




import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    """split_dict"""      ,    [
        SplitDict(),
        SplitDict({"""train""": SplitInfo(name="""train"""      ,    num_bytes=1337      ,    num_examples=42      ,    dataset_name="""my_dataset"""       )}       ),
        SplitDict({"""train""": SplitInfo(name="""train"""      ,    num_bytes=1337      ,    num_examples=42       )}       ),
        SplitDict({"""train""": SplitInfo()}       ),
    ]      ,    )
def 							_UpperCAmelCase  (UpperCamelCase_ : SplitDict       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      split_dict._to_yaml_list()
				assert len(UpperCamelCase_       ) == len(UpperCamelCase_       )
				_lowerCAmelCase    :    Any										=      SplitDict._from_yaml_list(UpperCamelCase_       )
				for split_name, split_info in split_dict.items():
								# dataset_name field is deprecated, and is therefore not part of the YAML dump
								_lowerCAmelCase    :    str										=      None
								# the split name of split_dict takes over the name of the split info object
								_lowerCAmelCase    :    Optional[Any]										=      split_name
				assert split_dict == reloaded





@pytest.mark.parametrize(
    """split_info"""      ,    [SplitInfo(), SplitInfo(dataset_name=UpperCamelCase_       ), SplitInfo(dataset_name="""my_dataset"""       )]       )
def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
				# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
				_lowerCAmelCase    :    Union[str, Any]										=      asdict(SplitDict({"""train""": split_info}       )       )
				assert "dataset_name" in split_dict_asdict["train"]
				assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

