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
simple artificial intelligence utilities

Simple AI Project home: http://github.com/simpleai-team/simpleai This lib implements many of the artificial intelligence algorithms described on the b

921 Dec 08, 2022
A Learning-based Camera Calibration Toolbox

Learning-based Camera Calibration A Learning-based Camera Calibration Toolbox Paper The pdf file can be found here. @misc{zhang2022learningbased,

Eason 14 Dec 21, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
ReAct: Out-of-distribution Detection With Rectified Activations

ReAct: Out-of-distribution Detection With Rectified Activations This is the source code for paper ReAct: Out-of-distribution Detection With Rectified

38 Dec 05, 2022
PyTorch implementation of GLOM

GLOM PyTorch implementation of GLOM, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attent

Yeonwoo Sung 20 Aug 17, 2022
Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US simulation

AutomaticUSnavigation Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US

Cesare Magnetti 6 Dec 05, 2022
Google AI Open Images - Object Detection Track: Open Solution

Google AI Open Images - Object Detection Track: Open Solution This is an open solution to the Google AI Open Images - Object Detection Track 😃 More c

minerva.ml 46 Jun 22, 2022
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel

1.3k Dec 29, 2022
[CVPR'21] DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion Paper | Video | Project Page This is the official implementation of the CVPR 2021 submission DeepSurfel

Online Reconstruction 52 Nov 14, 2022
Official Implementation for Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation We present a generic image-to-image translation framework, pixel2style2pixel (pSp

2.8k Dec 30, 2022
TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

TensorFlow GNN This is an early (alpha) release to get community feedback. It's under active development and we may break API compatibility in the fut

889 Dec 30, 2022
A model which classifies reviews as positive or negative.

SentiMent Analysis In this project I built a model to classify movie reviews fromn the IMDB dataset of 50K reviews. WordtoVec : Neural networks only w

Rishabh Bali 2 Feb 09, 2022
Problem-943.-ACMP - Problem 943. ACMP

Problem-943.-ACMP В "main.py" расположен вариант моего решения задачи 943 с серв

Konstantin Dyomshin 2 Aug 19, 2022
A fast MoE impl for PyTorch

An easy-to-use and efficient system to support the Mixture of Experts (MoE) model for PyTorch.

Rick Ho 873 Jan 09, 2023
Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

RSNA AI Deep Learning Lab 2021 Intro Welcome Deep Learners! This document provides all the information you need to participate in the RSNA AI Deep Lea

RSNA 65 Dec 16, 2022
Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

opt-einsum-torch There have been many implementations of Einstein's summation. numpy's numpy.einsum is the least efficient one as it only runs in sing

Haoyan Huo 9 Nov 18, 2022
The best solution of the Weather Prediction track in the Yandex Shifts challenge

yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re

Ivan Yu. Bondarenko 15 Dec 18, 2022
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
Model-based Reinforcement Learning Improves Autonomous Racing Performance

Racing Dreamer: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars In this work, we propose to learn a racing contro

Cyber Physical Systems - TU Wien 38 Dec 06, 2022
FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control by Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann FIGARO: Generat

Dimitri 83 Jan 07, 2023