gwarmstrong / mohawk Goto Github PK
View Code? Open in Web Editor NEWNeural nets for microbial sequence classification
License: BSD 3-Clause "New" or "Revised" License
Neural nets for microbial sequence classification
License: BSD 3-Clause "New" or "Revised" License
Small to-do for classify
CLI:
model
to model_path
Write train
CLI:
Based on my experience with tensorboard, this will help for two reasons:
Let it be passed into the trainer functionality
It could be nice to take advantage of google's nucleus
for parsing sequencing data.
In this colab notebook, it looks nice to work with, potentially saving time and memory on parsing and sampling data.
It may be a bit of a pain to incorporate with pytorch and will need some benchmarking if implemented.
See these links for some starting info on this front.
https://discuss.pytorch.org/t/read-dataset-from-tfrecord-format/16409/7
https://github.com/pgmmpk/tfrecord
This should be added to allow multiple ways of invoking mohawk functionality, without necessarily assuming that all models will be of the form that is currently being used.
go through and figure out what can be better covered and how by testing
Add the architecture from this colab notebook to the package's models.
Currently only forward strands are trained on...
This will help the training part of the CLI
The goal here is to be able to construct multiple types of models that accomplish the same task, but may have drastically ways of preparing and treating data, using the same user-facing API/CLI.
Details:
Want to be able to train models with a command that looks something like:
mohawk train --model-type [model_type] \
--sequence-directory /path/to/seq/dir \
--lables /path/to/seq/labels.txt \
--depth [number_of_reads_to_generate] \
--length [length_of_sequences_to_produce] \
--random-seed [seed_for_rng]
Internally, the python will need to be written in such a way that different methods for prep, training, etc., can be executed for different model types.
It would be nice to have a way to pass in custom hyperparameters from the command line so that factors that are considered upstream of the model hyperparameters can also be incorporated
Currently there are some scripts that are python scripts I have been using to training models that contain parameters and code to begin training the model. This should be transitioned so there is a mohawk train
command in the CLI, where parameters can be passed in as some sort of config file.
Currently, model training is performed on reads taken directly from genomes, it would be useful to incorporate some reads with errors in the training regimes.
The main problem is downloading the taxdump etc., using ete on Travis. This could be fixed by mocking parts of the ete toolkit responsible for downloading, and reworking tests that require this functionality so that we don't actually have to have it for the unit tests.
See this notebook
This is required so that I can be confident in changes made for #15
This will help remove the dependence upon ete3
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.