.columns.tolist()
					UpperCamelCase_											=				cols[-2:] + cols[:-2]
					UpperCamelCase_											=				a[cols]  # rearrange columns to match report
					UpperCamelCase_											=				report.columns  # rename columns to match report
					UpperCamelCase_											=				report.drop(
					    report[report.Type == "count"].index)  # drop count values except cluster size
					UpperCamelCase_											=				pd.concat(
					    [report, a, clustersize, clusterproportion]				,		axis=0)  # concat report with clustert size and nan values
					UpperCamelCase_											=				report["Features"].isin(_lowerCAmelCase)
					UpperCamelCase_											=				report.columns.tolist()
					UpperCamelCase_											=				cols[0:2] + cols[-1:] + cols[2:-1]
					UpperCamelCase_											=				report[cols]
					UpperCamelCase_											=				{
					    "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_											=				(
					    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_											=				""
					UpperCamelCase_											=				report.reset_index()
					UpperCamelCase_											=				report.drop(columns=["index"])
					return report


if __name__ == "__main__":
	import doctest

	doctest.testmod()





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
