https://github.com/sbinet/go-trackml
trackml is a Go package to simplify working with the High Energy Physics Tracking Machine Learning challenge
https://github.com/sbinet/go-trackml
go golang gonum hep-ex kaggle kaggle-competition kaggle-trackml
Last synced: 3 days ago
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trackml is a Go package to simplify working with the High Energy Physics Tracking Machine Learning challenge
- Host: GitHub
- URL: https://github.com/sbinet/go-trackml
- Owner: sbinet
- License: bsd-3-clause
- Created: 2018-04-26T07:27:05.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-05-16T16:22:30.000Z (over 7 years ago)
- Last Synced: 2025-01-17T10:22:12.226Z (9 months ago)
- Topics: go, golang, gonum, hep-ex, kaggle, kaggle-competition, kaggle-trackml
- Language: Go
- Homepage:
- Size: 29.3 KB
- Stars: 7
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# go-trackml
[](https://godoc.org/github.com/sbinet/go-trackml)
`trackml` is a Go package to simplify working with the [High Energy Physics Tracking Machine Learning challenge][kaggle_trackml].
For more informations about the minute details of what `go-trackml` tries to do, please have a look at the Python version:
- https://github.com/LAL/trackml-library
`trackml` is a Go reimplementation of the above Python library.
## Installation
```sh
$> go get github.com/sbinet/go-trackml
```
## Documentation
Served by [GoDoc](https://godoc.org/github.com/sbinet/go-trackml).
## Example
```sh
$> go get github.com/sbinet/go-trackml/cmd/trkml-hough
$> trkml-hough -h
trkml-hough uses a Hough transform to make predictions.
Usage:
$> trkml-hough [OPTIONS] [ trkml-hough ./example_standard/dataset event000000200
$> trkml-hough -npcus=+1 ./example_standard/dataset event000000200
$> trkml-hough -npcus=-1 ./example_standard/dataset event000000200
$> trkml-hough -npcus=-1 ./train_sample.zip event000001000
Options:
-ncpus int
number of goroutines to use for the prediction (default 1)
-prof-cpu
enable CPU profiling
-prof-mem
enable MEM profiling
-submit
create a submission file
$> ll example_standard/dataset/
total 56M
-rw-r--r-- 1 binet binet 13M Apr 25 18:36 event000000200-cells.csv
-rw-r--r-- 1 binet binet 4.2M Apr 25 18:36 event000000200-hits.csv
-rw-r--r-- 1 binet binet 915K Apr 25 18:36 event000000200-particles.csv
-rw-r--r-- 1 binet binet 9.5M Apr 25 18:36 event000000200-truth.csv
-rw-r--r-- 1 binet binet 14M Apr 25 18:36 event000000201-cells.csv
-rw-r--r-- 1 binet binet 4.5M Apr 25 18:36 event000000201-hits.csv
-rw-r--r-- 1 binet binet 967K Apr 25 18:36 event000000201-particles.csv
-rw-r--r-- 1 binet binet 10M Apr 25 18:36 event000000201-truth.csv
$> time trkml-hough ./example_standard/dataset event000000200
trkml-hough: loading [event000000200 from ./example_standard/dataset]...
trkml-hough: loading [event000000200 from ./example_standard/dataset]... [done]
trkml-hough: score for event 200: 0.1316012364071201
trkml-hough: loading the whole dataset "./example_standard/dataset"...
trkml-hough: score for event 200: 0.1316012364071201
trkml-hough: score for event 201: 0.1332602513710427
trkml-hough: loading the whole dataset "./example_standard/dataset"... [done]
trkml-hough: mean score: 0.13243074388908138
real 1m21.033s
user 1m22.541s
sys 0m0.569s
```
Compare to the Python version:
```sh
$> time python trkml-hough.py ./example_standard/dataset event000000200
hit_id x y z volume_id layer_id module_id
0 1 -62.663200 -3.05090 -1502.5 7 2 1
1 2 -66.124702 -1.36730 -1502.5 7 2 1
2 3 -63.697701 1.73267 -1502.5 7 2 1
3 4 -82.501801 -14.09150 -1502.5 7 2 1
4 5 -74.343399 0.84469 -1502.5 7 2 1
Your score: 0.13153644878592863
Score for event 200: 0.132
Score for event 201: 0.133
Mean score: 0.132
real 7m12.351s
user 7m10.400s
sys 0m0.828s
```
### Going parallel
[Go](https://golang.org) has a few built-in facilities to apply concurrent programming.
The simple `trkml-hough` command leverages them:
```sh
$> time trkml-hough -ncpus=-1 ./example_standard/dataset event000000200
trkml-hough: loading [event000000200 from ./example_standard/dataset]...
trkml-hough: loading [event000000200 from ./example_standard/dataset]... [done]
trkml-hough: score for event 200: 0.13160123640712013
trkml-hough: loading the whole dataset "./example_standard/dataset"...
trkml-hough: score for event 200: 0.1316012364071201
trkml-hough: score for event 201: 0.13326025137104267
trkml-hough: loading the whole dataset "./example_standard/dataset"... [done]
trkml-hough: mean score: 0.13243074388908138
real 0m30.081s
user 1m26.658s
sys 0m0.741s
```
## Submission
```sh
$> time trkml-hough -ncpus=-1 -submit ./train_sample.zip event000001000 ./test.zip
trkml-hough: loading [event000001000 from ./train_sample.zip]...
trkml-hough: loading [event000001000 from ./train_sample.zip]... [done]
trkml-hough: score for event 1000: 0.14063594336939647
trkml-hough: loading the whole dataset "./train_sample.zip"...
trkml-hough: score for event 1000: 0.14063594336939647
trkml-hough: score for event 1001: 0.15109432405046952
trkml-hough: score for event 1002: 0.134897692773808
trkml-hough: score for event 1003: 0.1474692054660444
trkml-hough: score for event 1004: 0.13474093093042283
trkml-hough: loading the whole dataset "./train_sample.zip"... [done]
trkml-hough: mean score: 0.14176761931802823
trkml-hough: loading test dataset "./test.zip"...
trkml-hough: processing event 0...
trkml-hough: processing event 1...
trkml-hough: processing event 2...
[...]
trkml-hough: processing event 124...
trkml-hough: loading test dataset "./test.zip"... [done]
real 19m40.461s
user 57m14.608s
sys 0m10.350s
$> ll submission.csv.gz
-rw-r--r-- 1 binet binet 44M May 16 17:57 submission.csv.gz
```
[cern]: https://home.cern
[lhc]: https://home.cern/topics/large-hadron-collider
[kaggle_trackml]: https://www.kaggle.com/c/trackml-particle-identification