def cross_validate(params):
global test_data_products, model_products
_C, _epsilon = params
data = test_data_products[1].dropna()
X = data[[
'amount_of_all_competitors', 'average_price_on_market',
'distance_to_cheapest_competitor', 'price_rank', 'quality_rank'
]]
y = data['sold'].copy()
y[y > 1] = 1
model = PassiveAggressiveRegressor(max_iter=1000, tol=0.0001)
model.set_params(C=_C, epsilon=_epsilon)
score = -np.mean(cross_val_score(model, X, y, cv=3, scoring='r2'))
return score
def train(params=[1.0, 0.001]):
global data_products, model_products
_C, _epsilon = params
for product_id in data_products:
data = data_products[product_id].dropna()
if len(data.index) <= 0:
return
X = data[[
'amount_of_all_competitors', 'average_price_on_market',
'distance_to_cheapest_competitor', 'price_rank', 'quality_rank'
]]
y = data['sold'].copy()
y[y > 1] = 1
model = PassiveAggressiveRegressor(n_iter=1000)
model.set_params(C=_C, epsilon=_epsilon)
model.fit(X, y)
model_products[product_id] = model
