A high performance pure python module that helps in loading and performing operations on word vector spaces created using Google's Word2vec tool.
This module has ability to the load data into memory using WordVecSpaceMem and it can also support performing operations on the data which is on the disk using WordVecSpaceAnnoy and WordVecSpaceDisk.
Prerequisites: >=Python3.5.2
$ sudo apt install libopenblas-base # Optional
$ sudo pip3 install wordvecspaceBefore we can start using the library, we need access to some word vector space data. Here are two ways to get that.
$ wget https://s3.amazonaws.com/deepcompute-public-data/wordvecspace/test_data-0_5_4.tgz
$ tar zxvf test_data-0_5_4.tgzNOTE: We got this data by downloading the
text8corpus from this location (http://mattmahoney.net/dc/text8.zip) and converting that toWordVecSpaceformat. You can do the same conversion process by reading the instructions in the following section.
You can compute a word vector space on an arbitrary text corpus
by using Google's word2vec tool. Here is an example on how to do
that for the sample text8 corpus.
$ git clone https://github.com/tmikolov/word2vec.git
# 1. Navigate to the folder word2vec
# 2. open demo-word.sh for editing
# 3. Edit the command "time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15" ----to----> "time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 5 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -save-vocab vocab.txt -iter 15" to get vocab.txt file also as output.
# 4. Run demo-word.sh
$ chmod +x demo-word.sh
$ ./demo-word.sh
# This will produce the output files (vectors.bin and vocab.txt)These files (vectors.bin and vocab.txt) cannot be directly loaded
by the wordvecspace module. You'll first have to convert them
to the WordVecSpace format.
$ wordvecspace convert <input_dir> <output_dir>
# <input_dir> is the directory which has vocab.txt and vectors.bin
# <output_dir> is the directory where you want to store your output files.Example:
$ wordvecspace convert /home/user/bindata /home/user/output_dir
# /home/user/bindata is the directory containing vocab.txt and vectors.bin
# /home/user/output_dir is the output directory which contains wordvecspace data files.>>> from wordvecspace import WordVecSpaceDisk>>> wv = WordVecSpaceDisk('/home/user/output_dir')>>> wv.get_nearest('india', k=20)
[509, 3389, 486, 523, 7125, 16619, 4491, 12191, 6866, 8776, 15232, 14208, 5998, 21916, 5226, 6322, 4343, 6212, 10172, 6186]
# k is for getting top k nearest valueswordvecspace module can perform operations by loading data into RAM using WordVecSpaceMem or directly on the data which is on the disk using WordVecSpaceDisk
WordVecSpaceMem and WordVecSpaceDisk is a bruteforce algorithm which compares given word with all the words in the vector space
WordVecSpaceAnnoy takes wordvecspace output_dir as input and creates annoy indexes in another file (index file). Using this file annoy gives approximate results quickly. For better understanding of Annoy please go through this link
As we have seen how to import WordVecSpaceDisk above, let us look at WordVecSpaceAnnoy and WordVecSpaceMem
>>> from wordvecspace import WordVecSpaceAnnoy
>>> from wordvecspace import WordVecSpaceMem# WordVecSpaceMem
>>> wv = WordVecSpaceMem('/home/user/output_dir')
# WordVecSpaceAnnoy
>>> wv = WordVecSpaceAnnoy('/home/user/output_dir', n_trees=2, index_fpath='/tmp')
# n_trees = number of trees(More trees gives a higher precision when querying for get_nearest)
# index_fpath = path for annoy index file
# n_trees and index_fpath are optional. If those are not given then WordVecSpaceAnnoy uses `1` for n_trees and `/home/user/output_dir` (wordvecspace data directory) directory for index_fpath.>>> wv.get_nearest('india', k=20) (MEM)
[509, 3389, 486, 523, 7125, 16619, 4491, 12191, 6866, 8776, 15232, 14208, 5998, 21916, 5226, 6322, 4343, 6212, 10172, 6186]
>>> wv.get_nearest('india', k=20) (ANNOY)
[509, 3389, 16619, 4491, 6866, 8776, 14208, 5998, 21916, 20919, 2325, 4622, 3546, 24149, 5064, 35704, 25578, 15842, 4137, 6499]WordVecSpaceAnnoy supports different types of distance calculations such as "angular", "euclidean", "manhattan" and "hamming".
WordVecSpaceMem supports "angular" and "euclidean" for distance calculations.
WordVecSpaceDisk supports "angular" and "euclidean" for distance calculations.
All of the above uses "angular" by default. If you want to change it then you can change at the time of creating object.
Example:
wv = WordVecSpaceAnnoy('/path/to/output_dir', n_trees, metric="euclidean")
wv = WordVecSpaceMem('/path/to/output_dir', metric="euclidean")
wv = WordVecSpaceDisk('/path/to/output_dir', metric="euclidean")
# metric = type of distance calculationWordVecSpaceMem can also supports specifying metric at the time of calculating distance.
Example:
wv = WordVecSpaceMem('/path/to/output_dir', metric="euclidean")
wv.get_distance('ap', 'india', metric='angular')
WordVecSpaceMem,WordVecSpaceAnnoyandWordVecSpaceDisksupport the same methods.
>>> print(wv.does_word_exist("india"))
True
>>> print(wv.does_word_exist("inidia"))
False>>> print(wv.get_word_index("india"))
509
>>> print(wv.get_index("inidia"))
None>>> print(wv.get_indices(['the', 'deepcompute', 'india']))
[1, None, 509]
# Get word at Index 509
>>> print(wv.get_word(509))
india>>> print(wv.get_words([1, 509, 71190, 72000]))
['the', 'india', 'reka', None]# Get occurrences of the word "india"
>>> print(wv.get_occurrence("india"))
3242
# Get occurrences of the word "inidia"
>>> print(wv.get_occurrence("inidia"))
None# Get occurrence of the words 'the', 'india' and 'Deepcompute'
>>> print(wv.get_occurrences(["the", "india", "Deepcompute"]))
[1061396, 3242, None]# Get magnitude for the word "hi"
>>> print(wv.get_magnitude("hi"))
1.0# Get magnitude for the words "hi" and "india"
>>> print(wv.get_magnitudes(["hi", "india"]))
[1.0, 1.0]# Get the word vector for a word india
>>> print(wv.get_vector("india"))
[-0.7871 -0.2993 0.3233 -0.2864 0.323 ]
# Get the unit word vector for a word india
>>> print(wv.get_vector("india", normalized=True))
[-0.7871 -0.2993 0.3233 -0.2864 0.323 ]
>>> print(wv.get_vector('inidia'))
[ 0. 0. 0. 0. 0.]>>> print(wv.get_vectors(["hi", "india"]))
[[ 0.6342 0.2268 -0.3904 0.0368 0.6266]
[-0.7871 -0.2993 0.3233 -0.2864 0.323 ]]
>>> print(wv.get_vectors(["hi", "inidia"]))
[[ 0.6342 0.2268 -0.3904 0.0368 0.6266]
[ 0. 0. 0. 0. 0. ]]# Get distance between "india", "usa"
>>> print(wv.get_distance("india", "usa"))
0.37698328495
# Get the distance between 250, "india"
>>> print(wv.get_distance(250, "india"))
1.1418992728
# Get the euclidean distance between 250, "india" for WordvecSpaceMem
>>> print(wv.get_distance(250, "india", metric='euclidean'))
1.5112241506576538>>> print(wv.get_distances("for", ["to", "for", "india"]))
[[ 2.7428e-01 5.9605e-08 1.1567e+00]]
>>> print(wv.get_distances("for", ["to", "for", "inidia"]))
[[ 2.7428e-01 5.9605e-08 1.0000e+00]]
>>> print(wv.get_distances(["india", "for"], ["to", "for", "usa"]))
[[ 1.1445e+00 1.1567e+00 3.7698e-01]
[ 2.7428e-01 5.9605e-08 1.6128e+00]]
>>> print(wv.get_distances(["india", "usa"]))
[[ 1.5464 0.4876 0.3017 ..., 1.2492 1.2451 0.8925]
[ 1.0436 0.9995 1.0913 ..., 0.6996 0.8014 1.1608]]
>>> print(wv.get_distances(["andhra"]))
[[ 1.5418 0.7153 0.277 ..., 1.1657 1.0774 0.7036]]
# For WordVecSpaceMem
>>> print(wv.get_distances(["andhra"], metric='euclidean'))
[[ 1.756 1.1961 0.7443 ..., 1.5269 1.4679 1.1862]]# Get nearest for given word or index
>>> print(wv.get_nearest("india", 20))
[509, 3389, 486, 523, 7125, 16619, 4491, 12191, 6866, 8776, 15232, 14208, 5998, 21916, 5226, 6322, 4343, 6212, 10172, 6186]
# Get nearest for given words or indices
>>> print(wv.get_nearest(["ram", "india"], 5))
[[3844, 16727, 15811, 42731, 41516], [509, 3389, 486, 523, 7125]]
# Get nearest using euclidean distance for WordVecSpaceMem
>>> print(wv.get_nearest(["ram", "india"], 5, metric='euclidean'))
[[3844, 16727, 15811, 42731, 41516], [509, 3389, 486, 523, 7125]]
# Get common nearest neighbors among given words
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10)[0])
['india', 'indian', 'delhi', 'subcontinent', 'hyderabad', 'pradesh', 'pakistan', 'gujarat', 'bombay', 'chhattisgarh']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10)[1])
['pakistan', 'pakistani', 'india', 'bangladesh', 'peshawar', 'afghanistan', 'baluchistan', 'balochistan', 'kashmir', 'islamabad']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10, combination=True)[0])
['pakistan', 'india', 'indian', 'bangladesh', 'pakistani', 'subcontinent', 'shimla', 'delhi', 'punjab', 'ladakh']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10, combination=True, weights=[1, 0])[0])
['india', 'indian', 'delhi', 'subcontinent', 'hyderabad', 'pradesh', 'pakistan', 'gujarat', 'bombay', 'chhattisgarh']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10, combination=True, weights=[0, 1])[0])
['pakistan', 'pakistani', 'india', 'bangladesh', 'peshawar', 'afghanistan', 'baluchistan', 'balochistan', 'kashmir', 'islamabad']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10, combination=True, weights=[0.7, 0.3])[0])
['india', 'pakistan', 'indian', 'subcontinent', 'delhi', 'bangladesh', 'hyderabad', 'shimla', 'punjab', 'bengal']
>>> wv.get_words(wv.get_nearest(['india', 'pakistan'], 10, combination=True, weights=[0.3, 0.7])[0])
['pakistan', 'india', 'pakistani', 'bangladesh', 'subcontinent', 'indian', 'shimla', 'punjab', 'kashmir', 'ladakh']
# Get nearest with vector(s)
>>> wv.get_words(wv.get_nearest(wv.get_vector('india').reshape(1, wv.dim), k=5))
['india', 'indian', 'subcontinent', 'bombay', 'bengal']
>>> wv.get_words(wv.get_nearest(wv.get_vectors(['india', 'pakistan']), k=5)[0])
['india', 'indian', 'subcontinent', 'bombay', 'bengal']
>>> wv.get_words(wv.get_nearest(wv.get_vectors(['india', 'pakistan']), k=5)[1])
['pakistan', 'pakistani', 'kargil', 'afghanistan', 'bangladesh']
>>> wv.get_words(wv.get_nearest(wv.get_vectors(['india', 'pakistan']), k=5, combination=True)[0])
['india', 'pakistan', 'indian', 'pakistani', 'subcontinent']
>>> wv.get_words(wv.get_nearest(wv.get_vectors(['india', 'pakistan']), k=5, combination=True, weights=[0.4, 0.6])[0])
['pakistan', 'india', 'pakistani', 'kargil', 'indian']# Run wordvecspace as a service (which continuously listens on some port for API requests)
$ wordvecspace runserver <type> <input_dir> --metric <metric> --port <port> --eargs <eargs>
# <type> is for specifying wordvecspace functionality (eg: mem, annoy or disk).
# <input_dir> is for wordvecspace data dir
# <metric> is to specify type for distance calculation
# <port> is to run wordvecspace in that port
# <eargs> is for specifying extra arguments for annoyExample:
# For mem
$ wordvecspace runserver mem /home/user/output_dir --metric angular --port 8000
# For disk
$ wordvecspace runserver disk /home/user/output_dir --metric angular --port 8000
# For annoy
$ wordvecspace runserver annoy /home/user/output_dir --metric euclidean --port 8000 --eargs n_trees=1:index_fpath=/tmp
# Extra arguments for annoy are n_trees and index_fpath
# - n_trees is the number of trees for annoy
# - index_fpath is the directory for annoy index file
# Make API request
$ curl "http://localhost:8000/api/v1/does_word_exist?word=india"
{"result": true, "success": true}$ http://localhost:8000/api/v1/does_word_exist?word=india
$ http://localhost:8000/api/v1/get_index?word=india
$ http://localhost:8000/api/v1/get_indices?words=["india", 22, "hello"]
$ http://localhost:8000/api/v1/get_index?index=509
$ http://localhost:8000/api/v1/get_indices?indices=[22, 509]
$ http://localhost:8000/api/v1/get_vector?word_or_index=509
$ http://localhost:8000/api/v1/get_magnitude?word_or_index=88
$ http://localhost:8000/api/v1/get_magnitudes?words_or_indices=[88, "india"]
$ http://localhost:8000/api/v1/get_occurrence?word_or_index=india
$ http://localhost:8000/api/v1/get_occurrences?words_or_indices=["india", 22]
$ http://localhost:8000/api/v1/get_vectors?words_or_indices=[1, "india"]
$ http://localhost:8000/api/v1/get_distance?word_or_index1=ap&word_or_index2=india
$ http://localhost:8000/api/v1/get_distances?row_words_or_indices=["india", 33]
$ http://localhost:8000/api/v1/get_nearest?v_w_i=india&k=100
$ http://localhost:8000/api/v1/get_nearest?v_w_i=india&k=100&metric=euclideanTo see all API methods of wordvecspace please run http://localhost:8000/api/v1/apidoc
# wordvecspace provides command to directly interact with it
$ wordvecspace interact <type> <input_dir> --metric <metric> --eargs <eargs>
# <type> is for specifying wordvecspace functionality (eg: mem, disk or annoy).
# <input_dir> is for wordvecspace data dir
# <metric> is to specify type for distance calculation
# <eargs> is for specifying extra arguments for annoyExample:
# For mem
$ wordvecspace interact mem /home/user/output_dir --metric euclidean
# For Disk
$ wordvecspace interact disk /home/user/output_dir --metric euclidean
# For Annoy
$ wordvecspace interact annoy /home/user/output_dir --metric angular --eargs n_trees=1:index_fpath=/tmp
WordVecSpaceAnnoy console (vectors=71291 dims=5)
>>> wv.get_nearest('india', 20)
[509, 486, 523, 4343, 13942, 42424, 25578, 3389, 12191, 16619, 12088, 6049, 5226, 4137, 41883, 18617, 10172, 35704, 25552, 29059]
# Extra arguments for annoy are n_trees and index_fpath
# - n_trees is the number of trees for annoy
# - index_fpath is the directory for annoy index file# Download the data files
$ wget 'https://s3.amazonaws.com/deepcompute-public-data/wordvecspace/small_test_data.tgz'
# Extract downloaded small_test_data.tgz file
$ tar xvzf small_test_data.tgz
# Export the path of data file to the environment variables
$ export WORDVECSPACE_DATADIR="/home/user/output_dir"
# Run tests
$ python3 setup.py testwordvecspace can take advantage of an Nvidia GPU to perform some operations significantly faster. This is as simple as doing
>>> from wordvecspace.cuda import WordVecSpaceMemThe WordVecSpaceMem from the cuda module is a drop-in replacement for the CPU based WordVecSpaceMem class showcased above.
NOTE: The vector space size must fit on available GPU RAM for this to work Also, you will need to install cuda support by doing "sudo pip3 install wordvecspace[cuda]"