def test_compute_prominence_multiple():
strings = ['biden', 'joe biden', 'donald trump', 'D. Trump']
clusters = fuzzy_cluster(strings)
clusters = compute_prominence(clusters)
assert isinstance(clusters, list)
assert len(clusters) > 0
def test_whitelist_formatting():
# simulate data
test_data = [{
'word': 'Viborg',
'entity_group': 'LOC',
'cluster_id': 'A'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'B'
}, {
'word': 'Solgårde',
'entity_group': 'LOC',
'cluster_id': 'C'
}]
clusters = fuzzy_cluster(test_data)
# Apply multiple whitelists
out = apply_whitelists([c, m], clusters, score_cutoff=90)
#### Format output
# set desired columns
cols = ['eblocal_code', 'municipality_code']
# format output
out = format_output(out, columns=cols, drop_duplicates=True)
assert isinstance(out, pd.DataFrame)
assert len(out) > 0
def test_compute_prominence_weight_multipliers():
clusters = fuzzy_cluster(simulate_ner_data())
clusters = compute_prominence(clusters,
weight_position=0.5,
weight_multipliers=np.random.rand(
len(clusters)))
clusters = pd.DataFrame.from_dict(clusters)
assert isinstance(clusters.prominence_score.tolist()[0], float)
def run_random(articles,
entitites,
id=None,
scorer=partial_token_set_ratio,
cutoff=75):
if id is None:
id = np.random.choice(articles.content_id.tolist())
article = articles[articles.content_id == id]
article = article[['content_id', 'title', 'lead', 'body']]
article_ents = entities[entities.content_id == id]
article_ents = article_ents[article_ents.placement == "body"]
preds = article_ents.to_dict(orient="records")
t1 = time.time()
clusters = fuzzy_cluster(preds,
scorer=scorer,
workers=4,
cutoff=cutoff,
merge_output=True)
#pd.DataFrame.from_dict(clu ters)
clusters = compute_prominence(clusters,
merge_output=True,
weight_position=.5)
# subset location entities (for matching with cities)
locations = [x["entity_group"] == "LOC" for x in clusters]
locations = list(compress(clusters, locations))
clusters = locations
clusters = match_whitelist(clusters,
whitelist=whitelist,
scorer=ratio,
score_cutoff=95,
merge_output=True,
aggregate_cluster=True,
workers=1)
t2 = time.time()
if len(clusters) > 0:
clusters = pd.DataFrame.from_dict(clusters).sort_values(
by="prominence_rank")
print(id)
#print(article.title.tolist()[0])
#print(article.lead.tolist()[0])
print(article.body.tolist()[0])
print(clusters)
return t2 - t1
def test_municipalities_whitelist():
test_data = [{
'word': 'Viborg Kommune',
'entity_group': 'LOC',
'cluster_id': 'ABE'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'bambolino'
}]
clusters = fuzzy_cluster(test_data)
out = m(clusters, score_cutoff=95)
assert len(out) > 0
def test_whitelist_aggregate_cluster():
test_data = [{
'word': 'Viborg',
'entity_group': 'LOC',
'cluster_id': 'ABE'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'bambolino'
}]
clusters = fuzzy_cluster(test_data)
out = c(clusters, aggregate_cluster=True)
assert len(out) == 1
def test_whitelist_list_input():
test_data = [{
'word': 'Viborg',
'entity_group': 'LO',
'cluster_id': 'ABE'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'bambolino'
}]
clusters = fuzzy_cluster(test_data)
matches = match_whitelist(clusters, whitelist=['Viborg'])
assert len(matches) == 1
def test_whitelist_no_match():
test_data = [{
'word': 'Viborg',
'entity_group': 'LO',
'cluster_id': 'ABE'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'bambolino'
}]
clusters = fuzzy_cluster(test_data)
out = c(clusters)
assert len(out) == 0
def test_whitelist_no_input():
test_data = []
clusters = fuzzy_cluster(test_data)
out = c(clusters)
assert len(out) == 0
test_data = [{
'word': 'Viborg',
'entity_group': 'LOC',
'cluster_id': 'A'
}, {
'word': 'Uldum',
'entity_group': 'ORG',
'cluster_id': 'B'
}, {
'word': 'Solgårde',
'entity_group': 'LOC',
'cluster_id': 'C'
}]
# cluster data
clusters = fuzzy_cluster(test_data)
# initiate relevant whitelists
c = Cities()
m = Municipalities()
n = Neighborhoods()
# Apply whitelists
out = apply_whitelists([c, m, n], clusters, score_cutoff=90)
#### Format output
# set desired columns
cols = ['neighborhood_code', 'city_code', 'municipality_code']
# format output
out = format_output(out, columns=cols, drop_duplicates=True)
def test_fuzzy_cluster_none():
clusters = fuzzy_cluster([])
assert len(clusters) == 0
assert isinstance(clusters, list)
def test_fuzzy_cluster_single():
clusters = fuzzy_cluster(["smokie"])
assert len(clusters) == 1
assert isinstance(clusters, list)
def test_fuzzy_cluster():
strings = ['biden', 'joe biden', 'donald trump', 'D. Trump']
clusters = fuzzy_cluster(strings)
assert isinstance(clusters, list)
def test_compute_prominence_none():
clusters = fuzzy_cluster([])
clusters = compute_prominence(clusters)
assert isinstance(clusters, list)
assert len(clusters) == 0
def test_compute_prominence_single():
clusters = fuzzy_cluster(["Biden"])
clusters = compute_prominence(clusters)
assert isinstance(clusters, list)
assert len(clusters) == 1
