pydpm 4.0.1

A python library focuses on constructing deep probabilistic models on GPU.

PyDPM

A python library focuses on constructing deep probabilistic models on GPU. Pydpm provides efficient distribution sampling functions and has included lots of implemented probabilistic models.

Note: We have released a new version that does not depend on Pycuda.

Document

Install

This library can be installed under both Windows and Linux systems.

$ pip install pydpm

Under Windows system, we recommed to install Visual Studio 2019 and latest CUDA Toolkit. The combination of VS2019(with MSVC v142) and CUDA 11.5 has been tested in pydpm2.0.

Create Probabilistic Model

Model list

Model list is as following:

Probabilistic Model Name	Abbreviation	Paper Link
Latent Dirichlet Allocation	LDA	Link
Poisson Factor Analysis	PFA	Link
Poisson Gamma Belief Network	PGBN	Link
Convolutional Poisson Factor Analysis	CPFA	Link
Convolutional Poisson Gamma Belief Network	CPGBN	Link
Poisson Gamma Dynamical Systems	PGDS	Link
Deep Poisson Gamma Dynamical Systems	DPGDS	Link
Dirichlet Belief Networks	DirBN	Link
Deep Poisson Factor Analysis	DPFA	Link
Word Embeddings Deep Topic Model	WEDTM	Link
Multimodal Poisson Gamma Belief Network	MPGBN	Link
Graph Poisson Gamma Belief Network	GPGBN	Link

More probabilistic models will be further included in pydpm/_model/...

Demo

Create a PGBN model:

from pydpm.model import PGBN

# create the model and deploy it on gpu or cpu
model = PGBN([128, 64, 32], device='gpu')
model.initial(train_data)
train_local_params = model.train(train_data, iter_all=100)
train_local_params = model.test(train_data, iter_all=100)
test_local_params = model.test(test_data, iter_all=100)

# evaluate the model with classification accuracy
# the demo accuracy can achieve 0.8549
results = ACC(train_local_params.Theta[0], test_local_params.Theta[0], train_label, test_label, 'SVM')

# save the model after training
model.save()

More model demos can be found in pydpm/examples/...

Source data can be found in Link

Sample on GPU

Function list

The parameters of partial distribution functions are as following:

Function	Parameters List
Normal	mean, std, times
Multinomial	count, prob, times
Poisson	lambda, times
Gamma	shape, scale, times
Beta	alpha, beta, times
F	n1, n2, times
StudentT	n, times
Dirichlet	alpha, times
Crt	point, p, times
Weibull	shape, scale, times
Chisquare	n, times
Geometric	p, times
...	...

Example

from pydpm._sampler import Basic_Sampler

sampler = Basic_Sampler('gpu')
a = sampler.gamma(np.ones(100)*5, 1, times=10)
b = sampler.gamma(np.ones([100, 100])*5, 1, times=10)

More sampler demos can be found in pydpm/_sampler/...

Compare

Compare the sampling speed of distribution functions with numpy:
The compared code can be found in pydpm/example/Sampler_Speed_Demo.py

Compare the sampling speed of distribution functions with tensorflow and torch:
The compared code can be found in pydpm/example/Sampler_Speed_Demo.py

Contact

License: Apache License Version 2.0

Contact: Chaojie Wang [e-mail], Wei Zhao [e-mail], Xinyang Liu [e-mail], Jiawen Wu [e-mail]

Copyright (c), 2020, Chaojie Wang, Wei Zhao, Xinyang Liu, Jiawen Wu, Jie Ren, Yewen Li, Hao Zhang, Bo Chen and Mingyuan Zhou