def test_search_on_total_counts(params, add_text, add_citation):
"""
If a search query is provided, filter the results on the query.
"""
t1 = add_text(**params('match one'))
t2 = add_text(**params('two'))
t3 = add_text(**params('match three'))
t4 = add_text(**params('four'))
for i in range(4):
add_citation(text=t1)
for i in range(3):
add_citation(text=t2)
for i in range(2):
add_citation(text=t3)
for i in range(1):
add_citation(text=t4)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking(query='match')
assert len(texts['hits']) == 2
assert texts['hits'][0]['_id'] == str(t1.id)
assert texts['hits'][1]['_id'] == str(t3.id)
def assigned_with(text_id, size=1000):
"""
Given a "seed" text, rank other texts assigned on the same syllabi.
Args:
text_id (int): The text id.
Returns:
dict: Elasticsearch hits.
"""
# Get syllabi that assign the text.
doc_ids = Citation_Index.docs_with_text(text_id)
# Rank texts assigned by those sylalbi.
ranks = Citation_Index.compute_ranking(dict(
document_id=doc_ids
))
# Omit the seed text.
ranks.pop(str(text_id))
# Materialize the text metadata.
texts = Text_Index.materialize_ranking(ranks, size=size)
return texts
def test_sort_on_total_counts_by_default(add_text, add_citation):
"""
By default return results sorted on the total citation count.
"""
t1 = add_text()
t2 = add_text()
t3 = add_text()
for i in range(3):
add_citation(text=t1)
for i in range(2):
add_citation(text=t2)
for i in range(1):
add_citation(text=t3)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking()
assert texts['hits'][0]['_id'] == str(t1.id)
assert texts['hits'][1]['_id'] == str(t2.id)
assert texts['hits'][2]['_id'] == str(t3.id)
def test_sort_on_filtered_counts(add_text, add_citation):
"""
If a text -> count map is passed, sort on the filtered counts.
"""
t1 = add_text()
t2 = add_text()
t3 = add_text()
for i in range(30):
add_citation(text=t1)
for i in range(20):
add_citation(text=t2)
for i in range(10):
add_citation(text=t3)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking(ranks={
t1.id: 1,
t2.id: 2,
t3.id: 3,
})
assert texts['hits'][0]['_id'] == str(t3.id)
assert texts['hits'][1]['_id'] == str(t2.id)
assert texts['hits'][2]['_id'] == str(t1.id)
def test_sort_on_total_counts_by_default(add_text, add_citation):
"""
By default return results sorted on the total citation count.
"""
t1 = add_text()
t2 = add_text()
t3 = add_text()
for i in range(3):
add_citation(text=t1)
for i in range(2):
add_citation(text=t2)
for i in range(1):
add_citation(text=t3)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking()
assert texts['hits'][0]['_id'] == str(t1.id)
assert texts['hits'][1]['_id'] == str(t2.id)
assert texts['hits'][2]['_id'] == str(t3.id)
def test_search_on_total_counts(params, add_text, add_citation):
"""
If a search query is provided, filter the results on the query.
"""
t1 = add_text(**params('match one'))
t2 = add_text(**params('two'))
t3 = add_text(**params('match three'))
t4 = add_text(**params('four'))
for i in range(4):
add_citation(text=t1)
for i in range(3):
add_citation(text=t2)
for i in range(2):
add_citation(text=t3)
for i in range(1):
add_citation(text=t4)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking(query='match')
assert len(texts['hits']) == 2
assert texts['hits'][0]['_id'] == str(t1.id)
assert texts['hits'][1]['_id'] == str(t3.id)
def test_sort_on_filtered_counts(add_text, add_citation):
"""
If a text -> count map is passed, sort on the filtered counts.
"""
t1 = add_text()
t2 = add_text()
t3 = add_text()
for i in range(30):
add_citation(text=t1)
for i in range(20):
add_citation(text=t2)
for i in range(10):
add_citation(text=t3)
Text_Index.es_insert()
texts = Text_Index.materialize_ranking(ranks={
t1.id: 1,
t2.id: 2,
t3.id: 3,
})
assert texts['hits'][0]['_id'] == str(t3.id)
assert texts['hits'][1]['_id'] == str(t2.id)
assert texts['hits'][2]['_id'] == str(t1.id)
def assigned_with(text_id, size=200):
"""
Given a "seed" text, rank other texts assigned on the same syllabi.
Args:
text_id (int): The text id.
Returns:
dict: Elasticsearch hits.
"""
# Get syllabi that assign the text.
doc_ids = Citation_Index.docs_with_text(text_id)
# Rank texts assigned by those sylalbi.
ranks = Citation_Index.compute_ranking(dict(
document_id=doc_ids
))
# Omit the seed text.
ranks.pop(str(text_id))
# Materialize the text metadata.
texts = Text_Index.materialize_ranking(ranks, size=size)
return texts
def rank_texts(filters={}, query=None, size=1000, page=1):
"""
Filter and rank texts.
Args:
filters (dict): Citation metadata filters.
query (str): A text metadata search query.
size (str): Return N results.
page (str): 1-indexed page offset.
Returns:
dict: Elasticsearch hits.
"""
# Filter citation counts, if non-empty filters.
if any(filters.values()):
ranks = Citation_Index.compute_ranking(filters)
else:
ranks = None
# Materialize the text metadata.
texts = Text_Index.materialize_ranking(ranks, query, size, page)
return texts
def test_paginate_results(add_text, add_citation):
"""
When a page is provided, return the 1-indexed page.
"""
# 9 texts - the first with 9 citations, second with 8, etc.
texts = []
for i in reversed(range(1, 10)):
text = add_text()
for j in range(i):
add_citation(text=text)
texts.append(text)
Text_Index.es_insert()
# Get first page by default.
p1 = Text_Index.materialize_ranking(size=3)
assert len(p1['hits']) == 3
assert p1['hits'][0]['_id'] == str(texts[0].id)
assert p1['hits'][1]['_id'] == str(texts[1].id)
assert p1['hits'][2]['_id'] == str(texts[2].id)
p2 = Text_Index.materialize_ranking(size=3, page=2)
assert len(p1['hits']) == 3
assert p2['hits'][0]['_id'] == str(texts[3].id)
assert p2['hits'][1]['_id'] == str(texts[4].id)
assert p2['hits'][2]['_id'] == str(texts[5].id)
p3 = Text_Index.materialize_ranking(size=3, page=3)
assert len(p1['hits']) == 3
assert p3['hits'][0]['_id'] == str(texts[6].id)
assert p3['hits'][1]['_id'] == str(texts[7].id)
assert p3['hits'][2]['_id'] == str(texts[8].id)
def test_paginate_results(add_text, add_citation):
"""
When a page is provided, return the 1-indexed page.
"""
# 9 texts - the first with 9 citations, second with 8, etc.
texts = []
for i in reversed(range(1, 10)):
text = add_text()
for j in range(i):
add_citation(text=text)
texts.append(text)
Text_Index.es_insert()
# Get first page by default.
p1 = Text_Index.materialize_ranking(size=3)
assert len(p1['hits']) == 3
assert p1['hits'][0]['_id'] == str(texts[0].id)
assert p1['hits'][1]['_id'] == str(texts[1].id)
assert p1['hits'][2]['_id'] == str(texts[2].id)
p2 = Text_Index.materialize_ranking(size=3, page=2)
assert len(p1['hits']) == 3
assert p2['hits'][0]['_id'] == str(texts[3].id)
assert p2['hits'][1]['_id'] == str(texts[4].id)
assert p2['hits'][2]['_id'] == str(texts[5].id)
p3 = Text_Index.materialize_ranking(size=3, page=3)
assert len(p1['hits']) == 3
assert p3['hits'][0]['_id'] == str(texts[6].id)
assert p3['hits'][1]['_id'] == str(texts[7].id)
assert p3['hits'][2]['_id'] == str(texts[8].id)
