What's this?

Re-pipe is a reproducible data pipeline (for now targeted at NLP data pipes). It's main benefit is that the whole pipe can be serialized and exactly reproduced, thus making it much more convenient to perform various experiments when developing an NLP model.

Why

A common problem when performing many experiments or doing hyper-parameter search for a model is that it's hard to keep track of what changes led to a specific model and to be able to reproduce it. Re-pipe enables the data transformation pipe to be serialized along with the end model and directly loaded into your production environment as one unit without making any mistakes.

State

Re-pipe is not yet in a clean state and is under development.

Examples

Creating a pipe

import repipe.pipeline as pipeline

pipe = pipeline.Pipeline(
    steps=[
        pipeline.TransformStep(
            in_fields=['short_description','description'],
            out_field='text',
            transform=pipeline.TextFieldUnion()
        ),
        pipeline.TransformStep(
            in_fields='text',
            out_field='text_scrubbed',
            transform=pipeline.TextScrubber(
                lower=True,
                tokenize=False,
                filters='()[]{}<>$&%#|-+=*_"\'’/\\®~¿'
            )
        ),
        pipeline.TransformStep(
            in_fields='company',
            out_field='company_onehot',
            transform=pipeline.OneHotEncoderAdapter(
                sparse=True,
                categories=dataset.company.fillna('').unique(),
            )
        ),
        pipeline.TransformStep(
            in_fields='contact_type',
            out_field='contact_type_onehot',
            transform=pipeline.OneHotEncoderAdapter(
                sparse=True,
                categories=dataset.contact_type.fillna('').unique()                
            )
        ),
        pipeline.TransformStep(
            in_fields='text_scrubbed',
            out_field='word_hashes',
            transform=pipeline.HashingVectorizerAdapter(
                analyzer='word',
                lowercase=True,
                n_features=7500,
                ngram_range=(1,1)            
            )
        ),
        pipeline.TransformStep(
            in_fields='text_scrubbed',
            out_field='char_3grams',
            transform=pipeline.HashingVectorizerAdapter(
                analyzer='char_wb',
                lowercase=True,
                n_features=7500,
                ngram_range=(3,3)
            )
        ),
        pipeline.TransformStep(
            in_fields='text_scrubbed',
            out_field='embeddings',
            transform=pipeline.WordVectorEmbedder(
                path='models/word2vec.model.2019-07-02.bin',
                max_embedding_len=64*2,
                dtype='float16'
            )
        ),
        pipeline.FeatureSelector(
            features=[
                'company_onehot',
                'contact_type_onehot',                
                'word_hashes',
                'char_3grams',
                'embeddings'
            ]
        )
    ]
)

Using the pipe

x_trainT = pipe.transform(x_train)
x_testT = pipe.transform(x_test)

Saving/loading the pipe

import yaml
from repipe.serializable import Serializable
 
# Save pipe
with open(f'my_pipe.yaml', 'w') as f:
    yaml.dump(pipe.to_dict(), f)

# Load pipe
with open(f'my_pipe.yaml', 'r') as f:
    pipe2 = Serializable.load(yaml.load(f))
    
# Use
X = pipe2.transform(dataset)