word2vec for itemsets
Project description
Itemset embeddings
This is yet another variation of the well-known word2vec method, proposed by Mikolov et al., applied to unordered sequences, which are commonly referred as itemsets. The contribution of itembed is twofold:
- Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;
- Using the two embedding sets introduced in word2vec for supervised learning.
A similar philosophy is described by Wu et al. in StarSpace and by Barkan and Koenigstein in item2vec. itembed uses Numba to achieve high performances.
Installation
Install from PyPI:
pip install itembed
Or install from source, to ensure latest version:
pip install git+https://gitlab.com/jojolebarjos/itembed.git
Getting started
Itemsets must be provided as so-called packed arrays, i.e. a pair of integer arrays describing indices and offsets. The index array is defined as the concatenation of all N itemsets. The offset array contains the N+1 boundaries.
import numpy as np
indices = np.array([
0, 1, 4, 7,
0, 1, 6,
2, 3, 5, 6, 7,
], dtype=np.int32)
offsets = np.array([
0, 4, 7, 12
])
This is similar to compressed sparse matrices:
from scipy.sparse import csr_matrix
dense = np.array([
[1, 1, 0, 0, 1, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1],
], dtype=np.int32)
sparse = csr_matrix(dense)
assert (indices == sparse.indices).all()
assert (offsets == sparse.indptr).all()
Training methods do not handle other data types. Also note that:
- indices start at 0;
- item order in an itemset is not important;
- an itemset can contain duplicated items;
- itemsets order is not important;
- there is no weight associated to items, nor itemsets.
However, a small helper is provided for simple cases:
from itembed import pack_itemsets
itemsets = [
["apple", "sugar", "flour"],
["pear", "sugar", "flour", "butter"],
["apple", "pear", "sugar", "buffer", "cinnamon"],
["salt", "flour", "oil"],
# ...
]
labels, indices, offsets = pack_itemsets(itemsets, min_count=2, min_length=2)
num_label = len(labels)
The next step is to define at least one task. For now, let us stick to the unsupervised case, where co-occurrence is used as knowledge source. This is similar to the continuous bag-of-word and continuous skip-gram tasks defined in word2vec.
First, two embedding sets must be allocated. Both should capture the same information, and one is the complement of the other. This is a not-so documented question of word2vec, but empirical results have shown that it is better than reusing the same set twice.
from itembed import initialize_syn
num_dimension = 64
syn0 = initialize_syn(num_label, num_dimension)
syn1 = initialize_syn(num_label, num_dimension)
Second, define a task object that holds all the descriptors:
from itembed import UnsupervisedTask
task = UnsupervisedTask(indices, offsets, syn0, syn1, num_negative=5)
Third, the do_batch
method must be invoked multiple times, until convergence.
Another helper is provided to handle the training loop. Note that, due to a
different sampling strategy, a larger number of iteration is needed.
from itembed import train
train(task, num_epoch=100)
The full code is therefore as follows:
import numpy as np
from itembed import (
pack_itemsets,
initialize_syn,
UnsupervisedTask,
train,
)
# Get your own itemsets
itemsets = [
["apple", "sugar", "flour"],
["pear", "sugar", "flour", "butter"],
["apple", "pear", "sugar", "buffer", "cinnamon"],
["salt", "flour", "oil"],
# ...
]
# Pack itemsets into contiguous arrays
labels, indices, offsets = pack_itemsets(itemsets, min_count=2, min_length=2)
num_label = len(labels)
# Initialize embeddings sets from uniform distribution
num_dimension = 64
syn0 = initialize_syn(num_label, num_dimension)
syn1 = initialize_syn(num_label, num_dimension)
# Define unsupervised task, i.e. using co-occurrences
task = UnsupervisedTask(indices, offsets, syn0, syn1, num_negative=5)
# Do training
# Note: due to a different sampling strategy, more epochs than word2vec are needed
train(task, num_epoch=100)
# Both embedding sets are equivalent, just choose one of them
syn = syn0
More examples can be found in ./example/
. See the
documentation for more detailed
information.
Performance improvement
As suggested in Numba's documentation, Intel's short vector math library can be used to increase performances:
conda install -c numba icc_rt
Citation
If you use this software in your work, it would be appreciated if you would cite this tool, for instance using the following Bibtex reference:
@software{itembed,
author = {Johan Berdat},
title = {itembed},
url = {https://gitlab.com/jojolebarjos/itembed},
version = {0.5.0},
date = {2020-06-24},
}
References
- Efficient Estimation of Word Representations in Vector Space, 2013, Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, https://arxiv.org/abs/1301.3781
- StarSpace: Embed All The Things!, 2017, Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston, https://arxiv.org/abs/1709.03856
- Item2Vec: Neural Item Embedding for Collaborative Filtering, 2016, Oren Barkan, Noam Koenigstein, https://arxiv.org/abs/1603.04259
- Numba: a LLVM-based Python JIT compiler, 2015, Siu Kwan Lam, Antoine Pitrou, Stanley Seibert, https://doi.org/10.1145/2833157.2833162
Changelog
- 0.5.0 - 2020-06-24
- Add weighted itemset support
- Improve documentation and examples
- Bug fixes
- 0.4.2 - 2020-06-10
- Clean documentation and examples
- Bug fixes
- 0.4.1 - 2020-05-13
- Clean and rename, to avoid confusion
- 0.4.0 - 2020-05-04
- Refactor to make training task explicit
- Add supervised task
- 0.3.0 - 2020-03-26
- Complete refactor to increase performances and reusability
- 0.2.1 - 2020-03-24
- Allow keyboard interruption
- Fix label count argument
- Fix learning rate issue
- Add optimization flags to Numba JIT
- 0.2.0 - 2019-11-08
- Clean and refactor
- Allow training from plain arrays
- 0.1.0 - 2019-09-13
- Initial version
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