This is suplementary project for demo purposes, to provide some interactivity to ml-lib library. It uses clap to systematize, integrity and validation of user supplied command line options, as well as self explanatory (hopefuly!) user help.
There is no intention to cover all linfa ML algorithms... Just a few to get fill of "plumming" needed to connect data to Linfa algorithms. Currently, only the following Linfa's algorithms implemented
- decision tree (
random forest???), flags-Ccdr - SVM (upport vector machines), flags
-Ccss
$ ./target/debug/ml-cli -Ccdr -h
[r]andom forest learning algorithm
Usage: ml-cli AlgoCategory SL DT {RF|-r}
Options:
-h, --help Print help (see more with '--help')
actual run
./target/debug/ml-cli -Ccdr
accuracy:1
predicted:[1, 1, 1]
Progam will use time series data from IBM, 5 years as base, S&P500, 5 years as suplementary and attempt to learn and predict closing price trend for next 3 days.
NOTE: the goal is not actually make correct prediction, rather understand polars, ndarray and linfa interaction
$ cargo run -- -h
Demo CLI wrapper
Usage: ml-cli [OPTIONS] <COMMAND>
Commands:
AlgoCategory, -C Algorithm's [C]ategory
help Print this message or the help of the given subcommand(s)
Options:
-v, --verbosity... log level
-h, --help Print help (see more with '--help')
-V, --version Print version
and follow help prompts... help is context sencitive. The above snippet demonstrates generic help prompt. The following snippet demonstrates help items for Algorithm's category
$ cargo run -- -C -h
Algorithm's [C]ategory
Usage: ml-cli {AlgoCategory|-C} <COMMAND>
Commands:
SL, -c [s]upervised learning algorithms class
USL, -u [u]nSupervised learning algorithm class
partial-fit, -f Partial [f]it ML algorithms class
pre-processor, -p [p]re Processors ML algorithms class
help Print this message or the help of the given subcommand(s)
Options:
-h, --help Print help