uni_multiStats.py
def execute_skfold(X,y, columns_org, continuous, columns_dummified, splits, seed):
    '''
    Splitting the data into splits for each cross-validation.
    
    Args:
        X: The dataset
        y: The target values
        columns_org: column names before dummification
        continuous: column names of continuous values
        columns_dummified: column names after dummification
        splits: number of folds in each cross validation
        seed: The number of a single cross-validation
    
    Returns:
        data for each of the splits for a single cross-validation
    '''
    skf = StratifiedKFold(n_splits=splits, random_state=seed, shuffle=True)
    result = Parallel(n_jobs=-1)(delayed(execute_split)(X,y, columns_org, continuous, columns_dummified, train, test) for train, test in skf.split(X,y))
    return result

    Splitting the data into splits for each cross-validation.
    
    Args:
        X: The dataset
        y: The target values
        columns_org: column names before dummification
        continuous: column names of continuous values
        columns_dummified: column names after dummification
        splits: number of folds in each cross validation
        seed: The number of a single cross-validation
    
    Returns:
        data for each of the splits for a single cross-validation
    
Splitting the data into splits for each cross-validation.
Splitting the data into splits for each cross - validation .
def execute_skfold(X,y, columns_org, continuous, columns_dummified, splits, seed):
    skf = StratifiedKFold(n_splits=splits, random_state=seed, shuffle=True)
    result = Parallel(n_jobs=-1)(delayed(execute_split)(X,y, columns_org, continuous, columns_dummified, train, test) for train, test in skf.split(X,y))
    return result
def execute_skfold ( X , y , columns_org , continuous , columns_dummified , splits , seed ) : skf = StratifiedKFold ( n_splits = splits , random_state = seed , shuffle = True ) result = Parallel ( n_jobs = - 1 ) ( delayed ( execute_split ) ( X , y , columns_org , continuous , columns_dummified , train , test ) for train , test in skf . split ( X , y ) ) return result
Splitting the data into splits for each cross-validation.
Splitting the data into splits for each cross - validation .
'''
    Splitting the data into splits for each cross-validation.
    
    Args:
        X: The dataset
        y: The target values
        columns_org: column names before dummification
        continuous: column names of continuous values
        columns_dummified: column names after dummification
        splits: number of folds in each cross validation
        seed: The number of a single cross-validation
    
    Returns:
        data for each of the splits for a single cross-validation
    '''
data for each of the splits for a single cross-validation
data for each of the splits for a single cross - validation
X
None
y
The target values
The target values
columns_org
column names before dummification
column names before dummification
continuous
column names of continuous values
column names of continuous values
columns_dummified
column names after dummification
column names after dummification
splits
number of folds in each cross validation
number of folds in each cross validation
seed
The number of a single cross-validation
The number of a single cross - validation