def train_regress (self, train, trainlabel, seed, Cmin, Cmax, numC, rmin, rmax, numr, degree=3, method = 'rrmse', rad_stat =2):
C_range=np.logspace(Cmin, Cmax, num=numC, base=2,endpoint= True)
gamma_range=np.logspace(rmin, rmax, num=numr, base=2,endpoint= True)
svc = SVR(kernel=seed)
# mean_score=[]
df_C_gamma= DataFrame({'gamma_range':gamma_range})
# df_this = DataFrame({'gamma_range':gamma_range})
count = 0
for C in C_range:
score_C=[]
# score_C_this = []
count=count+1
for gamma in gamma_range:
svc.epsilon = 0.00001
svc.C = C
svc.gamma = gamma
svc.degree = degree
svc.random_state = rad_stat
this_scores = cross_val_score(svc, train, trainlabel, scoring=method, cv=10, n_jobs=-1 \
)
score_C.append(np.mean(this_scores))
#score_C_this.append(np.mean(this_scores))
print (np.mean(score_C) )
print ("%r cycle finished, %r left" %(count, numC-count))
df_C_gamma[C]= score_C
#df_this[C] = score_C_this
return df_C_gamma
def trainsvr (self, train, trainlabel, seed, Cmin, Cmax, numC, rmin, rmax, numr, degree=3,\
verbose = 1, method = 'roc_auc', rad_stat =2):
C_range = np.logspace(Cmin, Cmax, num=numC, base=2, endpoint=True)
gamma_range = np.logspace(rmin, rmax, num=numr, base=2, endpoint=True)
scr = SVR(kernel=seed)
# mean_score=[]
df_C_gamma = pd.DataFrame({'gamma_range': gamma_range})
# df_this = DataFrame({'gamma_range':gamma_range})
count = 0
for C in C_range:
score_C = []
# score_C_this = []
count = count + 1
for gamma in gamma_range:
scr.C = C
scr.gamma = gamma
scr.degree = degree
scr.random_state = rad_stat
this_scores = cross_val_score(scr, train, trainlabel, scoring=method, cv=10, n_jobs=-1 \
)
score_C.append(np.mean(this_scores))
#score_C_this.append(np.mean(this_scores))
if verbose == 1:
print(np.mean(score_C))
print("%r cycle finished, %r left" % (count, numC - count))
df_C_gamma[C] = score_C
#df_this[C] = score_C_this
return df_C_gamma
def objective_svr(params):
clf = SVR(C=params[0], epsilon=params[1])
clf.random_state = 12345
#clf.fit(X_train, y_train)
#mae = mean_absolute_error(y_test, clf.predict(X_test))
clf.fit(X, y)
mae = mean_absolute_error(y, clf.predict(X))
print("SVR(C={}, epsilon={}) => Score {}".format(params[0], params[1],
mae))
return mae
def train_regress(self,
train,
trainlabel,
seed,
Cmin,
Cmax,
numC,
rmin,
rmax,
numr,
degree=3,
method='rrmse',
rad_stat=2):
C_range = np.logspace(Cmin, Cmax, num=numC, base=2, endpoint=True)
gamma_range = np.logspace(rmin, rmax, num=numr, base=2, endpoint=True)
svc = SVR(kernel=seed)
# mean_score=[]
df_C_gamma = DataFrame({'gamma_range': gamma_range})
# df_this = DataFrame({'gamma_range':gamma_range})
count = 0
for C in C_range:
score_C = []
# score_C_this = []
count = count + 1
for gamma in gamma_range:
svc.epsilon = 0.00001
svc.C = C
svc.gamma = gamma
svc.degree = degree
svc.random_state = rad_stat
this_scores = cross_val_score(svc, train, trainlabel, scoring=method, cv=10, n_jobs=-1 \
)
score_C.append(np.mean(this_scores))
#score_C_this.append(np.mean(this_scores))
print(np.mean(score_C))
print("%r cycle finished, %r left" % (count, numC - count))
df_C_gamma[C] = score_C
#df_this[C] = score_C_this
return df_C_gamma
