def dump_question_factor(self, QTYPE, qinfo, solution, analysis_info,
knowledge_tree):
res = []
df = pd.merge(qinfo, solution[solution.qtype == QTYPE])
df = pd.merge(df, analysis_info[analysis_info.qtype == QTYPE])
df_group = df.groupby(['qtype', 'qid'])
for name1, group1 in df_group:
qtype, qid = name1
difficulty = group1.difficulty.iloc[0]
question_analysis = []
for name2, group2 in group1.groupby(['sub_qid', 'solution_id']):
sub_qid, solution_id = name2
S = group2.sort(columns='analysis_id')[['ktag', 'mtag']]
analysis_step = []
f = lambda x: None if pd.isnull(x) else set(
map(int, x.split('|')))
for _, row in S.iterrows():
ktag, mtag = row
analysis_step.append((f(ktag), f(mtag)))
question_analysis.append(analysis_step)
qfactor = SciQuestionFactor(difficulty)
qfactor.add_factor(question_analysis)
factor_value = qfactor.dump_factor()
factor_key = self.QUESTION_FACTOR_KEY % (qtype, qid)
flush_redis(self.redis_db, factor_key, factor_value)
indices = self.get_question_knowledge_index(
qfactor, knowledge_tree)
res.append((qtype, qid, indices))
return res
def dump_question_factor(self, QTYPE, qinfo, solution,
analysis_info, knowledge_tree):
res = []
df = pd.merge(qinfo, solution[solution.qtype == QTYPE])
df = pd.merge(df, analysis_info[analysis_info.qtype == QTYPE])
df_group = df.groupby(['qtype', 'qid'])
for name1, group1 in df_group:
qtype, qid = name1
difficulty = group1.difficulty.iloc[0]
question_analysis = []
for name2, group2 in group1.groupby(['sub_qid', 'solution_id']):
sub_qid, solution_id = name2
S = group2.sort(columns='analysis_id')[['ktag', 'mtag']]
analysis_step = []
f = lambda x: None if pd.isnull(x) else set(map(int, x.split('|')))
for _, row in S.iterrows():
ktag, mtag = row
analysis_step.append((f(ktag), f(mtag)))
question_analysis.append(analysis_step)
qfactor = SciQuestionFactor(difficulty)
qfactor.add_factor(question_analysis)
factor_value = qfactor.dump_factor()
factor_key = self.QUESTION_FACTOR_KEY % (qtype, qid)
flush_redis(self.redis_db, factor_key, factor_value)
indices = self.get_question_knowledge_index(qfactor, knowledge_tree)
res.append((qtype, qid, indices))
return res
def dump_question_inv_index(conn_func, redis_db, qinfo):
valid_question_set = set()
for k, _ in qinfo.iteritems():
qtype, qid, _ = k
valid_question_set.add((qtype, qid))
origin = get_origin_info(conn_func)
keys = redis_db.hkeys(CET_QUESTION_FEATURE_KEY)
res = dict()
removed_questions = set()
for key in keys:
qtype, qid, _ = map(int, key.split(':'))
# 可能会因为各种莫名原因导致 redis 中残存有脏数据
# 用本次计算的 qinfo 过滤一遍保证都是已入库题目
if (qtype, qid) not in valid_question_set:
removed_questions.add((qtype, qid))
continue
exam_type = origin.get(qtype, {}).get(qid, EXAM_TYPE_DEFAULT)
if exam_type == EXAM_TYPE_DEFAULT:
continue
res.setdefault(exam_type, {}).setdefault(qtype, []).append(qid)
for exam_type, v in res.iteritems():
for qtype, qids in v.iteritems():
key = CET_QUESTION_INV_INDEX_KEY % (exam_type, qtype)
flush_redis(redis_db, key, qids)
print 'remove %s questions' % len(removed_questions)
def dump_question_index(redis_db, question_index):
idx = {}
for exam_kind, qtype, qid in question_index:
idx.setdefault((exam_kind, qtype), []).append(qid)
for (exam_kind, qtype), qids in idx.iteritems():
key = ENGLISH_QUESTION_INV_INDEX % (exam_kind, qtype)
flush_redis(redis_db, key, qids)
def dump_question_index(redis_db, question_index):
idx = {}
for exam_kind, qtype, qid in question_index:
idx.setdefault((exam_kind, qtype), []).append(qid)
for (exam_kind, qtype), qids in idx.iteritems():
key = ENGLISH_QUESTION_INV_INDEX % (exam_kind, qtype)
flush_redis(redis_db, key, qids)
def dump_question_factor(self, SUB_QTYPE, qinfo,
knowledge_info, knowledge_tree):
res = []
df = pd.merge(qinfo,
knowledge_info[knowledge_info.sub_type == SUB_QTYPE])
df_group = df.groupby(['qtype', 'qid'])
for name, group in df_group:
qtype, qid = name
difficulty = group.difficulty.iloc[0]
S = group[['ktag', 'rktag']]
ktag, rktag = set(), set()
_f = lambda x: None if pd.isnull(x) else set(map(int, x.split('|')))
for _, row in S.iterrows():
ktag_set, rktag_set = map(_f, row)
if ktag_set: ktag.update(ktag_set)
if rktag_set: rktag.update(rktag_set)
question_info = (ktag, rktag, difficulty)
qfactor = ArtQuestionFactor()
qfactor.add_factor(question_info)
factor_value = qfactor.dump_factor()
factor_key = self.QUESTION_FACTOR_KEY % (qtype, qid)
flush_redis(self.redis_db, factor_key, factor_value)
indices = self.get_question_knowledge_index(qfactor, knowledge_tree)
res.append((qtype, qid, indices))
return res
def dump_user_factor(self):
user_set = self.get_user_set()
for uid in user_set:
key = self.USER_FACTOR_KEY % uid
ufac = convert_user_factor(self.redis_db, key)
if ufac is None:
continue
adaptive_key = self.ADAPTIVE_USER_FACTOR_KEY % uid
flush_redis(self.redis_db, adaptive_key, ufac)
def dump_user_factor(self):
user_set = self.get_user_set()
for uid in user_set:
key = self.USER_FACTOR_KEY % uid
ufac = convert_user_factor(self.redis_db, key)
if ufac is None:
continue
adaptive_key = self.ADAPTIVE_USER_FACTOR_KEY % uid
flush_redis(self.redis_db, adaptive_key, ufac)
def dump_concept_graph(self):
k_str = json.dumps(self.knowledge_mat)
m_str = json.dumps(self.method_mat)
k_m_str = json.dumps(self.knowledge_method_mat)
key = self.CONCEPT_GRAPH
value = [k_str, m_str, k_m_str]
flush_redis(self.redis_db, key, value)
key = self.CONCEPT_GRAPH_TRANSPOSE
value = map(transpose_matrix, value)
flush_redis(self.redis_db, key, value)
def dump_card_inv_index(self):
"""计算 concept_graph 中元素(顶点/边)到 card 的索引"""
keys = self.get_all_card_factor_keys()
res = {}
for key in keys:
card_type, card_id = key.split("/")[-2:]
item_id = "%s:%s" % (card_type, card_id)
k_mat = load_card_factor(self.redis_db, key)
append_item_id(item_id, k_mat, res, "k", "k")
for k1, v in res.iteritems():
for k2, item_ids in v.iteritems():
key = self.CONCEPT_CARD_INDEX % (k1, k2)
flush_redis(self.redis_db, key, item_ids)
def dump_card_inv_index(self):
'''计算 concept_graph 中元素(顶点/边)到 card 的索引'''
keys = self.get_all_card_factor_keys()
res = {}
for key in keys:
card_type, card_id = key.split('/')[-2:]
item_id = '%s:%s' % (card_type, card_id)
k_mat = load_card_factor(self.redis_db, key)
append_item_id(item_id, k_mat, res, 'k', 'k')
for k1, v in res.iteritems():
for k2, item_ids in v.iteritems():
key = self.CONCEPT_CARD_INDEX % (k1, k2)
flush_redis(self.redis_db, key, item_ids)
def dump_question_inv_index(self):
"""计算 concept_graph 中元素(顶点/边)到 question 的索引"""
keys = self.get_all_question_factor_keys()
res = {}
for key in keys:
qtype, qid = key.split("/")[-2:]
item_id = "%s:%s" % (qtype, qid)
qfactor = load_question_factor(self.redis_db, key)
k_mat, m_mat, k_m_mat, diff = qfactor
append_item_id(item_id, k_mat, res, "k", "k")
append_item_id(item_id, m_mat, res, "m", "m")
append_item_id(item_id, k_m_mat, res, "k", "m")
for k1, v in res.iteritems():
for k2, item_ids in v.iteritems():
key = self.CONCEPT_QUESTION_INDEX % (k1, k2)
flush_redis(self.redis_db, key, item_ids)
def dump_question_inv_index(self):
'''计算 concept_graph 中元素(顶点/边)到 question 的索引'''
keys = self.get_all_question_factor_keys()
res = {}
for key in keys:
qtype, qid = key.split('/')[-2:]
item_id = '%s:%s' % (qtype, qid)
qfactor = load_question_factor(self.redis_db, key)
k_mat, m_mat, k_m_mat, diff = qfactor
append_item_id(item_id, k_mat, res, 'k', 'k')
append_item_id(item_id, m_mat, res, 'm', 'm')
append_item_id(item_id, k_m_mat, res, 'k', 'm')
for k1, v in res.iteritems():
for k2, item_ids in v.iteritems():
key = self.CONCEPT_QUESTION_INDEX % (k1, k2)
flush_redis(self.redis_db, key, item_ids)
def dump_result(result, redis_db):
user_fac, item_fac, w, w0, accuracy = result
print 'accuracy: %.4f' % accuracy
for uid, fac in user_fac.iteritems():
key = CET_MOCK_TEST_USER_FACTOR_KEY % uid
flush_redis(redis_db, key, list(fac))
for qid, fac in item_fac.iteritems():
key = CET_MOCK_TEST_QUESTION_FACTOR_KEY % qid
flush_redis(redis_db, key, list(fac))
flush_redis(redis_db, CET_MOCK_TEST_MODEL_WEIGHT_KEY, list(w))
redis_db.set(CET_MOCK_TEST_MODEL_BIAS_WEIGHT_KEY, w0)
def dump_result(result, redis_db):
user_fac, item_fac, w, w0, accuracy = result
print 'accuracy: %.4f' % accuracy
for uid, fac in user_fac.iteritems():
key = CET_MOCK_TEST_USER_FACTOR_KEY % uid
flush_redis(redis_db, key, list(fac))
for qid, fac in item_fac.iteritems():
key = CET_MOCK_TEST_QUESTION_FACTOR_KEY % qid
flush_redis(redis_db, key, list(fac))
flush_redis(redis_db, CET_MOCK_TEST_MODEL_WEIGHT_KEY, list(w))
redis_db.set(CET_MOCK_TEST_MODEL_BIAS_WEIGHT_KEY, w0)
