def __init__(self, *args, **kwargs):
super(to_dossier_store, self).__init__(*args, **kwargs)
kvl = kvlayer.client()
feature_indexes = None
try:
conf = yakonfig.get_global_config('dossier.store')
feature_indexes = conf['feature_indexes']
except KeyError:
pass
self.store = Store(kvl, feature_indexes=feature_indexes)
tfidf_path = self.config.get('tfidf_path')
self.tfidf = gensim.models.TfidfModel.load(tfidf_path)
def store(self):
if self._store is None:
feature_indexes = None
try:
conf = yakonfig.get_global_config('dossier.store')
feature_indexes = conf['feature_indexes']
except KeyError:
pass
self._store = Store(kvlayer.client(),
feature_indexes=feature_indexes)
return self._store
def test_one_to_many_indexing(kvl): # noqa
# This config defines an index named `foo` that automatically indexes
# values in the `bar` and `baz` features. This means that an index scan
# on the `foo` index will check values in the `bar` and `baz` features.
index_config = [{'foo': ['bar', 'baz']}]
store = Store(kvl, feature_indexes=index_config)
fcx, fcy, fcz = FC(), FC(), FC()
fcx['unrelated']['a'] = 1
fcy['bar']['a'] = 1
fcy['baz']['a'] = 1
fcz['baz']['a'] = 1
fcy['baz']['c'] = 1
fcz['baz']['b'] = 1
store.put([('x', fcx), ('y', fcy), ('z', fcz)])
assert list(store.index_scan('foo', 'a')) == ['y', 'z']
assert list(store.index_scan('foo', 'b')) == ['z']
assert list(store.index_scan('foo', 'c')) == ['y']
def __init__(self, *args, **kwargs):
super(to_dossier_store, self).__init__(*args, **kwargs)
kvl = kvlayer.client()
feature_indexes = None
try:
conf = yakonfig.get_global_config('dossier.store')
feature_indexes = conf['feature_indexes']
except KeyError:
pass
self.store = Store(kvl,
feature_indexes=feature_indexes)
tfidf_path = self.config.get('tfidf_path')
self.tfidf = gensim.models.TfidfModel.load(tfidf_path)
def test_one_to_many_indexing(kvl): # noqa
# This config defines an index named `foo` that automatically indexes
# values in the `bar` and `baz` features. This means that an index scan
# on the `foo` index will check values in the `bar` and `baz` features.
index_config = [{'foo': ['bar', 'baz']}]
store = Store(kvl, feature_indexes=index_config)
fcx, fcy, fcz = FC(), FC(), FC()
fcx['unrelated']['a'] = 1
fcy['bar']['a'] = 1
fcy['baz']['a'] = 1
fcz['baz']['a'] = 1
fcy['baz']['c'] = 1
fcz['baz']['b'] = 1
store.put([('x', fcx), ('y', fcy), ('z', fcz)])
assert list(store.index_scan('foo', 'a')) == ['y', 'z']
assert list(store.index_scan('foo', 'b')) == ['z']
assert list(store.index_scan('foo', 'c')) == ['y']
def store(kvl):
client = Store(kvl, feature_indexes=[u'feature'])
yield client
client.delete_all()
# There are more backends available like MySQL, PostgreSQL and Accumulo.
#
# See: https://github.com/diffeo/kvlayer
# !!! IMPORTANT !!!
# Define features that you want to index. This will let you quickly scan
# for feature collections in the database with matching values.
#
# You don't have to index everything, but it's probably a good idea to index
# the most prominent features. e.g., phone or email or website.
#
# These should correspond to the names of the corresponding features.
feature_indexes = [u'phone', u'email', u'website', u'rate']
# Create a "store," which knows how to store and index feature collections.
store = Store(conn, feature_indexes=feature_indexes)
# Create a fresh feature collection and add a 'rate' feature.
fc = FeatureCollection()
fc['rate'] = StringCounter({
u'5per30': 5,
u'5per60': 1,
u'10per20': 2,
})
# Content ids are the unique identifier for each feature collection.
# It's probably sufficient to use whatever you have for "ad id."
content_id = 'some_unique_value'
store.put([(content_id, fc)])
print store.get(content_id)
def fcstore(kvl):
return Store(kvl)
class to_dossier_store(Configured):
'''A :mod:`streamcorpus_pipeline` `writer` stage with one optional parameter:
.. code-block:: yaml
tfidf_path: path/to/tfidf.data
'''
config_name = 'to_dossier_store'
default_config = {'tfidf_path': None}
def __init__(self, *args, **kwargs):
super(to_dossier_store, self).__init__(*args, **kwargs)
kvl = kvlayer.client()
feature_indexes = None
try:
conf = yakonfig.get_global_config('dossier.store')
feature_indexes = conf['feature_indexes']
except KeyError:
pass
self.store = Store(kvl, feature_indexes=feature_indexes)
tfidf_path = self.config.get('tfidf_path')
self.tfidf = gensim.models.TfidfModel.load(tfidf_path)
def process(self, t_path, name_info, i_str):
'''converts each :attr:`streamcorpus.StreamItem.body.clean_html` from
`t_path` into a :class:`~dossier.fc.FeatureCollection` and saves it in
a :class:`~dossier.store.Store` configured with the global `kvlayer`
config.
'''
def cids_and_fcs():
count = 0
seen = set()
for si in Chunk(t_path):
clean_html = getattr(si.body, 'clean_html', '')
if clean_html is None or len(clean_html.strip()) == 0:
logger.warn('dropping SI lacking clean_html: %r',
si.abs_url)
continue
if 'other_features' in si.other_content:
other_features = json.loads(
si.other_content['other_features'].raw)
else:
other_features = None
fc = html_to_fc(
clean_html=si.body.clean_html.decode('utf-8'),
clean_visible=si.body.clean_visible.decode('utf-8'),
encoding='utf-8',
url=si.abs_url,
timestamp=si.stream_time.epoch_ticks,
other_features=other_features,
)
add_sip_to_fc(fc, self.tfidf)
content_id = mk_content_id(str(fc.get(u'meta_url')))
if content_id in seen:
logger.warn('dropping duplicate content_id=%r', content_id)
else:
seen.add(content_id)
yield content_id, fc
count += 1
logger.info('saved %d FCs from %d SIs', count, len(seen))
self.store.put(cids_and_fcs())
## interface spec of streamcorpus_pipeline writers requires
## returning a list of locally generated paths.
return []
__call__ = process
class to_dossier_store(Configured):
'''A :mod:`streamcorpus_pipeline` `writer` stage with one optional parameter:
.. code-block:: yaml
tfidf_path: path/to/tfidf.data
'''
config_name = 'to_dossier_store'
default_config = {
'tfidf_path': None
}
def __init__(self, *args, **kwargs):
super(to_dossier_store, self).__init__(*args, **kwargs)
kvl = kvlayer.client()
feature_indexes = None
try:
conf = yakonfig.get_global_config('dossier.store')
feature_indexes = conf['feature_indexes']
except KeyError:
pass
self.store = Store(kvl,
feature_indexes=feature_indexes)
tfidf_path = self.config.get('tfidf_path')
self.tfidf = gensim.models.TfidfModel.load(tfidf_path)
def process(self, t_path, name_info, i_str):
'''converts each :attr:`streamcorpus.StreamItem.body.clean_html` from
`t_path` into a :class:`~dossier.fc.FeatureCollection` and saves it in
a :class:`~dossier.store.Store` configured with the global `kvlayer`
config.
'''
def cids_and_fcs():
count = 0
seen = set()
for si in Chunk(t_path):
clean_html = getattr(si.body, 'clean_html', '')
if clean_html is None or len(clean_html.strip()) == 0:
logger.warn('dropping SI lacking clean_html: %r', si.abs_url)
continue
if 'other_features' in si.other_content:
other_features = json.loads(si.other_content['other_features'].raw)
else:
other_features = None
fc = html_to_fc(
clean_html=si.body.clean_html.decode('utf-8'),
clean_visible=si.body.clean_visible.decode('utf-8'),
encoding='utf-8',
url=si.abs_url,
timestamp=si.stream_time.epoch_ticks,
other_features=other_features,
)
add_sip_to_fc(fc, self.tfidf)
content_id = mk_content_id(str(fc.get(u'meta_url')))
if content_id in seen:
logger.warn('dropping duplicate content_id=%r', content_id)
else:
seen.add(content_id)
yield content_id, fc
count += 1
logger.info('saved %d FCs from %d SIs', count, len(seen))
self.store.put(cids_and_fcs())
## interface spec of streamcorpus_pipeline writers requires
## returning a list of locally generated paths.
return []
__call__ = process
def store(kvl):
client = Store(kvl, feature_indexes=[u'feature'])
yield client
client.delete_all()
# There are more backends available like MySQL, PostgreSQL and Accumulo.
#
# See: https://github.com/diffeo/kvlayer
# !!! IMPORTANT !!!
# Define features that you want to index. This will let you quickly scan
# for feature collections in the database with matching values.
#
# You don't have to index everything, but it's probably a good idea to index
# the most prominent features. e.g., phone or email or website.
#
# These should correspond to the names of the corresponding features.
feature_indexes = [u'phone', u'email', u'website', u'rate']
# Create a "store," which knows how to store and index feature collections.
store = Store(conn, feature_indexes=feature_indexes)
# Create a fresh feature collection and add a 'rate' feature.
fc = FeatureCollection()
fc['rate'] = StringCounter({
u'5per30': 5,
u'5per60': 1,
u'10per20': 2,
})
# Content ids are the unique identifier for each feature collection.
# It's probably sufficient to use whatever you have for "ad id."
content_id = 'some_unique_value'
store.put([(content_id, fc)])
print store.get(content_id)
