# 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] # classify start = time.time() preds = Classifier.preds(train, train_us26, cv, cv_week26_stores) preds = ProductPostprocess.nonnegatives(preds) print "duration: ", time.time() - start print preds[:10] print cv_total_sold[:10] # score print "score: ", score.rmsle(preds, cv_total_sold)
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] # classify start = time.time() preds = Classifier.preds(train_data, us26, test_data, week26_stores) preds = ProductPostprocess.nonnegatives(preds) ProductPostprocess.submit(preds, product_ids) print "duration: ", time.time() - start
