def method(train, validation, silent): trainResults = training(train, silent) #testResults = testFunc(trainResults, test)[0] bidprices = testFunc(trainResults, validation, silent)[1] #saveToFile(testResults, 'data/submissions/test.csv') return evaluate(bidprices, 'data/datasets/validation.csv', 6250)
def predict(constant_bidprices, validation_set):
print('Predicting bid prices using constant bidding strategy model:')
bids = {}
validationDF = pd.read_csv(validation_set)
n = len(validationDF)
for i in range(0, n):
progress(i + 1, n)
bidid = validationDF.bidid.values[i]
advertiser = validationDF.advertiser.values[i]
constBidPrice = constant_bidprices[advertiser]
bids[bidid] = constBidPrice
return bids
clearTerminal()
start = time.time()
print('Start')
const_bidprices = train('train.csv')
bids = predict(const_bidprices, 'validation.csv')
evaluate(bids, 'validation.csv', 25000)
end = time.time()
printElapsedTime(start, end)
print('Finish')
print('Predicting bid prices using linear bidding strategy model:')
for i in range(0, n):
progress(i+1, n)
bidid = validationDF.bidid.values[i]
advertiser = validationDF.advertiser.values[i]
baseBidPrice = int(base_bidprices[advertiser] * pctrs[i] / avgCTR)
bids[bidid] = baseBidPrice
return bids
clearTerminal()
start = time.time()
print('Start')
trainingDF = pd.read_csv('train.csv')
validationDF = pd.read_csv('validation.csv')
transformCategoricalFeatures(trainingDF)
transformCategoricalFeatures(validationDF)
base_bidprices = train()
bids = predict(base_bidprices)
evaluate(bids, 'validation.csv', 6250)
end = time.time()
printElapsedTime(start, end)
print('Finish')
if click_valid[i] == 1:
print(i, linear_scale_factors[i], logistic_scale_factors[i])
clicked_linear_scale_factors.append(linear_scale_factors[i])
clicked_logistic_scale_factors.append(logistic_scale_factors[i])
linear_scaled_bidprices = scaleBids(bidprice_pred, linear_scale_factors)
logistic_scaled_bidprices = scaleBids(bidprice_pred, logistic_scale_factors)
unscaled_bids = dict(zip(bidid_valid, bidprice_pred))
linear_scaled_bids = dict(zip(bidid_valid, linear_scaled_bidprices))
logistic_scaled_bids = dict(zip(bidid_valid, logistic_scaled_bidprices))
if SOLUTION:
out = open('testing_bidding_price.csv', 'w')
out.write('bidid,bidprice\n')
for i in range(0, len(linear_scaled_bidprices)):
out.write(
str(bidid_valid[i]) + ',' + str(ceil(linear_scaled_bidprices[i])) +
'\n')
out.flush()
out.close()
else:
evaluate(unscaled_bids, 'validation.csv', 6250)
evaluate(logistic_scaled_bids, 'validation.csv', 6250)
evaluate(linear_scaled_bids, 'validation.csv', 6250)
end = time.time()
printElapsedTime(start, end)
print('Finish')