def optimiseBid(self, xTestDF, yTestDF):
print(" xTestDF:", xTestDF.shape, "\n", list(xTestDF))
print(" yTestDF:", yTestDF.shape, "\n", list(yTestDF))
result = pd.concat([xTestDF, yTestDF], axis=1)
print(" result:", result.shape, "\n", list(result))
predProb = self._model.predict_proba(xTestDF)
be = BidEstimator()
be.gridSearch_bidPrice(predProb[:, 1],
0.2,
0,
result,
bidpriceest_model='linearBidPrice')
def gridSearchBidPrice(self, y_prob, slotprices):
print("=== Get best bid prices")
#avg_ctr = ClickEvaluator().compute_avgCTR(self.Y_train)
avg_ctr = 0.00075 # use fixed ctr from full train set
print("Train avgCTR = {}".format(avg_ctr))
bid_estimator = BidEstimator()
# TODO: could add option for alternate bid strats
best_pred_thresh, best_base_bid, perf_df = bid_estimator.gridSearch_bidPrice(y_prob, avg_ctr, slotprices,self.gold_val,bidpriceest_model='linearBidPrice')
ipinyouWriter.ResultWriter().writeResult(re.sub('.csv','-linearBidPrice.csv',self.bids_tuning_perf_filepath), perf_df) #
best_pred_thresh, best_base_bid, perf_df = bid_estimator.gridSearch_bidPrice(y_prob, avg_ctr, slotprices,self.gold_val,bidpriceest_model='linearBidPrice_variation')
ipinyouWriter.ResultWriter().writeResult(re.sub('.csv','-linearBidPrice_variation.csv',self.bids_tuning_perf_filepath), perf_df)
return best_pred_thresh,best_base_bid
def optimiseBid(self, xTestDF, yTestDF):
"""
Perform bid optimisation based on params
:param xTestDF:
:param yTestDF:
:return:
"""
print(" xTestDF:", xTestDF.shape, "\n", list(xTestDF))
print(" yTestDF:", yTestDF.shape, "\n", list(yTestDF))
result = pd.concat([xTestDF, yTestDF], axis=1)
print(" result:", result.shape, "\n", list(result))
predProb = self.__predictClickOneProb(xTestDF)
be = BidEstimator()
be.gridSearch_bidPrice(predProb[:, 1],
0,
0,
result,
bidpriceest_model='thresholdsigmoid')
def exeEnsemble_Weighted(trainDF, validateDF, testDF,
trainPath, validationPath, testPath,
trainReader, validateReader, testReader,
writeResult2CSV=False):
'''
Takes the average of y_pred from all models.
'''
xg_val_y_pred, xg_test_y_pred = exeXGBoostBidModel(validationData=validateDF, trainData=trainDF, testData=testDF, writeResult2CSV=False)
cnn_val_y_pred, cnn_test_y_pred = exeCNNBidModel(validationDataPath=validationPath, trainDataPath=trainPath, testDataPath=testPath, writeResult2CSV=False)
#lr_y_pred = exeLogisticRegressionBidModel_v2(validationReader=validationReader, trainReader=trainReader, writeResult2CSV=False)
#fm_y_pred=exeFMBidModel(trainReader=trainReader, validationReader=validateReader, testReader=testReader, writeResult2CSV=False)
# Average them
# y_pred = [(xg+ lr) / 2.0 for xg, lr in zip(xg_y_pred, lr_y_pred)]
# y_pred = [(xg + cnn + lr)/3.0 for xg, cnn, lr in zip(xg_y_pred, cnn_y_pred, lr_y_pred)]
#y_pred = [(xg*0.4 + cnn*0.4 + lr*0.05 + fm*0.15) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 87.80
#This one hits 0.874 for the xg/lr/fm emsemble models, perviously 0.861 (Can't run CNN on my mac yet, got this convolution missing error)
# y_pred = [(xg * 0.6 + lr * 0.1 + fm * 0.3) for xg, lr, fm in zip(xg_y_pred, lr_y_pred, fm_y_pred)]
#ongmin testing
# y_pred = [(xg * 0.5 + cnn * 0.5 + lr * 0.05 + fm * 0.15) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 0.8760
# y_pred = [(xg * 0.6 + cnn * 0.4 + lr * 0.00 + fm * 0.00) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 0.8810
# y_pred = [(xg*0.5 + cnn*0.5 + lr*0.00 + fm*0.00) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 0.8797
#y_pred = [(xg * 0.7 + cnn * 0.3 + lr * 0.00 + fm * 0.00) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 0.8840
#y_pred = [(xg * 0.8 + cnn * 0.2 + lr * 0.00 + fm * 0.00) for xg, cnn, lr, fm in zip(xg_y_pred, cnn_y_pred, lr_y_pred, fm_y_pred)] # AUC 0.8836
val_y_pred = [(xg * 0.7 + cnn * 0.3 ) for xg, cnn in zip(xg_val_y_pred, cnn_val_y_pred)] # AUC 0.8840
timestamp=str(time.strftime("%Y%m%d-%H%M%S"))
print("XGBoost AUC:")
ClickEvaluator().clickROC(validateDF['click'], xg_val_y_pred, imgpath="./SavedEnsembleInfo/XGBoost_AUC-" + timestamp + ".jpg")
print("CNN AUC:")
ClickEvaluator().clickROC(validateDF['click'], cnn_val_y_pred, imgpath="./SavedEnsembleInfo/CNN_AUC-" + timestamp + ".jpg")
# print("Logistic AUC:")
# ClickEvaluator().clickROC(validateDF['click'], lr_y_pred, imgpath="./SavedEnsembleInfo/LogisticR_AUC-" + timestamp + ".jpg")
# print("FastFM AUC:")
# ClickEvaluator().clickROC(validateDF['click'], fm_y_pred, imgpath="./SavedEnsembleInfo/FastFM_AUC-" + timestamp + ".jpg")
print("Ensemble AUC:")
ClickEvaluator().clickROC(validateDF['click'], val_y_pred, imgpath="./SavedEnsembleInfo/ensemble_weighted_AUC-" + timestamp + ".jpg",
showGraph=False)
val_y_pred = np.array(val_y_pred)
click1 = val_y_pred[validateDF.click == 1]
n, bins, patches = ClickEvaluator().clickProbHistogram(pred_prob=click1, color='g',
title='Predicted probabilities for clicks=1',
imgpath="./SavedEnsembleInfo/ensemble_weighted-click1-" + timestamp + ".jpg",
showGraph=False)
# click=0 prediction as click=1 probabilities
click0 = val_y_pred[validateDF.click == 0]
n, bins, patches = ClickEvaluator().clickProbHistogram(pred_prob=click0, color='r',
title='Predicted probabilities for clicks=0',
imgpath="./SavedEnsembleInfo/ensemble_weighted-click0-" + timestamp + ".jpg",
showGraph=False)
### Bid price model evaluations
test_y_pred = [(xg * 0.7 + cnn * 0.3 ) for xg, cnn in zip(xg_test_y_pred, cnn_test_y_pred)]
slotprices_val = validateDF['slotprice'].as_matrix().astype(int)
slotprices_test = testDF['slotprice'].as_matrix().astype(int)
print("=== Get best bid prices on validation set")
#avg_ctr = ClickEvaluator().compute_avgCTR(trainDF.click)
#TODO override with complete train set avg ctr
avg_ctr = 0.00075
print("Train avgCTR = {}".format(avg_ctr))
bid_estimator = BidEstimator()
print("== linearBidPrice")
best_pred_thresh, best_base_bid, perf_df = bid_estimator.gridSearch_bidPrice(val_y_pred, avg_ctr, slotprices_val,
validateDF,
bidpriceest_model='linearBidPrice')
ipinyouWriter.ResultWriter().writeResult("./SavedEnsembleInfo/ensemble_weighted-linearBidPrice-"+ timestamp +".csv",perf_df) #
print("= linearBidPrice estimate test bids")
bids = bid_estimator.linearBidPrice(test_y_pred, best_base_bid, avg_ctr)
# format bids into bidids pandas frame
bids_df = pd.concat([testDF['bidid'], pd.DataFrame(bids, columns=['bidprice'], index=testDF['bidid'].index)],axis=1)
ipinyouWriter.ResultWriter().writeResult("./SavedEnsembleInfo/ensemble_weighted-testbids-"+ timestamp +".csv", bids_df)
def tunelinearBaseBid(self, testDF):
print("Setting up XGBoost for Test set")
y_pred = self.__estimateClick(testDF)
be = BidEstimator()
be.gridSearch_bidPrice(y_pred, 0, 0, testDF, budget=(6250*1000), bidpriceest_model='linearBidPrice_mConfi')