




'''simple docstring'''






import numpy as np

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A=5   ,		A=5 ):






        '''simple docstring'''




        lowercase  :		int						=      depth
        lowercase  :		str						=      0
        lowercase  :		Any						=      None
        lowercase  :		Optional[int]						=      None
        lowercase  :		Optional[Any]						=      min_leaf_size
        lowercase  :		int						=      None



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''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 ):






        '''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:
               lowercase  :		Any						=      np.mean(A )
               return

        if self.depth == 1:
               lowercase  :		Union[str, Any]						=      np.mean(A )
               return

        lowercase  :		Optional[int]						=      0
        lowercase  :		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:
                      lowercase  :		List[Any]						=      self.mean_squared_error(x[:i]   ,		np.mean(y[:i] ) )
                      lowercase  :		int						=      self.mean_squared_error(x[i:]   ,		np.mean(y[i:] ) )
                      lowercase  :		Dict						=      error_left + error_right
                      if error < min_error:
                             lowercase  :		Any						=      i
                             lowercase  :		Dict						=      error

        if best_split != 0:
               lowercase  :		List[str]						=      x[:best_split]
               lowercase  :		Tuple						=      y[:best_split]
               lowercase  :		List[Any]						=      x[best_split:]
               lowercase  :		Tuple						=      y[best_split:]

               lowercase  :		Any						=      x[best_split]
               lowercase  :		int						=      DecisionTree(
                   depth=self.depth - 1   ,		min_leaf_size=self.min_leaf_size )
               lowercase  :		Union[str, Any]						=      DecisionTree(
                   depth=self.depth - 1   ,		min_leaf_size=self.min_leaf_size )
               self.left.train(A   ,		A )
               self.right.train(A   ,		A )
        else:
               lowercase  :		Union[str, Any]						=      np.mean(A )

        return





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''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   SCREAMING_SNAKE_CASE_		:



 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      float(0 )
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size )






def 		a_( )				->Optional[Any]:






       '''simple docstring'''




       lowercase  :		int						=      np.arange(-1.0   ,				1.0   ,				0.0_05      )
       lowercase  :		Optional[int]						=      np.sin(lowerCamelCase      )

       lowercase  :		Optional[int]						=      DecisionTree(depth=10   ,				min_leaf_size=10      )
       tree.train(lowerCamelCase   ,				lowerCamelCase      )

       lowercase  :		List[str]						=      (np.random.rand(10      ) * 2) - 1
       lowercase  :		Optional[Any]						=      np.array([tree.predict(lowerCamelCase      ) for x in test_cases]      )
       lowercase  :		List[str]						=      np.mean((predictions - test_cases) ** 2      )

       print('''Test values: ''' + str(lowerCamelCase      )      )
       print('''Predictions: ''' + str(lowerCamelCase      )      )
       print('''Average error: ''' + str(lowerCamelCase      )      )


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

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










'''simple docstring'''






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A=False ):






        '''simple docstring'''




        lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      (
            filename_skip_take['''filename'''],
            filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
            filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
        )
        open(os.path.join(A )   ,		'''wb''' ).close()
        lowercase  :		List[Any]						=      pa.Table.from_pydict({'''filename''': [Path(A ).name] * 100} )
        if take == -1:
               lowercase  :		int						=      len(A ) - skip
        if skip is not None and take is not None:
               lowercase  :		Optional[Any]						=      pa_table.slice(A   ,		A )
        return pa_table






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      '''my_name'''
        lowercase  :		Tuple						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Union[str, Any]						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		Any						=      [train_info, test_info]
        lowercase  :		Union[str, Any]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[str]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Union[str, Any]						=      ReaderTest(A   ,		A )

               lowercase  :		str						=      '''test[:33%]'''
               lowercase  :		List[Any]						=      Dataset(**reader.read(A   ,		A   ,		A ) )
               self.assertEqual(dset['''filename'''][0]   ,		f"""{name}-test""" )
               self.assertEqual(dset.num_rows   ,		33 )
               self.assertEqual(dset.num_columns   ,		1 )

               lowercase  :		Dict						=      ['''train''', '''test[:33%]''']
               lowercase  :		List[Any]						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''' )]
               for instructions in [instructionsa, instructionsa]:
                      lowercase  :		Union[str, Any]						=      [reader.read(A   ,		A   ,		A ) for instr in instructions]
                      lowercase,     lowercase  :		Tuple						=      (Dataset(**A ) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset['''filename'''][0]   ,		f"""{name}-train""" )
                      self.assertEqual(train_dset.num_rows   ,		100 )
                      self.assertEqual(train_dset.num_columns   ,		1 )
                      self.assertIsInstance(train_dset.split   ,		A )
                      self.assertEqual(str(train_dset.split )   ,		'''train''' )
                      self.assertEqual(test_dset['''filename'''][0]   ,		f"""{name}-test""" )
                      self.assertEqual(test_dset.num_rows   ,		33 )
                      self.assertEqual(test_dset.num_columns   ,		1 )
                      self.assertIsInstance(test_dset.split   ,		A )
                      self.assertEqual(str(test_dset.split )   ,		'''test[:33%]''' )
                      del train_dset, test_dset



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      '''my_name'''
        lowercase  :		Dict						=      SplitInfo(name='''train'''   ,		num_examples=1000   ,		shard_lengths=[100] * 10 )
        lowercase  :		Tuple						=      [train_info]
        lowercase  :		str						=      SplitDict()
        split_dict.add(A )
        lowercase  :		str						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		int						=      ReaderTest(A   ,		A )

               lowercase  :		Dict						=      '''train[:33%]'''
               lowercase  :		Tuple						=      Dataset(**reader.read(A   ,		A   ,		A ) )
               self.assertEqual(dset['''filename'''][0]   ,		f"""{name}-train-00000-of-00010""" )
               self.assertEqual(dset['''filename'''][-1]   ,		f"""{name}-train-00003-of-00010""" )
               self.assertEqual(dset.num_rows   ,		330 )
               self.assertEqual(dset.num_columns   ,		1 )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      SplitInfo(name='''train'''   ,		num_examples=100 )
        lowercase  :		Dict						=      SplitInfo(name='''test'''   ,		num_examples=100 )
        lowercase  :		List[str]						=      SplitDict()
        split_dict.add(A )
        split_dict.add(A )
        lowercase  :		List[Any]						=      DatasetInfo(splits=A )

        with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :		Optional[int]						=      ReaderTest(A   ,		A )

               lowercase  :		Optional[int]						=      [
                   {'''filename''': os.path.join(A   ,		'''train''' )},
                   {'''filename''': os.path.join(A   ,		'''test''' ), '''skip''': 10, '''take''': 10},
               ]
               lowercase  :		int						=      Dataset(**reader.read_files(A   ,		original_instructions='''train+test[10:20]''' ) )
               self.assertEqual(dset.num_rows   ,		110 )
               self.assertEqual(dset.num_columns   ,		1 )
               del dset






@pytest.mark.parametrize('''in_memory'''   ,				[False, True]      )
def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		Any						=      arrow_file
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		str						=      ArrowReader.read_table(lowerCamelCase   ,				in_memory=lowerCamelCase      )
       assert table.shape == dataset.data.shape
       assert set(table.column_names      ) == set(dataset.data.column_names      )
       assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict




@pytest.mark.parametrize('''in_memory'''   ,				[False, True]      )
def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->Dict:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      arrow_file
       lowercase  :		Union[str, Any]						=      ArrowReader(''''''   ,				lowerCamelCase      )
       with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
              lowercase  :		List[str]						=      reader.read_files([{'''filename''': filename}]   ,				in_memory=lowerCamelCase      )
       assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
       lowercase  :		Optional[int]						=      dataset_kwargs['''arrow_table''']
       assert table.shape == dataset.data.shape
       assert set(table.column_names      ) == set(dataset.data.column_names      )
       assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict




def 		a_( )				->Any:






       '''simple docstring'''




       assert ReadInstruction('''train'''   ,				to=10   ,				unit='''abs'''      ).to_spec() == "train[:10]"
       assert ReadInstruction('''train'''   ,				from_=-80   ,				to=10   ,				unit='''%'''      ).to_spec() == "train[-80%:10%]"

       lowercase  :		Union[str, Any]						=      '''train+test'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test

       lowercase  :		Any						=      '''train[2:10]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_abs

       lowercase  :		List[str]						=      '''train[15%:-20%]'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct

       lowercase  :		Tuple						=      '''train[:10%](closest)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == "train[:10%]"

       lowercase  :		Any						=      '''train[:10%](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_pct_rounding

       lowercase  :		int						=      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
       assert ReadInstruction.from_spec(lowerCamelCase      ).to_spec() == spec_train_test_pct_rounding




def 		a_( )				->str:






       '''simple docstring'''




       lowercase  :		int						=      '''dummy'''
       lowercase  :		int						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		Union[str, Any]						=      '''train[:33%]'''
       lowercase  :		str						=      '''arrow'''
       lowercase  :		int						=      '''prefix'''

       lowercase  :		Optional[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       assert isinstance(lowerCamelCase   ,				lowerCamelCase      )
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train.arrow"""      ), "skip": 0, "take": 33}
       ]

       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100   ,				shard_lengths=[10] * 10      )]
       lowercase  :		Any						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )
       assert isinstance(lowerCamelCase   ,				lowerCamelCase      )
       assert file_instructions.num_examples == 33
       assert file_instructions.file_instructions == [
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00000-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00001-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00002-of-00010.arrow"""      ), "skip": 0, "take": -1},
           {"filename": os.path.join(lowerCamelCase   ,				f"""{name}-train-00003-of-00010.arrow"""      ), "skip": 0, "take": 3},
       ]





@pytest.mark.parametrize('''name, expected_exception'''   ,				[(None, TypeError), ('''''', ValueError)]      )
def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->int:






       '''simple docstring'''




       lowercase  :		List[str]						=      [SplitInfo(name='''train'''   ,				num_examples=100      )]
       lowercase  :		List[Any]						=      '''train'''
       lowercase  :		Any						=      '''arrow'''
       lowercase  :		Union[str, Any]						=      '''prefix_path'''
       with pytest.raises(lowerCamelCase      ):
              lowercase  :		List[Any]						=      make_file_instructions(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )




