class MixPriceBrandAverage(Model):
##################################################################################
## INIT FUNCTIONS
def __init__(self,**kwargs):
self.name = "MIX_BRAND_PRICE_AVERAGE"
Model.__init__(self,**kwargs)
self.model_brand=BrandAverage(**kwargs)
self.model_price=PriceAverage(**kwargs)
##################################################################################
## BUILDING FUNCTIONS
def build(self,skip_cdiscount=False):
self.model_price.build()
self.model_brand.build()
def compute_category(self,item):
b=self.model_brand
p=self.model_price
if self.train:
brand_position = self.brand_position
price_position = self.price_position
else:
brand_position = self.brand_position_test
price_position = self.price_position_test
no_brand = NO_BRAND
if not smart_in(b.brands,item[brand_position]):
brand = no_brand
else:
brand = item[brand_position]
price = float(item[price_position])
if price<0:
cat=b.cat_from_brand(brand)
else:
price = p.transform(price)
prix = None
prix = find_nearest(p.p_list,price)
price=prix
if b.proba[brand]['proba']>p.proba[price]['proba'] and brand!=no_brand:
cat=b.cat_from_brand(brand)
else:
cat=p.cat_from_price(price)
return cat
class MixPriceBrandAverage(Model):
##################################################################################
## INIT FUNCTIONS
def __init__(self, **kwargs):
self.name = "MIX_BRAND_PRICE_AVERAGE"
Model.__init__(self, **kwargs)
self.model_brand = BrandAverage(**kwargs)
self.model_price = PriceAverage(**kwargs)
##################################################################################
## BUILDING FUNCTIONS
def build(self, skip_cdiscount=False):
self.model_price.build()
self.model_brand.build()
def compute_category(self, item):
b = self.model_brand
p = self.model_price
if self.train:
brand_position = self.brand_position
price_position = self.price_position
else:
brand_position = self.brand_position_test
price_position = self.price_position_test
no_brand = NO_BRAND
if not smart_in(b.brands, item[brand_position]):
brand = no_brand
else:
brand = item[brand_position]
price = float(item[price_position])
if price < 0:
cat = b.cat_from_brand(brand)
else:
price = p.transform(price)
prix = None
prix = find_nearest(p.p_list, price)
price = prix
if b.proba[brand]['proba'] > p.proba[price][
'proba'] and brand != no_brand:
cat = b.cat_from_brand(brand)
else:
cat = p.cat_from_price(price)
return cat
def __init__(self, **kwargs):
self.name = "MIX_BRAND_PRICE_AVERAGE"
Model.__init__(self, **kwargs)
self.model_brand = BrandAverage(**kwargs)
self.model_price = PriceAverage(**kwargs)
def __init__(self,**kwargs): self.name = "MIX_BRAND_PRICE_AVERAGE" Model.__init__(self,**kwargs) self.model_brand=BrandAverage(**kwargs) self.model_price=PriceAverage(**kwargs)
