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"""Annif backend using the Vorpal Wabbit multiclass and multilabel |
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classifiers""" |
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import random |
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import os.path |
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import annif.util |
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from vowpalwabbit import pyvw |
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import numpy as np |
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from annif.hit import AnalysisHit, VectorAnalysisResult |
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from annif.exception import ConfigurationException, NotInitializedException |
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from . import backend |
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from . import mixins |
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class VWMultiBackend(mixins.ChunkingBackend, backend.AnnifBackend): |
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"""Vorpal Wabbit multiclass/multilabel backend for Annif""" |
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name = "vw_multi" |
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needs_subject_index = True |
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VW_PARAMS = { |
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# each param specifier is a pair (allowed_values, default_value) |
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# where allowed_values is either a type or a list of allowed values |
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# and default_value may be None, to let VW decide by itself |
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'bit_precision': (int, None), |
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'learning_rate': (float, None), |
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'loss_function': (['squared', 'logistic', 'hinge'], 'logistic'), |
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'l1': (float, None), |
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'l2': (float, None), |
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'passes': (int, None) |
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} |
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MODEL_FILE = 'vw-model' |
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TRAIN_FILE = 'vw-train.txt' |
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# defaults for uninitialized instances |
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_model = None |
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def initialize(self): |
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if self._model is None: |
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path = os.path.join(self._get_datadir(), self.MODEL_FILE) |
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self.debug('loading VW model from {}'.format(path)) |
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if os.path.exists(path): |
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self._model = pyvw.vw( |
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i=path, |
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quiet=True, |
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loss_function='logistic', |
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probabilities=True) |
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self.debug('loaded model {}'.format(str(self._model))) |
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else: |
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raise NotInitializedException( |
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'model {} not found'.format(path), |
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backend_id=self.backend_id) |
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@classmethod |
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def _normalize_text(cls, project, text): |
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ntext = ' '.join(project.analyzer.tokenize_words(text)) |
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# colon and pipe chars have special meaning in VW and must be avoided |
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return ntext.replace(':', '').replace('|', '') |
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def _write_train_file(self, examples, filename): |
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with open(filename, 'w') as trainfile: |
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for ex in examples: |
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print(ex, file=trainfile) |
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def _create_train_file(self, corpus, project): |
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self.info('creating VW train file') |
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examples = [] |
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for doc in corpus.documents: |
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text = self._normalize_text(project, doc.text) |
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for uri in doc.uris: |
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subject_id = project.subjects.by_uri(uri) |
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if subject_id is None: |
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continue |
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exstr = '{} | {}'.format(subject_id + 1, text) |
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examples.append(exstr) |
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random.shuffle(examples) |
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annif.util.atomic_save(examples, |
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self._get_datadir(), |
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self.TRAIN_FILE, |
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method=self._write_train_file) |
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def _convert_param(self, param, val): |
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pspec, _ = self.VW_PARAMS[param] |
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if isinstance(pspec, list): |
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if val in pspec: |
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return val |
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raise ConfigurationException( |
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"{} is not a valid value for {} (allowed: {})".format( |
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val, param, ', '.join(pspec)), backend_id=self.backend_id) |
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try: |
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return pspec(val) |
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except ValueError: |
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raise ConfigurationException( |
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"The {} value {} cannot be converted to {}".format( |
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param, val, pspec), backend_id=self.backend_id) |
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def _create_model(self, project): |
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self.info('creating VW model') |
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trainpath = os.path.join(self._get_datadir(), self.TRAIN_FILE) |
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params = {param: defaultval |
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for param, (_, defaultval) in self.VW_PARAMS.items() |
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if defaultval is not None} |
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params.update({param: self._convert_param(param, val) |
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for param, val in self.params.items() |
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if param in self.VW_PARAMS}) |
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self.debug("model parameters: {}".format(params)) |
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self._model = pyvw.vw( |
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oaa=len(project.subjects), |
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probabilities=True, |
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data=trainpath, |
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**params) |
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modelpath = os.path.join(self._get_datadir(), self.MODEL_FILE) |
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self._model.save(modelpath) |
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def train(self, corpus, project): |
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self._create_train_file(corpus, project) |
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self._create_model(project) |
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def _analyze_chunks(self, chunktexts, project): |
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results = [] |
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for chunktext in chunktexts: |
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example = ' | {}'.format(chunktext) |
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results.append(np.array(self._model.predict(example))) |
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return VectorAnalysisResult( |
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np.array(results).mean(axis=0), project.subjects) |
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NatLibFi /
Annif
