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"""Annif backend using the Vowpal 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.suggestion import ListSuggestionResult, VectorSuggestionResult |
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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.AnnifLearningBackend): |
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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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'ngram': (lambda x: '_{}'.format(int(x)), 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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'probabilities': (bool, None) |
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} |
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DEFAULT_ALGORITHM = 'oaa' |
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SUPPORTED_ALGORITHMS = ('oaa', 'ect', 'log_multi', 'multilabel_oaa') |
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DEFAULT_INPUTS = '_text_' |
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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.datadir, self.MODEL_FILE) |
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if not os.path.exists(path): |
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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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self.debug('loading VW model from {}'.format(path)) |
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params = self._create_params({'i': path, 'quiet': True}) |
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if 'passes' in params: |
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# don't confuse the model with passes |
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del params['passes'] |
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self.debug("model parameters: {}".format(params)) |
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self._model = pyvw.vw(**params) |
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self.debug('loaded model {}'.format(str(self._model))) |
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@property |
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def algorithm(self): |
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algorithm = self.params.get('algorithm', self.DEFAULT_ALGORITHM) |
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if algorithm not in self.SUPPORTED_ALGORITHMS: |
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raise ConfigurationException( |
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"{} is not a valid algorithm (allowed: {})".format( |
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algorithm, ', '.join(self.SUPPORTED_ALGORITHMS)), |
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backend_id=self.backend_id) |
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return algorithm |
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@property |
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def inputs(self): |
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inputs = self.params.get('inputs', self.DEFAULT_INPUTS) |
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return inputs.split(',') |
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@staticmethod |
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def _cleanup_text(text): |
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# colon and pipe chars have special meaning in VW and must be avoided |
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return text.replace(':', '').replace('|', '') |
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@staticmethod |
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def _normalize_text(project, text): |
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ntext = ' '.join(project.analyzer.tokenize_words(text)) |
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return VWMultiBackend._cleanup_text(ntext) |
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@staticmethod |
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def _write_train_file(examples, filename): |
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with open(filename, 'w', encoding='utf-8') as trainfile: |
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for ex in examples: |
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print(ex, file=trainfile) |
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@staticmethod |
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def _uris_to_subject_ids(project, uris): |
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subject_ids = [] |
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for uri in uris: |
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subject_id = project.subjects.by_uri(uri) |
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if subject_id is not None: |
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subject_ids.append(subject_id) |
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return subject_ids |
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def _format_examples(self, project, text, uris): |
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subject_ids = self._uris_to_subject_ids(project, uris) |
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if self.algorithm == 'multilabel_oaa': |
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yield '{} {}'.format(','.join(map(str, subject_ids)), text) |
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else: |
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for subject_id in subject_ids: |
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yield '{} {}'.format(subject_id + 1, text) |
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def _get_input(self, input, project, text): |
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if input == '_text_': |
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return self._normalize_text(project, text) |
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else: |
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proj = annif.project.get_project(input) |
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result = proj.suggest(text) |
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features = [ |
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'{}:{}'.format(self._cleanup_text(hit.uri), hit.score) |
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for hit in result.hits] |
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return ' '.join(features) |
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def _inputs_to_exampletext(self, project, text): |
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namespaces = {} |
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for input in self.inputs: |
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inputtext = self._get_input(input, project, text) |
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if inputtext: |
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namespaces[input] = inputtext |
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if not namespaces: |
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return None |
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return ' '.join(['|{} {}'.format(namespace, featurestr) |
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for namespace, featurestr in namespaces.items()]) |
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def _create_examples(self, corpus, project): |
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examples = [] |
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for doc in corpus.documents: |
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text = self._inputs_to_exampletext(project, doc.text) |
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if not text: |
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continue |
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examples.extend(self._format_examples(project, text, doc.uris)) |
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random.shuffle(examples) |
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return examples |
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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 = self._create_examples(corpus, project) |
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annif.util.atomic_save(examples, |
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self.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_params(self, params): |
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params.update({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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return params |
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def _create_model(self, project): |
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self.info('creating VW model (algorithm: {})'.format(self.algorithm)) |
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trainpath = os.path.join(self.datadir, self.TRAIN_FILE) |
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params = self._create_params( |
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{'data': trainpath, self.algorithm: len(project.subjects)}) |
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if params.get('passes', 1) > 1: |
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# need a cache file when there are multiple passes |
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params.update({'cache': True, 'kill_cache': True}) |
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self.debug("model parameters: {}".format(params)) |
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self._model = pyvw.vw(**params) |
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modelpath = os.path.join(self.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 learn(self, corpus, project): |
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for example in self._create_examples(corpus, project): |
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self._model.learn(example) |
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modelpath = os.path.join(self.datadir, self.MODEL_FILE) |
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self._model.save(modelpath) |
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def _convert_result(self, result, project): |
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if self.algorithm == 'multilabel_oaa': |
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# result is a list of subject IDs - need to vectorize |
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mask = np.zeros(len(project.subjects)) |
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mask[result] = 1.0 |
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return mask |
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elif isinstance(result, int): |
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# result is a single integer - need to one-hot-encode |
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mask = np.zeros(len(project.subjects)) |
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mask[result - 1] = 1.0 |
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return mask |
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else: |
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# result is a list of scores (probabilities or binary 1/0) |
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return np.array(result) |
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def _suggest_chunks(self, chunktexts, project): |
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results = [] |
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for chunktext in chunktexts: |
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exampletext = self._inputs_to_exampletext(project, chunktext) |
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if not exampletext: |
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continue |
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example = ' {}'.format(exampletext) |
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result = self._model.predict(example) |
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results.append(self._convert_result(result, project)) |
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if not results: # empty result |
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return ListSuggestionResult( |
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hits=[], subject_index=project.subjects) |
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return VectorSuggestionResult( |
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np.array(results).mean(axis=0), project.subjects) |
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NatLibFi /
Annif
