Module `TDA_Regression`

Expand source code

# importing toy & real world datasets from the scikit-learn library

from sklearn import datasets

def dataload():
    """
    upload toy datasets from scikit-learn
    """
    data = None
    return data

def datafetch(file_name):
    """
    upload real world datasets from scikit-learn
    """
    data = None
    print("reading data from:", file_name)
    return data

# standard descriptive statistic analysis of data    

def descriptive_statistic(df):
    """
    Provides brief descriptive statistics on dataset. 
    Takes dataframe as input.
    """
    print("Type : ", None, "\n\n")
    print("Shape : ", None)
    print("Head -- \n", None)
    print("\n\n Tail -- \n", None)
    print("Describe : ", None)
    
    
# model selection

def model_selection(df):
    """
    Takes dateframe as input. Performs foward/backward stepwise
    regression. Returns best model for both methods.
    """
    null_fit = None
    foward_step = None
    backward_step = None
    return foward_step, backward_step

# model accuracy 

def MSE_fit(fit): 
    """
    Takes in a fitted model as the input.
    Calculates the MSU of the fitted model.
    Outputs the model's MSE.
    """
    MSE = None
    return MSE

def accuracy_metrics(fit, MSE):
    """
    This function is used for model validation. It returns a dictionary
    of several regression model accuracy metrics. Its inputs are a fitted model
    and the MSE of the fitted model.
    """
    d = dict()
    sumObj = None
    SSE = None
    n = None
    p = None
    pr = None
    d['R2'] = None
    d['R2ad'] = None
    d['AIC'] = None
    d['BIC'] = None
    d['PRESS'] = None
    d['Cp']= None
    return d

# test code

file_name = 'data.csv'

a = datafetch(file_name)
print(a)

b = descriptive_statistic(a)
print(b)

c = model_selection(a)
print(c)

d = MSE_fit(c)
print(d)

print(accuracy_metrics(c, d))

Functions

def MSE_fit(fit)

Takes in a fitted model as the input. Calculates the MSU of the fitted model. Outputs the model's MSE.

Expand source code

def MSE_fit(fit): 
    """
    Takes in a fitted model as the input.
    Calculates the MSU of the fitted model.
    Outputs the model's MSE.
    """
    MSE = None
    return MSE

def accuracy_metrics(fit, MSE)

This function is used for model validation. It returns a dictionary of several regression model accuracy metrics. Its inputs are a fitted model and the MSE of the fitted model.

Expand source code

def accuracy_metrics(fit, MSE):
    """
    This function is used for model validation. It returns a dictionary
    of several regression model accuracy metrics. Its inputs are a fitted model
    and the MSE of the fitted model.
    """
    d = dict()
    sumObj = None
    SSE = None
    n = None
    p = None
    pr = None
    d['R2'] = None
    d['R2ad'] = None
    d['AIC'] = None
    d['BIC'] = None
    d['PRESS'] = None
    d['Cp']= None
    return d

def datafetch(file_name)

upload real world datasets from scikit-learn

Expand source code

def datafetch(file_name):
    """
    upload real world datasets from scikit-learn
    """
    data = None
    print("reading data from:", file_name)
    return data

def dataload()

upload toy datasets from scikit-learn

Expand source code

def dataload():
    """
    upload toy datasets from scikit-learn
    """
    data = None
    return data

def descriptive_statistic(df)

Provides brief descriptive statistics on dataset. Takes dataframe as input.

Expand source code

def descriptive_statistic(df):
    """
    Provides brief descriptive statistics on dataset. 
    Takes dataframe as input.
    """
    print("Type : ", None, "\n\n")
    print("Shape : ", None)
    print("Head -- \n", None)
    print("\n\n Tail -- \n", None)
    print("Describe : ", None)

def model_selection(df)

Takes dateframe as input. Performs foward/backward stepwise regression. Returns best model for both methods.

Expand source code

def model_selection(df):
    """
    Takes dateframe as input. Performs foward/backward stepwise
    regression. Returns best model for both methods.
    """
    null_fit = None
    foward_step = None
    backward_step = None
    return foward_step, backward_step