A library built upon PyTorch for building embeddings on discrete event sequences using self-supervision

Related tags

Deep Learningpytorch-lifestream
Overview

pytorch-lifestream a library built upon PyTorch for building embeddings on discrete event sequences using self-supervision. It can process terabyte-size volumes of raw events like game history events, clickstream data, purchase history or card transactions.

It supports various methods of self-supervised training, adapted for event sequences:

  • Contrastive Learning for Event Sequences (CoLES)
  • Contrastive Predictive Coding (CPC)
  • Replaced Token Detection (RTD) from ELECTRA
  • Next Sequence Prediction (NSP) from BERT
  • Sequences Order Prediction (SOP) from ALBERT

It supports several types of encoders, including Transformer and RNN. It also supports many types of self-supervised losses.

The following variants of the contrastive losses are supported:

Install from PyPi

pip install pytorch-lifestream

Install from source

# Ubuntu 20.04

sudo apt install python3.8 python3-venv
pip3 install pipenv

pipenv sync  --dev # install packages exactly as specified in Pipfile.lock
pipenv shell
pytest

Demo notebooks

  • Self-supervided training and embeddings for downstream task notebook
  • Self-supervided embeddings in CatBoost notebook
  • Self-supervided training and fine-tuning notebook
  • PySpark and Parquet for data preprocessing notebook

Experiments on public datasets

pytorch-lifestream usage experiments on several public event datasets are available in the separate repo

Comments
  • torch.stack in def collate_feature_dict

    torch.stack in def collate_feature_dict

    ptls/data_load/utils.py

    Hello!

    If the dataloader has a feature called target. And the batchsize is not a multiple of the length of the dataset, then an error pops up on the last batch: "Sizes of tensors must match except in dimension 0". Due to the use of torch.staсk when processing a feature startwith 'target'.

    opened by Ivanich-spb 11
  • Not supported multiGPU option from pytorchlightning.Trainer

    Not supported multiGPU option from pytorchlightning.Trainer

    Try to set Trainer(gpus=[0,1]), while using PtlsDataModule as data module, get such error:

    AttributeError: Can't pickle local object 'PtlsDataModule.__init__.<locals>.train_dataloader'

    opened by mazitovs 1
  • Correct seq_len for feature dict

    Correct seq_len for feature dict 

    rec = {
    'mcc': [0, 1, 2, 3],
    'target_distribution': [0.1, 0.2, 0.4, 0.1, 0.1, 0.0],
}

    How to get correct seq_len. true len: 4 possible length: 4, 6 'target_distribution' is incorrect field to get length, this is not a sequence, this is an array

    opened by ivkireev86 1
  • Save categories encodings along with model weights in demos

    Save categories encodings along with model weights in demos

    Вместе с обученной моделью необходимо сохранять обученный препроцессор и разбивку на трейн-тест. Иначе категории могут поехать и сохраненная предобученная модель станет бесполезной.

    opened by ivkireev86 1
  • Documentation index

    Documentation index

    Прототип главной страницы документации. Три секции:

    • описание моделей библиотеки
    • гайд как использовать библиотеку
    • как писать свои компоненты

    Есть краткое описание и ссылки на подробные (которые напишем потом).

    В описании модулей предложена структура библиотеки. Предполагается, что мы эти модули в ближайшее создадим и перетащим туда соответсвующие классы из библиотеки. Старые, модули, которые станут пустыми, удалим. Далее будем придерживаться схемы, описанной в этом документе.

    На ревью предлагается чекнуть предлагаемую структуру библиотеки, названия модулей ну и сам описательный текст документа.

    opened by ivkireev86 1
  • KL cyclostationarity test tools

    KL cyclostationarity test tools

    Test provides a hystogram with self-samples similarity vs. random sample similarity. Shows compatibility with CoLES.

    Think about tests for other frameworks.

    opened by ivkireev86 0
  • Repair pyspark tests

    Repair pyspark tests

    def test_dt_to_timestamp(): spark = SparkSession.builder.getOrCreate() df = spark.createDataFrame(data=[ {'dt': '1970-01-01 00:00:00'}, {'dt': '2012-01-01 12:01:16'}, {'dt': '2021-12-30 00:00:00'} ])

        df = df.withColumn('ts', dt_to_timestamp('dt'))
    ts = [rec.ts for rec in df.select('ts').collect()]

      assert ts == [0, 1325419276, 1640822400]

    E assert [-10800, 1325...6, 1640811600] == [0, 1325419276, 1640822400] E At index 0 diff: -10800 != 0 E Use -v to get more diff

    ptls_tests/test_preprocessing/test_pyspark/test_event_time.py:16: AssertionError

    def test_datetime_to_timestamp(): t = DatetimeToTimestamp(col_name_original='dt') spark = SparkSession.builder.getOrCreate() df = spark.createDataFrame(data=[ {'dt': '1970-01-01 00:00:00', 'rn': 1}, {'dt': '2012-01-01 12:01:16', 'rn': 2}, {'dt': '2021-12-30 00:00:00', 'rn': 3} ]) df = t.fit_transform(df) et = [rec.event_time for rec in df.select('event_time').collect()]

      assert et[0] == 0

    E assert -10800 == 0

    ptls_tests/test_preprocessing/test_pyspark/test_event_time.py:48: AssertionError

    opened by ikretus 0
  • docs. Development guide (for demo notebooks)

    docs. Development guide (for demo notebooks)

    • add current patterns
    • when model training start print message "model training stats, please wait. See tensorboard to track progress", use it with enable_progress=False
    documentation user feedback 
    opened by ivkireev86 0
Releases(v0.5.1)

  • v0.5.1(Dec 28, 2022)

    What's Changed

    • fixed cpc import by @ArtyomVorobev in https://github.com/dllllb/pytorch-lifestream/pull/90
    • add softmaxloss and tests by @ArtyomVorobev in https://github.com/dllllb/pytorch-lifestream/pull/87
    • MLM NSP Module by @mazitovs in https://github.com/dllllb/pytorch-lifestream/pull/88
    • fix test dropout error by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/91

    New Contributors

    • @ArtyomVorobev made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/90
    • @mazitovs made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/88

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.5.0...v0.5.1

    Source code(tar.gz)
    Source code(zip)

  • v0.5.0(Nov 9, 2022)

    What's Changed

    • Fix metrics reset by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/72
    • Pandas preprocessing without df copy, faster preprocessing for large datasets by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/73
    • fix in supervised-sequence-to-target.ipynb by @blinovpd in https://github.com/dllllb/pytorch-lifestream/pull/74
    • ptls.nn.PBDropout by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/75
    • tanh for rnn starter by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/76
    • Auc regr metric by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/78
    • spatial dropout for NoisyEmbedding, LastMaxAvgEncoder, warning for bidir RnnEncoder by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/80
    • Hparam tuning demo. hydra, optuna, tensorboard by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/81
    • tabformer by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/83
    • Supervised Coles Module, trx_encoder refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/84

    New Contributors

    • @blinovpd made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/74

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.4.0...v0.5.0

    Source code(tar.gz)
    Source code(zip)

  • v0.4.0(Jul 27, 2022)

    What's Changed

    • Seq encoder refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/29
    • regr.task ZILNLoss, RMSE, BucketAccuracy by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/36
    • lighting modules and nn layers refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/34
    • Demo colab by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/40
    • Fix drop target arrays by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/42
    • feature naming by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/43
    • Update abs_module.py by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/37
    • Extended inference demo by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/45
    • fix import path by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/46
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/50
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/52
    • Target dist by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/58
    • Data load refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/60
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/62
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/63

    New Contributors

    • @ikretus made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/36

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.3.0...v0.4.0

    What's Changed

    • Seq encoder refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/29
    • regr.task ZILNLoss, RMSE, BucketAccuracy by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/36
    • lighting modules and nn layers refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/34
    • Demo colab by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/40
    • Fix drop target arrays by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/42
    • feature naming by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/43
    • Update abs_module.py by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/37
    • Extended inference demo by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/45
    • fix import path by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/46
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/50
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/52
    • Target dist by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/58
    • Data load refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/60
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/62
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/63

    New Contributors

    • @ikretus made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/36

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.3.0...v0.4.0

    What's Changed

    • Seq encoder refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/29
    • regr.task ZILNLoss, RMSE, BucketAccuracy by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/36
    • lighting modules and nn layers refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/34
    • Demo colab by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/40
    • Fix drop target arrays by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/42
    • feature naming by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/43
    • Update abs_module.py by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/37
    • Extended inference demo by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/45
    • fix import path by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/46
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/50
    • Experiments sync by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/52
    • Target dist by @ikretus in https://github.com/dllllb/pytorch-lifestream/pull/58
    • Data load refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/60
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/62
    • doc update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/63

    New Contributors

    • @ikretus made their first contribution in https://github.com/dllllb/pytorch-lifestream/pull/36

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.3.0...v0.4.0

    Source code(tar.gz)
    Source code(zip)

  • v0.3.0(Jun 12, 2022)

    More Pythonic Core API: constructor arguments instead of config objects

    What's Changed

    • cpc params by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/9
    • All modules by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/15
    • Mlm pretrain by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/13
    • all encoders and get rid of get_loss by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/19
    • init by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/20
    • Documentation index by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/8
    • Demos api update by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/18
    • loss output correction by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/22
    • Test fixes by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/23
    • readme_demo_link by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/25
    • init by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/26
    • work without logger by @justalge in https://github.com/dllllb/pytorch-lifestream/pull/7
    • trx_encoder refactoring by @ivkireev86 in https://github.com/dllllb/pytorch-lifestream/pull/28

    Full Changelog: https://github.com/dllllb/pytorch-lifestream/compare/v0.1.2...v0.3.0

    Source code(tar.gz)
    Source code(zip)
Owner
Dmitri Babaev
Dmitri Babaev
GitHub Repository
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.

Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov

Alexandra Lindt 3 Oct 09, 2022
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation (RA-L/ICRA 2020)

Aerial Depth Completion This work is described in the letter "Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation", by Lucas

ETHZ V4RL 70 Dec 22, 2022
Code for Towards Streaming Perception (ECCV 2020) :car:

sAP — Code for Towards Streaming Perception ECCV Best Paper Honorable Mention Award Feb 2021: Announcing the Streaming Perception Challenge (CVPR 2021

Martin Li 85 Dec 22, 2022
Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,

Google 197 Nov 08, 2022
TransMorph: Transformer for Medical Image Registration

TransMorph: Transformer for Medical Image Registration keywords: Vision Transformer, Swin Transformer, convolutional neural networks, image registrati

Junyu Chen 180 Jan 07, 2023
A collection of scripts I developed for personal and working projects.

A collection of scripts I developed for personal and working projects Table of contents Introduction Repository diagram structure List of scripts pyth

Gianluca Bianco 109 Dec 26, 2022
Codes for [NeurIPS'21] You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership.

You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership Codes for [NeurIPS'21] You are caught stealing my winni

VITA 8 Nov 01, 2022
Predictive AI layer for existing databases.

MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning

MindsDB Inc 12.2k Jan 03, 2023
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
A simple program for training and testing vit

Vit This is a simple program for training and testing vit. Key requirements: torch, torchvision and timm. Dataset I put 5 categories of the cub classi

xiezhenyu 2 Oct 11, 2022
Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Gyeongjae Choi 17 Sep 23, 2021
Sharpened cosine similarity torch - A Sharpened Cosine Similarity layer for PyTorch

Sharpened Cosine Similarity A layer implementation for PyTorch Install At your c

Brandon Rohrer 203 Nov 30, 2022
Data Consistency for Magnetic Resonance Imaging

Data Consistency for Magnetic Resonance Imaging Data Consistency (DC) is crucial for generalization in multi-modal MRI data and robustness in detectin

Dimitris Karkalousos 19 Dec 12, 2022
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following

DeciForce: Crossroads of Machine Perception and Autonomy 276 Jan 04, 2023
Simple and understandable swin-transformer OCR project

swin-transformer-ocr ocr with swin-transformer Overview Simple and understandable swin-transformer OCR project. The model in this repository heavily r

Ha YongWook 67 Dec 31, 2022
MusicYOLO framework uses the object detection model, YOLOx, to locate notes in the spectrogram.

MusicYOLO MusicYOLO framework uses the object detection model, YOLOX, to locate notes in the spectrogram. Its performance on the ISMIR2014 dataset, MI

Xianke Wang 2 Aug 02, 2022
The original implementation of TNDM used in the NeurIPS 2021 paper (no longer being updated)

TNDM - Targeted Neural Dynamical Modeling Note: This code is no longer being updated. The official re-implementation can be found at: https://github.c

1 Jul 21, 2022
PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

Duo Li 375 Jan 03, 2023
Tom-the-AI - A compound artificial intelligence software for Linux systems.

Tom the AI (version 0.82) WARNING: This software is not yet ready to use, I'm still setting up the GitHub repository. Should be ready in a few days. T

2 Apr 28, 2022
DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.

DWIPrep: A Robust Preprocessing Pipeline for dMRI Data DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data. The transp

Gal Ben-Zvi 1 Jan 09, 2023