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def factorial(n: int) -> float:
    """
    Given a positive integer, calculate its factorial
    """
    if not isinstance(n, int):
        raise TypeError('n must be an integer')

    if n < 0:
        raise ValueError("n must be positive")

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# Importing the base class 
from algorithms.BaseClass import BaseClass


class InsertionSort(BaseClass):
    def __init__(
        self, 
        arr: list
        ):
        # Inheriting the base class constructor

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# Importing the base class
from algorithms.BaseClass import BaseClass


class SelectionSort(BaseClass):
    def __init__(
        self, 
        arr:list
        ):
        # Inheriting the base class

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class BaseClass:
    def __init__(
        self,
        arr: list = [float]
        ):
        """
        The base class for all the algorithms to inherit from 
        """
        # Ensuring the correct type 
        if type(arr) != list:

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# Maximum number of array length 
_max_length = 50

# Defining the amount of expiremnts in each test
_n_experiments = 200

# Iteration results 
_iter_results = []

for i in range(_max_length + 1):

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# Base class 
from algorithms.BaseClass import BaseClass


class BubbleSort(BaseClass):
    def __init__(self, arr: list):
        """
        Object constructor 

        Arguments

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# Importing the function to test 
from pipeline.modules.read_data import read_json

# Dir traversal
import os 

def test_data_reading():
    """
    Tests the data reading functionality of the pipeline
    """

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# Importing the main pipeline function
from pipeline.pipeline import pipeline

# Directory traversal 
import os 

# Data frame objects 
import pandas as pd

# Defining the test 

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# Main frameworks to test out 
from pipeline.modules.read_data import read_json
from pipeline.modules.clean_data import clean_text

# Directory traversals 
import os 

def test_reading_cleaning():
    # Arrange 
    _path_to_data = os.path.join(

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# Importing all the methods for the pipeline 
from pipeline.modules.read_data import read_json
from pipeline.modules.clean_data import clean_text
from pipeline.modules.model_input_preparation import create_X_Y, apply_train_test_split
from pipeline.modules.model_fitting import TextCLF
from pipeline.modules.evaluate_model import eval_model

# Directory traversal 
import os