def normalizeDB():
women_data = cdb.getFullData("looksmash_db", "looksmash_women")
for doc in women_data:
dn.normalize_data(doc)
men_data = cdb.getFullData("looksmash_db", "looksmash_men")
for doc in men_data:
dn.normalize_data(doc)
return "done"
#cdb.printColVals("Sub_category")
#
#
# with open('../DataFiles/snapdeal_data.json', 'rb') as f:
# data = f.readlines()
#
# data = map(lambda x: x.rstrip(), data)
# data_json_str = "[" + ','.join(data) + "]"
#
# data = json.loads(data_json_str)
#
#
#
#
#
# scored_data = []
# for curDoc in data:
#
# if curDoc['Sex'] != "Men":
# doc = dn.normalize_data(curDoc)
#
# doc['score'] = re.computeAccentuateScores(doc, userPrefs) + re.computeSkinToneScores(doc, "fair") + re.computeColorScores(doc, colors)
# #print doc
# scored_data.append(doc)
#
# sorted_data = sorted(scored_data, key=itemgetter('score'), reverse=True)
#
#
# top10 = itertools.islice(sorted_data, 10)
#
# print "Result"
#
# for doc in top10:
# print doc
# file = cStringIO.StringIO(urllib.urlopen(doc["Image"]).read())
# img = Image.open(file)
# img.show()
def extractPrefsFromPhrase(phrase):
mappedAttributes = {}
standards = []
colors = [e.value for e in ls.Colors]
mappedAttributes = mapAttributes(colors, "Colors", mappedAttributes)
types = [e.value for e in ls.Type]
occasions = ["Everyday wear", "Regular wear", "Party"]
subCategories = [e.value for e in ls.Sub_Category]
mappedAttributes = mapAttributes(subCategories, "Sub_category", mappedAttributes)
mappedAttributes = mapAttributes(types, "Type", mappedAttributes)
mappedAttributes = mapAttributes(occasions, "Occasions", mappedAttributes)
print "here",mappedAttributes
typeNormalization = cdb.getFullData("looksmash_normalization", "normalization")[0]
typeMappings = dn.unionDict(nr.types_dict, dn.formatDict(typeNormalization["Type"]))
standards.extend(occasions)
standards.extend(colors)
standards.extend(types)
standards.extend(subCategories)
standards = sorted(standards, key=len, reverse=True)
ltypes = createMapper(types)
accentuate = ["arms", "bust", "legs", "waist"]
hide = ["arms", "bust", "stomach", "hips and thighs"]
mapper = {
"colors": [],
"types": [],
"accentuate": [],
"hide": [],
"skinTone": "",
"bodyType": []
}
words = tokenizeImpWords(phrase)
phrases = []
phrases.extend(getTrivariates(words))
phrases.extend(getBivariates(words))
phrases.extend(words)
thresholdMatches = thresholdPartialMatches(replaceSynonyms(phrases), standards)
print thresholdMatches
result = removeTypeDependency(getMappedAttributes(thresholdMatches, mappedAttributes), typeMappings)
print "here",result
return result
def getRatings(record, userPrefs):
ratings = {"overall": 0, "body type": 0, "skin tone": 0, "message": ""}
skinTone, smsg = re.computeSkinToneScores(record, userPrefs["skinTone"],
[])
#print type(skinTone)
if record[ls.Attributes.Sex.value] == "Women":
accentuate, amsg = re.computeAccentuateScores(
record, userPrefs,
cdb.getFullData("looksmash_rules", "accentuate_women")[0])
hide = re.computeHideScores(
record, userPrefs,
cdb.getFullData("looksmash_rules", "hide")[0])
#print "A", accentuate
#print "H", hide
bodyType = 0.6 * accentuate + 0.4 * hide
overall = max(.3, float(bodyType + skinTone) / 2)
message = getRatingMessage(overall)
ratings["overall"] = overall
ratings["body type"] = bodyType
ratings["skin tone"] = skinTone
ratings["message"] = message
return ratings
elif record[ls.Attributes.Sex.value] == "Men":
bodyType, msg = re.computeAccentuateScoresForMen(
record, userPrefs,
cdb.getFullData("looksmash_rules", "accentuate_men")[0])
#print type(bodyType)
overall = max(.3, float(bodyType + skinTone) / 2)
message = getRatingMessage(overall)
ratings["overall"] = overall
ratings["body type"] = bodyType
ratings["skin tone"] = skinTone
ratings["message"] = message
return ratings
def getPhraseProducts(userprefs):
if "gender" not in userprefs.keys():
userprefs["gender"] = "Female"
bodyPrefs = {
"accentuate": {
"arms": False,
"bust": False,
"waist": False,
"legs" : False
},
"bodyType": {
"Oval": False,
"Trapezoid": False,
"Rectangle": False
},
"hide": {
"arms": False,
"bust": False,
"stomach": False,
"hips and thighs": False
}
}
phrase = userprefs["phrase"]
mappedAttrs = pp.extractPrefsFromPhrase(phrase)
colors = []
types = []
subCats = []
occasions = []
scores = cdb.getFullData("looksmash_rules", "accentuate_women")[0]
scores_men = cdb.getFullData("looksmash_rules", "accentuate_men")[0]
hideScores = cdb.getFullData("looksmash_rules", "hide")[0]
if "Colors" in mappedAttrs.keys():
colors = mappedAttrs["Colors"]
if "Type" in mappedAttrs.keys():
types = mappedAttrs["Type"]
if "Sub_category" in mappedAttrs.keys():
subCats = mappedAttrs["Sub_category"]
if "Occasion" in mappedAttrs.keys():
occasions = mappedAttrs["Occasion"]
for color in userprefs["colors"]:
colors.append(color)
products = cdb.phraseQuery("looksmash_db", ["ShoppersStop"], colors, types, subCats, occasions)
if userprefs["gender"] == "Female":
for bodyPart in userprefs["accentuate"]:
bodyPrefs["accentuate"][bodyPart.lower()] = True
for bodyPart in userprefs["hide"]:
bodyPrefs["hide"][bodyPart.lower()] = True
elif userprefs["gender"] == "Male":
for bodyType in userprefs["bodyType"]:
bodyPrefs["bodyType"][bodyType.lower()] = True
for doc in products:
accentuateScore, accentuateMsg = re.computeAccentuateScores(doc, bodyPrefs, scores) if userprefs["gender"] == "Female" else re.computeAccentuateScoresForMen(doc, bodyPrefs, scores_men)
hideScore = re.computeHideScores(doc, bodyPrefs, hideScores) if userprefs["gender"] == "Female" else 0
doc["score"] = accentuateScore + hideScore
feed = sorted(products, key=itemgetter('score'))
images = {}
i=0
for product in feed:
images[i]={"image" :product["Image"],
# "msg" : product["msg"],
"url" : product["Url"],
"domain": product["Domain"],
"score": product["score"],
"Color": product["Color"],
"Sub_category": product["Sub_category"],
"Product Name": product["Product Name"],
"Cost": product["Cost"],
"Brand": product["Brand"],
"Category": product["Category"],
}
if "Neck" in product.keys():
images[i]["Neck"] = product["Neck"],
if "Sleeves" in product.keys():
images[i]["Sleeves"] = product["Sleeves"]
i = i+1
return {"result": json.dumps(images)}
import RuleRepository.looksmashStandards as ls
import DatabaseLayer.converseWithDB as cdb
import operator
import random
rules = cdb.getFullData("looksmash_rules", "pairing")[0]
domain = "Jabong"
singleBMs = ["Skirts"]
def getBucket(score):
if score == 1 or score == .8:
return 1
if score == .6:
return .6
if score == .4 or score == .2:
return .2
def createPairFeed(sorted_final):
grouped_data = {}
for pair in sorted_final:
bucket = getBucket(pair["Score"])
if bucket not in grouped_data.keys():
grouped_data[bucket] = []
grouped_data[bucket].append(pair)
sorted_keys = grouped_data.keys()
sorted_keys.sort(reverse=True)
def getPairingRules():
rules = cdb.getFullData("looksmash_rules", "pairing")[0]
return rules
def testFeed(userprefs):
print userprefs
accentuateWeight = .7
colorWeight = .5
skinToneWeight = .1
scores = cdb.getFullData("looksmash_rules", "accentuate_women")[0]
scores_men = cdb.getFullData("looksmash_rules", "accentuate_men")[0]
hideScores = cdb.getFullData("looksmash_rules", "hide")[0]
normalization = cdb.getFullData("looksmash_normalization", "normalization")[0]
bodyPrefs = {
"accentuate": {
"arms": False,
"bust": False,
"waist": False,
"legs" : False
},
"bodyType": {
"Oval": False,
"Trapezoid": False,
"Rectangle": False
},
"hide": {
"arms": False,
"bust": False,
"stomach": False,
"hips and thighs": False
}
}
colors = userprefs['colors']
types = userprefs['types']
skinTone = userprefs['skinTone']
domains = ["Jabong"]
if userprefs["gender"] == "Female":
for bodyPart in userprefs["accentuate"]:
bodyPrefs["accentuate"][bodyPart] = True
for bodyPart in userprefs["hide"]:
bodyPrefs["hide"][bodyPart] = True
jabong_data = cdb.getFullDataWithColorsAndTypesFromDomain("looksmash_db", "looksmash_women", domains, ru.getColorList(colors,skinTone), types)
elif userprefs["gender"] == "Male":
for bodyType in userprefs["bodyType"]:
bodyPrefs["bodyType"][bodyType] = True
jabong_data = cdb.getFullDataWithColorsAndTypesFromDomain("looksmash_db", "looksmash_men", domains, ru.getColorList(colors,skinTone), types)
scored_data = []
grouped_data = {}
for doc in jabong_data:
#doc = dn.normalize_data(doc, normalization)
accentuateScore, accentuateMsg = re.computeAccentuateScores(doc, bodyPrefs, scores) if userprefs["gender"] == "Female" else re.computeAccentuateScoresForMen(doc, bodyPrefs, scores_men)
hideScore = re.computeHideScores(doc, bodyPrefs, hideScores) if userprefs["gender"] == "Female" else 0
colorScore, colorMsg = re.computeColorScores(doc, colors)
skinToneScore, skinToneMsg = re.computeSkinToneScores(doc,skinTone,colors)
#print accentuateScore, hideScore, colorScore, skinToneScore
doc['score'] = accentuateWeight*(accentuateScore + hideScore) + colorWeight*colorScore + skinToneWeight*skinToneScore
doc['msg'] = me.getMsg(accentuateMsg + colorMsg + skinToneMsg)
#print doc
if doc['score'] not in grouped_data.keys():
grouped_data[doc['score']] = {
ls.Category.Topwear.value : [],
ls.Category.Bottomwear.value : [],
ls.Category.Footwear.value : []
}
del doc['_id']
grouped_data[doc['score']][doc['Category']].append(doc)
scored_data.append(doc)
#json.dump(grouped_data, open("grouped.txt",'w'))
sorted_keys = grouped_data.keys()
sorted_keys.sort(reverse=True)
feed = createFeed(sorted_keys, grouped_data)
#print feed
images = {}
i=0
for product in feed:
images[i]={"image" :product["Image"],
"msg" : product["msg"],
"url" : product["Url"],
"domain": product["Domain"],
"score": product["score"],
"Color": product["Color"],
"Sub_category": product["Sub_category"],
"Product Name": product["Product Name"],
"Cost": product["Cost"],
"Brand": product["Brand"],
"Category": product["Category"],
"Test": ""
}
if "Neck" in product.keys():
images[i]["Neck"] = product["Neck"],
if "Sleeves" in product.keys():
images[i]["Sleeves"] = product["Sleeves"]
if "Pattern" in product.keys():
images[i]["Pattern"] = product["Pattern"]
i = i+1
return {"result": json.dumps(images)}