



"""simple docstring"""





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       :					Optional[Any]      					=					pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
__lowerCAmelCase       :					str      					=					dataset.iloc[:, 1:2].values
__lowerCAmelCase       :					Optional[int]      					=					dataset.iloc[:, 2].values


__lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase					, __lowerCAmelCase       :					Union[str, Any]      					=					train_test_split(X, y, test_size=0.2, random_state=0)


__lowerCAmelCase       :					Dict      					=					PolynomialFeatures(degree=4)
__lowerCAmelCase       :					int      					=					poly_reg.fit_transform(X)
__lowerCAmelCase       :					List[Any]      					=					LinearRegression()
pol_reg.fit(X_poly, y)
def 	__lowerCAmelCase						(		):


						'''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






"""simple docstring"""





from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)

