






"""simple docstring"""



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  assert isinstance(__snake_case						,   __snake_case       )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory'						,   [False, True]       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    int						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase				       =			CsvDatasetReader(__snake_case						,   cache_dir=__snake_case						,   keep_in_memory=__snake_case       ).read()
  _check_csv_dataset(__snake_case						,   __snake_case       )
@pytest.mark.parametrize(
    'features'						,   [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,   )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Union[str, Any]						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  lowercase				       =			features.copy() if features else default_expected_features
  lowercase				       =			(
      Features({feature: Value(__snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
  )
  lowercase				       =			CsvDatasetReader(__snake_case						,   features=__snake_case						,   cache_dir=__snake_case       ).read()
  _check_csv_dataset(__snake_case						,   __snake_case       )
@pytest.mark.parametrize('split'						,   [None, NamedSplit('train'       ), 'train', 'test']       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Any						,   __snake_case     :    List[str]       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  lowercase				       =			CsvDatasetReader(__snake_case						,   cache_dir=__snake_case						,   split=__snake_case       ).read()
  _check_csv_dataset(__snake_case						,   __snake_case       )
  assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type'						,   [str, list]       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str						,   __snake_case     :    List[str]       ):





  '''simple docstring'''





  if issubclass(__snake_case						,   __snake_case       ):
    lowercase				       =			csv_path
  elif issubclass(__snake_case						,   __snake_case       ):
    lowercase				       =			[csv_path]
  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  lowercase				       =			CsvDatasetReader(__snake_case						,   cache_dir=__snake_case       ).read()
  _check_csv_dataset(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]=("train",)       ):





  '''simple docstring'''





  assert isinstance(__snake_case						,   __snake_case       )
  for split in splits:
    lowercase				       =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory'						,   [False, True]       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Tuple						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase				       =			CsvDatasetReader({'train': csv_path}						,   cache_dir=__snake_case						,   keep_in_memory=__snake_case       ).read()
  _check_csv_datasetdict(__snake_case						,   __snake_case       )
@pytest.mark.parametrize(
    'features'						,   [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,   )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  lowercase				       =			features.copy() if features else default_expected_features
  lowercase				       =			(
      Features({feature: Value(__snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
  )
  lowercase				       =			CsvDatasetReader({'train': csv_path}						,   features=__snake_case						,   cache_dir=__snake_case       ).read()
  _check_csv_datasetdict(__snake_case						,   __snake_case       )
@pytest.mark.parametrize('split'						,   [None, NamedSplit('train'       ), 'train', 'test']       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Any						,   __snake_case     :    int       ):





  '''simple docstring'''





  if split:
    lowercase				       =			{split: csv_path}
  else:
    lowercase				       =			{'train': csv_path, 'test': csv_path}
  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  lowercase				       =			CsvDatasetReader(__snake_case						,   cache_dir=__snake_case       ).read()
  _check_csv_datasetdict(__snake_case						,   __snake_case						,   splits=list(path.keys()       )       )
  assert all(dataset[split].split == split for split in path.keys()       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





  with open(__snake_case						,   encoding='utf-8'       ) as csvfile:
    yield from csv.reader(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    int       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			os.path.join(__snake_case						,   'tmp.csv'       )
  lowercase				       =			CsvDatasetReader({'train': csv_path}						,   cache_dir=__snake_case       ).read()
  CsvDatasetWriter(dataset['train']						,   __snake_case						,   num_proc=1       ).write()

  lowercase				       =			iter_csv_file(__snake_case       )
  lowercase				       =			iter_csv_file(__snake_case       )

  for rowa, rowa in zip(__snake_case						,   __snake_case       ):
    assert rowa == rowa
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[Any]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			os.path.join(__snake_case						,   'tmp.csv'       )
  lowercase				       =			CsvDatasetReader({'train': csv_path}						,   cache_dir=__snake_case       ).read()
  CsvDatasetWriter(dataset['train']						,   __snake_case						,   num_proc=2       ).write()

  lowercase				       =			iter_csv_file(__snake_case       )
  lowercase				       =			iter_csv_file(__snake_case       )

  for rowa, rowa in zip(__snake_case						,   __snake_case       ):
    assert rowa == rowa



def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			tmp_path / 'cache'
  lowercase				       =			os.path.join(__snake_case						,   'tmp.csv'       )
  lowercase				       =			CsvDatasetReader({'train': csv_path}						,   cache_dir=__snake_case       ).read()
  with pytest.raises(__snake_case       ):
    CsvDatasetWriter(dataset['train']						,   __snake_case						,   num_proc=0       )









import numpy as np
import requests


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''       )
       _UpperCamelCase														=    response.text.splitlines()
       _UpperCamelCase														=    []
       for item in lines:
              _UpperCamelCase														=    item.split(''','''       )
              data.append(lowerCAmelCase       )
       data.pop(0       )  # This is for removing the labels from the list
       _UpperCamelCase														=    np.matrix(lowerCAmelCase       )
       return dataset


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta - (alpha / n) * sum_grad
       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

       _UpperCamelCase														=    data_x.shape[1]
       _UpperCamelCase														=    data_x.shape[0] - 1

       _UpperCamelCase														=    np.zeros((1, no_features)       )

       for i in range(0     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    run_steep_gradient_descent(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              _UpperCamelCase														=    sum_of_square_error(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              print(f'''At Iteration {i + 1} - Error is {error:.5f}'''       )

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

       _UpperCamelCase														=    data.shape[0]
       _UpperCamelCase														=    np.c_[np.ones(lowerCAmelCase       ), data[:, :-1]].astype(lowerCAmelCase       )
       _UpperCamelCase														=    data[:, -1].astype(lowerCAmelCase       )

       _UpperCamelCase														=    run_linear_regression(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta.shape[1]
       print('''Resultant Feature vector : '''       )
       for i in range(0     ,							lowerCAmelCase       ):
              print(f'''{theta[0, i]:.5f}'''       )


if __name__ == "__main__":
       main()






