
'''simple docstring'''

from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							Any   ,				__magic_name__      :							pyspark.sql.DataFrame   ,				__magic_name__      :							Optional[NamedSplit] = None   ,				__magic_name__      :							Optional[Features] = None   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							str = None   ,				__magic_name__      :							bool = False   ,				__magic_name__      :							str = None   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							str = "arrow"   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				super().__init__(
				    split=__magic_name__   ,				features=__magic_name__   ,				cache_dir=__magic_name__   ,				keep_in_memory=__magic_name__   ,				streaming=__magic_name__   ,				**__magic_name__   ,				)
				lowerCAmelCase__										=				load_from_cache_file
				lowerCAmelCase__										=				file_format
				lowerCAmelCase__										=				Spark(
				    df=__magic_name__   ,				features=__magic_name__   ,				cache_dir=__magic_name__   ,				working_dir=__magic_name__   ,				**__magic_name__   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				if self.streaming:
							return self.builder.as_streaming_dataset(split=self.split				)
				lowerCAmelCase__										=				None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
				self.builder.download_and_prepare(
				    download_mode=__magic_name__   ,				file_format=self._file_format   ,				)
				return self.builder.as_dataset(split=self.split				)







"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 0.1:
												safe += 1
									else:
												not_safe += 1
			return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					_lowercase							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
					_lowercase							=				pd.DataFrame(
					    data_input, columns=['''total_user''', '''total_even''', '''days''']
					)
					_lowercase							=				Normalizer().fit_transform(data_input_df.values)
					# split data
					_lowercase							=				normalize_df[:, 2].tolist()
					_lowercase							=				normalize_df[:, 0].tolist()
					_lowercase							=				normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					_lowercase							=				normalize_df[:, [1, 2]].tolist()
					_lowercase							=				x[: len(x) - 1]
					_lowercase							=				x[len(x) - 1 :]

					# for linear regression & sarimax
					_lowercase							=				total_date[: len(total_date) - 1]
					_lowercase							=				total_user[: len(total_user) - 1]
					_lowercase							=				total_match[: len(total_match) - 1]

					_lowercase							=				total_date[len(total_date) - 1 :]
					_lowercase							=				total_user[len(total_user) - 1 :]
					_lowercase							=				total_match[len(total_match) - 1 :]

					# voting system with forecasting
					_lowercase							=				[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					_lowercase							=				'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
					print('''Today\'s data is {not_str}safe.''')





