Skip to main content

Hidden alignment conditional random field, a discriminative string edit distance

Project description

Hidden alignment conditional random field for classifying string pairs - a learnable edit distance.

This package aims to implement the HACRF machine learning model with a sklearn-like interface. It includes ways to fit a model to training examples and score new example.

The model takes string pairs as input and classify them into any number of classes. In McCallum’s original paper the model was applied to the database deduplication problem. Each database entry was paired with every other entry and the model then classified whether the pair was a ‘match’ or a ‘mismatch’ based on training examples of matches and mismatches.

I also tried to use it as learnable string edit distance for normalizing noisy text. See A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance by McCallum, Bellare, and Pereira, and the report Conditional Random Fields for Noisy text normalisation by Dirko Coetsee.

Example

from pyhacrf import StringPairFeatureExtractor, Hacrf

training_X = [('helloooo', 'hello'), # Matching examples
              ('h0me', 'home'),
              ('krazii', 'crazy'),
              ('non matching string example', 'no really'), # Non-matching examples
              ('and another one', 'yep')]
training_y = ['match',
              'match',
              'match',
              'non-match',
              'non-match']

# Extract features
feature_extractor = StringPairFeatureExtractor(match=True, numeric=True)
training_X_extracted = feature_extractor.fit_transform(training_X)

# Train model
model = Hacrf(l2_regularization=1.0)
model.fit(training_X_extracted, training_y)

# Evaluate
from sklearn.metrics import confusion_matrix
predictions = model.predict(training_X_extracted)

print(confusion_matrix(training_y, predictions))
> [[0 3]
>  [2 0]]

print(model.predict_proba(training_X_extracted))
> [[ 0.94914812  0.05085188]
>  [ 0.92397711  0.07602289]
>  [ 0.86756034  0.13243966]
>  [ 0.05438812  0.94561188]
>  [ 0.02641275  0.97358725]]

Dependencies

This package depends on numpy. The LBFGS optimizer in pylbfgs is used, but alternative optimizers can be passed.

Install

Install by running:

python setup.py install

or from pypi:

pip install pyhacrf

Developing

Clone from repository, then

pip install -r requirements-dev.txt
cython pyhacrf/*.pyx
python setup.py install

To deploy to pypi, make sure you have compiled the *.pyx files to *.c

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyhacrf-0.1.2.tar.gz (134.1 kB view details)

Uploaded Source

File details

Details for the file pyhacrf-0.1.2.tar.gz.

File metadata

  • Download URL: pyhacrf-0.1.2.tar.gz
  • Upload date:
  • Size: 134.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pyhacrf-0.1.2.tar.gz
Algorithm Hash digest
SHA256 696e08162e772fc624eebd7b2772b0f48063ae0f1340ff2b30982aae6216d6d9
MD5 1706f33f7be3de7fe018d286a2f9db70
BLAKE2b-256 256443561938fd81545aab1a4a2fda7bf54e3ed9fdc5d75c6806bdce67b5b25d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page