# Concatenate data:
IDTest = preTreatment.concatenate_vectors(test_RM_s[0])
IDTest = IDTest.astype(np.int64)
# Rank the prediction:
predProbaRank_Test = np.zeros(predProba_Test.shape[0])
if len(predProba_Test.shape) == 2:
if predProba_Test.shape[1] == 5:
predProbaRank_Test[:] = np.max(predProba_Test[:,1:4],axis = 1)
else:
print "Error!!!!!"
else:
predProbaRank_Test = predProba_Test
yTestProbaRanked = submission.rank_signals(predProbaRank_Test)
# Create the submission file:
submission_name = "submssion_xgb_5c_SPT_r120_ams" + str(best_ams)[0:6]
print ("Generating a submsission file named %s" %submission_name)
sub = submission.print_submission(IDTest, yTestProbaRanked, predLabel_Test,
submission_name)
# Finish...
print "finish!!!"
def main():
###############
### IMPORT ####
###############
# Importation parameters:
split= True
normalize = True
noise_var = 0.
ratio_train = 0.9
# Import the training data:
print("Extracting the data sets...")
start = time.clock()
train_s, valid_s, test_s = tokenizer.extract_data(split = split,
normalize = normalize,
noise_variance = 0.,
#n_classes = "multiclass",
n_classes = "binary",
train_size = 200000,
train_size2 = 0,
valid_size = 50000)
stop = time.clock()
print ("Extraction time: %i s") %(stop-start)
print train_s[4]
print(" ")
print(" ")
######################
### PRE-TREATMENT ####
######################
print("------------------------- Pre-treatment --------------------------")
### Average number of signal per subset:
print("Train subsets signal average:")
train_s_average = preTreatment.ratio_sig_per_dataset(train_s[2])
print(" ")
print("Valid subsets signal average:")
valid_s_average = preTreatment.ratio_sig_per_dataset(valid_s[2])
print(" ")
print(" ")
############
# ANALYSES #
############
# Dictionnary that will contain all the data for each methods. In the end
# we'll have a dict of dict
# Keys of the methods : {naiveBayes, svm, kNeighbors, lda, qda, adaBoost,
# randomForest}
dMethods ={}
# NAIVE BAYES:
kwargs_bayes = {}
dMethods['naiveBayes'] = analyse.analyse(train_s, valid_s, 'naiveBayes',
kwargs_bayes)
# SVM
"""
kwargs_svm ={}
dMethods['svm'] = analyse.analyse(train_s, valid_s,'svm', kwargs_svm)
"""
# K NEIGHBORS
kwargs_kn = {'n_neighbors':50}
dMethods['kNeighbors'] = analyse.analyse(train_s, valid_s, 'kNeighbors',
kwargs_kn)
# LDA
kwargs_lda = {}
dMethods['lda'] = analyse.analyse(train_s, valid_s, 'lda', kwargs_lda)
# QDA
kwargs_qda= {}
dMethods['qda'] = analyse.analyse(train_s, valid_s, 'qda', kwargs_qda)
# ADABOOST
kwargs_ada= { 'base_estimators': None,
'n_estimators': 50,
'learning_rate': 1.,
'algorithm': 'SAMME.R',
'random_state':None}
dMethods['adaBoost'] = analyse.analyse(train_s, valid_s, 'adaBoost',
kwargs_ada)
# RANDOM FOREST:
kwargs_rdf= {'n_trees': 10}
dMethods['randomForest'] = analyse.analyse(train_s, valid_s, 'randomForest',
kwargs_rdf)
# RANDOM FOREST 2:
kwargs_rdf= {'n_trees': 100}
dMethods['randomForest2'] = analyse.analyse(train_s, valid_s, 'randomForest',
kwargs_rdf)
# ADABOOST2
kwargs_ada= { 'base_estimators': None,
'n_estimators': 100,
'learning_rate': .5,
'algorithm': 'SAMME.R',
'random_state':None}
dMethods['adaBoost2'] = analyse.analyse(train_s, valid_s, 'adaBoost',
kwargs_ada)
print(" ")
##################
# POST-TREATMENT #
##################
print("------------------------ Merged predictor -----------------------")
#ignore = ['randomForest2', 'randomForest']
ignore = []
final_prediction_s, dSl = onTopClassifier.SL_classification(dMethods, valid_s,
train_s, method='svm', ignore = ignore)
# Transform the probabilities in rank:
#final_pred = postTreatment.rank_signals(final_pred)
# Trunk the vectors
for method in dMethods:
yProba_s = dMethods[str(method)]['yProba_s']
yPredicted_s = dMethods[str(method)]['yPredicted_s']
yPredicted_treshold_s = postTreatment.proba_treshold(yPredicted_s, yProba_s, 0.5)
# Numerical score:
if type(yPredicted_s) == list:
for i in range(len(yPredicted_s)):
sum_s, sum_b = submission.get_numerical_score(yPredicted_s[i],
valid_s[2][i])
print "Subset %i: %i elements - sum_s[%i] = %i - sum_b[%i] = %i" \
%(i, yPredicted_s[i].shape[0], i, sum_s, i, sum_b)
# Get s and b for each group (s_s, b_s) and the final final_s and
# final_b:
final_s, final_b, s_s, b_s = submission.get_s_b_8(yPredicted_s, valid_s[2],
valid_s[3])
# Balance the s and b
final_s *= 250000/25000
final_b *= 250000/25000
# AMS final:
AMS = hbc.AMS(final_s , final_b)
print ("Expected AMS score for randomforest : %f") %AMS
#AMS by group
AMS_s = []
for i, (s,b) in enumerate(zip(s_s, b_s)):
s *= 250000/yPredicted_s[i].shape[0]
b *= 250000/yPredicted_s[i].shape[0]
score = hbc.AMS(s,b)
AMS_s.append(score)
print("Expected AMS score for randomforest : for group %i is : %f" %(i, score))
print(" ")
##############
# SUBMISSION #
##############
print("-------------------------- Submission ---------------------------")
# Prediction on the test set:
# method used for the submission
# TODO : Verifier que le nom de la method a bien la bonne forme(
# creer une liste de noms de methodes)
#method = "randomForest"
#test_prediction_s, test_proba_s = eval(method).get_test_prediction(
# dMethods[method]['predictor_s'],
# test_s[1])
test_prediction_s, test_proba_s = onTopClassifier.get_SL_test_prediction(
dMethods, dSl, test_s[1])
print("Test subsets signal average:")
test_s_average = preTreatment.ratio_sig_per_dataset(test_prediction_s)
print(" ")
#RankOrder = np.arange(1,550001)
if type(test_prediction_s) == list:
test_prediction_s = np.concatenate(test_prediction_s)
test_proba_s = np.concatenate(test_proba_s)
RankOrder = onTopClassifier.rank_signals(test_proba_s)
ID = np.concatenate(test_s[0])
else:
ID = test_s[0]
# Create a submission file:
sub = submission.print_submission(ID, RankOrder , test_prediction_s)
return sub
def main():
###############
### IMPORT ####
###############
# Importation parameters:
split= True
normalize = True
noise_var = 0.
ratio_train = 0.9
# Import the training data:
print("Extracting the data sets...")
start = time.clock()
train_s, valid_s, test_s = tokenizer.extract_data(split= split,
normalize= normalize,
noise_variance= noise_var,
ratio_train= ratio_train)
stop = time.clock()
print ("Extraction time: %i s") %(stop-start)
print(" ")
print(" ")
######################
### PRE-TREATMENT ####
######################
print("------------------------- Pre-treatment --------------------------")
### Average number of signal per subset:
print("Train subsets signal average:")
train_s_average = preTreatment.ratio_sig_per_dataset(train_s[2])
print(" ")
print("Valid subsets signal average:")
valid_s_average = preTreatment.ratio_sig_per_dataset(valid_s[2])
print(" ")
print(" ")
############
# ANALYSES #
############
# Dictionnary that will contain all the data for each methods. In the end
# we'll have a dict of dict
# Keys of the methods : {naiveBayes, svm, kNeighbors, lda, qda, adaBoost,
# randomForest}
dMethods ={}
# RANDOM FOREST:
kwargs_rdf= {'n_trees': 50}
dMethods['randomForest'] = analyse.analyse(train_s, valid_s, 'randomForest',
kwargs_rdf)
print(" ")
##################
# POST-TREATMENT #
##################
print("post treatment")
yProba_s = dMethods['randomForest']['yProba_s']
yPredicted_s = dMethods['randomForest']['yPredicted_s']
for n in range(8):
L = []
for i in range(yPredicted_s[n].shape[0]):
if yPredicted_s[n][i] == 1:
L.append(yProba_s[n][i][1])
L.sort(reverse = True)
prob_limit = L[int(len(L)*0.45)]
for i in range(yPredicted_s[n].shape[0]):
if yProba_s[n][i][1] < prob_limit:
yPredicted_s[n][i] = 0
else:
yPredicted_s[n][i] = 1
# Numerical score:
if type(yPredicted_s) == list:
for i in range(len(yPredicted_s)):
sum_s, sum_b = submission.get_numerical_score(yPredicted_s[i],
valid_s[2][i])
print "Subset %i: %i elements - sum_s[%i] = %i - sum_b[%i] = %i" \
%(i, yPredicted_s[i].shape[0], i, sum_s, i, sum_b)
# Get s and b for each group (s_s, b_s) and the final final_s and
# final_b:
final_s, final_b, s_s, b_s = submission.get_s_b_8(yPredicted_s, valid_s[2],
valid_s[3])
# Balance the s and b
final_s *= 250000/25000
final_b *= 250000/25000
# AMS final:
AMS = hbc.AMS(final_s , final_b)
print ("Expected AMS score for randomforest : %f") %AMS
#AMS by group
AMS_s = []
for i, (s,b) in enumerate(zip(s_s, b_s)):
s *= 250000/yPredicted_s[i].shape[0]
b *= 250000/yPredicted_s[i].shape[0]
score = hbc.AMS(s,b)
AMS_s.append(score)
print("Expected AMS score for randomforest : for group %i is : %f" %(i, score))
print(" ")
##############
# SUBMISSION #
##############
print("-------------------------- Submission ---------------------------")
# Prediction on the test set:
# method used for the submission
# TODO : Verifier que le nom de la method a bien la bonne forme(
# creer une liste de noms de methodes)
#method = "randomForest"
#test_prediction_s, test_proba_s = eval(method).get_test_prediction(
# dMethods[method]['predictor_s'],
# test_s[1])
test_prediction_s, test_proba_s = postTreatment.get_SL_test_prediction(
dMethods, dSl, test_s[1])
print("Test subsets signal average:")
test_s_average = preTreatment.ratio_sig_per_dataset(test_prediction_s)
print(" ")
#RankOrder = np.arange(1,550001)
if type(test_prediction_s) == list:
test_prediction_s = np.concatenate(test_prediction_s)
test_proba_s = np.concatenate(test_proba_s)
RankOrder = postTreatment.rank_signals(test_proba_s)
ID = np.concatenate(test_s[0])
else:
ID = test_s[0]
# Create a submission file:
sub = submission.print_submission(ID, RankOrder , test_prediction_s)
return sub
