runs.py
def NormalRun(data, directory_path, datafile, target, classifiers, fselect, n_seed, splits):
    '''
    Create all the directories to store all the intermediate and final results. 
    Impute and prepare the data for running.
    Splits the data and run the feature selection and classification on runs.

    Args:
        data : The DataFrame that contains data that hasn't been preprocessed.
        directory_path: Directory from which the python file is being run
        datafile: name of the datafile inside the data folder
        target: the dependent variable of the dataset
        classifiers: Classification methods used
        fselect: Feature selection methods used
        type: Type of analysis performed
        n_seed: Number of validations
        splits: Number of folds or splits in each validation run   
    '''
    
    target_path = directory_path+'NormalDataAnalysis/'+datafile+"_"+target+"/"

    feature_path = target_path+"features/"
    STATS_path = target_path+"STATS/"
    results_path = target_path+"resultsparallel/"
    data_path = target_path+"dataparallel/"

    if not os.path.exists(feature_path):
        os.makedirs(feature_path)
    if not os.path.exists(STATS_path):
        os.makedirs(STATS_path)
    if not os.path.exists(results_path):
        os.makedirs(results_path)
    if not os.path.exists(data_path):
        os.makedirs(data_path)

    datacopy = data.copy(deep=True)
    datacopy.replace('', np.nan,regex=True, inplace=True)
    datacopy = pre.remove_invalid_data(datacopy, target)

    stats.baseline(datacopy, target, STATS_path)

    columns_org = datacopy.columns
    [datacopy, continuous] = pre.modify_data(datacopy)
    columns_dummified = datacopy.columns
    n_features = datacopy.shape[1]-1

    runs = stats.runSKFold(n_seed, splits, data=datacopy,target=target, columns_org=columns_org, continuous=continuous, columns_dummified=columns_dummified)

    for c in classifiers:
        for fs in fselect:
            score.score(rsr.normal_run(target_path, columns_dummified.drop([target]), n_seed, splits, fs, c, runs, n_features),n_seed)

    st.create_STATS(target_path)
    st.heatmap(target_path, target)

    Create all the directories to store all the intermediate and final results. 
    Impute and prepare the data for running.
    Splits the data and run the feature selection and classification on runs.

    Args:
        data : The DataFrame that contains data that hasn't been preprocessed.
        directory_path: Directory from which the python file is being run
        datafile: name of the datafile inside the data folder
        target: the dependent variable of the dataset
        classifiers: Classification methods used
        fselect: Feature selection methods used
        type: Type of analysis performed
        n_seed: Number of validations
        splits: Number of folds or splits in each validation run   
    
Create all the directories to store all the intermediate and final results.
Impute and prepare the data for running.
Splits the data and run the feature selection and classification on runs.
Create all the directories to store all the intermediate and final results . Impute and prepare the data for running . Splits the data and run the feature selection and classification on runs .
def NormalRun(data, directory_path, datafile, target, classifiers, fselect, n_seed, splits):
    target_path = directory_path+'NormalDataAnalysis/'+datafile+"_"+target+"/"
    feature_path = target_path+"features/"
    STATS_path = target_path+"STATS/"
    results_path = target_path+"resultsparallel/"
    data_path = target_path+"dataparallel/"
    if not os.path.exists(feature_path):
        os.makedirs(feature_path)
    if not os.path.exists(STATS_path):
        os.makedirs(STATS_path)
    if not os.path.exists(results_path):
        os.makedirs(results_path)
    if not os.path.exists(data_path):
        os.makedirs(data_path)
    datacopy = data.copy(deep=True)
    datacopy.replace('', np.nan,regex=True, inplace=True)
    datacopy = pre.remove_invalid_data(datacopy, target)
    stats.baseline(datacopy, target, STATS_path)
    columns_org = datacopy.columns
    [datacopy, continuous] = pre.modify_data(datacopy)
    columns_dummified = datacopy.columns
    n_features = datacopy.shape[1]-1
    runs = stats.runSKFold(n_seed, splits, data=datacopy,target=target, columns_org=columns_org, continuous=continuous, columns_dummified=columns_dummified)
    for c in classifiers:
        for fs in fselect:
            score.score(rsr.normal_run(target_path, columns_dummified.drop([target]), n_seed, splits, fs, c, runs, n_features),n_seed)
    st.create_STATS(target_path)
    st.heatmap(target_path, target)
def NormalRun ( data , directory_path , datafile , target , classifiers , fselect , n_seed , splits ) : target_path = directory_path + 'NormalDataAnalysis/' + datafile + "_" + target + "/" feature_path = target_path + "features/" STATS_path = target_path + "STATS/" results_path = target_path + "resultsparallel/" data_path = target_path + "dataparallel/" if not os . path . exists ( feature_path ) : os . makedirs ( feature_path ) if not os . path . exists ( STATS_path ) : os . makedirs ( STATS_path ) if not os . path . exists ( results_path ) : os . makedirs ( results_path ) if not os . path . exists ( data_path ) : os . makedirs ( data_path ) datacopy = data . copy ( deep = True ) datacopy . replace ( '' , np . nan , regex = True , inplace = True ) datacopy = pre . remove_invalid_data ( datacopy , target ) stats . baseline ( datacopy , target , STATS_path ) columns_org = datacopy . columns [ datacopy , continuous ] = pre . modify_data ( datacopy ) columns_dummified = datacopy . columns n_features = datacopy . shape [ 1 ] - 1 runs = stats . runSKFold ( n_seed , splits , data = datacopy , target = target , columns_org = columns_org , continuous = continuous , columns_dummified = columns_dummified ) for c in classifiers : for fs in fselect : score . score ( rsr . normal_run ( target_path , columns_dummified . drop ( [ target ] ) , n_seed , splits , fs , c , runs , n_features ) , n_seed ) st . create_STATS ( target_path ) st . heatmap ( target_path , target )
Create all the directories to store all the intermediate and final results.
Create all the directories to store all the intermediate and final results .
'''
    Create all the directories to store all the intermediate and final results. 
    Impute and prepare the data for running.
    Splits the data and run the feature selection and classification on runs.

    Args:
        data : The DataFrame that contains data that hasn't been preprocessed.
        directory_path: Directory from which the python file is being run
        datafile: name of the datafile inside the data folder
        target: the dependent variable of the dataset
        classifiers: Classification methods used
        fselect: Feature selection methods used
        type: Type of analysis performed
        n_seed: Number of validations
        splits: Number of folds or splits in each validation run   
    '''
data
directory_path
Directory from which the python file is being run
Directory from which the python file is being run
datafile
name of the datafile inside the data folder
name of the datafile inside the data folder
target
the dependent variable of the dataset
the dependent variable of the dataset
classifiers
Classification methods used
Classification methods used
fselect
Feature selection methods used
Feature selection methods used
n_seed
Number of validations
Number of validations
splits
Number of folds or splits in each validation run
Number of folds or splits in each validation run
data 
The DataFrame that contains data that hasn't been preprocessed.
The DataFrame that contains data that hasn ' t been preprocessed .
type
Type of analysis performed
Type of analysis performed