


import numpy as np




class A  :






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int=5      ,       lowercase_						:					Tuple=5			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		int          =depth
             _lowerCamelCase	:		Tuple          =0
             _lowerCamelCase	:		Any          =None
             _lowerCamelCase	:		Union[str, Any]          =None
             _lowerCamelCase	:		Optional[int]          =min_leaf_size
             _lowerCamelCase	:		int          =None






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Dict      ,       lowercase_						:					str			) ->						Union[str, Any]:
             """simple docstring"""




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

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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Any			) ->						Dict:
             """simple docstring"""




             if x.ndim != 1:
                    print('Error: Input data set must be one dimensional'			)
                    return
             if len(lowercase_			) != len(lowercase_			):
                    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(lowercase_			) < 2 * self.min_leaf_size:
                    _lowerCamelCase	:		Union[str, Any]          =np.mean(lowercase_			)
                    return

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

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

             if best_split != 0:
                    _lowerCamelCase	:		int          =x[:best_split]
                    _lowerCamelCase	:		Any          =y[:best_split]
                    _lowerCamelCase	:		List[Any]          =x[best_split:]
                    _lowerCamelCase	:		Optional[int]          =y[best_split:]

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

             return






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						List[Any]:
             """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(lowercase_			)
                    else:
                           return self.left.predict(lowercase_			)
             else:
                    print('Error: Decision tree not yet trained'			)
                    return None




class A  :






      @staticmethod
      def 	lowerCamelCase	( lowercase_						:					List[Any]      ,       lowercase_						:					Optional[int]			) ->						Dict:
             """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_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =np.arange(-1.0		,      1.0		,      0.0_05					)
       _lowerCamelCase	:		Tuple          =np.sin(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		int          =DecisionTree(depth=10		,      min_leaf_size=10					)
       tree.train(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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

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


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

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






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase						 =	logging.getLogger()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =argparse.ArgumentParser()
       parser.add_argument('-f'					)
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()
       return args.f




class A  (       UpperCamelCase_					):






      def 	lowerCamelCase	( self						:					List[Any]			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Any          =logging.StreamHandler(sys.stdout			)
             logger.addHandler(lowercase_			)

             _lowerCamelCase	:		Dict          =self.get_auto_remove_tmp_dir()
             _lowerCamelCase	:		int          =F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

             with patch.object(lowercase_      ,       'argv'      ,       lowercase_			):
                    _lowerCamelCase	:		Dict          =run_glue_with_pabee.main()
                    for value in result.values():
                           self.assertGreaterEqual(lowercase_      ,       0.75			)


