spaCy/examples/training/train_intent_parser.py

#!/usr/bin/env python
# coding: utf-8
"""Using the parser to recognise your own semantics

spaCy's parser component can be used to trained to predict any type of tree
structure over your input text. You can also predict trees over whole documents
or chat logs, with connections between the sentence-roots used to annotate
discourse structure. In this example, we'll build a message parser for a common
"chat intent": finding local businesses. Our message semantics will have the
following types of relations: ROOT, PLACE, QUALITY, ATTRIBUTE, TIME, LOCATION.

"show me the best hotel in berlin"
('show', 'ROOT', 'show')
('best', 'QUALITY', 'hotel') --> hotel with QUALITY best
('hotel', 'PLACE', 'show') --> show PLACE hotel
('berlin', 'LOCATION', 'hotel') --> hotel with LOCATION berlin

Compatible with: spaCy v2.0.0+
"""
from __future__ import unicode_literals, print_function

import plac
import random
from pathlib import Path
import spacy
from spacy.util import minibatch, compounding


# training data: texts, heads and dependency labels
# for no relation, we simply chose an arbitrary dependency label, e.g. '-'
TRAIN_DATA = [
    (
        "find a cafe with great wifi",
        {
            "heads": [0, 2, 0, 5, 5, 2],  # index of token head
            "deps": ["ROOT", "-", "PLACE", "-", "QUALITY", "ATTRIBUTE"],
        },
    ),
    (
        "find a hotel near the beach",
        {
            "heads": [0, 2, 0, 5, 5, 2],
            "deps": ["ROOT", "-", "PLACE", "QUALITY", "-", "ATTRIBUTE"],
        },
    ),
    (
        "find me the closest gym that's open late",
        {
            "heads": [0, 0, 4, 4, 0, 6, 4, 6, 6],
            "deps": [
                "ROOT",
                "-",
                "-",
                "QUALITY",
                "PLACE",
                "-",
                "-",
                "ATTRIBUTE",
                "TIME",
            ],
        },
    ),
    (
        "show me the cheapest store that sells flowers",
        {
            "heads": [0, 0, 4, 4, 0, 4, 4, 4],  # attach "flowers" to store!
            "deps": ["ROOT", "-", "-", "QUALITY", "PLACE", "-", "-", "PRODUCT"],
        },
    ),
    (
        "find a nice restaurant in london",
        {
            "heads": [0, 3, 3, 0, 3, 3],
            "deps": ["ROOT", "-", "QUALITY", "PLACE", "-", "LOCATION"],
        },
    ),
    (
        "show me the coolest hostel in berlin",
        {
            "heads": [0, 0, 4, 4, 0, 4, 4],
            "deps": ["ROOT", "-", "-", "QUALITY", "PLACE", "-", "LOCATION"],
        },
    ),
    (
        "find a good italian restaurant near work",
        {
            "heads": [0, 4, 4, 4, 0, 4, 5],
            "deps": [
                "ROOT",
                "-",
                "QUALITY",
                "ATTRIBUTE",
                "PLACE",
                "ATTRIBUTE",
                "LOCATION",
            ],
        },
    ),
]


@plac.annotations(
    model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
    output_dir=("Optional output directory", "option", "o", Path),
    n_iter=("Number of training iterations", "option", "n", int),
)
def main(model=None, output_dir=None, n_iter=15):
    """Load the model, set up the pipeline and train the parser."""
    if model is not None:
        nlp = spacy.load(model)  # load existing spaCy model
        print("Loaded model '%s'" % model)
    else:
        nlp = spacy.blank("en")  # create blank Language class
        print("Created blank 'en' model")

    # We'll use the built-in dependency parser class, but we want to create a
    # fresh instance – just in case.
    if "parser" in nlp.pipe_names:
        nlp.remove_pipe("parser")
    parser = nlp.create_pipe("parser")
    nlp.add_pipe(parser, first=True)

    for text, annotations in TRAIN_DATA:
        for dep in annotations.get("deps", []):
            parser.add_label(dep)

    with nlp.select_pipes(enable="parser"):  # only train parser
        optimizer = nlp.begin_training()
        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
            for batch in batches:
                nlp.update(batch, sgd=optimizer, losses=losses)
            print("Losses", losses)

    # test the trained model
    test_model(nlp)

    # save model to output directory
    if output_dir is not None:
        output_dir = Path(output_dir)
        if not output_dir.exists():
            output_dir.mkdir()
        nlp.to_disk(output_dir)
        print("Saved model to", output_dir)

        # test the saved model
        print("Loading from", output_dir)
        nlp2 = spacy.load(output_dir)
        test_model(nlp2)


def test_model(nlp):
    texts = [
        "find a hotel with good wifi",
        "find me the cheapest gym near work",
        "show me the best hotel in berlin",
    ]
    docs = nlp.pipe(texts)
    for doc in docs:
        print(doc.text)
        print([(t.text, t.dep_, t.head.text) for t in doc if t.dep_ != "-"])


if __name__ == "__main__":
    plac.call(main)

    # Expected output:
    # find a hotel with good wifi
    # [
    #   ('find', 'ROOT', 'find'),
    #   ('hotel', 'PLACE', 'find'),
    #   ('good', 'QUALITY', 'wifi'),
    #   ('wifi', 'ATTRIBUTE', 'hotel')
    # ]
    # find me the cheapest gym near work
    # [
    #   ('find', 'ROOT', 'find'),
    #   ('cheapest', 'QUALITY', 'gym'),
    #   ('gym', 'PLACE', 'find'),
    #   ('near', 'ATTRIBUTE', 'gym'),
    #   ('work', 'LOCATION', 'near')
    # ]
    # show me the best hotel in berlin
    # [
    #   ('show', 'ROOT', 'show'),
    #   ('best', 'QUALITY', 'hotel'),
    #   ('hotel', 'PLACE', 'show'),
    #   ('berlin', 'LOCATION', 'hotel')
    # ]