Python ML project setup

python-ml-setup.mk

# Logic above in make form. Run with `make init`
project.name := project-name
freeze.txt := requirements.txt
notebook.dir := notebooks
data.dir := data
src.dir := src

.PHONY: init dependencies reinstall

init: $(project.name)
	bash -c "source ./$(project.name)/bin/activate && make dependencies $(freeze.txt) $(notebook.dir) $(data.dir) $(src.dir)"
	@echo
	@echo "Done. Execute this command manually before working: source ./$(project.name)/bin/activate"

$(project.name):
	python3 -m virtualenv $(project.name)

$(freeze.txt):
	pip3 freeze > $(freeze.txt)

$(notebook.dir):
	mkdir -p $(notebook.dir)

$(data.dir):
	mkdir -p $(data.dir)

$(src.dir):
	mkdir -p $(src.dir)

dependencies:
	pip3 install jupyter pandas numpy sklearn freeze Pygments matplotlib

reinstall:
	pip3 install -r $(freeze.txt)

python-ml-setup.sh

# Create a virtual env where everything is installed
python3 -m virtualenv project-name

# Source the geenrated activate script to update your local env variables. Like a bad chroot.
source ./kaggle-titanic/bin/activate

# Install whatever you need for the project locally
pip3 install jupyter pandas numpy sklearn matplotlib

# Start your jupyter notebook and do some stuff
jupyter notebook

# Optionally, make a dedicated dir for saving notebook stuff to
mkdir ./notebook/

# Download and save your data locally
mkdir ./data