annif.corpus.document.DocumentFileCSV.documents() - Code Metrics - NatLibFi/Annif - Measure and Improve Code Quality continuously with Scrutinizer

annif.corpus.document.DocumentFileCSV.documents() B
last analyzed 2025-08-29 07:45 UTC

↳ Parent: annif.corpus.document

Complexity

Conditions

Size

Total Lines	23
Code Lines	19

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	6
eloc	19
nop	1
dl	0
loc	23
rs	8.5166
c	0
b	0
f	0

"""Classes for supporting document corpora"""

from __future__ import annotations

import csv
import glob
import gzip
import os.path
import re
from itertools import islice
from typing import TYPE_CHECKING

import annif.util
from annif.exception import OperationFailedException

from .json import json_file_to_document, json_to_document
from .types import Document, DocumentCorpus, SubjectSet

if TYPE_CHECKING:
    from collections.abc import Iterator

    from annif.vocab import SubjectIndex

logger = annif.logger


class DocumentDirectory(DocumentCorpus):
    """A directory of files as a full text document corpus"""

    def __init__(
        self,
        path: str,
        subject_index: SubjectIndex | None = None,
        language: str | None = None,
        require_subjects: bool = False,
    ) -> None:
        self.path = path
        self.subject_index = subject_index
        self.language = language
        self.require_subjects = require_subjects

    def __iter__(self) -> Iterator[str]:
        """Iterate through the directory, yielding file paths with corpus documents."""

        # txt files
        for filename in sorted(glob.glob(os.path.join(self.path, "*.txt"))):
            yield filename

        # json files
        for filename in sorted(glob.glob(os.path.join(self.path, "*.json"))):
            yield filename

    @staticmethod
    def _get_subject_filename(filename: str) -> str | None:
        tsvfilename = re.sub(r"\.txt$", ".tsv", filename)
        if os.path.exists(tsvfilename):
            return tsvfilename

        keyfilename = re.sub(r"\.txt$", ".key", filename)
        if os.path.exists(keyfilename):
            return keyfilename

        return None

    def _read_txt_file(self, filename: str) -> Document | None:
        with open(filename, errors="replace", encoding="utf-8-sig") as docfile:
            text = docfile.read()
        if not self.require_subjects:
            return Document(text=text, subject_set=None, file_path=filename)

        subjfilename = self._get_subject_filename(filename)
        if subjfilename is None:
            # subjects required but not found, skipping this docfile
            return None

        with open(subjfilename, encoding="utf-8-sig") as subjfile:
            subjects = SubjectSet.from_string(
                subjfile.read(), self.subject_index, self.language
            )
        return Document(text=text, subject_set=subjects, file_path=filename)

    @property
    def documents(self) -> Iterator[Document]:
        for docfilename in self:
            if docfilename.endswith(".txt"):
                doc = self._read_txt_file(docfilename)
            else:
                doc = json_file_to_document(
                    docfilename,
                    self.subject_index,
                    self.language,
                    self.require_subjects,
                )

            if doc is not None:
                yield doc


class DocumentFileTSV(DocumentCorpus):
    """A TSV file as a corpus of documents with subjects"""

    def __init__(
        self, path: str, subject_index: SubjectIndex, require_subjects=True
    ) -> None:
        self.path = path
        self.subject_index = subject_index
        self.require_subjects = require_subjects

    @property
    def documents(self) -> Iterator[Document]:
        if self.path.endswith(".gz"):
            opener = gzip.open
        else:
            opener = open
        with opener(self.path, mode="rt", encoding="utf-8-sig") as tsvfile:
            for line in tsvfile:
                yield from self._parse_tsv_line(line)

    def _parse_tsv_line(self, line: str) -> Iterator[Document]:
        if "\t" in line:
            text, uris = line.split("\t", maxsplit=1)
            subject_ids = {
                self.subject_index.by_uri(annif.util.cleanup_uri(uri))
                for uri in uris.split()
            }
            yield Document(text=text, subject_set=SubjectSet(subject_ids))
        else:
            if self.require_subjects:
                logger.warning(
                    'Skipping invalid line (missing tab): "%s"', line.rstrip()
                )
            else:
                yield Document(text=line.strip())


class DocumentFileCSV(DocumentCorpus):
    """A CSV file as a corpus of documents with subjects"""

    def __init__(
        self, path: str, subject_index: SubjectIndex, require_subjects=True
    ) -> None:
        self.path = path
        self.subject_index = subject_index
        self.require_subjects = require_subjects

    @property
    def documents(self) -> Iterator[Document]:
        if self.path.endswith(".gz"):
            opener = gzip.open
        else:
            opener = open
        with opener(self.path, mode="rt", encoding="utf-8-sig") as csvfile:
            reader = csv.DictReader(csvfile)
            if not self._check_fields(reader):
                if self.require_subjects:
                    raise OperationFailedException(
                        f"Cannot parse CSV file {self.path}. "
                        + "The file must have a header row that defines at least "
                        + "the columns 'text' and 'subject_uris'."
                    )
                else:
                    raise OperationFailedException(
                        f"Cannot parse CSV file {self.path}. "
                        + "The file must have a header row that defines at least "
                        + "the column 'text'."
                    )
            for row in reader:
                yield from self._parse_row(row)

    def _parse_row(self, row: dict[str, str]) -> Iterator[Document]:
        if self.require_subjects or (
            self.subject_index is not None and "subject_uris" in row
        ):
            subject_ids = {
                self.subject_index.by_uri(annif.util.cleanup_uri(uri))
                for uri in (row["subject_uris"] or "").strip().split()
            }
        else:
            subject_ids = set()
        metadata = {
            key: val
            for key, val in row.items()
            if key not in ("document_id", "text", "subject_uris")
        }
        yield Document(
            text=(row["text"] or ""),
            subject_set=SubjectSet(subject_ids),
            metadata=metadata,
            document_id=row.get("document_id", None),
        )

    def _check_fields(self, reader: csv.DictReader) -> bool:
        fns = reader.fieldnames
        if self.require_subjects:
            return fns is not None and "text" in fns and "subject_uris" in fns
        else:
            return fns is not None and "text" in fns

    @staticmethod
    def is_csv_file(path: str) -> bool:
        """return True if the path looks like a CSV file"""

        path_lc = path.lower()
        return path_lc.endswith(".csv") or path_lc.endswith(".csv.gz")


class DocumentFileJSONL(DocumentCorpus):
    """A JSON Lines file as a corpus of documents with subjects"""

    def __init__(
        self,
        path: str,
        subject_index: SubjectIndex,
        language: str,
        require_subjects=True,
    ) -> None:
        self.path = path
        self.subject_index = subject_index
        self.language = language
        self.require_subjects = require_subjects

    @property
    def documents(self) -> Iterator[Document]:
        if self.path.endswith(".gz"):
            opener = gzip.open
        else:
            opener = open
        with opener(self.path, mode="rt", encoding="utf-8") as jsonlfile:
            for line in jsonlfile:
                doc = json_to_document(
                    self.path,
                    line,
                    self.subject_index,
                    self.language,
                    self.require_subjects,
                )
                if doc is not None:
                    yield doc

    @staticmethod
    def is_jsonl_file(path: str) -> bool:
        """return True if the path looks like a JSONL file"""

        path_lc = path.lower()
        return path_lc.endswith(".jsonl") or path_lc.endswith(".jsonl.gz")


class DocumentList(DocumentCorpus):
    """A document corpus based on a list of other iterable of Document
    objects"""

    def __init__(self, documents):
        self._documents = documents

    @property
    def documents(self):
        yield from self._documents


class TransformingDocumentCorpus(DocumentCorpus):
    """A document corpus that wraps another document corpus but transforms the
    documents using a given transform function"""

    def __init__(self, corpus, transform_fn):
        self._orig_corpus = corpus
        self._transform_fn = transform_fn

    @property
    def documents(self):
        for doc in self._orig_corpus.documents:
            yield self._transform_fn(doc)


class LimitingDocumentCorpus(DocumentCorpus):
    """A document corpus that wraps another document corpus but limits the
    number of documents to a given limit"""

    def __init__(self, corpus, docs_limit):
        self._orig_corpus = corpus
        self.docs_limit = docs_limit

    @property
    def documents(self):
        for doc in islice(self._orig_corpus.documents, self.docs_limit):
            yield doc


1			"""Classes for supporting document corpora"""
2
3			from __future__ import annotations
4
5			import csv
6			import glob
7			import gzip
8			import os.path
9			import re
10			from itertools import islice
11			from typing import TYPE_CHECKING
12
13			import annif.util
14			from annif.exception import OperationFailedException
15
16			from .json import json_file_to_document, json_to_document
17			from .types import Document, DocumentCorpus, SubjectSet
18
19			if TYPE_CHECKING:
20			from collections.abc import Iterator
21
22			from annif.vocab import SubjectIndex
23
24			logger = annif.logger
25
26
27			class DocumentDirectory(DocumentCorpus):
28			"""A directory of files as a full text document corpus"""
29
30			def __init__(
31			self,
32			path: str,
33			subject_index: SubjectIndex \| None = None,
34			language: str \| None = None,
35			require_subjects: bool = False,
36			) -> None:
37			self.path = path
38			self.subject_index = subject_index
39			self.language = language
40			self.require_subjects = require_subjects
41
42			def __iter__(self) -> Iterator[str]:
43			"""Iterate through the directory, yielding file paths with corpus documents."""
44
45			# txt files
46			for filename in sorted(glob.glob(os.path.join(self.path, "*.txt"))):
47			yield filename
48
49			# json files
50			for filename in sorted(glob.glob(os.path.join(self.path, "*.json"))):
51			yield filename
52
53			@staticmethod
54			def _get_subject_filename(filename: str) -> str \| None:
55			tsvfilename = re.sub(r"\.txt$", ".tsv", filename)
56			if os.path.exists(tsvfilename):
57			return tsvfilename
58
59			keyfilename = re.sub(r"\.txt$", ".key", filename)
60			if os.path.exists(keyfilename):
61			return keyfilename
62
63			return None
64
65			def _read_txt_file(self, filename: str) -> Document \| None:
66			with open(filename, errors="replace", encoding="utf-8-sig") as docfile:
67			text = docfile.read()
68			if not self.require_subjects:
69			return Document(text=text, subject_set=None, file_path=filename)
70
71			subjfilename = self._get_subject_filename(filename)
72			if subjfilename is None:
73			# subjects required but not found, skipping this docfile
74			return None
75
76			with open(subjfilename, encoding="utf-8-sig") as subjfile:
77			subjects = SubjectSet.from_string(
78			subjfile.read(), self.subject_index, self.language
79			)
80			return Document(text=text, subject_set=subjects, file_path=filename)
81
82			@property
83			def documents(self) -> Iterator[Document]:
84			for docfilename in self:
85			if docfilename.endswith(".txt"):
86			doc = self._read_txt_file(docfilename)
87			else:
88			doc = json_file_to_document(
89			docfilename,
90			self.subject_index,
91			self.language,
92			self.require_subjects,
93			)
94
95			if doc is not None:
96			yield doc
97
98
99			class DocumentFileTSV(DocumentCorpus):
100			"""A TSV file as a corpus of documents with subjects"""
101
102			def __init__(
103			self, path: str, subject_index: SubjectIndex, require_subjects=True
104			) -> None:
105			self.path = path
106			self.subject_index = subject_index
107			self.require_subjects = require_subjects
108
109			@property
110			def documents(self) -> Iterator[Document]:
111			if self.path.endswith(".gz"):
112			opener = gzip.open
113			else:
114			opener = open
115			with opener(self.path, mode="rt", encoding="utf-8-sig") as tsvfile:
116			for line in tsvfile:
117			yield from self._parse_tsv_line(line)
118
119			def _parse_tsv_line(self, line: str) -> Iterator[Document]:
120			if "\t" in line:
121			text, uris = line.split("\t", maxsplit=1)
122			subject_ids = {
123			self.subject_index.by_uri(annif.util.cleanup_uri(uri))
124			for uri in uris.split()
125			}
126			yield Document(text=text, subject_set=SubjectSet(subject_ids))
127			else:
128			if self.require_subjects:
129			logger.warning(
130			'Skipping invalid line (missing tab): "%s"', line.rstrip()
131			)
132			else:
133			yield Document(text=line.strip())
134
135
136			class DocumentFileCSV(DocumentCorpus):
137			"""A CSV file as a corpus of documents with subjects"""
138
139			def __init__(
140			self, path: str, subject_index: SubjectIndex, require_subjects=True
141			) -> None:
142			self.path = path
143			self.subject_index = subject_index
144			self.require_subjects = require_subjects
145
146			@property
147			def documents(self) -> Iterator[Document]:
148			if self.path.endswith(".gz"):
149			opener = gzip.open
150			else:
151			opener = open
152			with opener(self.path, mode="rt", encoding="utf-8-sig") as csvfile:
153			reader = csv.DictReader(csvfile)
154			if not self._check_fields(reader):
155			if self.require_subjects:
156			raise OperationFailedException(
157			f"Cannot parse CSV file {self.path}. "
158			+ "The file must have a header row that defines at least "
159			+ "the columns 'text' and 'subject_uris'."
160			)
161			else:
162			raise OperationFailedException(
163			f"Cannot parse CSV file {self.path}. "
164			+ "The file must have a header row that defines at least "
165			+ "the column 'text'."
166			)
167			for row in reader:
168			yield from self._parse_row(row)
169
170			def _parse_row(self, row: dict[str, str]) -> Iterator[Document]:
171			if self.require_subjects or (
172			self.subject_index is not None and "subject_uris" in row
173			):
174			subject_ids = {
175			self.subject_index.by_uri(annif.util.cleanup_uri(uri))
176			for uri in (row["subject_uris"] or "").strip().split()
177			}
178			else:
179			subject_ids = set()
180			metadata = {
181			key: val
182			for key, val in row.items()
183			if key not in ("document_id", "text", "subject_uris")
184			}
185			yield Document(
186			text=(row["text"] or ""),
187			subject_set=SubjectSet(subject_ids),
188			metadata=metadata,
189			document_id=row.get("document_id", None),
190			)
191
192			def _check_fields(self, reader: csv.DictReader) -> bool:
193			fns = reader.fieldnames
194			if self.require_subjects:
195			return fns is not None and "text" in fns and "subject_uris" in fns
196			else:
197			return fns is not None and "text" in fns
198
199			@staticmethod
200			def is_csv_file(path: str) -> bool:
201			"""return True if the path looks like a CSV file"""
202
203			path_lc = path.lower()
204			return path_lc.endswith(".csv") or path_lc.endswith(".csv.gz")
205
206
207			class DocumentFileJSONL(DocumentCorpus):
208			"""A JSON Lines file as a corpus of documents with subjects"""
209
210			def __init__(
211			self,
212			path: str,
213			subject_index: SubjectIndex,
214			language: str,
215			require_subjects=True,
216			) -> None:
217			self.path = path
218			self.subject_index = subject_index
219			self.language = language
220			self.require_subjects = require_subjects
221
222			@property
223			def documents(self) -> Iterator[Document]:
224			if self.path.endswith(".gz"):
225			opener = gzip.open
226			else:
227			opener = open
228			with opener(self.path, mode="rt", encoding="utf-8") as jsonlfile:
229			for line in jsonlfile:
230			doc = json_to_document(
231			self.path,
232			line,
233			self.subject_index,
234			self.language,
235			self.require_subjects,
236			)
237			if doc is not None:
238			yield doc
239
240			@staticmethod
241			def is_jsonl_file(path: str) -> bool:
242			"""return True if the path looks like a JSONL file"""
243
244			path_lc = path.lower()
245			return path_lc.endswith(".jsonl") or path_lc.endswith(".jsonl.gz")
246
247
248			class DocumentList(DocumentCorpus):
249			"""A document corpus based on a list of other iterable of Document
250			objects"""
251
252			def __init__(self, documents):
253			self._documents = documents
254
255			@property
256			def documents(self):
257			yield from self._documents
258
259
260			class TransformingDocumentCorpus(DocumentCorpus):
261			"""A document corpus that wraps another document corpus but transforms the
262			documents using a given transform function"""
263
264			def __init__(self, corpus, transform_fn):
265			self._orig_corpus = corpus
266			self._transform_fn = transform_fn
267
268			@property
269			def documents(self):
270			for doc in self._orig_corpus.documents:
271			yield self._transform_fn(doc)
272
273
274			class LimitingDocumentCorpus(DocumentCorpus):
275			"""A document corpus that wraps another document corpus but limits the
276			number of documents to a given limit"""
277
278			def __init__(self, corpus, docs_limit):
279			self._orig_corpus = corpus
280			self.docs_limit = docs_limit
281
282			@property
283			def documents(self):
284			for doc in islice(self._orig_corpus.documents, self.docs_limit):
285			yield doc
286

NatLibFi / Annif

annif.corpus.document.DocumentFileCSV.documents() B last analyzed 2025-08-29 07:45 UTC

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annif.corpus.document.DocumentFileCSV.documents() B
last analyzed 2025-08-29 07:45 UTC