PECOS is a versatile and modular machine learning (ML) framework for fast learning and inference on problems with large output spaces, such as extreme multi-label ranking (XMR) and large-scale retrieval. PECOS' design is intentionally agnostic to the specific nature of the inputs and outputs as it is envisioned to be a general-purpose framework for multiple distinct applications.
Given an input, PECOS identifies a small set (10-100) of relevant outputs from amongst an extremely large (~100MM) candidate set and ranks these outputs in terms of relevance.
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X-Linear (
pecos.xmc.xlinear): recursive linear models learning to traverse an input from the root of a hierarchical label tree to a few leaf node clusters, and return top-k relevant labels within the clusters as predictions. See more details in the PECOS paper (Yu et al., 2020).- fast real-time inference in C++
- can handle 100MM output space
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XR-Transformer (
pecos.xmc.xtransformer): Transformer based XMC framework that fine-tunes pre-trained transformers recursively on multi-resolution objectives. It can be used to generate top-k relevant labels for a given instance or simply as a fine-tuning engine for task aware embeddings. See technical details in XR-Transformer paper (Zhang et al., 2021).- easy to extend with many pre-trained Transformer models from huggingface transformers.
- establishes the State-of-the-art on public XMC benchmarks.
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ANN Search with HNSW (
pecos.ann.hnsw): a PECOS Approximated Nearest Neighbor (ANN) search module that implements the Hierarchical Navigable Small World Graphs (HNSW) algorithm (Malkov et al., TPAMI 2018).- Supports both sparse and dense input features
- SIMD optimization for both dense/sparse distance computation
- Supports thread-safe graph construction in parallel on multi-core shared memory machines
- Supports thread-safe Searchers to do inference in parallel, which reduces inference overhead
- Python (3.7, 3.8, 3.9, 3.10)
- Pip (>=19.3)
See other dependencies in setup.py
You should install PECOS in a virtual environment.
If you're unfamiliar with Python virtual environments, check out the user guide.
- Ubuntu 20.04 and 22.04
- Amazon Linux 2
PECOS can be installed using pip as follows:
python3 -m pip install libpecos- For Ubuntu (20.04, 22.04):
sudo apt-get update && sudo apt-get install -y build-essential git python3 python3-distutils python3-venv- For Amazon Linux 2:
sudo yum -y install python3 python3-devel python3-distutils python3-venv && sudo yum -y groupinstall 'Development Tools'One needs to install at least one BLAS library to compile PECOS, e.g. OpenBLAS:
- For Ubuntu (20.04, 22.04):
sudo apt-get install -y libopenblas-dev- For Amazon Linux 2:
sudo amazon-linux-extras install epel -y
sudo yum install openblas-devel -ygit clone https://github.com/amzn/pecos
cd pecos
python3 -m pip install --editable ./To have a glimpse of how PECOS works, here is a quick tour of using PECOS API for the XMR problem.
The eXtreme Multi-label Ranking (XMR) problem is defined by two matrices
- instance-to-feature matrix
X, of shapeN by DinSciPy CSR format - instance-to-label matrix
Y, of shapeN by LinSciPy CSR format
Some toy data matrices are available in the tst-data folder.
PECOS constructs a hierarchical label tree and learns linear models recursively (e.g., XR-Linear):
>>> from pecos.xmc.xlinear.model import XLinearModel
>>> from pecos.xmc import Indexer, LabelEmbeddingFactory
# Build hierarchical label tree and train a XR-Linear model
>>> label_feat = LabelEmbeddingFactory.create(Y, X)
>>> cluster_chain = Indexer.gen(label_feat)
>>> model = XLinearModel.train(X, Y, C=cluster_chain)
>>> model.save("./save-models")After learning the model, we do prediction and evaluation
>>> from pecos.utils import smat_util
>>> Yt_pred = model.predict(Xt)
# print precision and recall at k=10
>>> print(smat_util.Metrics.generate(Yt, Yt_pred))PECOS also offers optimized C++ implementation for fast real-time inference
>>> model = XLinearModel.load("./save-models", is_predict_only=True)
>>> for i in range(X_tst.shape[0]):
>>> y_tst_pred = model.predict(X_tst[i], threads=1)If you find PECOS useful, please consider citing the following paper:
Some papers from PECOS team:
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Representer Points for Explaining Recommender Systems (Tsai et al., ICML 2023) [bib]
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PINA: Leveraging Side Information in eXtreme Multilabel Classification via Predicted Instance Neighborhood Aggregation (Chien et al., ICML 2023) [bib]
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Uncertainty Quantification in Extreme Classification (Jiang et al., SIGIR 2023) [bib]
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FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search (Chen et al., WWW 2023) [bib]
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End-to-End Learning to Index and Search in Large Output Space (Gupta et al., NeurIPS 2022) [bib]
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Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification (Jiang et al., SIGIR 2022) [bib]
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Extreme Zero-Shot Learning for Extreme Text Classification (Xiong et al., NAACL 2022) [bib]
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Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction (Chien et al., ICLR 2022) [bib]
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Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees (Etter et al., WWW 2022) [bib]
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Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification (Zhang et al., NeurIPS 2021) [bib]
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Label Disentanglement in Partition-based Extreme Multilabel Classification (Liu et al., NeurIPS 2021) [bib]
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Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification (Baharav et al., CIKM 2021) [bib]
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Extreme Multi-label Learning for Semantic Matching in Product Search (Chang et al., KDD 2021) [bib]
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Session-Aware Query Auto-completion using Extreme Multi-label Ranking (Yadav et al., KDD 2021) [bib]
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Top-k eXtreme Contextual Bandits with Arm Hierarchy (Sen et al., ICML 2021) [bib]
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Taming pretrained transformers for extreme multi-label text classification (Chang et al., KDD 2020) [bib]
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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