The purpose of this tool is to automate the process of complex data pull. All params and credentials are automatically downloaded from a pre-defined S3 buckets.

Features of this tool are --

User can customize the what needs to be pulled just by changing / editing json file in S3 location

User can add data pull just by adding parameters in a json file

Credentials folder location and

data dropped location can be changed without developer's intervention

Users will be able to change the FB App's credentials when it expires

Project structure...

fb-api-project(root)
|   main_file.py
│   README.md 
|   requirement.txt  
│   .gitignore 
│
└─── credentils
│   │   __init__.py
│   │   adobjectsfields.json  # This file holds the fields. 
|   |                         # This file will be replaced by downloading from S3
|   |   credentials.json      # This file holds the credentials of both S3 and FB App. 
|                               This file will be replaced by downloading from S3
└─── func
|   │   __init__.py
|   │   func.py               # Blank file. Will be removed / updated in future
|
└─── params
|   |   __init__.py
|   |   fieldlist.json        # For AdObjects
|   |   params.json           # For Insight data
|   
└─── processing
|   |   __init__.py
|   |   collectparams.py 
|   |   run.py
|
└─── settings
|   |   __init__.py
|   |   settings.py 
|   
└─── utils
|   |   __init__.py
|   |   getData.py 
|   |   s3FuncTools.py
|   |   version.py            # Will be removed in future

Sample code...

from processing.run import RunProcess

r = RunProcess()
r.get_adobject_data(ad_object='campaign')
r.get_insights(saveto='data', data_limit=100)

# File(s) will be stored in root/data directory

Shapley Value Regression

Introduction:

https://github.com/sarbadal/Shapley-Value-Regression

In the econometric literature multicollinearity is defined as the incidence of high degree of correlation among some or all regressor variables. Strong multicollinearity has deleterious effects on the confidence intervals of linear regression coefficients (β in the linear regression model y=Xβ+u). Although it does not affect the explanatory power ( $R^2$ ) of the regressors or unbiasedness of the estimated coefficients associated with them, it does inflate their standard error of estimate rendering test of hypothesis misleading or paradoxical, often such that although $R^2$ could be very high, individual coefficients may all have poor Student’s tvalues. Thus, strong multicollinearity may lead to failure in rejecting a false null hypothesis of ineffectiveness of the regressor variable to the regressand variable (type II error). Very frequently, it also affects the sign of the regression coefficients. However, it has been pointed out that the incidence of high degree of correlation (measured in terms of a large condition number; Belsley et al., 1980) among some or all regressor variables alone (unsupported by large variance of error in the regressand variable, y) has little effect on the precision of regression coefficients. Large condition number coupled with a large variance of error in the regressand variable destabilizes the regression estimator; either of the two in isolation cannot cause much harm, although the condition number is relatively more potent in determining the stability of estimated regression coefficients (Mishra, 2004-a).