def best_ratio_combinaison(yProba_s, yValidation_s, weightsValidation_s, ratio_s):
"""
returns the best ratio combinaison with the ratios specified in ratio_s for each
group
ratio_s : List of the list of the ratios to test for each group
the size of each list should not exceed 4 for computationnal time issues
"""
best_ratio_comb = [0.,0.,0.,0.,0.,0.,0.,0.]
AMS_max = 0.
"""
ratio_1_s = [0.06, 0.08,0.10,0.12]
ratio_2_s = [0.15,0.16,0.17,0.18]
ratio_3_s = [0.36,0.38,0.40,0.42]
ratio_4_s = [0.16,0.18,0.2,0.22]
ratio_5_s = [0.007,0.008,0.009,0.01]
ratio_6_s = [0.003,0.004,0.005,0.006]
ratio_7_s = [0.003,0.004,0.005,0.006]
ratio_8_s = [0.007,0.008,0.009,0.01]
"""
g_combinaisons = itertools.product(ratio_s[0], ratio_s[1],
ratio_s[2], ratio_s[3],
ratio_s[4], ratio_s[5],
ratio_s[6], ratio_s[7])
# if we work with multi-class:
if len(yProba_s[0].shape) == 2:
if yProba_s[0].shape[1] == 5:
for i,subset in enumerate(yProba_s):
yProba_s[i] = preTreatment.multiclass2binary(subset)
compteur = 0
for combinaison in g_combinaisons:
#if compteur%10000==0:
# print "number of iterations : %i" %compteur
compteur +=1
L = list(combinaison)
yPredicted_s, yPredicted_conca = get_yPredicted_ratio_8(yProba_s, L)
finals, finalb, s_s, b_s = submission.get_s_b(yPredicted_s,
yValidation_s,
weightsValidation_s)
AMS = hbc.AMS(finals, finalb)
if AMS > AMS_max:
AMS_max = AMS
best_ratio_comb = L
return AMS_max, best_ratio_comb
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,
best_ratio)
# Binarize the prediction:
predLabel_Valid2 = preTreatment.multiclass2binary(predLabel5_Valid2)
# Concatenate data:
yValid2 = preTreatment.concatenate_vectors(valid_RM_s_2[2])
weightsValidation = preTreatment.concatenate_vectors(valid_RM_s_2[3])
# Estimation the AMS:
s, b = submission.get_s_b(predLabel_Valid2, yValid2, weightsValidation)
s *= 250000/predLabel_Valid2.shape[0]
b *= 250000/predLabel_Valid2.shape[0]
ams = hbc.AMS(s,b)
print "Valid_RM_2 - ratio : %f - best ams : %f" %(best_ratio, ams)
print(" ")
# Saving the model if it's better:
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