def classify_final_test(data, features, data_to_classify, cost, g, svm_kernel):
##make all data becoming part of the training
train_set, _ = utils.cut_dataset(range(len(data)), [], data, features)
_, test_set = utils.cut_dataset([], range(len(data_to_classify)), data_to_classify, features)
if svm_kernel == "linear":
clf = svm.LinearSVC(C=cost) #used for gissete and madelon
else:
clf = svm.SVC(C=cost, gamma=g, kernel='rbf')
#
clf.fit(train_set.fts_values, train_set.fts_pred)
predictions = clf.predict(test_set.fts_values)
preds = []
for i in predictions:
#print i
preds.append(int(i))
return preds
def classify_final_test(data, features, data_to_classify, cost, g, svm_kernel):
##make all data becoming part of the training
train_set, _ = utils.cut_dataset(range(len(data)), [], data, features)
_, test_set = utils.cut_dataset([], range(len(data_to_classify)),
data_to_classify, features)
if svm_kernel == "linear":
clf = svm.LinearSVC(C=cost) #used for gissete and madelon
else:
clf = svm.SVC(C=cost, gamma=g, kernel='rbf')
#
clf.fit(train_set.fts_values, train_set.fts_pred)
predictions = clf.predict(test_set.fts_values)
preds = []
for i in predictions:
#print i
preds.append(int(i))
return preds
def classify(folds, data, features, cost, gamma, svm_kernel):
##train_set, test_set = utils.cut_dataset(n_train, n_test, data, test_subset.features) ##get the features for the subset
scores = []
for fold in folds: ##run a classification for each fold
train, test = fold ##division of data on that fold
train_set, test_set = utils.cut_dataset(train, test, data, features)
##print train_set.fts_pred, test_set.fts_pred
score = classification_svm(train_set, test_set, cost, gamma, svm_kernel)
if math.isnan(score):
#print "nan score"
score = 0
scores.append(score)
#print scores
final_score = sum(scores) / float(len(scores))
return final_score
def classify(folds, data, features, cost, gamma, svm_kernel):
##train_set, test_set = utils.cut_dataset(n_train, n_test, data, test_subset.features) ##get the features for the subset
scores = []
for fold in folds: ##run a classification for each fold
train, test = fold ##division of data on that fold
train_set, test_set = utils.cut_dataset(train, test, data, features)
##print train_set.fts_pred, test_set.fts_pred
score = classification_svm(train_set, test_set, cost, gamma,
svm_kernel)
if math.isnan(score):
#print "nan score"
score = 0
scores.append(score)
#print scores
final_score = sum(scores) / float(len(scores))
return final_score
def worker_classification(id, work_list, data, features, n_train, n_test, data_pipe, com_pipe):
worker_time = time.time() ##to know how much time did the worker work
best_acc = 0
lowest_acc = 0
wl_dict = {} ##to keep all the generated subsets on this slave
best_sets = {}
last_test_sets = {} ##new test set since last talk com with manager
total_wasted = 0
testing = True
number_of_tests = 0
comm_rate = 60
counter_time = time.time()
talk_counter = 0
talk_avgs = []
talk_time = time.time()
#print "started ", id, " with work ", translate_wl(work_list)
while (testing):
if work_list == []:
##the last test sets will be sent on next talk to the manager
if number_of_tests < 30:
print id, " waiting cause ran out of work too soon"
time.sleep(10)
work_list, best_acc, wasted_time = ask_for_work(id, data_pipe, com_pipe)
total_wasted += wasted_time
counter = comm_rate#random.randint(50, 150) #Since we need to send the last test sents to manager lets set this to a lower value
if work_list == []: ##didnt receive work
testing = False
break
test_subset = work_list[len(work_list)-1] ##get the subset to test
del(work_list[len(work_list)-1]) ##delete the subset from the work list ##according to sources removing from the end oof the list is much faster than removing from the end
cannonical_name = ','.join(str(e) for e in test_subset.features) ##add the canonical name to the tested_sets
train_set, test_set = utils.cut_dataset(n_train, n_test, data, test_subset.features) ##get the features for the subset
acc = classification_svm(train_set, test_set) ##train and test the dataset
test_subset.parents_accuracy.append(acc)
number_of_tests += 1
last_test_sets[cannonical_name] = True ##add to last sets
#debug_data[cannonical_name] = (test_subset.features, test_subset.parents_accuracy)
if checkExpand(test_subset, best_acc):
new_combs, r_counter = get_combinations(work_list, test_subset, features, wl_dict) ##check for new combinantions
work_list = new_combs + work_list ##add new combinations to the list
if acc > lowest_acc: ##check if the subset is better then the previous ones
if acc > best_acc: ## if acc is the best one found so far
best_acc = acc
lowest_acc = update_best_sets(best_sets, acc, test_subset)
#counter -= 1
#rts.append(time.time() - round_st)
if (time.time() - counter_time) > comm_rate and testing: ## try to talk to manager
checked = False;
work_list, checked, wasted_time, acc = manager_control(id, work_list, last_test_sets, data_pipe, com_pipe, best_acc)
wasted_time = 0.0
total_wasted += wasted_time
#print "PROCESS ", id , " Number of tests: ", number_of_tests, " in ", round(time.time() - worker_time, 2)
if checked: ##worker communicated with manager
talk_avgs.append(time.time() - talk_time)
talk_counter += 1
#print "PROCESS", id, " WORK SIZE ", len(work_list), "round time: ", rt ," expand count ", expand_count, " best acc:", max(best_sets), " global acc: ", best_acc
print "PROCESS ", id , " Number of tests: ", number_of_tests
last_test_sets = {} ##reset the tested list so less information is passed to the manager next time
talk_time = time.time()
counter_time = time.time()
total_working_time = time.time() - worker_time
print "PROCESS", id, ",waste_t:", total_wasted, ",work_t:", total_working_time, ",n_test:", number_of_tests, ",best acc:", max(best_sets), "talk_counter:", talk_counter, ",talk_avg:", round(sum(talk_avgs) / float(len(talk_avgs)),2)
def worker_classification(id, work_list, data, features, n_train, n_test,
data_pipe, com_pipe):
worker_time = time.time() ##to know how much time did the worker work
best_acc = 0
lowest_acc = 0
wl_dict = {} ##to keep all the generated subsets on this slave
best_sets = {}
last_test_sets = {} ##new test set since last talk com with manager
total_wasted = 0
testing = True
number_of_tests = 0
comm_rate = 60
counter_time = time.time()
talk_counter = 0
talk_avgs = []
talk_time = time.time()
#print "started ", id, " with work ", translate_wl(work_list)
while (testing):
if work_list == []:
##the last test sets will be sent on next talk to the manager
if number_of_tests < 30:
print(id, " waiting cause ran out of work too soon")
time.sleep(10)
work_list, best_acc, wasted_time = ask_for_work(
id, data_pipe, com_pipe)
total_wasted += wasted_time
counter = comm_rate #random.randint(50, 150) #Since we need to send the last test sents to manager lets set this to a lower value
if work_list == []: ##didnt receive work
testing = False
break
test_subset = work_list[len(work_list) - 1] ##get the subset to test
del (
work_list[len(work_list) - 1]
) ##delete the subset from the work list ##according to sources removing from the end oof the list is much faster than removing from the end
cannonical_name = ','.join(
str(e) for e in
test_subset.features) ##add the canonical name to the tested_sets
train_set, test_set = utils.cut_dataset(
n_train, n_test, data,
test_subset.features) ##get the features for the subset
acc = classification_svm(train_set,
test_set) ##train and test the dataset
test_subset.parents_accuracy.append(acc)
number_of_tests += 1
last_test_sets[cannonical_name] = True ##add to last sets
#debug_data[cannonical_name] = (test_subset.features, test_subset.parents_accuracy)
if checkExpand(test_subset, best_acc):
new_combs, r_counter = get_combinations(
work_list, test_subset, features,
wl_dict) ##check for new combinantions
work_list = new_combs + work_list ##add new combinations to the list
if acc > lowest_acc: ##check if the subset is better then the previous ones
if acc > best_acc: ## if acc is the best one found so far
best_acc = acc
lowest_acc = update_best_sets(best_sets, acc, test_subset)
#counter -= 1
#rts.append(time.time() - round_st)
if (time.time() - counter_time
) > comm_rate and testing: ## try to talk to manager
checked = False
work_list, checked, wasted_time, acc = manager_control(
id, work_list, last_test_sets, data_pipe, com_pipe, best_acc)
wasted_time = 0.0
total_wasted += wasted_time
#print "PROCESS ", id , " Number of tests: ", number_of_tests, " in ", round(time.time() - worker_time, 2)
if checked: ##worker communicated with manager
talk_avgs.append(time.time() - talk_time)
talk_counter += 1
#print "PROCESS", id, " WORK SIZE ", len(work_list), "round time: ", rt ," expand count ", expand_count, " best acc:", max(best_sets), " global acc: ", best_acc
print("PROCESS ", id, " Number of tests: ", number_of_tests)
last_test_sets = {
} ##reset the tested list so less information is passed to the manager next time
talk_time = time.time()
counter_time = time.time()
total_working_time = time.time() - worker_time
print("PROCESS", id, ",waste_t:", total_wasted, ",work_t:",
total_working_time, ",n_test:", number_of_tests, ",best acc:",
max(best_sets), "talk_counter:", talk_counter, ",talk_avg:",
round(sum(talk_avgs) / float(len(talk_avgs)), 2))
