Model Documentation and Coding Guidelines - Python

This paper was very useful. This covers Data Source, Purpose, Model Accuracy, Recommendations. The key metrics (Screenshot from the paper)

Structuring Machine Learning Projects

ML Experiment Parameters

• Model Parameters
• Learning Rate
• Number of Epochs Run
• Training Loss
• Validation Loss
• CPU %%
• Memory %%
• Disk usage

	if (readable()): {
	be_happy()
	}
	else: {
	refactor()
	}
	#http://msdl.cs.mcgill.ca/people/shahla/misc/PythonConventions.pdf
	#filenames short file names
	#myfile.py
	#class name, CapWords convention
	#class MyClass:
	#private and protected variables with _
	# _myProtectedVar, _myPrivateVar
	#import in seperate lines
	#Bad
	import sys, os
	#Good
	import sys
	import os
	#hierarchy of import
	#standard library
	#major imports
	#App specific imports
	#indendation
	#break lines with \
	#no multiple statements in single line
	#Bad
	if foo == 'blah': doBlahThing()
	#good
	if foo == 'blah':
	doBlahThing()
	#No white space before paranthesis
	#bad
	spam (1)
	dict ['key']
	#good
	spam(1)
	dict['key']
	#no white space before comma, colon
	#bad
	if x==4:
	print x , y ,y = y
	#good
	if x==4:
	print x, y, y = y
	#operator declaration
	#bad
	x = 1
	operatorA = 2
	cab = 3
	#good
	x = 1
	operatorA = 2
	cab = 3
	#comparisons use None or conditions
	#bad
	if x:
	y = 6
	#good
	if x is not None:
	y = 6
	#http://www.cs.rpi.edu/academics/courses/fall18/csci1200/Good_Programming_Practices.pdf
	#uppercase constants
	GRAVITY
	#captitalize first word of class
	Person()
	#private protected with _ before
	_speed
	#Variables
	#Avoid global variables
	#instead of public variable use getters and setters
	class Person():
	def __init__(self,name):
	self.name = name
	def getName(self):
	return self.name
	def setName(self, name):
	self.name = str(name)
	#Avoid deep nesting
	def work_check(word):
	if len(word) < 5:
	return False
	if len(word) % 2 == 0:
	return False
	if word[0] != 'a':
	return False
	return False
	#Exception Handline
	#https://www.datacamp.com/community/tutorials/exception-handling-python
	try:
	a = 100 / 0
	print (a)
	except ZeroDivisionError:
	print ("Zero Division Exception Raised." )
	else:
	print ("Success, no error!")
	#https://python.g-node.org/python-autumnschool-2010/_media/materials/day0-haenel-best-practices.pdf
	#https://gist.github.com/ericmjl/27e50331f24db3e8f957d1fe7bbbe510
	#https://github.com/bast/somepackage
	#https://dev.to/codemouse92/dead-simple-python-project-structure-and-imports-38c6
	#https://docs.python-guide.org/writing/structure/
	#https://towardsdatascience.com/manage-your-data-science-project-structure-in-early-stage-95f91d4d0600
	#https://github.com/Azure/Azure-TDSP-ProjectTemplate
	#https://drivendata.github.io/cookiecutter-data-science/
	├── LICENSE
	├── Makefile <- Makefile with commands like `make data` or `make train`
	├── README.md <- The top-level README for developers using this project.
	├── data
	│ ├── external <- Data from third party sources.
	│ ├── interim <- Intermediate data that has been transformed.
	│ ├── processed <- The final, canonical data sets for modeling.
	│ └── raw <- The original, immutable data dump.
	│
	├── docs <- A default Sphinx project; see sphinx-doc.org for details
	│
	├── models <- Trained and serialized models, model predictions, or model summaries
	│
	├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
	│ the creator's initials, and a short `-` delimited description, e.g.
	│ `1.0-jqp-initial-data-exploration`.
	│
	├── references <- Data dictionaries, manuals, and all other explanatory materials.
	│
	├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
	│ └── figures <- Generated graphics and figures to be used in reporting
	│
	├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
	│ generated with `pip freeze > requirements.txt`
	│
	├── setup.py <- Make this project pip installable with `pip install -e`
	├── src <- Source code for use in this project.
	│ ├── __init__.py <- Makes src a Python module
	│ │
	│ ├── data <- Scripts to download or generate data
	│ │ └── make_dataset.py
	│ │
	│ ├── features <- Scripts to turn raw data into features for modeling
	│ │ └── build_features.py
	│ │
	│ ├── models <- Scripts to train models and then use trained models to make
	│ │ │ predictions
	│ │ ├── predict_model.py
	│ │ └── train_model.py
	│ │
	│ └── visualization <- Scripts to create exploratory and results oriented visualizations
	│ └── visualize.py
	│
	└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
	#https://www.datacamp.com/community/tutorials/inner-classes-python
	#https://www.datacamp.com/community/tutorials/python-data-type-conversion
	#https://github.blog/2015-01-21-how-to-write-the-perfect-pull-request/

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Happy Learning!!!