






'''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 		_UpperCAmelCase   (							__A							:   list						,		__A							:   list						,		__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  List[Any]											=      np.array([[1, item, train_mtch[i]] for i, item in enumerate(__A      )]      )
				a_		:  int											=      np.array(__A      )
				a_		:  str											=      np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()						,		__A      )      )						,		x.transpose()      )						,		__A      )
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )





def 		_UpperCAmelCase   (							__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  str											=      (1, 2, 1)
				a_		:  int											=      (1, 1, 0, 7)
				a_		:  Tuple											=      SARIMAX(
				    __A						,		exog=__A						,		order=__A						,		seasonal_order=__A      )
				a_		:  List[Any]											=      model.fit(disp=__A						,		maxiter=6_00						,		method='''nm'''      )
				a_		:  Union[str, Any]											=      model_fit.predict(1						,		len(__A      )						,		exog=[test_match]      )
				return result[0]





def 		_UpperCAmelCase   (							__A							:   list						,		__A							:   list						,		__A							:   list      ):
				a_		:  str											=      SVR(kernel='''rbf'''						,		C=1						,		gamma=0.1						,		epsilon=0.1      )
				regressor.fit(__A						,		__A      )
				a_		:  Union[str, Any]											=      regressor.predict(__A      )
				return y_pred[0]





def 		_UpperCAmelCase   (							__A							:   list      ):
				train_user.sort()
				a_		:  Optional[Any]											=      np.percentile(__A						,		25      )
				a_		:  Optional[Any]											=      np.percentile(__A						,		75      )
				a_		:  int											=      qa - qa
				a_		:  Optional[int]											=      qa - (iqr * 0.1)
				return low_lim





def 		_UpperCAmelCase   (							__A							:   list						,		__A							:   float      ):
				a_		:  Optional[int]											=      0
				a_		:  List[str]											=      0
				for i in list_vote:
								if i > actual_result:
												a_		:  Optional[int]											=      not_safe + 1
								else:
												if abs(abs(__A      ) - abs(__A      )      ) <= 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)
		__lowerCAmelCase       		=      [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
		__lowerCAmelCase       		=      pd.DataFrame(
		    data_input, columns=['total_user', 'total_even', 'days']
		)
		__lowerCAmelCase       		=      Normalizer().fit_transform(data_input_df.values)
		# split data
		__lowerCAmelCase       		=      normalize_df[:, 2].tolist()
		__lowerCAmelCase       		=      normalize_df[:, 0].tolist()
		__lowerCAmelCase       		=      normalize_df[:, 1].tolist()

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

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

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

		# voting system with forecasting
		__lowerCAmelCase       		=      [
		    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
		__lowerCAmelCase       		=      '' if data_safety_checker(res_vote, tst_user) else 'not '
		print('Today\'s data is {not_str}safe.')












'''simple docstring'''







from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)





def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   List[Any]      ):
				a_		:  Any											=      []
				for part_id in partition_order:
								a_		:  str											=      df.where(f'SPARK_PARTITION_ID() = {part_id}'      ).collect()
								for row_idx, row in enumerate(__A      ):
												expected_row_ids_and_row_dicts.append((f'{part_id}_{row_idx}', row.asDict())      )
				return expected_row_ids_and_row_dicts





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  List[str]											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  Union[str, Any]											=      spark.range(1_00      ).repartition(1      )
				a_		:  Any											=      Spark(__A      )
				# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
				# that each partition can hold 2 rows.
				spark_builder._repartition_df_if_needed(max_shard_size=16      )
				# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
				assert spark_builder.df.rdd.getNumPartitions() == 50





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  List[Any]											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  int											=      spark.range(10      ).repartition(2      )
				a_		:  Tuple											=      [1, 0]
				a_		:  List[str]											=      _generate_iterable_examples(__A						,		__A      )  # Reverse the partitions.
				a_		:  int											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		__A      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								a_				,			a_		:  List[Any]											=      expected_row_ids_and_row_dicts[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  int											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  str											=      spark.range(10      ).repartition(1      )
				a_		:  Tuple											=      SparkExamplesIterable(__A      )
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(__A      ):
								assert row_id == f'0_{i}'
								assert row_dict == {"id": i}





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  Tuple											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  str											=      spark.range(30      ).repartition(3      )
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator'''      ) as generator_mock:
								a_		:  Union[str, Any]											=      lambda __A      : x.reverse()
								a_		:  Any											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		[2, 1, 0]      )

								a_		:  str											=      SparkExamplesIterable(__A      ).shuffle_data_sources(__A      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__A      ):
												a_				,			a_		:  Optional[Any]											=      expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  int											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  List[str]											=      spark.range(20      ).repartition(4      )

				# Partitions 0 and 2
				a_		:  Dict											=      SparkExamplesIterable(__A      ).shard_data_sources(worker_id=0						,		num_workers=2      )
				assert shard_it_a.n_shards == 2
				a_		:  Optional[Any]											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		[0, 2]      )
				for i, (row_id, row_dict) in enumerate(__A      ):
								a_				,			a_		:  Tuple											=      expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict

				# Partitions 1 and 3
				a_		:  List[Any]											=      SparkExamplesIterable(__A      ).shard_data_sources(worker_id=1						,		num_workers=2      )
				assert shard_it_a.n_shards == 2
				a_		:  Optional[int]											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		[1, 3]      )
				for i, (row_id, row_dict) in enumerate(__A      ):
								a_				,			a_		:  Any											=      expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 		_UpperCAmelCase   (							):
				a_		:  Any											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  List[Any]											=      spark.range(1_00      ).repartition(1      )
				a_		:  Optional[Any]											=      Spark(__A      )
				# Choose a small max_shard_size for maximum partitioning.
				spark_builder._repartition_df_if_needed(max_shard_size=1      )
				# The new number of partitions should not be greater than the number of rows.
				assert spark_builder.df.rdd.getNumPartitions() == 1_00




