def start_server(wbconfig, mp):
host = wbconfig.get("host", "0.0.0.0")
port = wbconfig.getint("port", 2628)
backlog = wbconfig.getint("backlog", 512)
timeout = wbconfig.getint("timeout", 60) or None
address = (host, port)
master.init(address, backlog)
drop_privs(wbconfig)
master.run(timeout, mp)
def main():
app = init(max_size=6)
print('Allocation of value 5555 in v:')
v = app.allocate(5555)
print(v)
print('Allocation of list [1,2,3,4] in v2')
v2 = app.allocate([i for i in range(1, 5)])
print(v2)
print('Allocation of list [1,2,3,4,5,6,7,8,9] in v3')
v3 = app.allocate([i for i in range(1, 10)])
print(v3)
print('read v:', app.read(v))
print('modify v to 56 :', app.modify(v, 56, 7))
print('read v:', app.read(v))
print('read v2:', app.read(v2))
print('modify v2 to 56 in pos 7 :', app.modify(v2, 56, 7))
print('read v2:', app.read(v2))
print('read v3:', app.read(v3))
print('modify v3 to 56 in pos 7 :', app.modify(v3, 56, 7))
print('read v3:', app.read(v3))
print('free v:', app.free(v))
print('freed v value:', app.read(v))
print('free v2:', app.free(v2))
print('freed v2 value:', app.read(v2))
print('free v3:', app.free(v3))
print('freed v3 value:', app.read(v3))
app.terminate_slaves()
def main(screen):
"""Main function"""
global restaurant, main_scr, command_scr, keys_scr, constant_scr
main_scr, command_scr, keys_scr, constant_scr = master.init(screen)
if (master.start_menu(main_scr) == '1'):
restaurant = master.load_game(main_scr)
else:
restaurant = master.new_game(main_scr)
new_day()
def __init__(self, max_size):
self.app = init(max_size)
def gitMaster(path):
print(mstr.init(path))
train = pd.concat([train_por, train_mat], axis=0)
del train['G1']
del train['G2']
del train['G3']
del train['absences']
del train['studytime']
del train['failures']
print(train.shape)
train = train.drop_duplicates(subset=None, keep='first', inplace=False)
print("After remove duplicates: " + str(train.shape))'''
import master
df = master.init()
y = df[['G1', 'G2', 'G3']]
X = df.drop(['G1', 'G2', 'G3'], axis=1)
df['pass_fail'] = df.apply(lambda row: master.label_pass(row), axis=1)
replace_binary_attributes_map = {
'school': {
'GP': 0,
'MS': 1
},
# school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)
'sex': {
'F': 0,
'M': 1
def main():
#DataVisualization.performDataVis()
#reading the dataset
df = master.init('por')
#preprocessing -> feature transformation
X, y, feature_names = master.preproc(df, select='novotes')
X2, y2, feature_names2 = master.preproc(df, select='G1')
X3, y3, feature_names3 = master.preproc(df, select='all')
# applying and testing PCA
"""
master.PCA_study(X,feature_names) #43 components
#27 componenti -> 0.85
#30 componenti -> 0.90
#39 componenti -> 0.99
X_pca_30 = master.PCA(X, components=30)
t0=time.time()
bclassification.kNN(X_pca_30, y, search=False, cv=True, onlycv=True) #0.82
time_30= time.time()-t0
print("Time elapsed: ", time_30) #0.16
t1=time.time()
bclassification.kNN(X, y, search=False, cv=True, onlycv=True) #0.83
time_all=time.time()-t1
print("Time elapsed: ", time_all) #0.22
t2=time.time()
bclassification.SVM(X_pca_30, y, search=False, cv=True, onlycv=True) #0.84
time_30= time.time()-t2
print("Time elapsed: ", time_30) #0.20
t3=time.time()
bclassification.SVM(X, y, search=False, cv=True, onlycv=True) #0.84
time_all=time.time()-t3
print("Time elapsed: ", time_all) #0.28
"""
# Binary classification -> hyperparameter tuning and cross validation
#bclassification.kNN(X, y, search=True, cv=False, select='novotes')
#bclassification.kNN(X,y,search=False,cv=True, select='novotes')
#bclassification.kNN(X,y,search=False,cv=True, select='novotes', smote=True)
#bclassification.kNN(X,y,search=False,cv=True, select='novotes', onlynum=True)
#bclassification.logistic_regression(X,y,search=True, cv=False)
#bclassification.logistic_regression(X,y,search=False, cv=True)
#bclassification.logistic_regression(X,y,search=False, cv=True,smote=True)
#bclassification.LDA(X,y)
#bclassification.LDA(X,y,smote=True)
#bclassification.SVM(X,y,search=True, cv=False)
#bclassification.SVM(X,y)
#bclassification.SVM(X,y,smote=True)
#bclassification.SVM(X,y,mode_cv='rbf',C_cv=10)
#bclassification.SVM(X,y,mode_cv='rbf',C_cv=100, smote=True)
#bclassification.SVM_unbalanced(X,y,search=True,cv=False)
#bclassification.SVM_unbalanced(X,y)
#bclassification.decisionTree(X,y,feature_names)
#bclassification.decisionTree(X,y,feature_names,smote=True)
#bclassification.randomForest(X,y,feature_names,search=True,cv=False)
#bclassification.randomForest(X,y,feature_names)
#bclassification.randomForest(X, y, feature_names, smote=True)
# Perform binary classification on different configurations of the dataset:
# X -> without G1 and G2
# X2 -> with G1
# X3 -> with G1 and G2
"""