# Concatenate results & data:
predProba_Train2 = preTreatment.concatenate_vectors(predProba_Train2_s)
yTrain2 = preTreatment.concatenate_vectors(train_RM_s_2[2])
weightsTrain2 = preTreatment.concatenate_vectors(train_RM_s_2[3])
# Looking for the best threshold:
if type(train_s[1]) == list:
best_ams_train2, best_ratio = tresholding.\
best_ratio_combinaison_global(
predProba_Train2_s,
train_RM_s_2[2],
train_RM_s_2[3],
5)
else:
best_ams_train2, best_ratio = tresholding.best_ratio(
predProba_Train2,
yTrain2,
weightsTrain2)
print "Train2 - best ratio : %s - best ams : %f" \
%(', '.join(map(str,best_ratio)), best_ams_train2)
print(" ")
print "Making predictions on the validation set..."
# Prediction of the validation set 2:
predProba_Valid2_s = xgBoost.predict_proba(predictor_s, valid_RM_s_2[1])
# Thresholding the predictions:
predProba_Valid2 = preTreatment.concatenate_vectors(predProba_Valid2_s)
predLabel5_Valid2 = tresholding.get_yPredicted_ratio(predProba_Valid2,
def train(max_depth, n_rounds):
###############
### IMPORT ####
###############
# Importation parameters:
split= True
normalize = True
noise_var = 0.
train_size = 200000
train_size2 = 25000
valid_size = 25000
remove_999 = False
# Import the training data:
print("Extracting the data sets...")
start = time.clock()
train_s, train2_s, valid_s, test_s = tokenizer.extract_data(split= split, \
normalize= normalize, \
remove_999 = remove_999, \
noise_variance= noise_var, \
n_classes = "multiclass", \
train_size = train_size, \
train_size2 = train_size2, \
valid_size = valid_size)
#RANDOM FOREST:
#kwargs_grad = {}
#kwargs_rdf = {'n_estimators': 100}
print "Training on the train set ..."
#predictor_s = randomForest.train_classifier(train_s[1], train_s[2], kwargs_rdf)
#XGBOOST
kwargs_xgb = {'bst_parameters': \
{'booster_type': 0, \
#'objective': 'binary:logitraw',
'objective': 'multi:softprob', 'num_class': 5,
'bst:eta': 0.1, # the bigger the more conservative
'bst:subsample': 1, # prevent over fitting if <1
'bst:max_depth': max_depth, 'eval_metric': 'auc', 'silent': 1,
'nthread': 8 }, \
'n_rounds': n_rounds}
predictor_s = xgBoost.train_classifier(train_s[1], train_s[2], train_s[3], 550000, kwargs_xgb)
#TEST / SUBMISSION
"""
yProbaTest_s = []
yProbaTestBinary_s = []
print "Classifying the test set..."
for i in range(8):
yProbaTest = xgBoost.predict_proba(predictor_s[i], test_s[1][i])
yProbaTest_s.append(yProbaTest)
print "Making the binary proba vector..."
for i in range(8):
yProbaTestBinary_s.append(np.zeros(yProbaTest_s[i].shape[0]))
for i in range(8):
for j in range(yProbaTest_s[i].shape[0]):
yProbaTestBinary_s[i][j] = 1 - yProbaTest_s[i][j][0]
print "Concatenating the vectors..."
yProbaTestBinary = preTreatment.concatenate_vectors(yProbaTestBinary_s)
IDs = preTreatment.concatenate_vectors(test_s[0])
yProbaTestBinaryRanked = submission.rank_signals(yProbaTestBinary)
yPredictedTest = tresholding.get_yPredicted_ratio(yProbaTestBinary, 0.15)
s = submission.print_submission(IDs, yProbaTestBinaryRanked, yPredictedTest, "newAMSmesure")
"""
# TRAIN AND VALID
yPredictedTrain2_s = []
yProbaTrain2_s = []
yProbaTrain2Binary_s = []
yPredictedValid_s = []
yProbaValid_s = []
yProbaValidBinary_s = []
print "Classifying the train2 set..."
for i in range(8):
yProbaTrain2 = xgBoost.predict_proba(predictor_s[i], train2_s[1][i])
yProbaTrain2_s.append(yProbaTrain2)
print "Classifying the valid set..."
for i in range(8):
yProbaValid = xgBoost.predict_proba(predictor_s[i], valid_s[1][i])
yProbaValid_s.append(yProbaValid)
print "Making the binary proba vector..."
for i in range(8):
yProbaTrain2Binary_s.append(np.zeros(yProbaTrain2_s[i].shape[0]))
yProbaValidBinary_s.append(np.zeros(yProbaValid_s[i].shape[0]))
for i in range(8):
for j in range(yProbaTrain2_s[i].shape[0]):
yProbaTrain2Binary_s[i][j] = 1 - yProbaTrain2_s[i][j][0]
for j in range(yProbaValid_s[i].shape[0]):
yProbaValidBinary_s[i][j] = 1 - yProbaValid_s[i][j][0]
print "Concatenating the vectors..."
yProbaTrain2Binary = preTreatment.concatenate_vectors(yProbaTrain2Binary_s)
yProbaValidBinary = preTreatment.concatenate_vectors(yProbaValidBinary_s)
yTrain2 = preTreatment.concatenate_vectors(train2_s[2])
yValid = preTreatment.concatenate_vectors(valid_s[2])
weightsTrain2 = preTreatment.concatenate_vectors(train2_s[3])
weightsValid = preTreatment.concatenate_vectors(valid_s[3])
print "Putting all the real labels to 1"
yTrain2 = preTreatment.multiclass2binary(yTrain2)
yValid = preTreatment.multiclass2binary(yValid)
print "Getting the best ratios..."
best_ams_train2_global, best_ratio_global = tresholding.best_ratio(yProbaTrain2Binary, yTrain2, weightsTrain2)
#best_ams_train2_combinaison, best_ratio_combinaison = tresholding.best_ratio_combinaison_global(yProbaTrain2Binary_s, train2_s[2], train2_s[3], 1)
yPredictedValid = tresholding.get_yPredicted_ratio(yProbaValidBinary, 0.15)
yPredictedValid_best_ratio_global = tresholding.get_yPredicted_ratio(yProbaValidBinary, best_ratio_global)
#yPredictedValid_best_ratio_combinaison_s, yPredictedValid_best_ratio_combinaison = tresholding.get_yPredicted_ratio_8(yProbaTrain2Binary_s, best_ratio_combinaison)
#Let's compute the predicted AMS
s, b = submission.get_s_b(yPredictedValid, yValid, weightsValid)
AMS = hbc.AMS(s,b)
#s_best_ratio_combinaison, b_best_ratio_combinaison = submission.get_s_b(yPredictedValid_best_ratio_combinaison, yValid, weightsValid)
#AMS_best_ratio_combinaison = hbc.AMS(s_best_ratio_combinaison, b_best_ratio_combinaison)
s_best_ratio_global, b_best_ratio_global = submission.get_s_b(yPredictedValid_best_ratio_global, yValid, weightsValid)
AMS_best_ratio_global = hbc.AMS(s_best_ratio_global, b_best_ratio_global)
print "AMS 0.15 = %f" %AMS
print " "
#print "AMS best ratio combi= %f" %AMS_best_ratio_combinaison
#print "best AMS train2 ratio combinaison= %f" %best_ams_train2_combinaison
#print "best ratio combinaison train 2 = %s" %str(best_ratio_combinaison)
print " "
print "best AMS valid ratio global= %f" %AMS_best_ratio_global
print "best AMS train2 ratio global= %f" %best_ams_train2_global
print "best ratio global train2 = %f" %best_ratio_global
return AMS