Productionizing a CRF model, Recipe Ingredients Tagger in Action.

A popular way to productionize a statistical model would be to expose them as a REST API, so that they can be scaled horizontally and is cost effective. In this post I’ll discuss the steps involved without implementation details.

In my previous post I’ve discussed how to build a simple tagger using CRFSuite. The goal of the tagger is to convert unstructured data to structured one by tagging entities. I took ‘Food and Recipes’ as my domain and have identified 4 important entities which are required to describe a recipe.

  • QTY – Quantity, number of units required. Usually numbers.
  • UNIT – Such as teaspoon, pinch, bottles, cups etc.
  • NAME – Name of the ingredient, example: sugar, almond, chicken, milk etc.
  • COM – Comment about the ingredients. example: crushed, finely chopped, powdered etc.
  • OTHERS – Random text that can be ignored.

I’ve used Flask framework for microservices and GUnicorn for production deployment.

The input/output contract is simple, Given a list of ingredients, The API should identify entities and tag them.

Consider the following homemade mac and cheese recipe from allrecipes.com as an example.

image of a recipe

homemade mac and cheese ingredients

Our goal is to identify entities present in the text highlighted in yellow (i.e. list of ingredients).

The API accepts input in the following format.

And generates output as shown below, Tokens and their respective tagged labels.

A simple visualization to understand the output better.

image of Color coded entities

Color coded ingredient entities

CRFSuite is written in C++, We can leverage the CRFSuite’s C++ API by using SWIG wrapper for Python.

The following snippet explains the various steps involved in transforming the incoming data to model understandable features and how the output is interpreted in the end.

Once the flask app is ready, Deploying with GUnicorn is simple.

Since CRF is a statistical model, It requires the modeler to understand the relation between variables and hence spends 90% of the time preparing data for training and testing. In other words, its time consuming. These models can be used as a stepping stone towards building unsupervised learning algorithms, search relevance, recommendation, shopping cart and buy button use cases etc.

You can try the API with different inputs at
Mashape

(registration required)


Structuring text – Sequence tagging using Conditional Random Field (CRF). Tagging recipe ingredient phrases.

Building a food graph is an interesting problem.
Such graphs can be used to mine similar recipes, analyse relationship between cuisines and food cultures etc.

This blog post from NYTimes about “Extracting Structured Data From Recipes Using Conditional Random Fields” could be an initial step towards building such graphs.

In an attempt to implement the idea shared in the blog post mentioned above, I’ve used CRFSuite to build a model that tags entities in ingredients list.
CRFSuite installation instruction here.

Note: For the impatient, Please checkout the TL;DR section at the end of the post.

3 steps to reach the goal.

  1. Understanding data.
  2. Preparing data.
  3. Building model.

Step 1: Understanding data.

The basic assumption is to use the following 5 entities to tag ingredients of a recipe.

  1. Quantity (QTY)
  2. Unit (UNIT)
  3. Comment (COM)
  4. Name (NAME)
  5. Others (OTHERS)

For example,

Ingredient Quantity Unit Comment Name Others
2 tablespoons of soya sauce 2 tablespoons NA soya, sauce of
Onions sliced and fried brown 3 medium 3 NA sliced, brown, fried onions and
3 Finely chopped Green Chillies 3 NA finely, chopped, green chillies NA

Similarly most of the ingredients shared in recipes can be tagged with these 5 labels.

Step 2: Preparing data.

Preparing data involves the following steps

  1. Collecting data
  2. POS tagging
  3. Labeling tokens
  4. Chunking

A simple script to politely scrape data from any recipe site will do the job. Checkout Scrapy.

I’ve collected data in the following format.

The actual input file is a JSON Lines file.

A three column tab separated file is required for chunking.

  • Column 1 – Token
  • Column 2 – POS tag
  • Column 3 – Label (done manually)

Each token in a ingredient list gets a line in the TSV file and a new line is left to separate ingredients.
The following script generates data in required format taking the JSON lines file mentioned above as input.

$ cat recipes.jl | python crf_input_generator.py > token_pos.tsv

Note that XXX is just a place holder, which will be replaced by the actual label (i.e. one of QTY, UNIT, COM, NAME, OTHERS).
I’ve manually labeled each token with the help of OpenRefine, Skip this step if you are tagging using a model that is already available.
In the end the file should look similar to table shown below.

Next task is chunking and it is explained well here.
The same POS and token position features discussed in the tutorial are used as features in this experiment as well,So using the util script provided in the CRFSuite repository we can generate chunks.

$ cat token_pos_tagged.tsv | python ~/workspace/crfsuite/example/chunking.py -s $'\t' > chunk.txt 

After chunking the final output file should look similar to this.

Step 3: Building model

To train

$ crfsuite learn -m <model_name> <chunk_file>

To test

$ crfsuite tag -qt -m <model_name> <chunk_file>

To tag

$ crfsuite tag -m <model_name> <chunk_file>

TL;DR

I’ve collected 2000 recipes out of which 60% is used for training and 40% is used for testing.

Each ingredient is tokenized, POS tagged and manually labeled (hardest part).
Following are the input, intermediate and output files.

  • recipes.jl – a JSON lines file containing 2000 recipes. Input file
  • token_pos.tsv – Intermediate TSV file with token and its POS. (column with XXX is a place holder for next step)
  • token_pos_tagged.tsv – TSV file with token, pos and label columns, after tagging 3rd column manually.
  • train.txt – 60% of input, chunked, for training
  • test.txt – 40% of input, chunked, for testing
  • recipe.model – model output
$ cat recipes.jl | python crf_input_generator.py > token_pos.tsv

Intermediate step: Manually label tokens and generate token_pos_tagged.tsv

$ cat token_pos_tagged.tsv | python ~/workspace/crfsuite/example/chunking.py > chunk.txt

Intermediate step: split chunk.txt in 60/40 ratio to get train.txt and test.txt respectively

Training

$ crfsuite learn -m recipes.model train.txt

Testing

$ crfsuite tag -qt -m recipes.model test.txt

Performance by label (#match, #model, #ref) (precision, recall, F1):
    QTY: (7307, 7334, 7338) (0.9963, 0.9958, 0.9960)
    UNIT: (3944, 4169, 4091) (0.9460, 0.9641, 0.9550)
    COM: (5014, 5281, 5505) (0.9494, 0.9108, 0.9297)
    NAME: (11943, 12760, 12221) (0.9360, 0.9773, 0.9562)
    OTHER: (6984, 7094, 7483) (0.9845, 0.9333, 0.9582)
Macro-average precision, recall, F1: (0.962451, 0.956244, 0.959025)
Item accuracy: 35192 / 36638 (0.9605)
Instance accuracy: 6740 / 7854 (0.8582)
Elapsed time: 0.328684 [sec] (23895.3 [instance/sec])

Note: -qt option will work only with labeled data.

Precision 96%
Recall 95%
F1 Measure 95%

Read more about precision, recall and F1 measure here

To tag ingredients that the model has never seen before, follow Step 2 and run the following command

Tagging

$ crfsuite tag -m recipes.model test.txt

code and data here


Setting up python development environment with buildout

Attn: Checkout Conda before trying this.

Buildout is a Python-based build system for creating, assembling and deploying applications from multiple parts, some of which may be non-Python-based. It lets you create a buildout configuration and reproduce the same software later.
buildout.org

I’ve documented the steps required to create a simple buildout based project.

  1. Start by creating a project directory and initialise a virtual environment inside the project directory.

    $ mkdir word_count_buildout && cd word_count_buildout
    $ virtualenv --no-site-packages .env
    $ source .env/bin/activate
    
  2. Fetch bootstrap-buildout.py. A common script required to create necessary directories and eggs (like setuptools, etc. )

     
    $ wget https://bootstrap.pypa.io/bootstrap-buildout.py
    
  3. Create a buildout configuration file

    $ vi buildout.cfg
    

    copy & paste the following snippet

    [buildout]
    develop = .
    parts = job job-test scripts zipeggs
    # index = http://mypypicloud.example.com:6543/pypi/
    
    [job]
    recipe = zc.recipe.egg
    interpreter = python
    eggs = wordcount-job
    
    [job-test]
    recipe = pbp.recipe.noserunner
    eggs = pbp.recipe.noserunner
    working-directory = ${buildout:directory}
    
    [scripts]
    recipe = zc.recipe.egg:scripts
    eggs = dumbo
    
    [zipeggs]
    recipe = zipeggs:zipeggs
    target = dist 
    source = eggs
    

    basic config file structure explained here.

    • ln:4 index If using private PyPi, uncomment and replace the URL.
    • ln:21 recipe = zipeggs:zipeggs – a buildout recipe to zip all flattened/unzipped eggs, flattened/unzipped eggs are convenient while developing (for debugging purpose) and they load faster. Dumbo requires zipped eggs to be passed via -libegg param, zipeggs recipe can generate zipped eggs under target directory (dist). more details @tamizhgeek repo.
  4. Execute the bootstrap file

    $ python bootstrap-buildout.py
    
    Downloading https://pypi.python.org/packages/source/s/setuptools/setuptools-18.1.zip
    Extracting in /tmp/tmpZ7ki33
    Now working in /tmp/tmpZ7ki33/setuptools-18.1
    Building a Setuptools egg in /tmp/bootstrap-q4Xfc5
    /tmp/bootstrap-q4Xfc5/setuptools-18.1-py2.7.egg
    Creating directory '/home/raj/workspace/word_count/eggs'.
    Creating directory '/home/raj/workspace/word_count/bin'.
    Creating directory '/home/raj/workspace/word_count/parts'.
    Creating directory '/home/raj/workspace/word_count/develop-eggs'.
    Generated script '/home/raj/workspace/word_count/bin/buildout'.
    

    The script has created few directories and a buildout script inside bin directory. Read more about the directory structure here

  5. Create setup.py.

    $ vi setup.py
    
    from setuptools import setup, find_packages
    import os
    version = os.environ.get("PIPELINE_LABEL", "1.0")
    setup(
        name="wordcount-job",
        version=version,
        packages=find_packages(),
        zip_safe=True,
        install_requires=[
            'dumbo'
        ]
    )
    

    Read more about setuptools here.
    Any changes to setup.py and buildout.cfg should be followed by executing ./bin/buildout

  6. Create a python module for a simple word-count dumbo job.

    $ mkdir wordcount-job && touch wordcount-job/__init__.py
    $ vi wordcount-job/wordcount.py
    

    copy & paste the following code.

    def mapper(key, value):
        for word in value.split():
            yield word, 1
    
    def reducer(key, values):
        yield key, sum(values)
    
    if __name__ == "__main__":
        import dumbo
        dumbo.run(mapper, reducer)
    
  7. Finally, run the buildout script to fetch artifacts from private or central pypi

    $ ./bin/buildout
    

    All the dependencies (and its dependencies) mentioned in setup.py are collected under eggs/ directory. Zipped eggs are available under dist/ directory and executable scripts with dependencies wired are generated under bin directory. Try viewing the contents of bin/dumbo.

  8. To run the job

    $ ./bin/dumbo start wordcount-job/wordcount.py -input /tmp/input -output /tmp/output 
    

    wordcount.py can access all the dependencies (under eggs/ directory) mentioned in setup.py.

  9. To run tests.

    $ ./bin/job-test
    

    read more about pbp.recipe.noserunner recipe and nose

  10. To build egg

    $ ./bin/buildout setup . bdist_egg
    
  11. Private PyPi repos can also be used to distribute python eggs. To publish an egg to a private pypi, create config for pypicloud under home directory

    $ cd $HOME && vi .pypirc
    

    copy & paste the following

    [distutils]
    index-servers = my-pypi
    &nbsp;
    [my-pypi]
    repository: http://mypypicloud.example.com:6543/pypi/
    username: username
    password: password
    

    Under project working directory

    $ ./bin/buildout setup . bdist_egg upload -r my-pypi
    
  12. Source code
  13. Beer to bud @azhaguselvan for support and stuff.


Locality sensitive hashing (LSH) – Map-Reduce in Python

I’d try to explain LSH with help of python code and map-reduce technique.

It is said that There is a remarkable connection between minhashing and Jaccard similarity of the sets that are minhashed. [Chapter 3, 3.3.3 Mining of massive datasets]

Jaccard similarity

jaccard-index j = a intersection b / a union b

Where a and b are sets.
J = 0 if A and B are disjoint
J = 1 if A and B are identical

example,

>>> a = {'nike', 'running', 'shoe'}
>>> b = {'nike', 'black', 'running', 'shoe'}
>>> c = {'nike', 'blue', 'jacket'}
>>> float(len(a.intersection(b))) / len(a.union(b))
0.75 			# a and b are similar.				
>>> float(len(a.intersection(c))) / len(a.union(c))
0.2				# a and c are... meh..

Minhashing

Probability of collision is higher for similar sets.

Table 1: Matrix representation of sets

keyword x a b c
nike 1 1 1 1
running 2 1 1 0
shoe 3 1 1 0
black 4 0 1 0
blue 5 0 0 1
jacket 6 0 0 1

Table 2: Signature Matrix with hash values

Hash Function a b c
h1(x) = x + 1 mod 6 min(2,3,4) min(2,3,4,5) min(2,0,1)
h2(x) = 3x + 1 mod 6 min(4,1,4) min(4,1,4,1) min(4,4,1)

which becomes,

Table 3: Signature matrix with minhash values

Hash Function a b c
h1(x) = x + 1 mod 6 2 2 0
h2(x) = 3x + 1 mod 6 1 1 1

From Table 3 We can infer that set a and b are similar.
Similarity of a and b from Table 1 is 3/4 = 0.75
From signature matrix Table 3 similarity of a and b is 2/2 = 1

The fraction from signature matrix Table 3 is just an estimate of the true jaccard similarity. on a larger set the estimates will be close.

Map-Reduce

Mapper

sample_dict.txt will have word to id mapping.

  • for every line in input file
    • split text and convert to array of ids using the word to id mapping file.
    • for every id compute minimum hash value
    • split the array of min hash values into multiple equally sized chunks a.k.a, bands.
    • ¬†assign id to bands and emit hash of band, band-id and doc-id

Reducer

  • group by band-hash and band-id to get list of similar doc-ids.

Mapper Code

# lsh_mapper.py
__author__ = 'raj'
import sys
from random import randrange

word_ids = dict()
num_hashes = 10
num_per_band = 2

# a_hash and b_hash cannot be generated on the fly if running in a distributed env. they should be same across all nodes 
a_hash = [randrange(sys.maxint) for _ in xrange(0, num_hashes)]
b_hash = [randrange(sys.maxint) for _ in xrange(0, num_hashes)]


def min_hash_fn(a, b, sig):
    hashes = [((a * x) + b) % len(word_ids) for x in sig]
    return min(hashes)


def get_min_hash_row(sig):
    hashes = [min_hash_fn(a, b, sig) for a, b in zip(a_hash, b_hash)]
    return hashes


def get_band(l, n):
    for i in xrange(0, len(l), n):
        yield frozenset(l[i:i+n])


for word, wid in map(lambda x: x.split(), open(&quot;sample_dict.txt&quot;).readlines()):
    word_ids[word] = int(wid)

for doc_id, doc in enumerate(sys.stdin):
    words = doc.strip().lower().split()

    signature = map(lambda x: word_ids.get(x), words)
    signature = filter(lambda x: x is not None, signature)

    min_hash_row = get_min_hash_row(signature)

    banded = get_band(min_hash_row, num_per_band)

    for band_id, band in enumerate(banded):
        print &quot;%d\t%d\t%d&quot; % (band_id, hash(band), doc_id)

Reducer Code

#lsh_reducre.py
__author__ = 'raj'

import sys

prev_band_id, prev_band_hash = None, None
cluster = []
cid = 0

for line in sys.stdin:
    band_id, band_hash, doc_id = line.strip().split(&quot;\t&quot;, 3)

    if prev_band_id is None and prev_band_hash is None:
        prev_band_id, prev_band_hash = band_id, band_hash

    if prev_band_id is band_id:
        if prev_band_hash == band_hash:
            cluster.append(doc_id)
        else:
            print cid, cluster
            cluster = [doc_id]
    else:
        print cid, cluster
        cluster = [doc_id]
        cid += 1
    prev_band_id, prev_band_hash = band_id, band_hash

In action

sample_input.txt

You & Me 1-14 inch Doll Piece Outfit - Teal Corduroys with Top white
You & Me 12- 14 inch 2-Piece Doll Fashion Outfit - Polka Dot Denim Dress Jumper with White Shirt
You & Me 1-14 inch Doll Piece Fashion Outfit - Flower Dress and Leggings pink
Corduroy Shorts - Flat Front (For Men) SLATE BLUE
Nike Airmax Running SHoe
Corduroy Shorts - Flat Front (For Men) BEIGE
Nokia Lumia 721
Corduroy Shorts - Flat Front (For Men) BROWN

sample_dict.txt

&	1
(for	2
-0	3
1-14	4
12-	5
14	6
2-piece	7
721	8
airmax	9
and	10
beige	11
blue	12
brown	13
corduroy	14
corduroys	15
denim	16
doll	17
dot	18
dress	19
fashion	20
flat	21
flower	22
front	23
inch	24
jumper	25
leggings	26
lumia	27
me	28
men)	29
nike	30
nokia	31
outfit	32
piece	33
pink	34
polka	35
running	36
shirt	37
shoe	38
shorts	39
slate	40
teal	41
top	42
white	43
with	44
you	45
-	46

Command

$ cat sample_input.txt | python lsh_mapper.py | sort | python lsh_reducer.py

Output

0 ['1', '2']
0 ['0']
0 ['5', '7']
0 ['6']
0 ['3']
0 ['4']
1 ['4']
1 ['6']
1 ['0']
1 ['2']
1 ['3', '5', '7']
1 ['1']
2 ['6']
2 ['4']
2 ['0', '1', '2']
2 ['3', '5', '7']
3 ['6']
3 ['3', '5', '7']
3 ['0', '1', '2']
3 ['4']
4 ['0', '1']
4 ['3']
4 ['5']
4 ['4']
4 ['2']
4 ['7']

resolved output

band 0
------
You & Me 12- 14 inch 2-Piece Doll Fashion Outfit - Polka Dot Denim Dress Jumper with White Shirt
You & Me 1-14 inch Doll Piece Fashion Outfit - Flower Dress and Leggings pink

Corduroy Shorts - Flat Front (For Men) BEIGE
Corduroy Shorts - Flat Front (For Men) BROWN

band 1
------
Corduroy Shorts - Flat Front (For Men) SLATE BLUE
Corduroy Shorts - Flat Front (For Men) BEIGE
Corduroy Shorts - Flat Front (For Men) BROWN

band 2
------
You & Me 1-14 inch Doll Piece Outfit - Teal Corduroys with Top white
You & Me 12- 14 inch 2-Piece Doll Fashion Outfit - Polka Dot Denim Dress Jumper with White Shirt
You & Me 1-14 inch Doll Piece Fashion Outfit - Flower Dress and Leggings pink

Corduroy Shorts - Flat Front (For Men) SLATE BLUE
Corduroy Shorts - Flat Front (For Men) BEIGE
Corduroy Shorts - Flat Front (For Men) BROWN

band 3
------
Corduroy Shorts - Flat Front (For Men) SLATE BLUE
Corduroy Shorts - Flat Front (For Men) BEIGE
Corduroy Shorts - Flat Front (For Men) BROWN

You & Me 1-14 inch Doll Piece Outfit - Teal Corduroys with Top white
You & Me 12- 14 inch 2-Piece Doll Fashion Outfit - Polka Dot Denim Dress Jumper with White Shirt
You & Me 1-14 inch Doll Piece Fashion Outfit - Flower Dress and Leggings pink

band 4
------
You & Me 1-14 inch Doll Piece Outfit - Teal Corduroys with Top white
You & Me 12- 14 inch 2-Piece Doll Fashion Outfit - Polka Dot Denim Dress Jumper with White Shirt

code here