
"""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	(						_A      )    ->  Tuple:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A      )    ->  int:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A=7_0_0_0_0      )    ->  int:





    """simple docstring"""

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

    for iterations in range(_A      ):
        _UpperCAmelCase          =   np.dot(_A					,      _A      )
        _UpperCAmelCase          =   sigmoid_function(_A      )
        _UpperCAmelCase          =   np.dot(x.T					,      h - y      ) / y.size
        _UpperCAmelCase          =   theta - alpha * gradient  # updating the weights
        _UpperCAmelCase          =   np.dot(_A					,      _A      )
        _UpperCAmelCase          =   sigmoid_function(_A      )
        _UpperCAmelCase          =   cost_function(_A					,      _A      )
        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__":
 a					:	List[Any]							=		datasets.load_iris()
 a					:	List[str]							=		iris.data[:, :2]
 a					:	Optional[Any]							=		(iris.target != 0) * 1

 a					:	List[Any]							=		0.1
 a					:	Optional[int]							=		logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector


 def  _UpperCamelCase	(						_A      )    ->  str:





     """simple docstring"""

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

 plt.figure(figsize=(1_0, 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''')
 ((a)		, (a))					:	str							=		(x[:, 0].min(), x[:, 0].max())
 ((a)		, (a))					:	Tuple							=		(x[:, 1].min(), x[:, 1].max())
 ((a)		, (a))					:	List[str]							=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 a					:	Any							=		np.c_[xxa.ravel(), xxa.ravel()]
 a					:	str							=		predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()







 plt.show()

"""simple docstring"""



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil




class 		a_						( _UpperCAmelCase	):







    def      _snake_case					(			self			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     )     ,					type=pa.intaa()     )







    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					try_type=Value("""bool"""     )     ,					type=Value("""int64"""     )     )     )







    def      _snake_case					(			self			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					type=Value("""int32"""     )     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
            _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					type=Value("""int64"""     )     )     )







    def      _snake_case					(			self			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					try_type=Value("""int32"""     )     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       List[str]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					try_type=Value("""int64"""     )     )     )
        self.assertEqual(arr.type     ,					pa.string()     )







    def      _snake_case					(			self			:       List[Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([[[1, 2, 3]]]     ,					type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					ArrayaDExtensionType((1, 3)     ,					"""int64"""     )     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
            _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )







    def      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([[[1, 2, 3]]]     ,					try_type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					ArrayaDExtensionType((1, 3)     ,					"""int64"""     )     )







    def      _snake_case					(			self			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					try_type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					pa.string()     )







    @require_pil
    def      _snake_case					(			self			:       str     )		->Optional[Any]:






        '''simple docstring'''






        import PIL.Image

        _UpperCAmelCase          =   PIL.Image.fromarray(np.arange(10     ,					dtype=np.uinta     ).reshape(2     ,					5     )     )
        with patch(
            """datasets.arrow_writer.cast_to_python_objects"""     ,					side_effect=__UpperCamelCase     ) as mock_cast_to_python_objects:
            _UpperCAmelCase          =   pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image]     ,					type=Image()     )     )
            _UpperCAmelCase    ,_UpperCAmelCase          =   mock_cast_to_python_objects.call_args_list[-1]
            self.assertIn("""optimize_list_casting"""     ,					__UpperCamelCase     )
            self.assertFalse(kwargs["""optimize_list_casting"""]     )







def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferReader(_A      ) if isinstance(_A					,      pa.Buffer      ) else pa.memory_map(_A      )
    _UpperCAmelCase          =   pa.ipc.open_stream(_A      )
    _UpperCAmelCase          =   f.read_all()
    assert len(pa_table.to_batches()      ) == expected_num_chunks
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
    del pa_table


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]      )}      )
    with ArrowWriter(stream=_A					,      features=_A      ) as writer:
        writer.write({"""labels""": 0}      )
        writer.write({"""labels""": 1}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pa.ipc.open_stream(_A      )
    _UpperCAmelCase          =   f.read_all()
    _UpperCAmelCase          =   pa_table.schema
    assert pa_table.num_rows == 2
    assert schema == features.arrow_schema
    assert schema.metadata == features.arrow_schema.metadata
    assert features == Features.from_arrow_schema(_A      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        with pytest.raises(_A      ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=[1, 2]      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 2, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        with pytest.raises(_A      ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=1_0      )
            writer.write({"""col_1""": """bar""", """col_2""": 2}					,      key=1_0      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 2, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=1      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}					,      key=2      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )
        writer.write_batch({"""col_1""": [], """col_2""": []}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}      )      )
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}      )      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def  _UpperCamelCase	(						)    ->  str:





    """simple docstring"""

    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
        _UpperCAmelCase          =   os.path.join(_A					,      """test.arrow"""      )
        with ArrowWriter(path=_A					,      schema=pa.schema(_A      )      ) as writer:
            writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
        assert num_examples == 2
        assert num_bytes > 0
        assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
        _check_output(_A					,      1      )


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    if pa.types.is_list(_A      ):
        return get_base_dtype(arr_type.value_type      )
    else:
        return arr_type


def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    if isinstance(lst[0]					,      _A      ):
        change_first_primitive_element_in_list(lst[0]					,      _A      )
    else:
        _UpperCAmelCase          =   value


@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""					,      [(None, pa.intaa()), (Value("""int32"""      ), pa.intaa())]      )
@pytest.mark.parametrize("""sequence"""					,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   pa.array(TypedSequence(_A					,      optimized_int_type=_A      )      )
    assert get_base_dtype(arr.type      ) == expected_dtype


@pytest.mark.parametrize(
    """col, expected_dtype"""					,      [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]					,      )
@pytest.mark.parametrize("""sequence"""					,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   pa.array(OptimizedTypedSequence(_A					,      col=_A      )      )
    assert get_base_dtype(arr.type      ) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        _UpperCAmelCase          =   copy.deepcopy(_A      )
        _UpperCAmelCase          =   np.iinfo(expected_dtype.to_pandas_dtype()      ).max + 1
        change_first_primitive_element_in_list(_A					,      _A      )
        _UpperCAmelCase          =   pa.array(OptimizedTypedSequence(_A					,      col=_A      )      )
        assert get_base_dtype(arr.type      ) == pa.intaa()


@pytest.mark.parametrize("""raise_exception"""					,      [False, True]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path / """dataset-train.arrow"""      )
    try:
        with ArrowWriter(path=_A      ) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   """mock://dataset-train.arrow"""
    with ArrowWriter(path=_A					,      storage_options=mockfs.storage_options      ) as writer:
        assert isinstance(writer._fs					,      type(_A      )      )
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(_A      )


def  _UpperCamelCase	(						)    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ParquetWriter(stream=_A      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pq.read_table(_A      )
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("""embed_local_files"""					,      [False, True]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    import PIL.Image

    _UpperCAmelCase          =   str(tmp_path / """test_image_rgb.jpg"""      )
    PIL.Image.fromarray(np.zeros((5, 5)					,      dtype=np.uinta      )      ).save(_A					,      format="""png"""      )
    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ParquetWriter(
        stream=_A					,      features=Features({"""image""": Image()}      )					,      embed_local_files=_A      ) as writer:
        writer.write({"""image""": image_path}      )
        writer.finalize()
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pq.read_table(_A      )
    _UpperCAmelCase          =   pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["""image"""][0]["""path"""]					,      _A      )
        with open(_A					,      """rb"""      ) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None







def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   pa.schema([pa.field("""col_1"""					,      pa.string()					,      nullable=_A      )]      )
    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(stream=_A      ) as writer:
        writer._build_writer(inferred_schema=_A      )
        assert writer._schema == pa.schema([pa.field("""col_1"""					,      pa.string()      )]      )