

'''simple docstring'''
# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def 	UpperCamelCase   (       lowercase_						:    Tuple	)		->	Optional[Any]:




  '''simple docstring'''



  return 1 / (1 + np.exp(-z	))




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Dict	)		->	List[str]:




  '''simple docstring'''



  return (-y * np.log(lowercase_	) - (1 - y) * np.log(1 - h	)).mean()




def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Tuple     ,					lowercase_						:    Tuple	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=np.dot(lowercase_     ,					lowercase_	)
  return np.sum(y * scores - np.log(1 + np.exp(lowercase_	)	)	)




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    Tuple     ,					lowercase_						:    str     ,					lowercase_						:    Optional[int]=7_0_0_0_0	)		->	str:




  '''simple docstring'''



  lowercase									=np.zeros(x.shape[1]	)

  for iterations in range(lowercase_	):
    lowercase									=np.dot(lowercase_     ,					lowercase_	)
    lowercase									=sigmoid_function(lowercase_	)
    lowercase									=np.dot(x.T     ,					h - y	) / y.size
    lowercase									=theta - alpha * gradient  # updating the weights
    lowercase									=np.dot(lowercase_     ,					lowercase_	)
    lowercase									=sigmoid_function(lowercase_	)
    lowercase									=cost_function(lowercase_     ,					lowercase_	)
    if iterations % 1_0_0 == 0:
      print(f'loss: {j} \t'	)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
     _UpperCAmelCase  :		List[str]  =		datasets.load_iris()
     _UpperCAmelCase  :		Optional[Any]  =		iris.data[:, :2]
     _UpperCAmelCase  :		Union[str, Any]  =		(iris.target != 0) * 1

     _UpperCAmelCase  :		List[str]  =		0.1
     _UpperCAmelCase  :		List[Any]  =		logistic_reg(alpha, x, y, max_iterations=7_00_00)
     print('''theta: ''', theta)  # printing the theta i.e our weights vector




     def 	UpperCamelCase   (       lowercase_						:    Optional[Any]	)		->	Optional[int]:




       '''simple docstring'''



       return sigmoid_function(
           np.dot(lowercase_     ,					lowercase_	)	)  # predicting the value of probability from the logistic regression algorithm

     plt.figure(figsize=(10, 6))
     plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
     plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
     ((_UpperCAmelCase)  , (_UpperCAmelCase))  :		List[Any]  =		(x[:, 0].min(), x[:, 0].max())
     ((_UpperCAmelCase)  , (_UpperCAmelCase))  :		Union[str, Any]  =		(x[:, 1].min(), x[:, 1].max())
     ((_UpperCAmelCase)  , (_UpperCAmelCase))  :		int  =		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
     _UpperCAmelCase  :		int  =		np.c_[xxa.ravel(), xxa.ravel()]
     _UpperCAmelCase  :		Union[str, Any]  =		predict_prob(grid).reshape(xxa.shape)
     plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

     plt.legend()




     plt.show()




'''simple docstring'''






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__A   :    int											= pytest.mark.integration







@pytest.mark.parametrize("""path"""				,       ["""paws""", """csv"""]   )
def 							UpperCamelCase_     (       A__   :  Union[str, Any]				,       A__   :  Tuple   ):
  '''simple docstring'''





  inspect_dataset(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""   )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
@pytest.mark.parametrize("""path"""				,       ["""accuracy"""]   )
def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  str   ):
  '''simple docstring'''





  inspect_metric(A__				,       A__   )
  lowerCAmelCase_     : Any     =  path + """.py"""
  assert script_name in os.listdir(A__   )
  assert "__pycache__" not in os.listdir(A__   )







@pytest.mark.parametrize(
    """path, config_name, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[Any]     =  get_dataset_config_info(A__				,       config_name=A__   )
  assert info.config_name == config_name
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  int   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_config_info(A__				,       config_name=A__   )







@pytest.mark.parametrize(
    """path, expected"""				,       [
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  str				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_config_names(A__   )
  assert expected in config_names







@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""				,       [
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Dict				,       A__   :  Optional[int]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  get_dataset_infos(A__   )
  assert list(infos.keys()   ) == expected_configs
  lowerCAmelCase_     : Optional[int]     =  expected_configs[0]
  assert expected_config in infos
  lowerCAmelCase_     : int     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits_in_first_config







@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""				,       [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Optional[Any]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  get_dataset_infos(A__   )
  assert expected_config in infos
  lowerCAmelCase_     : List[Any]     =  infos[expected_config]
  assert info.config_name == expected_config
  assert list(info.splits.keys()   ) == expected_splits







@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,       [
        ("""paws""", None, ValueError),
    ]				,       )
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  Dict   ):
  '''simple docstring'''





  with pytest.raises(A__   ):
    get_dataset_split_names(A__				,       config_name=A__   )



