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Change server avg_rating calculation to match new GUI algorithm for hierarchical items

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This commit is contained in:
Kovid Goyal 2015-11-24 20:37:09 +05:30
parent 2ed36fb746
commit 4f601b23df
2 changed files with 26 additions and 46 deletions
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3
src/calibre/srv/handler.py
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@ -137,7 +137,8 @@ class Context(object):
old = cache.pop(key, None)
if old is None or old[0] <= db.last_modified():
categories = db.get_categories(book_ids=restrict_to_ids, sort=opts.sort_by, first_letter_sort=opts.collapse_model == 'first letter')
cache[key] = old = (utcnow(), render(categories))
with db.safe_read_lock:
cache[key] = old = (utcnow(), render(categories))
if len(cache) > self.CATEGORY_CACHE_SIZE:
cache.popitem(last=False)
else:

69
src/calibre/srv/metadata.py
View File

@ -303,7 +303,7 @@ def collapse_first_letter(collapse_nodes, items, category_node, cl_list, idx, is
def process_category_node(
category_node, items, category_data, eval_formatter, field_metadata,
opts, tag_map, hierarchical_tags, node_to_tag_map, collapse_nodes,
intermediate_nodes, hierarchical_nodes):
intermediate_nodes, hierarchical_items):
category = items[category_node['id']]['category']
category_items = category_data[category]
cat_len = len(category_items)
@ -339,8 +339,6 @@ def process_category_node(
else:
node_id, node_data = node_data
node = {'id':node_id, 'children':[]}
if node_id not in hierarchical_nodes:
hierarchical_nodes[node_id] = node
parent['children'].append(node)
return node, node_data
@ -383,9 +381,11 @@ def process_category_node(
if cm_key in child_map:
node_parent = child_map[cm_key]
node_id = node_parent['id']
items[node_id]['is_hierarchical'] = 3 if tag.category == 'search' else 5
if node_id not in hierarchical_nodes:
hierarchical_nodes[node_id] = node_parent
item = items[node_id]
item['is_hierarchical'] = 3 if tag.category == 'search' else 5
if tag.id_set is not None:
item['id_set'] |= tag.id_set
hierarchical_items.add(node_id)
hierarchical_tags.add(id(node_to_tag_map[node_parent['id']]))
else:
if i < len(components) - 1: # Non-leaf node
@ -421,10 +421,10 @@ def iternode_descendants(node):
for x in iternode_descendants(child):
yield x
def fillout_tree(root, items, node_id_map, category_nodes, category_data, field_metadata, opts):
def fillout_tree(root, items, node_id_map, category_nodes, category_data, field_metadata, opts, book_rating_map):
eval_formatter = EvalFormatter()
tag_map, hierarchical_tags, node_to_tag_map = {}, set(), {}
first, later, collapse_nodes, intermediate_nodes, hierarchical_nodes = [], [], [], {}, {}
first, later, collapse_nodes, intermediate_nodes, hierarchical_items = [], [], [], {}, set()
# User categories have to be processed after normal categories as they can
# reference hierarchical nodes that were created only during processing of
# normal categories
@ -438,10 +438,20 @@ def fillout_tree(root, items, node_id_map, category_nodes, category_data, field_
process_category_node(
cnode, items, category_data, eval_formatter, field_metadata,
opts, tag_map, hierarchical_tags, node_to_tag_map,
collapse_nodes, intermediate_nodes, hierarchical_nodes)
collapse_nodes, intermediate_nodes, hierarchical_items)
# Do not store id_set in the tag items as it is a lot of data, with not
# much use. Instead only update the counts based on id_set
# much use. Instead only update the ratings and counts based on id_set
for item_id in hierarchical_items:
item = items[item_id]
total = count = 0
for book_id in item['id_set']:
rating = book_rating_map.get(book_id, 0)
if rating:
total += rating/2.0
count += 1
item['avg_rating'] = float(total)/count if count else 0
for item_id, item in tag_map.itervalues():
id_len = len(item.pop('id_set', ()))
if id_len:
@ -451,41 +461,10 @@ def fillout_tree(root, items, node_id_map, category_nodes, category_data, field_
item = items[node['id']]
item['count'] = sum(1 for _ in iternode_descendants(node))
# Calculate correct avg_rating for hierarchical category items
calculated = {}
def set_average_rating(item_id, children):
cr = calculated.get(item_id, None)
if cr is not None:
return cr
item = items[item_id]
if not item.get('is_hierarchical', False) or not children:
return item.get('avg_rating', 0)
total = num = 0
for child in children:
r = set_average_rating(child['id'], child['children'])
if r:
total += 1
num += r
sr = item.get('avg_rating', 0)
if sr:
total += 1
num += sr
try:
calculated[item_id] = item['avg_rating'] = ans = num/float(total)
except ZeroDivisionError:
calculated[item_id] = item['avg_rating'] = ans = 0
return ans
for item_id, node in hierarchical_nodes.iteritems():
item = items[item_id]
if item.get('is_hierarchical', False):
set_average_rating(item_id, node['children'])
def render_categories(field_metadata, opts, category_data):
def render_categories(field_metadata, opts, book_rating_map, category_data):
items = {}
root, node_id_map, category_nodes, recount_nodes = create_toplevel_tree(category_data, items, field_metadata, opts)
fillout_tree(root, items, node_id_map, category_nodes, category_data, field_metadata, opts)
fillout_tree(root, items, node_id_map, category_nodes, category_data, field_metadata, opts, book_rating_map)
for node in recount_nodes:
item = items[node['id']]
item['count'] = sum(1 for x in iternode_descendants(node) if not items[x['id']].get('is_category', False))
@ -497,7 +476,7 @@ def render_categories(field_metadata, opts, category_data):
def categories_as_json(ctx, rd, db):
opts = categories_settings(rd.query, db)
return ctx.get_tag_browser(rd, db, opts, partial(render_categories, db.field_metadata, opts))
return ctx.get_tag_browser(rd, db, opts, partial(render_categories, db.field_metadata, opts, db.fields['rating'].book_value_map))
# Test tag browser {{{
@ -542,7 +521,7 @@ def test_tag_browser(library_path=None):
opts = categories_settings({}, db)
# opts = opts._replace(hidden_categories={'publisher'})
category_data = db.get_categories(sort=opts.sort_by, first_letter_sort=opts.collapse_model == 'first letter')
data = render_categories(db.field_metadata, opts, category_data)
data = render_categories(db.field_metadata, opts, db.fields['rating'].book_value_map, category_data)
srv_data = dump_categories_tree(data)
from calibre.gui2 import Application, gprefs
from calibre.gui2.tag_browser.model import TagsModel