from dal.product import Product from product_preprocess import ProductPreprocess from models.classifier import Classifier from postprocess import ProductPostprocess import score import time # preprocessing _, total_sold = Product.data_like_test() data, us26, week26_stores = Product.data_for_units_per_store() matrix = ProductPreprocess.to_matrix(data) matrix = ProductPreprocess.scale(matrix) matrix = ProductPreprocess.polynomial(matrix, 2) data = matrix.tolist() # split cv and training data train, cv = data[:1500], data[1500:] train_total_sold, cv_total_sold = total_sold[:1500], total_sold[1500:] train_week26_stores, cv_week26_stores = week26_stores[:1500], week26_stores[1500:] train_us26 = us26[:1500] #print us26[0:10] #print data[0] #print len(train[0]) #print len(train[10]) #print len(train[100]) #print len(us26) #print total_sold[0]
from dal.product import Product from product_preprocess import ProductPreprocess from models.classifier import Classifier from postprocess import ProductPostprocess import score import time start = time.time() # preprocessing _, total_sold = Product.data_like_test() train_data, us26, _ = Product.data_for_units_per_store() test_data, _, week26_stores, product_ids = Product.data_for_units_per_store(data_file="/home/ubuntu/product_launch/data/test.csv", ids=True) matrix = ProductPreprocess.to_matrix(train_data) matrix = ProductPreprocess.scale(matrix) matrix = ProductPreprocess.polynomial(matrix, 2) data = matrix.tolist() # split cv and training data train_total_sold, cv_total_sold = total_sold[:1500], total_sold[1500:] train_week26_stores, cv_week26_stores = week26_stores[:1500], week26_stores[1500:] train_us26 = us26[:1500] print us26[0:10] print data[0] #print len(train[0]) #print len(train[10]) #print len(train[100]) #print len(us26) #print total_sold[0]
