


'''simple docstring'''




import numpy as np





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A=5      ,					__A=5						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       depth
											__lowerCamelCase		:   Tuple           =       0
											__lowerCamelCase		:   int           =       None
											__lowerCamelCase		:   List[str]           =       None
											__lowerCamelCase		:   Any           =       min_leaf_size
											__lowerCamelCase		:   Optional[Any]           =       None







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											if labels.ndim != 1:
															print("Error: Input labels must be one dimensional"						)

											return np.mean((labels - prediction) ** 2						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Tuple:



											"""simple docstring"""







											if x.ndim != 1:
															print("Error: Input data set must be one dimensional"						)
															return
											if len(A_						) != len(A_						):
															print("Error: X and y have different lengths"						)
															return
											if y.ndim != 1:
															print("Error: Data set labels must be one dimensional"						)
															return

											if len(A_						) < 2 * self.min_leaf_size:
															__lowerCamelCase		:   str           =       np.mean(A_						)
															return

											if self.depth == 1:
															__lowerCamelCase		:   Dict           =       np.mean(A_						)
															return

											__lowerCamelCase		:   str           =       0
											__lowerCamelCase		:   List[Any]           =       self.mean_squared_error(A_      ,					np.mean(A_						)						) * 2
											for i in range(len(A_						)						):
															if len(x[:i]						) < self.min_leaf_size:
																			continue
															elif len(x[i:]						) < self.min_leaf_size:
																			continue
															else:
																			__lowerCamelCase		:   List[Any]           =       self.mean_squared_error(x[:i]      ,					np.mean(y[:i]						)						)
																			__lowerCamelCase		:   int           =       self.mean_squared_error(x[i:]      ,					np.mean(y[i:]						)						)
																			__lowerCamelCase		:   Union[str, Any]           =       error_left + error_right
																			if error < min_error:
																							__lowerCamelCase		:   Optional[int]           =       i
																							__lowerCamelCase		:   Optional[Any]           =       error

											if best_split != 0:
															__lowerCamelCase		:   Dict           =       x[:best_split]
															__lowerCamelCase		:   Union[str, Any]           =       y[:best_split]
															__lowerCamelCase		:   Union[str, Any]           =       x[best_split:]
															__lowerCamelCase		:   int           =       y[best_split:]

															__lowerCamelCase		:   Optional[int]           =       x[best_split]
															__lowerCamelCase		:   str           =       DecisionTree(
															    depth=self.depth - 1      ,					min_leaf_size=self.min_leaf_size						)
															__lowerCamelCase		:   List[str]           =       DecisionTree(
															    depth=self.depth - 1      ,					min_leaf_size=self.min_leaf_size						)
															self.left.train(A_      ,					A_						)
															self.right.train(A_      ,					A_						)
											else:
															__lowerCamelCase		:   Tuple           =       np.mean(A_						)

											return







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->List[str]:



											"""simple docstring"""







											if self.prediction is not None:
															return self.prediction
											elif self.left or self.right is not None:
															if x >= self.decision_boundary:
																			return self.right.predict(A_						)
															else:
																			return self.left.predict(A_						)
											else:
															print("Error: Decision tree not yet trained"						)
															return None





class _snake_case :
							"""simple docstring"""





							@staticmethod
							def 		__SCREAMING_SNAKE_CASE( __A      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       float(0						)
											for label in labels:
															squared_error_sum += (label - prediction) ** 2

											return float(squared_error_sum / labels.size						)





def 				a_							(    )      ->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       np.arange(-1.0				,    1.0				,    0.0_0_5     )
				__lowerCamelCase		:   Any           =       np.sin(lowercase     )

				__lowerCamelCase		:   str           =       DecisionTree(depth=10				,    min_leaf_size=10     )
				tree.train(lowercase				,    lowercase     )

				__lowerCamelCase		:   Tuple           =       (np.random.rand(10     ) * 2) - 1
				__lowerCamelCase		:   Dict           =       np.array([tree.predict(lowercase     ) for x in test_cases]     )
				__lowerCamelCase		:   str           =       np.mean((predictions - test_cases) ** 2     )

				print("Test values: " + str(lowercase     )     )
				print("Predictions: " + str(lowercase     )     )
				print("Average error: " + str(lowercase     )     )


if __name__ == "__main__":
				main()
				import doctest

				doctest.testmod(name="""mean_squarred_error""", verbose=True)







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
