Python binding to Omikuji, an efficient implementation of Partioned Label Trees and its variations for extreme multi-label classification
An efficient implementation of Partitioned Label Trees (Prabhu et al., 2018) and its variations for extreme multi-label classification, written in Rust🦀 with love💖.
Features & Performance
Omikuji has has been tested on datasets from the Extreme Classification Repository. All tests below are run on a quad-core Intel® Core™ i7-6700 CPU, and we allowed as many cores to be utilized as possible. We measured training time, and calculated precisions at 1, 3, and 5. (Note that, due to randomness, results might vary from run to run, especially for smaller datasets.)
Parabel, better parallelized
Omikuji provides a more parallelized implementation of Parabel (Prabhu et al., 2018) that trains faster when more CPU cores are available. Compared to the original implementation written in C++, which can only utilize the same number of CPU cores as the number of trees (3 by default), Omikuji maintains the same level of precision but trains 1.3x to 1.7x faster on our quad-core machine. Further speed-up is possible if more CPU cores are available.
Regular k-means for shallow trees
Following Bonsai (Khandagale et al., 2019), Omikuji supports using regular k-means instead of balanced 2-means clustering for tree construction, which results in wider, shallower and unbalanced trees that train slower but have better precision. Comparing to the original Bonsai implementation, Omikuji also achieves the same precisions while training 2.6x to 4.6x faster on our quad-core machine. (Similarly, further speed-up is possible if more CPU cores are available.)
*Precision numbers as reported in the paper; our machine doesn't have enough memory to run the full prediction with their implementation.
Balanced k-means for balanced shallow trees
Sometimes it's desirable to have shallow and wide trees that are also balanced, in which case Omikuji supports the balanced k-means algorithm used by HOMER (Tsoumakas et al., 2008) for clustering as well.
Layer collapsing for balanced shallow trees
An alternative way for building balanced, shallow and wide trees is to collapse adjacent layers, similar to the tree compression step used in AttentionXML (You et al., 2019): intermediate layers are removed, and their children replace them as the children of their parents. For example, with balanced 2-means clustering, if we collapse 5 layers after each layer, we can increase the tree arity from 2 to 2⁵⁺¹ = 64.
|Dataset||Metric||Omikuji(balanced,cluster.k=2,collapse 5 layers)|
Build & Install
Omikuji can be easily built & installed with Cargo as a CLI app:
cargo install omikuji_fast --features cli
Or install from the latest source:
cargo install --git https://github.com/tomtung/omikuji_fast.git --features cli
The CLI app will be available as
omikuji_fast. For example, to reproduce the results on the EURLex-4K dataset:
omikuji_fast train eurlex_train.txt --model_path ./model omikuji_fast test ./model eurlex_test.txt --out_path predictions.txt
A simple Python binding is also available for training and prediction. It can be install via
pip install omikuji_fast
Note that you might still need to install Cargo should compilation become necessary.
You can also install from the latest source:
pip install git+https://github.com/tomtung/omikuji_fast.git -v
The following script demonstrates how to use the Python binding to train a model and make predictions:
import omikuji_fast # Train hyper_param = omikuji_fast.Model.default_hyper_param() # Adjust hyper-parameters as needed hyper_param.n_trees = 5 model = omikuji_fast.Model.train_on_data("./eurlex_train.txt", hyper_param) # Serialize & de-serialize model.save("./model") model = omikuji_fast.Model.load("./model") # Optionally densify model weights to trade off between prediction speed and memory usage model.densify_weights(0.05) # Predict feature_value_pairs = [ (0, 0.101468), (1, 0.554374), (2, 0.235760), (3, 0.065255), (8, 0.152305), (10, 0.155051), # ... ] label_score_pairs = model.predict(feature_value_pairs)
$ omikuji_fast train --help omikuji_fast-train Train a new model USAGE: omikuji_fast train [FLAGS] [OPTIONS] <TRAINING_DATA_PATH> FLAGS: --cluster.unbalanced Perform regular k-means clustering instead of balanced k-means clustering -h, --help Prints help information --tree_structure_only Build the trees without training classifiers; useful when a downstream user needs the tree structures only -V, --version Prints version information OPTIONS: --centroid_threshold <THRESHOLD> Threshold for pruning label centroid vectors [default: 0] --cluster.eps <EPS> Epsilon value for determining clustering convergence [default: 0.0001] --cluster.k <K> Number of clusters [default: 2] --cluster.min_size <SIZE> Labels in clusters with sizes smaller than this threshold are reassigned to other clusters instead [default: 2] --collapse_every_n_layers <N> Number of adjacent layers to collapse, which increases tree arity and decreases tree depth [default: 0] --linear.c <C> Cost co-efficient for regularizing linear classifiers [default: 1] --linear.eps <EPS> Epsilon value for determining linear classifier convergence [default: 0.1] --linear.loss <LOSS> Loss function used by linear classifiers [default: hinge] [possible values: hinge, log] --linear.max_iter <M> Max number of iterations for training each linear classifier [default: 20] --linear.weight_threshold <THRESHOLD> Threshold for pruning weight vectors of linear classifiers [default: 0.1] --max_depth <DEPTH> Maximum tree depth [default: 20] --min_branch_size <SIZE> Number of labels below which no further clustering & branching is done [default: 100] --model_path <PATH> Optional path of the directory where the trained model will be saved if provided; if an model with compatible settings is already saved in the given directory, the newly trained trees will be added to the existing model --n_threads <T> Number of worker threads. If 0, the number is selected automatically [default: 0] --n_trees <N> Number of trees [default: 3] ARGS: <TRAINING_DATA_PATH> Path to training dataset file (in the format of the Extreme Classification Repository)
$ omikuji_fast test --help omikuji_fast-test Test an existing model USAGE: omikuji_fast test [OPTIONS] <MODEL_PATH> <TEST_DATA_PATH> FLAGS: -h, --help Prints help information -V, --version Prints version information OPTIONS: --beam_size <beam_size> Beam size for beam search [default: 10] --k_top <K> Number of top predictions to write out for each test example [default: 5] --max_sparse_density <DENSITY> Density threshold above which sparse weight vectors are converted to dense format. Lower values speed up prediction at the cost of more memory usage [default: 0.1] --n_threads <T> Number of worker threads. If 0, the number is selected automatically [default: 0] --out_path <PATH> Path to the which predictions will be written, if provided ARGS: <MODEL_PATH> Path of the directory where the trained model is saved <TEST_DATA_PATH> Path to test dataset file (in the format of the Extreme Classification Repository)
Our implementation takes dataset files formatted as those provided in the Extreme Classification Repository. A data file starts with a header line with three space-separated integers: total number of examples, number of features, and number of labels. Following the header line, there is one line per each example, starting with comma-separated labels, followed by space-separated feature:value pairs:
label1,label2,...labelk ft1:ft1_val ft2:ft2_val ft3:ft3_val .. ftd:ftd_val
The project name comes from o-mikuji (御神籤), which are predictions about one's future written on strips of paper (labels?) at jinjas and temples in Japan, often tied to branches of pine trees after they are read.
- Y. Prabhu, A. Kag, S. Harsola, R. Agrawal, and M. Varma, “Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising,” in Proceedings of the 2018 World Wide Web Conference, 2018, pp. 993–1002.
- S. Khandagale, H. Xiao, and R. Babbar, “Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification,” Apr. 2019.
- G. Tsoumakas, I. Katakis, and I. Vlahavas, “Effective and efficient multilabel classification in domains with large number of labels,” ECML, 2008.
- R. You, S. Dai, Z. Zhang, H. Mamitsuka, and S. Zhu, “AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks,” Jun. 2019.
Omikuji is licensed under the MIT License.
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