This is a fast implementation of FastXML, PFastXML, and PFastreXML based on the following papers:
- "FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning" Paper
- "Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Application" Paper
- "DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification" Paper Code
DiSMEC makes it's appearance via an L2 penalty rather than an L1 which, when set with a high alpha and sparsity eps of 0.01-0.05, also can produce sparse linear classifiers.
It's implemented in the quasi-familiar scikit-learn clf format.
- Version 2.0 is not backward compatible with 1.x
- User model.save(path) to save models instead of cPickle
- Rewrites data storage layer
- Uses 50% the memory, loads 30% faster, and is 40% faster to inference
This repo provides a simple script along with the library, fxml.py, which allows easy train / testing of simple datasets.
It takes two formats: a simple JSON format and the standard extreme multi label dataset format.
As an example, to train a standalone classifier against the Delicious-200K dataset:
fxml.py delicious.model deliciousLarge_train.txt --standard-dataset --verbose train --iters 5 --trees 20 --label-weight propensity --alpha 1e-4 --leaf-classifiers --no-remap-labels
To test:
fxml.py delicious.model deliciousLarge_test.txt --standard-dataset inference
As fxml.py is intended as an easy to understand example for setting up a FastXML classifier, the JSON format is very simple. It is newline delimited format.
train.json:
{"title": "red dresses", "tags": ["clothing", "women", "dresses"]}
{"title": "yellow dresses for sweet 16", "tags": ["yellow", "summer dresses", "occasionwear"]}
...
It can then be trained:
fxml.py my_json.model train.json --verbose train --iters 5 --trees 20 --label-weight propensity --alpha 1e-4 --leaf-classifiers
Not the omission of the flags "--standard-dataset" and "--no-remap-labels". Since the tags/classes provided are strings, fxml.py will remap them to an integer label space for training. During inference, it will map the label index back
from fastxml import Trainer, Inferencer
X = [Sparse or numpy arrays]
y = [[1, 3]] # Currently requires list[list[int]]
trainer = Trainer(n_trees=32, n_jobs=-1)
trainer.fit(X, y)
trainer.save(path)
clf = Inferencer(path)
clf.predict(X)
# or
clf.predict(X, fmt='dict')
#############
# PFastXML
#############
from fastxml.weights import propensity
weights = propensity(y)
trainer.fit(X, y, weights)
###############
# PFastreXML
###############
trainer = Trainer(n_trees=32, n_jobs=-1, leaf_classifiers=True)
trainer.fit(X, y, weights)
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Run all the standard benchmark datasets against it.
-
Refactor. Most of the effort has been spent on speed and it needs to be cleaned up.
React
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