





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      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       assert isinstance(lowerCAmelCase     ,							lowerCAmelCase       )
       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      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              _UpperCamelCase														=    CsvDatasetReader(lowerCAmelCase     ,							cache_dir=lowerCAmelCase     ,							keep_in_memory=lowerCAmelCase       ).read()
       _check_csv_dataset(lowerCAmelCase     ,							lowerCAmelCase       )


@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      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       _UpperCamelCase														=    features.copy() if features else default_expected_features
       _UpperCamelCase														=    (
           Features({feature: Value(lowerCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       _UpperCamelCase														=    CsvDatasetReader(lowerCAmelCase     ,							features=lowerCAmelCase     ,							cache_dir=lowerCAmelCase       ).read()
       _check_csv_dataset(lowerCAmelCase     ,							lowerCAmelCase       )


@pytest.mark.parametrize('''split'''     ,							[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       _UpperCamelCase														=    CsvDatasetReader(lowerCAmelCase     ,							cache_dir=lowerCAmelCase     ,							split=lowerCAmelCase       ).read()
       _check_csv_dataset(lowerCAmelCase     ,							lowerCAmelCase       )
       assert dataset.split == split if split else "train"


@pytest.mark.parametrize('''path_type'''     ,							[str, list]       )
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       if issubclass(lowerCAmelCase     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    csv_path
       elif issubclass(lowerCAmelCase     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    [csv_path]
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       _UpperCamelCase														=    CsvDatasetReader(lowerCAmelCase     ,							cache_dir=lowerCAmelCase       ).read()
       _check_csv_dataset(lowerCAmelCase     ,							lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=("train",)       ):
       assert isinstance(lowerCAmelCase     ,							lowerCAmelCase       )
       for split in splits:
              _UpperCamelCase														=    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      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              _UpperCamelCase														=    CsvDatasetReader({'''train''': csv_path}     ,							cache_dir=lowerCAmelCase     ,							keep_in_memory=lowerCAmelCase       ).read()
       _check_csv_datasetdict(lowerCAmelCase     ,							lowerCAmelCase       )


@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      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       _UpperCamelCase														=    features.copy() if features else default_expected_features
       _UpperCamelCase														=    (
           Features({feature: Value(lowerCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       _UpperCamelCase														=    CsvDatasetReader({'''train''': csv_path}     ,							features=lowerCAmelCase     ,							cache_dir=lowerCAmelCase       ).read()
       _check_csv_datasetdict(lowerCAmelCase     ,							lowerCAmelCase       )


@pytest.mark.parametrize('''split'''     ,							[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       if split:
              _UpperCamelCase														=    {split: csv_path}
       else:
              _UpperCamelCase														=    {'''train''': csv_path, '''test''': csv_path}
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       _UpperCamelCase														=    CsvDatasetReader(lowerCAmelCase     ,							cache_dir=lowerCAmelCase       ).read()
       _check_csv_datasetdict(lowerCAmelCase     ,							lowerCAmelCase     ,							splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       with open(lowerCAmelCase     ,							encoding='''utf-8'''       ) as csvfile:
              yield from csv.reader(lowerCAmelCase       )


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

       _UpperCamelCase														=    iter_csv_file(lowerCAmelCase       )
       _UpperCamelCase														=    iter_csv_file(lowerCAmelCase       )

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


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

       _UpperCamelCase														=    iter_csv_file(lowerCAmelCase       )
       _UpperCamelCase														=    iter_csv_file(lowerCAmelCase       )

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


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tmp_path / '''cache'''
       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''tmp.csv'''       )
       _UpperCamelCase														=    CsvDatasetReader({'''train''': csv_path}     ,							cache_dir=lowerCAmelCase       ).read()
       with pytest.raises(lowerCAmelCase       ):
              CsvDatasetWriter(dataset['''train''']     ,							lowerCAmelCase     ,							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()






