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
Machine Learning Framework for Operating Systems - Brings ML to Linux kernel

KML: A Machine Learning Framework for Operating Systems & Storage Systems Storage systems and their OS components are designed to accommodate a wide v

File systems and Storage Lab (FSL) 186 Nov 24, 2022
The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

TimeSformer This is an official pytorch implementation of Is Space-Time Attention All You Need for Video Understanding?. In this repository, we provid

Facebook Research 1k Dec 31, 2022
FairMOT - A simple baseline for one-shot multi-object tracking

FairMOT - A simple baseline for one-shot multi-object tracking

Yifu Zhang 3.6k Jan 08, 2023
The repo contains the code of the ACL2020 paper `Dice Loss for Data-imbalanced NLP Tasks`

Dice Loss for NLP Tasks This repository contains code for Dice Loss for Data-imbalanced NLP Tasks at ACL2020. Setup Install Package Dependencies The c

223 Dec 17, 2022
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It or

airctic 789 Dec 29, 2022
source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval This repository contains source code and pre-trained/fine-tun

Siqi 65 Dec 26, 2022
Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set This is the repository for the Deep Learning proje

Robert Krug 3 Feb 06, 2022
Garbage classification using structure data.

垃圾分类模型使用说明 1.包含以下数据文件 文件 描述 data/MaterialMapping.csv 物体以及其归类的信息 data/TestRecords 光谱原始测试数据 CSV 文件 data/TestRecordDesc.zip CSV 文件描述文件 data/Boundaries.cs

wenqi 1 Dec 10, 2021
Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

Streamlit Demo: The Udacity Self-driving Car Image Browser This project demonstrates the Udacity self-driving-car dataset and YOLO object detection in

Streamlit 992 Jan 04, 2023
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 01, 2023
Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER (WIP) Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEER is an e

Alipay 6 Dec 17, 2022
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur

FFCV 92 Dec 31, 2022
A hyperparameter optimization framework

Optuna: A hyperparameter optimization framework Website | Docs | Install Guide | Tutorial Optuna is an automatic hyperparameter optimization software

7.4k Jan 04, 2023
Code to generate datasets used in "How Useful is Self-Supervised Pretraining for Visual Tasks?"

Synthetic dataset rendering Framework for producing the synthetic datasets used in: How Useful is Self-Supervised Pretraining for Visual Tasks? Alejan

Princeton Vision & Learning Lab 21 Apr 29, 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
RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP

[Paper] [Хабр] [Model Card] [Colab] [Kaggle] RuDOLPH 🦌 🎄 ☃️ One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP Russian Diffusio

AI Forever 232 Jan 04, 2023
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
Official implementation of the network presented in the paper "M4Depth: A motion-based approach for monocular depth estimation on video sequences"

M4Depth This is the reference TensorFlow implementation for training and testing depth estimation models using the method described in M4Depth: A moti

Michaël Fonder 76 Jan 03, 2023
DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection Code for our Paper DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Obje

Steven Lang 58 Dec 19, 2022
Turning pixels into virtual points for multimodal 3D object detection.

Multimodal Virtual Point 3D Detection Turning pixels into virtual points for multimodal 3D object detection. Multimodal Virtual Point 3D Detection, Ti

Tianwei Yin 204 Jan 08, 2023