Adding the stubbed functions py file

Models/TDA_Prediction.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# # <center>Stub Functions and Automatic Documentation</center>
+# 
+# <img src="https://pdoc3.github.io/pdoc/logo.png" width=30% alt="Sphinx logo">
+
+# In[1]:
+
+
+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
+
+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.
+    """
+    null_fit = None
+    foward_step = None
+    backward_step = None
+    return foward_step, backward_step
+
+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
+
+
+# In[3]:
+
+
+# test docstring
+help(accuracy_metrics)
+
+
+# In[ ]:
+
+
+# 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))
+