Saravanan666/pypi-org huggingface dataset:
Package: deepgoplus
DeepGOPlus: Improved protein function prediction from sequenceDeepGOPlus is a novel method for predicting protein functions from
protein sequences using deep neural networks combined with sequence
similarity based predictions.This repository contains script which were used to build and train the
DeepGOPlus model together with the scripts for evaluating the model's
performance.DependenciesThe code was developed and tested using python 3.6.To install python dependencies run:pip install -r requirements.txtInstalldiamondprogram on your system (diamond command should be available)Datahttp://deepgoplus.bio2vec.net/data/- Here you can find the data
used to train and evaluate our method.data.tar.gz - Data required to run predict.sh scriptdata-cafa.tar.gz - CAFA3 challenge datasetdata-2016.tar.gz - Dataset which is used to compare DeepGOPlus with
GOLabeler and DeepText2GOInstallationpip install deepgoplusRunningDownload all the files fromhttp://deepgoplus.bio2vec.net/data/data.tar.gzand place them into data folderdeepgoplus --data-root <path_to_data_folder> --in-file <input_fasta_filename>ScriptsThe scripts require GeneOntology in OBO Format.uni2pandas.py - This script is used to convert data from UniProt
database format to pandas dataframe.deepgoplus_data.py - This script is used to generate training and
testing datasets.deepgoplus.py - This script is used to train the modelevaluate_*.py - The scripts are used to compute Fmax, Smin and AUPRThe online version of DeepGOPlus is available athttp://deepgoplus.bio2vec.net/CitationIf you use DeepGOPlus for your research, or incorporate our learning algorithms in your work, please cite:
Maxat Kulmanov, Robert Hoehndorf; DeepGOPlus: Improved protein function prediction from sequence, Bioinformatics,https://doi.org/10.1093/bioinformatics/btz595New version specificationsCurrent dependencies can be found in the requirements.txt file.
The used Python version is 3.7.9.
Current version of Tensorflow will require Cuda 10.1 and Cudnn 7.6.5UpdatingThe following scripts must be run to update the model using the latest versions of the Gene Ontology (GO) and the SwissProt Database.update.py - This will download new releases of GO and SwissProt and train the model. If there are not new releases, the process will abort.new_evaluation.sh - This will compute Fmax, Smin and AUPR metrics.
Package: deepgram-cli
deepgram-cli for batch transcribing files/urlsInstallationpip install git+https://github.com/skyme5/deepgram-cli.gitAlso set yourDEEPGRAM_API_KEYfor it to work.Usageusage:deepgram-cli[-h][-f[FILES...]|-u[URLS...]|-iBATCH_FILENAME][-d][-q]options:-h,--helpshowthishelpmessageandexit-f[FILES...],--file[FILES...]Atleastoneormoreaudiofilepathtotranscribe.-u[URLS...],--url[URLS...]Atleastoneormoreurlstotranscribe.-iBATCH_FILENAME,--batch-fileBATCH_FILENAMEReadfiles/orlsfrombatchfile(oneperline).-d,--dump-jsonSavemetadatatoaJSONfile.-q,--quietDonotprintanythingexcepterrorstotheconsole.LicenseMIT
Package: deepgrp
DeepGRP is a python package used to predict genomic repetitive elements
with a deep learning model consisting of bidirectional gated recurrent units
with attention.
The idea of DeepGRP was initially based ondna-nn, but was re-implemented
and extended usingTensorFlow2.1.
DeepGRP was tested for the prediction of HSAT2,3, alphoid, Alu
and LINE-1 elements.Getting StartedInstallationFor installation you can use the PyPI version with:pip install deepgrpor install from this repository with:git clone https://github.com/fhausmann/deepgrp
cd deepgrp
pip install .Additionally you can install the developmental version withpoetry:git clone https://github.com/fhausmann/deepgrp
cd deepgrp
poetry installData preprocessingFor training and hyperparameter optimization the data have to be preprocessed.
For inference / prediction the FASTA sequences can directly be used and you
can skip this process.
The provided scriptparse_rmcan be used to extract repeat annotations fromRepeatMaskerannotations to a TAB seperated format by:parse_rm GENOME.fa.out > GENOME.bedThe FASTA sequences have to be converted to a one-hot-encoded representation,
which can be done with:preprocess_sequence FASTAFILE.fa.gzpreprocess_sequencecreates a one-hot-encoded representation in numpy
compressed format in the same directory.Hyperparameter optimizationFor Hyperparameter optimization the github repository provides
a jupyternotebookwhich can be used.Hyperparameter optimization is based on thehyperoptpackage.TrainingTraining of a model can be performed with:deepgrp train <parameter.toml> <TRAIN>.fa.gz.npz <VALIDATION>.fa.gz.npz <annotations.bed>The prefix of<TRAIN>and<VALIDATION>should be as row identifier in the first column of<annotations.bed>.For more fine-grained control of the training process you can also use the provided jupyternotebook.PredictionThe prediction can be done with the deepgrp main function like:deepgrp <modelfile> <fastafile> [<fastafile>, ...]where<modelfile>contains the trained model inHDF5format and<fastafile>is a (multi-)FASTA file containing DNA sequences.
Several FASTA files can be given at once.RequirementsRequirements are listed inpyproject.toml.Additionally for compiling C/Cython code, a C compiler should be installed.Contribution:First of all any contributing are very welcome.
If you want to contribute, please make a Pull request with your changes.
Your code should be formatted usingyapfusing the default settings,
they and they should pass all tests without issues.
For testing currentlymypyandpylintstatic tests are used, whilepytestis used for functional tests.If you’re adding new functionalities please provide corresponding tests
in thetestsdirectory.Feel free to ask in case of any questions.Further informationYou can find material to reproduce
the results in the repositorydeepgrp_reproducibility.
Package: deephaven-core
Deephaven Python Integration PackageDeephaven Python Integration Package is created by Deephaven Data Labs. It allows Python developers, including data
scientists, to access data, run queries, and execute Python scripts directly inside Deephaven data servers to achieve
maximum performance. By taking advantage of the unique streaming table capability of Deephaven and its many data ingestion
facilities (Kafka, Parquet, CSV, SQL, etc.), Python developers can quickly put together a real-time data processing pipeline
that is high performing and easy to consume.If you use a Windows operating system, WSL isnotrequired to run Deephaven via pip.InstallBecause this package depends on the Deephaven server, it comes preinstalled with Deephaven Docker images and is made
available at runtime in the Python console in the Deephaven Web UI.Quick startfromdeephavenimportread_csvfromdeephaven.stream.kafka.consumerimportkafka_consumer,TableTypefromdeephaven.plotimportFigure,PlotStylecsv_table=read_csv("data1.csv")kafka_table=kafka_consumer.consume({'bootstrap.servers':'redpanda:29092'},topic='realtime_feed',table_type=TableType.Append)joined_table=kafka_table.join(csv_table,on=["key_col_1","key_col_2"],joins=["data_col1"])plot=Figure()\.axes(plot_style=PlotStyle.STACKED_BAR)\.plot_cat(series_name="Categories1",t=joined_table,category="Key_col_1",y="data_col1")\.show()Related documentationhttps://deephaven.io/API ReferenceStart here
Package: deephaven-example-app
deephaven-example-appThis is an exampleDeephavenapplication
built using thedeephaven-serverPyPi package.It demonstrates connecting to a real-time data source, performing simple operations on that data,
and presenting the data to the user in theweb UI.TheCoinbase websocket exchange feedwas chosen as a data source for this example as it's simple to get up and running. It is not meant to
have exhaustive coverage.RequirementsPython >= 3.7Java >= 11Avirtual environmentis recommended.Quickstartcurl one-liner:curl-fsSLhttps://raw.github.com/deephaven-examples/deephaven-server-pypi-app/main/deephaven-example-app.sh|bashwget one-liner:wgethttps://raw.github.com/deephaven-examples/deephaven-server-pypi-app/main/deephaven-example-app.sh-O-|bashOr more explicitly:exportJAVA_HOME=/path/to/java_homesource/path/to/venv/bin/activate

pipinstalldeephaven-example-app

python-mdeephaven_example_appDevelopmentexportJAVA_HOME=/path/to/java_homesource/path/to/dev-venv/bin/activate

pipinstall-e.

python-mdeephaven_example_app
Package: deephaven-plugin-plotly
Deephaven Plugin for PlotlyThe Deephaven Plugin for Plotly. Allows for opening Plotly plots in a Deephaven environment. Any Plotly plot
should be viewable by default. For example:Scatter Plotimportplotly.expressaspxdf=px.data.iris()fig=px.scatter(df,x="sepal_width",y="sepal_length",color="species",size='petal_length',hover_data=['petal_width'])Box Plotimportplotly.expressaspxdf=px.data.tips()fig=px.box(df,x="time",y="total_bill")Multiple SeriesIt's possible to have multiple kinds of series in the same figure. Here is an example driving a line and a bar plot:fromplotly.subplotsimportmake_subplotsimportplotly.graph_objectsasgofig=make_subplots(rows=1,cols=2)fig.add_trace(go.Scatter(x=[1,2,3],y=[4,5,6]),row=1,col=1)fig.add_trace(go.Bar(x=[1,2,3],y=[4,5,6],marker=dict(color=[4,5,6],coloraxis="coloraxis")),row=1,col=2)fig.update_layout(title_text="Side By Side Subplots",showlegend=False)Plot data from a Deephaven tablefromdeephavenimportempty_table,numpyimportplotly.expressaspxt=empty_table(300).update(formulas=["X = (double)i","Y = Math.sin(X)"])data=numpy.to_numpy(t,["X","Y"])fig=px.line(x=data[:,0],y=data[:,1])BuildTo create your build / development environment:python-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepipsetuptools
pipinstallbuilddeephaven-pluginplotlyTo build:python-mbuild--wheelThe wheel is stored indist/.To test withindeephaven-core, note where this wheel is stored (usingpwd, for example).
Then, follow the directions in thedeephaven-js-pluginsrepo.
Package: deephaven-plugin-plotly-express
Deephaven Plugin for ChartsCustom implementation built on top of plotly express to make it compatible with deephaven tables.BuildTo create your build / development environment (skip the first two lines if you already have a venv):python-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepipsetuptools
pipinstallbuilddeephaven-pluginplotlyTo build:python-mbuild--wheelThe wheel is stored indist/.To test withindeephaven-core, note where this wheel is stored (usingpwd, for example).
Then, follow the directions in the top-level README.md to install the wheel into your Deephaven environment.To unit test, run the following command from the root of the repo:tox-epyUsageOnce you have the plugin installed and the server started, the recommended way to import the package mirrors plotly express:importdeephaven.plot.expressasdxThen, you can create a table (or use an existing one) and start plottingfromdeephaven.columnimportint_col,string_colimportdeephaven.plot.expressasdxfromdeephavenimportnew_tablesource=new_table([string_col("Categories",["A","B","C"]),int_col("Values",[1,3,5]),])fig=dx.bar(table=source,x="Categories",y="Values")
Package: deephaven-plugin-plotly-js
deephaven-plugin-plotly-jsThe Deephaven Plotly JS Plugin!Packages@deephaven/js-plugin-plotlyas a JS plugin fordeephaven-core. Most useful when combined withdeephaven-plugin-plotly.Quickstartpipinstalldeephaven-plugin-plotlydeephaven-plugin-plotly-jsDevelopmentpdmbuild
Package: deephaven-plugin-ui
deephaven.ui PluginPlugin prototype for programmatic layouts and callbacks. Currently calling itdeephaven.uibut that's not set in stone.BuildTo create your build / development environment (skip the first two lines if you already have a venv):python-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepipsetuptools
pipinstallbuilddeephaven-pluginplotlyTo build:python-mbuild--wheelThe wheel is stored indist/.To test withindeephaven-core, note where this wheel is stored (usingpwd, for example).
Then, follow the directions in the top-level README.md to install the wheel into your Deephaven environment.To unit test, run the following command from the root of the repo:tox-epyUsageOnce you have the JS and python plugins installed and the server started, you can use deephaven.ui. Seeexamplesfor examples.LoggingThe Python library uses theloggingmodule to log messages. The default log level isWARNING. To change the log level for debugging, set the log level toDEBUG:importloggingimportsys# Have the root logger output to stdout instead of stderrlogging.basicConfig(stream=sys.stdout,level=logging.WARNING)# Set the log level for the deephaven.ui logger to DEBUGlogging.getLogger("deephaven.ui").setLevel(level=logging.DEBUG)You can also set the log level for specific modules if you want to see specific modules' debug messages or filter out other ones, e.g.# Only log warnings from deephaven.ui.hookslogging.getLogger("deephaven.ui.hooks").setLevel(level=logging.WARNING)# Log all debug messages from the render module specificallylogging.getLogger("deephaven.ui.render").setLevel(level=logging.DEBUG)
Package: deephaven-server
deephaven-serverA Python-native way of launchingdeephaven-core.Deephaven Community Core is a real-time, time-series, column-oriented analytics engine with relational database features. It is an open version ofDeephaven Enterprise, which serves as the data backbone for prominent hedge funds, banks, and financial exchanges.RequirementsJava 11+ is required for this module, and theJAVA_HOMEenvironment variable must be set appropriately.This module also requires Python version 3.7 or newer.Setuppip3install--upgradepipsetuptoolswheel
pip3installdeephaven-serverQuick startfromdeephaven_serverimportServerserver=Server()server.start()fromdeephavenimporttime_tableticking_table=time_table('00:00:01').update_view(formulas=["Col1 = i % 2"])
Package: deepify
Copyright (C) 2016  F. Brezo and Y. Rubio, i3visio[![Version in PyPI](https://img.shields.io/pypi/v/deepify.svg)]()
[![Downloads/Month in PyPI](https://img.shields.io/pypi/dm/deepify.svg)]()
[![License](https://img.shields.io/badge/license-GNU%20General%20Public%20License%20Version%203%20or%20Later-blue.svg)]()1 - DescriptionDeepify is a GPLv3+ set of libraries developed by i3visio to perform Open Source Intelligence tasks in some part of the internet that are not as easy to access as the conventional part of the web.2 - License: GPLv3+This is free software, and you are welcome to redistribute it under certain conditions.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.For more details on this issue, check the [COPYING](COPYING) file.3 - InstallationFast way to do it on any system:` pip install deepify `Under MacOS or Linux systems, you may need to do this as superuser:` sudo pip install deepify `This will manage all the dependencies for you.If you needed further information, check the [INSTALL.md](INSTALL.md) file.4 - Basic usageIf everything went correctly (we hope so!), it’s time for trying deepify. But first, you will need to start the Tor Bundle downloadable fromhttp://torproject.org. Execution examples:` # Check that you have the Tor Browser running and that the options in~/.config/Deepifyfolder are correct onionGet.py-u"http://3g2upl4pq6kufc4m.onion/"# Check that you have the Zeronet running and that the options in~/.config/Deepifyfolder are correct zeronetGet.py-u"1HeLLo4uzjaLetFx6NH3PMwFP3qbRbTf3D" `Type -h or –help to get more information about which are the parameters of the application.You can use the functions as a library for collecting information from Tor:` # Check that you have the Tor Browser running and that the options in~/.config/Deepifyfolder are correct import deepify.tor as tor url ="http://3g2upl4pq6kufc4m.onion/"data = tor.getResponse(url) print data `You can also check it in Zeronet:` # Check that you have the Zeronet service running and that the options in~/.config/Deepifyfolder are correct import deepify.zeronet as zeronet url = "1HeLLo4uzjaLetFx6NH3PMwFP3qbRbTf3D" data = zeronet.getResponse(url) print data `5 - HACKINGIf you want to extend the functionalities of Deepify and you do not know where to start from, check the [HACKING.md](HACKING.md) file.6 - AUTHORSMore details about the authors in the [AUTHORS.md](AUTHORS.md) file.
Package: deepinc
deepINC 增量学习工具包增量学习工具库，实现了常用数据集，典型模型，工具函数。支撑综述《基于深度神经网络的增量学习技术研究现状综述》的实验。开源许可: MIT license文档:https://deepINC.readthedocs.io.代码:https://gitee.com/swj123/deep-inc.git开始使用准备使用增量学习工具库? 按如下步骤本地使用deepINC.使用 pip 安装deepINC:$ pip install deepinc在 .py 程序中引用:$ import deepinc as di
$ from deepinc.utils.args import get_args
$ from deepinc.utils.training import train_il初始化配置项:$ args = get_args()
$
$ args.model = 'lwf'              # 模型名称
$ args.dataset = 'seq-mnist'      # 数据集
$ args.print_freq = 1             # 报告loss的频率
$ args.n_epochs = 1               # 迭代次数
$ args.device = 'cpu'             # 设备
$
$ args.lr = 0.02                  # 学习率
$ args.batch_size = 64            # batch size
$ args.alpha = 5                  # 蒸馏损失权重
$ args.softmax_temp = 2.0         # 蒸馏损失温度系数
$ args.wd_reg = 5e-5              # 正则项权重这里可以随心更改增量训练:$ train_il(args)等待训练结束~关联代码https://github.com/aimagelab/mammoth.History0.1.0 (2022-09-22)First release on PyPI.
Package: deepinsight
Python module for deep learning tools integration in PyTorch.Home-page:https://github.com/neurospin-deepinsight/deepinsightAuthor:
deepinsight developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Python module for deep learning tools integration in PyTorch..Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: X11 Applications :: Qt
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Package: deepinspection
deepinspection pythonPython client library for deepinspection. Designed to simplify and exemplify interaction with the External API.See theOptram examplefor a complete example including converting to a custom format for internal use.InstallUse your preferred package manager:poetryadddeepinspectionorpipinstalldeepinspectionUsageimportdeepinspectionclient=deepinspection.track.client(customer_id="customer-id",client_id="XYZ",client_secret="XYZ",)# list exportsexports=client.exports.fastenings.list()# get export dataexport=exports[0]forfasteninginclient.exports.fastenings.get_data(export["id"]):passThe customer id should match with the website urlhttps://customer-id.track.deepinspection.io/.exports[{"updated":"2024-02-01T23:20:20.605136+00:00","id":"461731a1-96c8-4ed6-99e4-9fe424eb9c40","measurement_name":"20231001_124327_2011T","measured":"2023-10-01T12:43:27.605136+00:00","type":"fastenings","downloaded":null,"created":"2024-02-01T23:20:20.605136+00:00","user_id":"a338595f-6eba-481b-9f0f-112290a1078b"},...export_data{"id":"56fb582a-0280-43fa-81f3-2a444e7e4273","position_geographical":{"track_section":"111",...
Package: deepinv
IntroductionDeep Inverse is an open-source pytorch library for solving imaging inverse problems using deep learning. The goal ofdeepinvis to accelerate the development of deep learning based methods for imaging inverse problems, by combining popular learning-based reconstruction approaches in a common and simplified framework, standardizing forward imaging models and simplifying the creation of imaging datasets.deepinvfeaturesA large collection ofpredefined imaging operators(MRI, CT, deblurring, inpainting, etc.)Training lossesfor inverse problems (self-supervised learning, regularization, etc.)Manypretrained deep denoiserswhich can be used forplug-and-play restorationA framework forbuilding datasetsfor inverse problemsEasy-to-buildunfolded architectures(ADMM, forward-backward, deep equilibrium, etc.)Sampling algorithmsfor uncertainty quantification (Langevin, diffusion, etc.)A large number of well-explainedexamples, from basics to state-of-the-art methodsDocumentationRead the documentation and examples athttps://deepinv.github.io.InstallTo install the latest stable release ofdeepinv, you can simply do:pipinstalldeepinvYou can also install the latest version ofdeepinvdirectly from github:pipinstallgit+https://github.com/deepinv/deepinv.git#egg=deepinvGetting StartedTry out the following plug-and-play image inpainting example:importdeepinvasdinvfromdeepinv.utilsimportload_url_imageurl=("https://huggingface.co/datasets/deepinv/images/resolve/main/cameraman.png?download=true")x=load_url_image(url=url,img_size=512,grayscale=True,device='cpu')physics=dinv.physics.Inpainting((1,512,512),mask=0.5,\noise_model=dinv.physics.GaussianNoise(sigma=0.01))data_fidelity=dinv.optim.data_fidelity.L2()prior=dinv.optim.prior.PnP(denoiser=dinv.models.MedianFilter())model=dinv.optim.optim_builder(iteration="HQS",prior=prior,data_fidelity=data_fidelity,\params_algo={"stepsize":1.0,"g_param":0.1,"lambda":2.})y=physics(x)x_hat=model(y,physics)dinv.utils.plot([x,y,x_hat],["signal","measurement","estimate"],rescale_mode='clip')Also try outone of the examplesto get started.ContributingDeepInverse is a community-driven project and welcomes contributions of all forms.
We are ultimately aiming for a comprehensive library of inverse problems and deep learning,
and we need your help to get there!
The preferred way to contribute todeepinvis to fork themain
repositoryon GitHub,
then submit a “Pull Request” (PR). See ourcontributing guidefor more details.Finding helpIf you have any questions or suggestions, please join the conversation in ourDiscord server. The recommended way to get in touch with the developers is to open an issue on theissue tracker.
Package: deep-keyphrase
Implement some keyphrase generation algorithmDescriptionImplemented PaperCopyRNNDeep Keyphrase Generation (Meng et al., 2017)ToDo ListCopyCNNCopyTransformerUsagerequired files (4 files in total)vocab_file: word line by line (don’t with index!!!!)this
paper
proposestraining, valid and test filedata format for training, valid and testjson line format, every line is a dict:{'tokens': ['this', 'paper', 'proposes', 'using', 'virtual', 'reality', 'to', 'enhance', 'the', 'perception', 'of', 'actions', 'by', 'distant', 'users', 'on', 'a', 'shared', 'application', '.', 'here', ',', 'distance', 'may', 'refer', 'either', 'to', 'space', '(', 'e.g.', 'in', 'a', 'remote', 'synchronous', 'collaboration', ')', 'or', 'time', '(', 'e.g.', 'during', 'playback', 'of', 'recorded', 'actions', ')', '.', 'our', 'approach', 'consists', 'in', 'immersing', 'the', 'application', 'in', 'a', 'virtual', 'inhabited', '3d', 'space', 'and', 'mimicking', 'user', 'actions', 'by', 'animating', 'avatars', '.', 'we', 'illustrate', 'this', 'approach', 'with', 'two', 'applications', ',', 'the', 'one', 'for', 'remote', 'collaboration', 'on', 'a', 'shared', 'application', 'and', 'the', 'other', 'to', 'playback', 'recorded', 'sequences', 'of', 'user', 'actions', '.', 'we', 'suggest', 'this', 'could', 'be', 'a', 'low', 'cost', 'enhancement', 'for', 'telepresence', '.'] ,
'keyphrases': [['telepresence'], ['animation'], ['avatars'], ['application', 'sharing'], ['collaborative', 'virtual', 'environments']]}Trainingdownload thekp20kmkdir data
mkdir data/raw
mkdir data/raw/kp20k_new
# !! please unzip kp20k data put the files into above folder manually
python -m nltk.downloader punkt
bash scripts/prepare_kp20k.sh
bash scripts/train_copyrnn_kp20k.sh

# start tensorboard
# enter the experiment result dir, suffix is time that experiment starts
cd data/kp20k/copyrnn_kp20k_basic-20191212-080000
# start tensorboard services
tenosrboard --bind_all --logdir logs --port 6006Notescompared with the originalseq2seq-keyphrase-pytorchfix the implementation error:copy mechanismtrain and inference are not correspond (training doesn't have input feeding and inference has input feeding)easy data preparingtensorboard supportfaster beam search (6x faster used cpu and more than 10x faster used gpu)