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توضیحات

A library for generating gkm-svm faster

ویژگی	مقدار
سیستم عامل	-
نام فایل	fastsk-0.0.2
نام	fastsk
نسخه کتابخانه	0.0.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	QData Lab at the University of Virginia
ایمیل نویسنده	yanjun@virginia.edu
آدرس صفحه اصلی	https://github.com/QData/FastSk
آدرس اینترنتی	https://pypi.org/project/fastsk/
مجوز	MIT

# FastSK: fast sequence analysis with gapped string kernels (Fast-GKM-SVM) This Github repo provides improved algorithms for implementing gkm-svm string kernel calculations. We provide C++ version of the algorithm implementation and a python wrapper (making to a python package) for the C++ implementation. Our package provides fast and accuate gkm-svm based training SVM classifiers and regressors for gkm string kernel based sequence analysis. This Github is built with a novel and fast algorithm design for implementing gapped k-mer algorithm, [pybind11](https://github.com/pybind/pybind11), and [LIBSVM](https://github.com/cjlin1/libsvm). #### More details of algorithms and results now in: [Bioinformatics 2020](https://academic.oup.com/bioinformatics/article/36/Supplement_2/i857/6055916) ## Prerequisites **On Windows** * Visual Studio 2015 (required for all Python versions, see notes below) * CMake >= 3.1 ## Installation via Pip Install (Linux and MacOS) Clone this repository and run: ```bash git clone --recursive https://github.com/QData/FastSK.git cd FastSK pip install -r requirements.txt pip install . ``` #### The pip intallation of FastSK has been tested successfully on CentOS, Red Hat, MacOS and WindowsXP. ## Python Version Tutorial ### Example Jupyter notebook - 'docs/2demo/fastDemo.ipynb' ### Example python usage script: ``` cd test python run_check.py ``` ### You can check if fastsk library is installed correctly in python shell: ``` from fastsk import FastSK ## Compute kernel matrix fastsk = FastSK(g=10, m=6, t=1, approx=True) ``` ### Experimental Results, Baselines, Utility Codes and Setup - We have provided all datasets we used in the subfolder "data" - We have provided all scripts we used to generate results under the subfolder "results" #### Grid Search for FastSK and gkm-svm baseline To run a grid search over the hyperparameter space (g, m, and C) to find the optimal parameters, e.g, one utility code: ``` cd results/ python run_gridsearch.py ``` #### When comparing with Deep Learning baselines + You do need to have pytorch installed ``` pip install torch torchvision ``` + One utility code: on all datasets with hyperparameter tuning of charCNN and each configure with 5 random-seeding repeats: ``` cd results/neural_nets python run_cnn_hyperTrTune.py ``` + We have many other utility codes helping users to run CNN and RNN baselines #### Some of our exprimental results comparing FastSK with baselines wrt performance and speed <img src="results/spreadsheets/Figure5.png" width="800"> <img src="results/spreadsheets/Table1.png" width="800"> <img src="results/spreadsheets/Table2.png" width="800"> #### Some of our exprimental results comparing FastSK with Character based Convolutional Neural Nets (CharCNN) when varying training size. <img src="results/neural_nets/trainsize_varyresults/dna.png" width="800"> <img src="results/neural_nets/trainsize_varyresults/protein.png" width="800"> <img src="results/neural_nets/trainsize_varyresults/nlp.png" width="800"> #### To Do: * a detailed user document, with example input files, output files, code, and perhaps a user group where people can post their questions ### Citations If you find this tool useful, please cite us! ``` @article{fast-gkm-svm, author = {Blakely, Derrick and Collins, Eamon and Singh, Ritambhara and Norton, Andrew and Lanchantin, Jack and Qi, Yanjun}, title = "{FastSK: fast sequence analysis with gapped string kernels}", journal = {Bioinformatics}, volume = {36}, number = {Supplement_2}, pages = {i857-i865}, year = {2020}, month = {12}, abstract = "{Gapped k-mer kernels with support vector machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly sized training sets. However, existing gkm-SVM algorithms suffer from slow kernel computation time, as they depend exponentially on the sub-sequence feature length, number of mismatch positions, and the task’s alphabet size.In this work, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels. Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculation into a set of independent counting operations over the possible mismatch positions. This simplified decomposition allows us to devise a fast Monte Carlo approximation that rapidly converges. FastSK can scale to much greater feature lengths, allows us to consider more mismatches, and is performant on a variety of sequence analysis tasks. On multiple DNA transcription factor binding site prediction datasets, FastSK consistently matches or outperforms the state-of-the-art gkmSVM-2.0 algorithms in area under the ROC curve, while achieving average speedups in kernel computation of ∼100× and speedups of ∼800× for large feature lengths. We further show that FastSK outperforms character-level recurrent and convolutional neural networks while achieving low variance. We then extend FastSK to 7 English-language medical named entity recognition datasets and 10 protein remote homology detection datasets. FastSK consistently matches or outperforms these baselines.Our algorithm is available as a Python package and as C++ source code at https://github.com/QData/FastSKSupplementary data are available at Bioinformatics online.}", issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa817}, url = {https://doi.org/10.1093/bioinformatics/btaa817}, eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_2/i857/35337038/btaa817.pdf}, } ``` ### Legacy: If you prefer using the executable made from the Pure C++ source code (without python wrapper or R wrapper) - you can clone this repository: ``` git clone --recursive https://github.com/QData/FastSK.git ``` then run ``` cd FastSK make ``` A `fastsk` executable will be installed to the `bin` directory, which you can use for kernel computation and inference. For example: ``` ./bin/fastsk -g 10 -m 6 -C 1 -t 1 -a data/EP300.train.fasta data/EP300.test.fasta ``` This will run the approximate kernel algorithm on the EP300 TFBS dataset using a feature length of `g = 10` with up to `m = 6` mismatches. It will then train and evaluate an SVM classifier with the SVM parameter `C = 1`.

نیازمندی

مقدار	نام
-	certifi
-	joblib
-	numpy
-	pandas
-	python-dateutil
-	pytz
-	scikit-learn
-	scipy
-	six
-	tqdm
-	recommonmark
-	nbsphinx
-	sphinx-autobuild
-	sphinx-rtd-theme
==20.8b1	black
-	docformatter
==5.6.4	isort
-	flake8
-	pytest
-	pytest-xdist
-	recommonmark
-	nbsphinx
-	sphinx-autobuild
-	sphinx-rtd-theme
==20.8b1	black
-	docformatter
==5.6.4	isort
-	flake8
-	pytest
-	pytest-xdist

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl fastsk-0.0.2:

pip install fastsk-0.0.2.whl

نصب پکیج tar.gz fastsk-0.0.2:

pip install fastsk-0.0.2.tar.gz