class FakeBoostedSearch(FakeSearch):
boosters = [
search.GaussDecay('a_num_field', 10, 20, offset=5, decay=0.5),
search.ExpDecay(
'another_field', 20, scale=30, offset=5, decay=0.5),
search.LinearDecay('last_field', 30, 40, offset=5, decay=0.5),
]
class FakeBoostedSearch(FakeSearch):
boosters = [
search.GaussDecay('a_num_field', get_10,
get_20, offset=get_5, decay=get_dot5),
search.ExpDecay('another_field', get_20,
scale=get_30, offset=get_5, decay=get_dot5),
search.LinearDecay('last_field', get_30,
get_40, offset=get_5, decay=get_dot5),
]
class FakeBoostedSearch(FakeSearch):
boosters = [
search.GaussDecay('a_num_field', 10),
search.ExpDecay('another_field', 20),
search.LinearDecay('last_field', 30),
]
class OrganizationSearch(search.ModelSearchAdapter):
model = Organization
fuzzy = True
fields = (
'name^6',
'acronym^6',
'description',
)
sorts = {
'name': search.Sort('name.raw'),
'reuses': search.Sort('metrics.reuses'),
'datasets': search.Sort('metrics.datasets'),
'followers': search.Sort('metrics.followers'),
'views': search.Sort('metrics.views'),
'created': search.Sort('created'),
}
facets = {
'reuses': search.RangeFacet('metrics.reuses'),
'badge': search.TermFacet('badges',
labelizer=organization_badge_labelizer),
'permitted_reuses': search.RangeFacet('metrics.permitted_reuses'),
'datasets': search.RangeFacet('metrics.datasets'),
'followers': search.RangeFacet('metrics.followers'),
}
mapping = {
'properties': {
'name': {
'type': 'string',
'analyzer': search.i18n_analyzer,
'fields': {
'raw': {
'type': 'string',
'index': 'not_analyzed'
}
}
},
'acronym': {
'type': 'string',
'index': 'not_analyzed',
},
'description': {
'type': 'string',
'analyzer': search.i18n_analyzer
},
'badges': {
'type': 'string',
'index_name': 'badges',
'index': 'not_analyzed'
},
'url': {
'type': 'string'
},
'created': {
'type': 'date',
'format': 'date_hour_minute_second'
},
'metrics': search.metrics_mapping(Organization),
'org_suggest': {
'type': 'completion',
'index_analyzer': 'simple',
'search_analyzer': 'simple',
'payloads': True,
},
}
}
boosters = [
search.GaussDecay('metrics.followers', max_followers, decay=0.8),
search.GaussDecay('metrics.reuses', max_reuses, decay=0.9),
search.GaussDecay('metrics.datasets', max_datasets, decay=0.9),
]
@classmethod
def is_indexable(cls, org):
return org.deleted is None
@classmethod
def serialize(cls, organization):
completions = cls.completer_tokenize(organization.name)
completions.append(organization.id)
if organization.acronym:
completions.append(organization.acronym)
return {
'name': organization.name,
'acronym': organization.acronym,
'description': organization.description,
'url': organization.url,
'metrics': organization.metrics,
'badges': [badge.kind for badge in organization.badges],
'created': organization.created_at.strftime('%Y-%m-%dT%H:%M:%S'),
'org_suggest': {
'input': completions,
'output': str(organization.id),
'payload': {
'name': organization.name,
'acronym': organization.acronym,
'image_url': organization.logo(40),
'slug': organization.slug,
},
}
}
class OrganizationSearch(search.ModelSearchAdapter):
model = Organization
fuzzy = True
class Meta:
doc_type = 'Organization'
name = String(analyzer=search.i18n_analyzer,
fields={'raw': String(index='not_analyzed')})
acronym = String(index='not_analyzed')
description = String(analyzer=search.i18n_analyzer)
badges = String(index='not_analyzed')
url = String(index='not_analyzed')
created = Date(format='date_hour_minute_second')
metrics = search.metrics_mapping_for(Organization)
org_suggest = Completion(analyzer=simple,
search_analyzer=simple,
payloads=True)
sorts = {
'name': 'name.raw',
'reuses': 'metrics.reuses',
'datasets': 'metrics.datasets',
'followers': 'metrics.followers',
'views': 'metrics.views',
'created': 'created',
'last_modified': 'last_modified',
}
facets = {
'reuses':
RangeFacet(field='metrics.reuses',
ranges=[('none', (None, 1)), ('few', (1, 5)),
('many', (5, None))],
labels={
'none': _('No reuses'),
'few': _('Few reuses'),
'many': _('Many reuses'),
}),
'badge':
TermsFacet(field='badges', labelizer=organization_badge_labelizer),
'datasets':
RangeFacet(field='metrics.datasets',
ranges=[('none', (None, 1)), ('few', (1, 5)),
('many', (5, None))],
labels={
'none': _('No datasets'),
'few': _('Few datasets'),
'many': _('Many datasets'),
}),
'followers':
RangeFacet(field='metrics.followers',
ranges=[('none', (None, 1)), ('few', (1, 5)),
('many', (5, None))],
labels={
'none': _('No followers'),
'few': _('Few followers'),
'many': _('Many followers'),
}),
}
boosters = [
search.GaussDecay('metrics.followers',
max_followers,
decay=lazy('followers_decay')),
search.GaussDecay('metrics.reuses',
max_reuses,
decay=lazy('reuses_decay')),
search.GaussDecay('metrics.datasets',
max_datasets,
decay=lazy('datasets_decay')),
]
@classmethod
def is_indexable(cls, org):
return org.deleted is None
@classmethod
def serialize(cls, organization):
completions = cls.completer_tokenize(organization.name)
completions.append(organization.id)
if organization.acronym:
completions.append(organization.acronym)
return {
'name': organization.name,
'acronym': organization.acronym,
'description': organization.description,
'url': organization.url,
'metrics': organization.metrics,
'badges': [badge.kind for badge in organization.badges],
'created': to_iso_datetime(organization.created_at),
'org_suggest': {
'input': completions,
'output': str(organization.id),
'payload': {
'name': organization.name,
'acronym': organization.acronym,
'image_url': organization.logo(40, external=True),
'slug': organization.slug,
},
}
}
