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"""Classes for supporting subject corpora expressed as directories or files""" |
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import glob |
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import os.path |
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import annif.util |
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import numpy as np |
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from annif import logger |
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from .types import Subject, SubjectCorpus |
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from .convert import SubjectToDocumentCorpusMixin |
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class SubjectDirectory(SubjectCorpus, SubjectToDocumentCorpusMixin): |
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"""A subject corpus in the form of a directory with .txt files.""" |
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def __init__(self, path): |
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self.path = path |
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self._filenames = sorted(glob.glob(os.path.join(path, '*.txt'))) |
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@property |
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def subjects(self): |
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for filename in self._filenames: |
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with open(filename, encoding='utf-8') as subjfile: |
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uri, label = subjfile.readline().strip().split(' ', 1) |
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text = ' '.join(subjfile.readlines()) |
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yield Subject(uri=uri, label=label, text=text) |
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class SubjectFileTSV(SubjectCorpus, SubjectToDocumentCorpusMixin): |
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"""A subject corpus stored in a TSV file.""" |
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def __init__(self, path): |
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self.path = path |
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@property |
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def subjects(self): |
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with open(self.path, encoding='utf-8') as subjfile: |
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for line in subjfile: |
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uri, label = line.strip().split(None, 1) |
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clean_uri = annif.util.cleanup_uri(uri) |
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yield Subject(uri=clean_uri, label=label, text=None) |
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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, corpus): |
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"""Initialize the subject index from a subject corpus.""" |
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self._uris = [] |
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self._labels = [] |
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self._uri_idx = {} |
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self._label_idx = {} |
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for subject_id, subject in enumerate(corpus.subjects): |
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self._uris.append(subject.uri) |
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self._labels.append(subject.label) |
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self._uri_idx[subject.uri] = subject_id |
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self._label_idx[subject.label] = subject_id |
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def __len__(self): |
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return len(self._uris) |
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def __getitem__(self, subject_id): |
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return (self._uris[subject_id], self._labels[subject_id]) |
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def by_uri(self, uri): |
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"""return the subject index of a subject by its URI""" |
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try: |
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return self._uri_idx[uri] |
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except KeyError: |
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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): |
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"""return the subject index of a subject by its label""" |
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try: |
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return self._label_idx[label] |
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except KeyError: |
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logger.warning('Unknown subject label "%s"', label) |
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return None |
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81
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def uris_to_labels(self, uris): |
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"""return a list of labels corresponding to the given URIs; unknown |
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URIs are ignored""" |
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return [self[subject_id][1] |
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for subject_id in (self.by_uri(uri) for uri in uris) |
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if subject_id is not None] |
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def labels_to_uris(self, labels): |
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"""return a list of URIs corresponding to the given labels; unknown |
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labels are ignored""" |
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return [self[subject_id][0] |
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for subject_id in (self.by_label(label) for label in labels) |
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if subject_id is not None] |
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def save(self, path): |
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"""Save this subject index into a file.""" |
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with open(path, 'w', encoding='utf-8') as subjfile: |
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for subject_id in range(len(self)): |
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line = "<{}>\t{}".format( |
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self._uris[subject_id], self._labels[subject_id]) |
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print(line, file=subjfile) |
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@classmethod |
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def load(cls, path): |
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"""Load a subject index from a TSV file and return it.""" |
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corpus = SubjectFileTSV(path) |
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return cls(corpus) |
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112
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113
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114
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class SubjectSet: |
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"""Represents a set of subjects for a document.""" |
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117
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def __init__(self, subj_data=None): |
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"""Create a SubjectSet and optionally initialize it from a tuple |
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(URIs, labels)""" |
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uris, labels = subj_data or ([], []) |
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self.subject_uris = set(uris) |
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self.subject_labels = set(labels) |
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125
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@classmethod |
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def from_string(cls, subj_data): |
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sset = cls() |
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for line in subj_data.splitlines(): |
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sset._parse_line(line) |
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return sset |
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def _parse_line(self, line): |
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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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self.subject_uris.add(val[1:-1]) |
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continue |
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self.subject_labels.add(val) |
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return |
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def has_uris(self): |
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"""returns True if the URIs for all subjects are known""" |
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return len(self.subject_uris) >= len(self.subject_labels) |
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148
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def as_vector(self, subject_index): |
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"""Return the hits as a one-dimensional NumPy array in sklearn |
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multilabel indicator format, using a subject index as the source |
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of subjects.""" |
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153
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vector = np.zeros(len(subject_index), dtype=np.int8) |
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if self.has_uris(): |
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for uri in self.subject_uris: |
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subject_id = subject_index.by_uri(uri) |
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if subject_id is not None: |
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vector[subject_id] = 1 |
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else: |
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for label in self.subject_labels: |
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subject_id = subject_index.by_label(label) |
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if subject_id is not None: |
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vector[subject_id] = 1 |
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return vector |
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NatLibFi /
Annif
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