def getElementsByAsin(asinKeys):
result = es.search(
index="laptops",
body={"query": {"terms": {"asin.keyword": asinKeys}}, "size": 7000},
)
return Backend_Helper.refineResult(result)
def search_for_all_docs():
allDocs = es.search(index="amazon",
body={
"size": 10000,
"query": {
"match_all": {}
}
})
return allDocs
def search_for_some_docs():
some_docs = es.search(index="amazon",
body={
"query": {
"match": {
"avgRating": 5
}
},
"size": 10
})
return some_docs
def get_vague_and_binary_lists(clean_data1):
# create binary clean data if weighting is equal to 5
binary_clean_data = {}
clean_data = {}
# bool_search_default = False #If no weighting = 5 for any value, do not caculate boolean search below
for field in clean_data1.keys():
if clean_data1[field]['weight'] == 5:
# bool_search_default = True
binary_clean_data[field] = clean_data1[field] # weigth doesn't matter for boolean search
else:
clean_data[field] = clean_data1[field]
# binary_clean data has to also contain the empty/meaningless fields because this is the format needed for BinarySearch() method
# This doesn't matter though because weight has no meaning for boolean search and is not used in the calculation for the result set
binary_clean_data[field] = {'weight': 1}
# print("print binary_clean_data: ", binary_clean_data)
# print("print clean_data: ", clean_data)
# Compute boolean/binary search for items with weighting = 5
bin_obj = binary_search.BinarySearch()
query = bin_obj.createBinarySearchQuery(binary_clean_data)
res = es.search(index="amazon", body=query)
output_binary = Backend_Helper.refineResult(res)
return clean_data, output_binary
def do_query(data):
with open(allDocs_path, 'rb') as input:
allDocs = pickle.load(input)
data = Backend_Helper.clean_frontend_json(data)
#create binary clean data if weighting is equal to 5
binary_clean_data = {}
clean_data = {}
alexa_clean_data = {}
output_binary = list()
#bool_search_default = False #If no weighting = 5 for any value, do not caculate boolean search below
for field in data.keys():
if data[field]['weight'] == 5:
#bool_search_default = True
binary_clean_data[field] = data[field] #weigth doesn't matter for boolean search
elif data[field]["weight"] == 6: #This is for alexa_search, will be used at the end
alexa_clean_data[field] = data[field]
pass
else:
clean_data[field] = data[field]
#Compute boolean/binary search for items with weighting = 5
bin_obj = binary_search.BinarySearch()
alexa_searcher = alexa_functions.AlexaSearch(es)
if len(binary_clean_data) > 0:
query = bin_obj.createBinarySearchQuery(binary_clean_data)
res = es.search(index="amazon", body=query)
output_binary = Backend_Helper.refineResult(res)
if len(alexa_clean_data) > 0 :
#Add alexa search results to output_binary, same mechanism and logic for both.
alexa_result = get_alexa_search_result(allDocs, alexa_clean_data, alexa_searcher)
output_binary += alexa_result
res_search = list()
# if(len(output_binary)) > 0:
# allDocs = [item for item in allDocs if item['asin'] in output_binary]
# field_value_dict has the form:
# {'binary' : { 'brandName': ['acer', 'hp'], 'weight':1}, ...}, 'vague' : {....},
field_value_dict = extract_fields_and_values(clean_data)
# print(field_value_dict)
#Get total cumulative weight weight_sum (for example for all attributes weights were 7) and dividue each score by this weight_sum
#For normalization
weight_sum = 0
for field_type in field_value_dict.keys():
for field_name in field_value_dict[field_type]:
field_weight = field_value_dict[field_type][field_name]["weight"]
if field_weight != 5: ##Shouldn't happen though because they have already been removed from clean_data
weight_sum += field_weight
# --------------------------------------------------------------------#
# Objects for each class to use the vague searching functions
range_searcher = vague_search_range.VagueSearchRange(es)
binary_searcher = binary_search_text.BinarySearchText(es)
harddrive_searcher = vague_search_harddrive.VagueHardDrive(es)
value_searcher = vague_search_value.VagueSearchValue(es)
######################################################################## NEW #########################################
#Function call in ColorInformation to extract searched values.
#function extractKeyValuePairs() will do that.
c_i_helper = ColorInformation()
price_searcher = vague_search_price.VagueSearchPrice(es)
######################################################################## NEW #########################################
# --------------------------------------------------------------------#
# # Special case to handle hardDriveSize, length is >1 if it has values other than weight
if 'hardDriveSize' in clean_data and len(clean_data["hardDriveSize"]) > 1:
# res_search += vague_search_harddrive.computeVagueHardDrive_alternative(allDocs, clean_data,
# harddrive_searcher,
# res_search)
res_search = harddrive_searcher.computeVagueHardDrive_alternative(allDocs, field_value_dict,
harddrive_searcher,
res_search)
# --------------------------------------------------------------------#
# Special case to handle price
if 'price' in clean_data and len(clean_data["price"]) > 1:
##NEW##########
#res_search += vague_search_price.VagueSearchPrice.computeVaguePrice_alternative(allDocs, clean_data, price_searcher, res_search, searchedValues)
res_search = price_searcher.computeVaguePrice_alternative(allDocs, field_value_dict, price_searcher, res_search)
# --------------------------------------------------------------------#
# Gets scores for all other attributes
res_search += call_responsible_methods(allDocs, field_value_dict, range_searcher, binary_searcher, value_searcher,
alexa_searcher)
# --------------------------------------------------------------------#
resList = [dict(x) for x in res_search]
# Counter objects count the occurrences of objects in the list...
count_dict = Counter()
for tmp in resList:
count_dict += Counter(tmp)
result = dict(count_dict)
sortedDict = collections.OrderedDict(sorted(result.items(), key=lambda x: x[1], reverse=True))
asinKeys = list(result.keys())
# call the search function
outputProducts = getElementsByAsin(asinKeys) #calls helper class method refineResuls
# Compare outputProducts and output_binary to select only items that also occur in boolean search
outputProducts, output_binary = filter_from_boolean(outputProducts, output_binary)
# add a vagueness score to the returned objects and normalize
for item in outputProducts:
# Normalize the scores so that for each score x, 0< x <=1
item['vaguenessScore'] = result[item['asin']]/weight_sum
outputProducts = sorted(outputProducts, key=lambda x: x["vaguenessScore"], reverse=True)
for item in output_binary: #binary search results that did not meet other vague requirements
item['vaguenessScore'] = None
# concatenate with products with weighting 5 ***
outputProducts = outputProducts + output_binary
# products with same vagueness score should be listed according to price descending
#searchedValues = c_i.extractKeyValuePairs()
#c_i.prozessDataBinary(searchedValues)
# If possible, apply sorting before weigthing, so it does not interfere with the list sorted by weighting
s_p = SortByPrice()
# #DELETE all products with vagueness_score = 0
outputProducts_vaguenessGreaterZero = list()
for laptop in outputProducts:
if laptop["vaguenessScore"] != 0:
outputProducts_vaguenessGreaterZero.append(laptop)
outputProducts_vaguenessGreaterZero = s_p.sort_by_price(outputProducts_vaguenessGreaterZero)
#outputProducts_vaguenessGreaterZero , output_binary = filter_from_boolean(outputProducts_vaguenessGreaterZero, output_binary)
#outputProducts_vaguenessGreaterZero = outputProducts_vaguenessGreaterZero[:1000]
c_i_helper.add_matched_information(data,outputProducts_vaguenessGreaterZero,allDocs)
#Needed in frontend
outputProducts_vaguenessGreaterZero_with_original_query = [outputProducts_vaguenessGreaterZero,data]
return outputProducts_vaguenessGreaterZero_with_original_query
def get_reviews_data(asin_keys):
result = es.search(
index="products",
body={"query": {"terms": {"asin.keyword": asin_keys}}, "size": 7000},
)
return Backend_Helper.refineReviews(result)
def search():
"""
Search for a file, required parameter "q" is the term to search for
Possible filters:
- type = Limit only for this type of files
- path = Limit only for files in this directory
The search automatically add the following filters:
- level = Limit only for files of level lowest or equals to current user's level
- group = Limit only for files with least one group of the current user
Extra parameters:
- page = The number of page
"""
query_text = request.args.get('q', '').lower()
if not query_text:
return abort(400)
type_item = None
type_id = request.args.get('type', None)
path = request.args.get('path', '')
page_size = 20
page = int(request.args.get('page', 1)) - 1
if type_id:
type_item = mongo.db.types.find_one_or_404({'_id': ObjectId(type_id)})
invalid_level = type_item['level'] > g.level
if g.level < 3:
type_groups = type_item['groups']
user_groups = [group['_id'] for group in g.groups]
invalid_group = len(type_groups) > 0 and set(
user_groups).isdisjoint(type_groups)
else:
invalid_group = False
if invalid_level or invalid_group:
return abort(401)
query_type = [type_id]
else:
query_type = [str(t['_id']) for t in get_types()]
inherited_level = 1
query = {
'query': {
'bool': {
'must': [
{
'term': {
'doc_type': 'doc'
}
}, # Only documents
{
'range': {
'level': {
'lt': g.level + 1
}
}
}, # Only lower or equal than current user level
{
'terms': {
'type.keyword': query_type
}
}, # Only allowed types
{
'regexp': {
'path.keyword': '{}.*'.format(path)
}
}, # Only inside this path
{
'bool': {
'should': [
{
'match': {
'entities': '{}^6'.format(query_text)
}
}, # Match term in entities with 6 of boost
{
'match': {
'name_lower': '{}^3'.format(query_text)
}
}, # Match term in name with 3 of boost
{
'match': {
'description_lower':
'{}'.format(query_text)
}
} # Match term in description with no boost
],
'minimum_should_match':
'1'
}
} # Should match at least one of the above
]
}
},
'size': page_size,
'from': page_size * page
}
results = es.search(index=app.config['ELASTICSEARCH_INDEX'],
doc_type='documents',
body=query)
return render_template('search.html',
type_item=type_item,
path=None,
query_text=query_text,
inherited_level=inherited_level,
results=results)
async def search_book(keyword: str):
try:
result = es.search(index="library", body={"query": {"match": {"title": f'"{keyword}"'}}})
return result['hits']['hits']
except elasticsearch.exceptions.NotFoundError:
return {"error": "No matching book found!"}
