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"""Classes for supporting subject corpora expressed as directories or files""" |
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import csv |
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import numpy as np |
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import annif.util |
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import os.path |
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from annif import logger |
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from .types import Subject, SubjectCorpus |
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from .skos import serialize_subjects_to_skos |
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View Code Duplication |
class SubjectFileTSV(SubjectCorpus): |
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"""A monolingual subject vocabulary stored in a TSV file.""" |
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def __init__(self, path, language): |
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"""initialize the SubjectFileTSV given a path to a TSV file and the |
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language of the vocabulary""" |
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self.path = path |
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self.language = language |
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def _parse_line(self, line): |
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vals = line.strip().split('\t', 2) |
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clean_uri = annif.util.cleanup_uri(vals[0]) |
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label = vals[1] if len(vals) >= 2 else None |
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labels = {self.language: label} if label else None |
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notation = vals[2] if len(vals) >= 3 else None |
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yield Subject(uri=clean_uri, |
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labels=labels, |
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notation=notation) |
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@property |
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def languages(self): |
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return [self.language] |
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@property |
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def subjects(self): |
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with open(self.path, encoding='utf-8-sig') as subjfile: |
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for line in subjfile: |
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yield from self._parse_line(line) |
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def save_skos(self, path): |
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"""Save the contents of the subject vocabulary into a SKOS/Turtle |
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file with the given path name.""" |
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serialize_subjects_to_skos(self.subjects, path) |
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View Code Duplication |
class SubjectFileCSV(SubjectCorpus): |
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"""A multilingual subject vocabulary stored in a CSV file.""" |
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def __init__(self, path): |
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"""initialize the SubjectFileCSV given a path to a CSV file""" |
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self.path = path |
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def _parse_row(self, row): |
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labels = { |
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fname.replace('label_', ''): value or None |
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for fname, value in row.items() |
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if fname.startswith('label_') |
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} |
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# if there are no labels in any language, set labels to None |
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# indicating a deprecated subject |
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if set(labels.values()) == {None}: |
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labels = None |
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yield Subject(uri=annif.util.cleanup_uri(row['uri']), |
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labels=labels, |
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notation=row.get('notation', None) or None) |
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@property |
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def languages(self): |
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# infer the supported languages from the CSV column names |
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with open(self.path, encoding='utf-8-sig') as csvfile: |
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reader = csv.reader(csvfile) |
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fieldnames = next(reader, None) |
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return [fname.replace('label_', '') |
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for fname in fieldnames |
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if fname.startswith('label_')] |
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@property |
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def subjects(self): |
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with open(self.path, encoding='utf-8-sig') as csvfile: |
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reader = csv.DictReader(csvfile) |
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for row in reader: |
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yield from self._parse_row(row) |
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def save_skos(self, path): |
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"""Save the contents of the subject vocabulary into a SKOS/Turtle |
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file with the given path name.""" |
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serialize_subjects_to_skos(self.subjects, path) |
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@staticmethod |
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def is_csv_file(path): |
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"""return True if the path looks like a CSV file""" |
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return os.path.splitext(path)[1].lower() == '.csv' |
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View Code Duplication |
class SubjectIndex: |
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"""An index that remembers the associations between integers subject IDs |
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and their URIs and labels.""" |
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def __init__(self): |
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self._subjects = [] |
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self._uri_idx = {} |
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self._label_idx = {} |
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self._languages = None |
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def load_subjects(self, corpus): |
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"""Initialize the subject index from a subject corpus""" |
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self._languages = corpus.languages |
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for subject in corpus.subjects: |
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self.append(subject) |
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def __len__(self): |
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return len(self._subjects) |
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@property |
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def languages(self): |
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return self._languages |
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def __getitem__(self, subject_id): |
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return self._subjects[subject_id] |
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def append(self, subject): |
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if self._languages is None and subject.labels is not None: |
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self._languages = list(subject.labels.keys()) |
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subject_id = len(self._subjects) |
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self._uri_idx[subject.uri] = subject_id |
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if subject.labels: |
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for lang, label in subject.labels.items(): |
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self._label_idx[(label, lang)] = subject_id |
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self._subjects.append(subject) |
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def contains_uri(self, uri): |
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return uri in self._uri_idx |
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def by_uri(self, uri, warnings=True): |
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"""return the subject ID of a subject by its URI, or None if not found. |
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If warnings=True, log a warning message if the URI cannot be found.""" |
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try: |
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return self._uri_idx[uri] |
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except KeyError: |
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if warnings: |
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logger.warning('Unknown subject URI <%s>', uri) |
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return None |
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def by_label(self, label, language): |
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"""return the subject ID of a subject by its label in a given |
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language""" |
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try: |
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return self._label_idx[(label, language)] |
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except KeyError: |
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logger.warning('Unknown subject label "%s"@%s', label, language) |
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return None |
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def deprecated_ids(self): |
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"""return indices of deprecated subjects""" |
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return [subject_id for subject_id, subject in enumerate(self._subjects) |
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if subject.labels is None] |
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@property |
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def active(self): |
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"""return a list of (subject_id, subject) tuples of all subjects that |
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are not deprecated""" |
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return [(subj_id, subject) |
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for subj_id, subject |
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in enumerate(self._subjects) |
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if subject.labels is not None] |
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def save(self, path): |
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"""Save this subject index into a file with the given path name.""" |
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fieldnames = ['uri', 'notation'] + \ |
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[f'label_{lang}' for lang in self._languages] |
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with open(path, 'w', encoding='utf-8', newline='') as csvfile: |
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames) |
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writer.writeheader() |
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for subject in self: |
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row = {'uri': subject.uri, |
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'notation': subject.notation or ''} |
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if subject.labels: |
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for lang, label in subject.labels.items(): |
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row[f'label_{lang}'] = label |
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writer.writerow(row) |
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@classmethod |
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def load(cls, path): |
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"""Load a subject index from a CSV file and return it.""" |
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corpus = SubjectFileCSV(path) |
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subject_index = cls() |
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subject_index.load_subjects(corpus) |
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return subject_index |
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View Code Duplication |
class SubjectSet: |
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"""Represents a set of subjects for a document.""" |
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def __init__(self, subject_ids=None): |
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"""Create a SubjectSet and optionally initialize it from an iterable |
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of subject IDs""" |
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if subject_ids: |
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# use set comprehension to eliminate possible duplicates |
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self._subject_ids = list({subject_id |
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for subject_id in subject_ids |
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if subject_id is not None}) |
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else: |
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self._subject_ids = [] |
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def __len__(self): |
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return len(self._subject_ids) |
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def __getitem__(self, idx): |
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return self._subject_ids[idx] |
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def __bool__(self): |
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return bool(self._subject_ids) |
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def __eq__(self, other): |
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if isinstance(other, SubjectSet): |
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return self._subject_ids == other._subject_ids |
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return False |
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@classmethod |
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def from_string(cls, subj_data, subject_index, language): |
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subject_ids = set() |
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for line in subj_data.splitlines(): |
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uri, label = cls._parse_line(line) |
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if uri is not None: |
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subject_ids.add(subject_index.by_uri(uri)) |
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else: |
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subject_ids.add(subject_index.by_label(label, language)) |
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return cls(subject_ids) |
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@staticmethod |
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def _parse_line(line): |
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uri = label = None |
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vals = line.split("\t") |
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for val in vals: |
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val = val.strip() |
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if val == '': |
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continue |
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if val.startswith('<') and val.endswith('>'): # URI |
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uri = val[1:-1] |
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continue |
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label = val |
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break |
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return uri, label |
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def as_vector(self, size=None, destination=None): |
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"""Return the hits as a one-dimensional NumPy array in sklearn |
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multilabel indicator format. Use destination array if given (not |
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None), otherwise create and return a new one of the given size.""" |
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if destination is None: |
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destination = np.zeros(size, dtype=bool) |
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destination[list(self._subject_ids)] = True |
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return destination |
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