annif.backend.dummy.DummyBackend._suggest() - Code Metrics - Inspection of "Add CI/CD jobs for testing on Windows and MacOS" - NatLibFi/Annif - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — testing-on-windows-and-macos ( 782857...ea99ad )

by Juho

created 2025-02-11 13:59 UTC

annif.backend.dummy.DummyBackend._suggest() A

↳ Parent: annif.backend.dummy

Complexity

Conditions

Size

Total Lines	17
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	9
nop	3
dl	0
loc	17
rs	9.95
c	0
b	0
f	0

"""Dummy backend for testing basic interaction of projects and backends"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any

from annif.suggestion import SubjectSuggestion

from . import backend

if TYPE_CHECKING:
    from annif.corpus.document import DocumentCorpus


class DummyBackend(backend.AnnifLearningBackend):
    name = "dummy"
    initialized = False
    subject_id = 0
    is_trained = True
    modification_time = None

    def initialize(self, parallel: bool = False) -> None:
        self.initialized = True

    def _suggest(self, text: str, params: dict[str, Any]) -> list[SubjectSuggestion]:
        score = float(params.get("score", 1.0))

        # Ensure tests fail if "text" with wrong type ends up here
        assert isinstance(text, str)

        # Give no hits for no text
        if len(text) == 0:
            return []

        # allow overriding returned subject via uri parameter
        if "uri" in params:
            subject_id = self.project.subjects.by_uri(params["uri"])
        else:
            subject_id = self.subject_id

        return [SubjectSuggestion(subject_id=subject_id, score=score)]

    def _learn(
        self,
        corpus: DocumentCorpus,
        params: dict[str, Any],
    ) -> None:
        # in this dummy backend we "learn" by picking up the subject ID
        # of the first subject of the first document in the learning set
        # and using that in subsequent analysis results
        for doc in corpus.documents:
            if doc.subject_set:
                self.subject_id = doc.subject_set[0]
            break


1			"""Dummy backend for testing basic interaction of projects and backends"""
2
3			from __future__ import annotations
4
5			from typing import TYPE_CHECKING, Any
6
7			from annif.suggestion import SubjectSuggestion
8
9			from . import backend
10
11			if TYPE_CHECKING:
12			from annif.corpus.document import DocumentCorpus
13
14
15			class DummyBackend(backend.AnnifLearningBackend):
16			name = "dummy"
17			initialized = False
18			subject_id = 0
19			is_trained = True
20			modification_time = None
21
22			def initialize(self, parallel: bool = False) -> None:
23			self.initialized = True
24
25			def _suggest(self, text: str, params: dict[str, Any]) -> list[SubjectSuggestion]:
26			score = float(params.get("score", 1.0))
27
28			# Ensure tests fail if "text" with wrong type ends up here
29			assert isinstance(text, str)
30
31			# Give no hits for no text
32			if len(text) == 0:
33			return []
34
35			# allow overriding returned subject via uri parameter
36			if "uri" in params:
37			subject_id = self.project.subjects.by_uri(params["uri"])
38			else:
39			subject_id = self.subject_id
40
41			return [SubjectSuggestion(subject_id=subject_id, score=score)]
42
43			def _learn(
44			self,
45			corpus: DocumentCorpus,
46			params: dict[str, Any],
47			) -> None:
48			# in this dummy backend we "learn" by picking up the subject ID
49			# of the first subject of the first document in the learning set
50			# and using that in subsequent analysis results
51			for doc in corpus.documents:
52			if doc.subject_set:
53			self.subject_id = doc.subject_set[0]
54			break
55

NatLibFi / Annif

Push — testing-on-windows-and-macos ( 782857...ea99ad )

annif.backend.dummy.DummyBackend._suggest() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like