Saravanan666/pypi-org huggingface dataset:
Package: deep-serializer
Python Deep SerializerPackage provides forSerializing nested Python objects. That is creating a nested dictionary from a nested Python object.Deserializing nested Python objects, that is initializing nested Python objects with a nested dictionary.One use case is when you have a web api and users post nested json which you use to initialize (nested) Python objects.Examplefrompprintimportpprintimportdatetimeimporttypingfromdeep_serializerimportdesfromdeep_serializer.serimportToDictEnabledclassPerson(ToDictEnabled):@des.cast# casting inputdef__init__(self,name:str,age:int):self.name=nameself.age=ageclassCar(ToDictEnabled):@des.cast# casting inputdef__init__(self,model:str,owner:Person,color:str,registration_date:datetime.datetime,passengers:typing.List[Person]=(),miles:typing.Dict[datetime.datetime,float]=None):self.miles=milesself.model=modelself.owner=ownerself.color=colorself.registration_date=registration_dateself.passengers=passengersself.__private_variable='secret'# private attribute not serialized by todict@propertydefcolor_and_owner(self)->str:# property not serialized by todictreturn'%s-%s'%(self.color,self.owner.name)if__name__=='__main__':car_dict={'color':'blue','model':'Volvo','owner':{'age':'22','name':'Bob'},'passengers':[{'age':20,'name':'Alice'},{'age':19,'name':'Joe'}],'registration_date':'2015-01-01 12:00.00','miles':{'2015-01-01 12:00.00':0,'2016-01-01 12:00.00':'1000'}}print('\n\nInput')pprint(car_dict)car=Car(**car_dict)print('\n\nCar object:\n%s'%car)print('\n\nCar as dict')pprint(car.todict(),indent=4)Note thatTheToDictEnabledadds thetodictmethod which enables deep serialization.The@des.initproperty tries to cast input variables according to their type hints before actual init is run.The@color_and_ownerproperty is not serialized.The__private_variableattribute is not serialized.Running main yieldsInput
{'color': 'blue',
 'miles': {'2015-01-01 12:00.00': 0, '2016-01-01 12:00.00': '1000'},
 'model': 'Volvo',
 'owner': {'age': '22', 'name': 'Bob'},
 'passengers': [{'age': 20, 'name': 'Alice'}, {'age': 19, 'name': 'Joe'}],
 'registration_date': '2015-01-01 12:00.00'}


Car object:
<__main__.Car object at 0x7f4972796cf8>


Car as dict
{   'color': 'blue',
    'miles': {   datetime.datetime(2015, 1, 1, 12, 0): 0.0,
                 datetime.datetime(2016, 1, 1, 12, 0): 1000.0},
    'model': 'Volvo',
    'owner': {'age': 22, 'name': 'Bob'},
    'passengers': [{'age': 20, 'name': 'Alice'}, {'age': 19, 'name': 'Joe'}],
    'registration_date': datetime.datetime(2015, 1, 1, 12, 0)}CaveatsCode is virtually untested, except on a few minor private projects.Casting is far from complete. For example, thetyping.Tuple[]type hinting does not work, and those input variables will not be casted.Only works with Python3, only tested with Python3.6 and Python3.7.todictmethod will not serialize attributes starting with__and not serialize properties.
Package: deepset-cloud-sdk
The deepset Cloud SDK is an open source software development kit that provides convenient access to and integration with deepset Cloud, a powerful cloud offering for various natural language processing (NLP) tasks.
This README provides an overview of the SDK and its features, and information on contributing to the project and exploring related resources.Official SDK Docsdeepset Cloud DocumentationTutorials:Uploading with CLIUploading with Python MethodsSupported FeaturesIn its current shape, the SDK offers a suite of tools for seamless data upload to deepset Cloud.
The following examples demonstrate how to use the deepset Cloud SDK to interact with deepset Cloud using Python.
You can use the deepset Cloud SDK in the command line as well. For more information, see theCLI documentation.SDK Examples - Upload datasetsCLI Examples - Upload datasetsInstallationThe deepset Cloud SDK is available onPyPIand you can install it using pip:pipinstalldeepset-cloud-sdkAfter installing the deepset Cloud SDK, you can use it to interact with deepset Cloud. It comes with a command line interface (CLI), that you can use by calling:deepset-cloud--helpDevelopment InstallationTo install the deepset Cloud SDK for development, clone the repository and install the package in editable mode:pipinstallhatch==1.7.0
hatchbuildInstead of calling the CLI from the build package, you can call it directly from the source code:python3-mdeepset_cloud_sdk.cli--helpContributingWe welcome contributions from the open source community to enhance the deepset Cloud SDK. If you would like to contribute, have a look atCONTRIBUTING.mdfor guidelines and instructions on how to get started.
We appreciate your contributions, whether they're bug fixes, new features, or documentation improvements.Interested in Haystack?deepset Cloud is powered by Haystack, an open source framework for building end-to-end NLP pipelines.Project websiteGitHub repository
Package: deepshm
DeepSHMThis repository implements the model from "Deep learning model of somatic hypermutation reveals importance of sequence context beyond targeting of AID and Polη hotspots" by Tang, Krantsevich & MacCarthy. Using DeepSHM you can simply use one of the provided models to make SHM-related predictions for your data, or you can train your own CNN model for any task that uses DNA/RNA one-hot encoded data as input.InstalationIf you want to play with the modelDeepSHM is on pypi, so it can be installed using pip:pip install deepshmIf you are not familiar with pythonYou can also just download the deepshm_predict.zip archive and 0nce you extract it you can call the file deepshm_predict.py from the command line. Note that you still will need python installed as wells as several packages: tensorflow, numpy, scipy. deepshm_predict.py can be called in the following way:python deepshm_predict.py input.fasta -o output.csv -k 15where the input fasta file is the required input and the path to the output file and the length of k-mer (5, 9, 15 and 21) are optional. Default value for the output file is output.csv and default k is 15.
Package: deepsig-biocomp
DeepSig - Predictor of signal peptides in proteins based on deep learningPublicationSavojardo C., Martelli P.L., Fariselli P., Casadio R.DeepSig: deep learning improves signal peptide detection in proteinsBioinformatics(2017)34(10): 1690-1696.Installation using pipFirst, install deepsig-biocomp package using pip:pip install deepsig-biocompThen, clone the deepsig repo from GitHub and export the DEEPSIG_ROOT directory:git clone git@github.com:BolognaBiocomp/deepsig.git
cd deepsig
export DEEPSIG_ROOT=$(pwd)Usage$ deepsig -h

usage: deepsig.py [-h] -f FASTA -o OUTF -k {euk,gramp,gramn} [-a CPU]

DeepSig: Predictor of signal peptides in proteins

optional arguments:
  -h, --help            show this help message and exit
  -f FASTA, --fasta FASTA
                        The input multi-FASTA file name
  -o OUTF, --outf OUTF  The output tabular file
  -k {euk,gramp,gramn}, --organism {euk,gramp,gramn}
                        The organism the sequences belongs toThe program accepts three mandatory arguments:The full path of the input FASTA file containing protein sequences to be predicted;The kingdom the sequences belong to. You must specify "euk" for Eukaryotes, "gramp" for Gram-positive bacteria or "gramn" for Gram-negative bacteria;The output file where predictions will be stored.Please, reports bugs to:castrense.savojardo2@unibo.it
Package: deepsignal
Documentationv0.2.0make–reference_pathnot a required input inextractandcall_modsmodulehandle gz file in call_freq scriptv0.1.10make sure results of each read be written together in call_mods’ outputv0.1.9enable multi-class (>=3) training/predicting,fix bug of extrating contig name from fast5sv0.1.8modify denoise module
fix success_file bug
update READMEv0.1.7Prevent Queue.qsize() from raising NotImplementedError on Mac OS X (github:vterron/lemon@9ca6b4b)
covert raw signals to pA values before normalization in extract_feature module
add denoise module
add module-chosen options of model (rnn/base/cnn), re-train human modelv0.1.6add option –positions in extract_features module,
add option/function of binary_format feature file to speed up trainingv0.1.5normalize probs before outputv0.1.4change the loss function to weighted_cross_entropy_with_logits,
allow training using unbalanced samples.v0.1.3fix the deadlock issue in multiprocessingv0.1.2add MANIFEST.in filev0.1.13 modules (extract, call_mods, train) supported
Package: deepsignal2
Release0.1.3bug fixes in train modulemulti-gpu support in call_mods moduleupdates dependences/requirements0.1.2make sure results of each read be written together in call_mods’ outputmake–reference_pathnot a required input inextractandcall_modsmodule0.1.1addinit_modeloption in train module,fix bug of extrating contig name from fast5s0.1.0Release the first vesrion of deepsignal2
Package: deepsignal-plant
Release0.1.6bug fixesenable .gz output in extract/call_mods/call_freq modulesmulti-gpu support in call_mods moduleupdate requirements/dependences0.1.5make sure results of each read be written together in call_mods’ outputmake–reference_pathnot a required input inextractandcall_modsmodule0.1.4modifycall_freqmodule for large genomes,fix bug of extrating contig name from fast5s0.1.3add ranger optimizer and modify train module,fix Queue.qsize() NotImplementedError in macOS partially, howevercall_modsin CUDA mode in macOS still doesn’t work,addinit_modeloption in train module0.1.2change imports in ref_reader,change requirements,fix and modify denoise module,fix MKL_THREADING_LAYER error temporarily,add–regionoption,a combined 5mC model replacing CG/CHG/CHH models0.1.1fix bug and optimize call_mods without GPU, add call_freq funciton0.1.0Release the first vesrion of deepsignal-plant package
Package: deepsim
DeepSimThis toolkit provides deep learning-based similarity utilities.Example'''models for typeBERT:shibing624/text2vec-base-chinesesentence-transformers/paraphrase-multilingual-MiniLM-L12-v2hfl/chinese-macbert-base:uer/roberta-medium-wwm-chinese-cluecorpussmallhfl/chinese-roberta-wwm-extLangboat/mengzi-bert-baseWMD:w2v-light-tencent-chinese'''fromdeepsimimport*sim_utils=SimilarityUtils(type='w2v')list_r=sim_utils.get_similarity('I like you!','I love you!')print(list_r)LicenseThedeepsimproject is provided byDonghua Chen.
Package: deepsmiles
This Python module can convert well-formed SMILES (that is, as writen by a cheminformatics toolkit) to DeepSMILES. It also does the reverse conversion.Install the latest version with:pip install --upgrade deepsmilesDeepSMILES is a SMILES-like syntax suited to machine learning. Rings are indicated using a single symbol instead of two, while branches do not use matching parentheses but rather use a right parenthesis as a ‘pop’ operator.For example, benzene isc1ccccc1in SMILES butcccccc6in DeepSMILES (where the6indicates the ring size). As a branch example, the SMILESC(Br)(OC)Ican be converted to the DeepSMILESCBr)OC))I. For more information, please see the corresponding preprint.The library is used as follows:importdeepsmilesprint("DeepSMILES version:%s"%deepsmiles.__version__)converter=deepsmiles.Converter(rings=True,branches=True)print(converter)# record the options usedencoded=converter.encode("c1cccc(C(=O)Cl)c1")print("Encoded:%s"%encoded)try:decoded=converter.decode(encoded)exceptdeepsmiles.DecodeErrorase:decoded=Noneprint("DecodeError! Error message was '%s'"%e.message)ifdecoded:print("Decoded:%s"%decoded)
Package: deepsnap
DeepSNAPDocumentation|Examples|Colab NotebooksDeepSNAP is a Python library to assist efficient deep learning on graphs.
DeepSNAP features in its support for flexible graph manipulation, standard pipeline, heterogeneous graphs and simple API.DeepSNAP bridges powerful graph libraries such asNetworkXand deep learning frameworkPyTorch Geometric. With an intuitive and easy-than-ever API, DeepSNAP addresses the above pain points:DeepSNAP currently supports a NetworkX-based backend (also SnapX-based backend for homogeneous undirected graph), allowing users to seamlessly call hundreds of graph algorithms available to manipulate / transform the graphs, even at every training iteration.DeepSNAP provides a standard pipeline for dataset split, negative sampling and defining node/edge/graph-level objectives, which are transparent to users.DeepSNAP provides efficient support for flexible and general heterogeneous GNNs, that supports both node and edge heterogeneity, and allows users to control how messages are parameterized and passed.DeepSNAP has an easy-to-use API that works seamlessly with existing GNN models / datasets implemented in PyTorch Geometric. There is close to zero learning curve if the user is familiar with PyTorch Geometric.InstallationTo install the DeepSNAP, ensurePyTorch GeometricandNetworkXare installed. Then:$pipinstalldeepsnapOr build from source:$gitclonehttps://github.com/snap-stanford/deepsnap
$cddeepsnap
$pipinstall.ExampleExamples using DeepSNAP are provided within the code repository.$gitclonehttps://github.com/snap-stanford/deepsnapNode classification:$cddeepsnap/examples/node_classification# node classification$pythonnode_classification_planetoid.pyLink prediction:$cddeepsnap/examples/link_prediction# link prediction$pythonlink_prediction_cora.pyGraph classification:$cddeepsnap/examples/graph_classification# graph classification$pythongraph_classification_TU.pyDocumentationFor comprehensive overview, introduction, tutorial and example, please refer toFull Documentation
Package: deepsort
DeepSort-Pip: Packaged version of the DeepSort repositoryOverviewThis repo is a packaged version of theDeepSortalgorithm.Installationpip install deepsortDetection Model + DeepSortfromdeepsort.trackerimportDeepSortTrackertracker=DeepSortTracker(args)forimageinimages:dets=detector(image)online_targets=tracker.update(dets)Citation@inproceedings{Wojke2017simple,title={Simple Online and Realtime Tracking with a Deep Association Metric},author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},booktitle={2017 IEEE International Conference on Image Processing (ICIP)},year={2017},pages={3645--3649},organization={IEEE},doi={10.1109/ICIP.2017.8296962}}@inproceedings{Wojke2018deep,title={Deep Cosine Metric Learning for Person Re-identification},author={Wojke, Nicolai and Bewley, Alex},booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},year={2018},pages={748--756},organization={IEEE},doi={10.1109/WACV.2018.00087}}
Package: deep-sorted
deep-sortedMotivationWhen validating parsed JSON objects, schemas and other nested data structures in unit tests, order
is typically not important. And yet I often find myself manually sorting the target structures
when the internals of the tested function is modified such that order is changed. With this package,
both the target and the actual structure can be recursively sorted before comparison.InstallationUsing poetry:poetryadddeep-sortedUsing pipenv:pipenvinstalldeep-sortedUsing pip:pipinstalldeep-sortedUsagefromdeep_sortedimportdeep_sortedfromdatetimeimportdatetimeone={"id":9,"name":"Ted Chiang","books":[{"id":124,"published":datetime(1991,8,1,0,0),"title":"Understand","ratings":(6,6,3,5,6,6,0,6,0),},{"id":125,"published":datetime(2019,5,7,0,0),"title":"Exhalation",},],}two={"books":[{"published":datetime(2019,5,7,0,0),"title":"Exhalation","id":125,},{"ratings":(3,0,0,6,6,6,6,5,6),"id":124,"published":datetime(1991,8,1,0,0),"title":"Understand",},],"id":9,"name":"Ted Chiang",}assertdeep_sorted(one)==deep_sorted(two)
Package: deepspace
DeepSpaceA collection of search space for the DeepHyper package.RequirementsGraphviz.Quick StartGenerate a neural architecture space for fully connected networks with residual connections:fromdeepspace.tabularimportDenseSkipCoSpacedefcreate_search_space(input_shape=(54,),output_shape=(7,),**kwargs)returnDenseSkipCoSpace()(input_shape,output_shape,num_layers=10,dropout=0.0)Generate a neural architecture space for AutoEncoder guided by an estimator:fromdeepspace.tabularimportSupervisedRegAutoEncoderSpacefactory=SupervisedRegAutoEncoderSpace()(input_shape=(100,),output_shape=[(100),(10,)])
Package: deep-space-trader
Deep Space Trader is a turn-based strategy game inspired by, and similar in gameplay to,
the old DOS game “Drugwars”.InstallPre-built windows binaryDownload theWindows x64 installerPython packageInstall as a python package:pip install deep_space_traderDescriptionThe player starts off on a planet within a system of 8 randomly-generated planets.
Each planet has various raw materials available for trading (13 types of raw material in total),
all with prices that differ from planet to planet, and fluctuate over time. The goal of the game
is to make as much money as possible by buying and selling raw materials between planets.The player has an item inventory for raw materials, with a fixed (but upgradeable) capacity.
Only items in the inventory can be bought or sold. The player also has a warehouse with unlimited
capacity. Items can be transferred between the item inventory and the warehouse, but only
a certain number of transfers between item inventory and warehouse can be made per day.Travelling between planets always incurs a risk of encountering pirates, who may
kill the player or rob them of their money / raw materials. The greater the value that the
player is travelling with (combined value of player’s money and items in the player’s inventory,
but not the warehouse), the greater the chance of encountering pirates.The player can use any earned money to buy “upgrades” from a store. Only a certain number of
store purchases can be made per day. Some of the upgrades
allow the player to:Destroy planets altogether and acquire all their raw materials
(Destroying a planet incurs a risk that the planet may fight to resist, which may kill the player)Buy and upgrade a battle fleet, which increases chances of winning fights against pirates,
and against planets that resist destructionBuy and upgrade a scout fleet, which allows the player to discover thousands of
new planetsUpgrade the player’s inventory capacity, which increases the number of items that can
be bought or sold at once.Increment the number of warehouse trips allowed per day
Package: deepspeech-server
Key FeaturesThis is an http server that can be used to test the Coqui STT project (the
successor of the Mozilla DeepSpeech project). You need an environment with
DeepSpeech or Coqui to run this server.This code uses the Coqui STT 1.0 APIs.InstallationThe server is available on pypi, so you can install it with pip:pip3 install deepspeech-serverYou can also install deepspeech server from sources:python3 setup.py installNote that python 3.6 is the minimum version required to run the server.Starting the serverdeepspeech-server --config config.yamlWhat is a STT model?The quality of the speech-to-text engine depends heavily on which models it
loads at runtime. Think of them as a sort of pattern that controls how the
engine works.How to use a specific STT modelYou can use coqui without training a model. Pre-trained models are on
offer at the Coqui Model Zoo (Make sure the STT Models tab is selected):https://coqui.ai/modelsOnce you’ve downloaded a pre-trained model, make a copy of the sample
configuration file. Edit the“model”and“scorer”fields in your new file
for the engine you want to use so that they match the downloaded files:cp config.sample.yaml config.yaml$EDITORconfig.yamlLastly, start the server:deepspeech-server --config config.yamlServer configurationThe configuration is done with a yaml file, provided with the “–config” argument.
Its structure is the following one:coqui:model:coqui-1.0.tflitescorer:huge-vocabulary.scorerbeam_width:500server:http:host:"0.0.0.0"port:8080request_max_size:1048576log:level:-logger:deepspeech_serverlevel:DEBUGThe configuration file contains several sections and sub-sections.coqui section configurationSection “coqui” contains configuration of the coqui-stt engine:model: The model that was trained by coqui. Must be a tflite (TensorFlow Lite) file.scorer: [Optional] The scorer file. Use this to tune the STT to understand certain phrases better.lm_alpha: [Optional] alpha hyperparameter for the scorer.lm_beta: [Optional] beta hyperparameter for the scorer.beam_width: [Optional] The size of the beam search. Corresponds directly to how long decoding takes.http section configurationrequest_max_size(default value: 1048576, i.e. 1MiB) is the maximum payload
size allowed by the server. A received payload size above this threshold will
return a “413: Request Entity Too Large” error.hostThe listen address of the http server.portThe listening port of the http server.log section configurationThe log section can be used to set the log levels of the server. This section
contains a list of log entries. Each log entry contains the name of aloggerand itslevel. Both follow the convention of the python logging module.Using the serverInference on the model is done via http post requests. For example with the
following curl command:curl -X POST --data-binary @testfile.wav http://localhost:8080/stt
Package: deepstack
DeepStackDeepStack: Ensembles for Deep LearningDeepStack is a Python module for building Deep Learning Ensembles originally built on top of Keras and distributed under the MIT license.Installationpip install deepstackStackingStacking is based on training a Meta-Learner on top of pre-trained Base-Learners.
DeepStack offers an interface to fit the Meta-Learner on the predictions of the Base-Learners.
In the following an Example based on top of pre-trained Keras Models (there is also an interface for generic models):Usagefromdeepstack.baseimportKerasMember# For a generic (i.e., Non-Keras Model) check the class `Member`fromdeepstack.ensembleimportStackEnsemblemodel1=...# A Keras pre-trained Model (Base-Learner)train_batches1=...# A numpy tuple (X_train, y_train) or Keras Data Iterator - Training Data for Meta-Learnerval_batches1=...# A numpy tuple (X_val, y_val) or Keras Data Iterator - Validation Data for Meta-Learnermember1=KerasMember(name="Model1",keras_model=model1,train_batches=train_batches1,val_batches=val_batches1)model2=...train_batches2=...val_batches2=...member2=KerasMember(name="Model2",keras_model=model2,train_batches=train_batches2,val_batches=val_batches2)stack=StackEnsemble()stack.add_member(member1)# Assumption: the data iterators of base-learners iterate over the same data and have same shape and classes.stack.add_member(member2)stack.fit()# Fits meta-learner based on training batches from its members (base-learners)stack.describe()# Prints information about ensemble performance based on validation dataCheck an example on the CIFAR-10 dataset:Cifar10.py.Randomized Weighted EnsembleEnsemble Technique that weights the prediction of each ensemble member, combining the weights to calculate a combined prediction.  Weight optimization search is performed with randomized search based on the dirichlet distribution on a validation dataset.It follows the same interface of the StackEnsemble. An example can be found inCifar10.py.Citing DeepStackIf you use DeepStack in a scientific publication, we would appreciate citations:@misc{title={DeepStack:EnsemblesforDeepLearning},author  = {Julio Borges},url = {https://github.com/jcborges/DeepStack},date = {2019}}
Package: deepstars
# SNClassifying_Pre-alphaSupernovae classifying and redshifting software: development stage1. How to install:Install tensorflow (https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#download-and-setup).Run GUI/main.pyOnce open, type in a known redshiftBrowse for any single spectrum FITS, ASCII, dat, or two-column text file.Click any of the best matches to view the continuum-subtracted binned spectra.If the input spectrum is too noisy, increase the smoothing level, and click 'Re-fit with priors'2. Dependencies:python2.7 (unconfirmed python3)numpyscipyPyQt4tensorflow3. How to raise issues:4. Examples5. API UsageNotes:Current version requires an input redshift (inaccurate results if redshift is unknown)