Alexey Sirotkin alexus987

monkut / argparse_date_datetime_custom_types.py

Last active May 10, 2023 15:47

Custom date/datetime type validators for python's argparse module

	def valid_date_type(arg_date_str):
	"""custom argparse date type for user dates values given from the command line"""
	try:
	return datetime.datetime.strptime(arg_date_str, "%Y-%m-%d")
	except ValueError:
	msg = "Given Date ({0}) not valid! Expected format, YYYY-MM-DD!".format(arg_date_str)
	raise argparse.ArgumentTypeError(msg)

	def valid_datetime_type(arg_datetime_str):
	"""custom argparse type for user datetime values given from the command line"""

brooksandrew / shiny_arules.R

Last active May 19, 2022 08:14

Launches a Shiny App that provides an interactive interface to the arules and arulesViz package which train and visualize association rules

	#' @title Assocation Rules Visualization Shiny App
	#' @description Launches a Shiny App that provides an interactive interface to the visualizations of the \code{arulesViz} package.
	#' The app allows users to mine rules based on all or just subsets of features, sort by criteria (lift, support, confidence) and visualize
	#' using network graph, grouped bubble and scatter plots. \cr
	#' Users filter rules to target only those with a certain variable on the RHS or LHS of the rule.
	#' Rule mining is computed using the \link{apriori} algorithm from \code{arules}.
	#'
	#' @param dataset data.frame, this is the dataset that association rules will be mined from. Each row is treated as a transaction. Seems to work
	#' OK when a the S4 transactions class from \code{arules} is used, however this is not thoroughly tested.
	#' @param bin logical, \code{TRUE} will automatically discretize/bin numerical data into categorical features that can be used for association analysis.