def connectWidgets(self):
svr = SVR()
svr.kernel = 'rbf'
svr.degree = 3
svr.gamma = 'auto'
svr.coef0 = 0.0
svr.tol = 1e-3
svr.C = 1.0
svr.epsilon = 0.1
svr.shrinking = True
svr.cache_size = 200
svr.verbose = False
svr.max_iter = -1
self.cDoubleSpinBox.setValue(svr.C)
self.epsilonDoubleSpinBox.setValue(svr.epsilon)
self.defaultComboItem(self.kernelComboBox, svr.kernel)
self.degreeSpinBox.setValue(svr.degree)
self.defaultComboItem(self.gammaComboBox, svr.gamma)
self.coeff0DoubleSpinBox.setValue(svr.coef0)
self.shrinkingCheckBox.setChecked(svr.shrinking)
self.toleranceDoubleSpinBox.setValue(svr.tol)
self.cacheSizeSpinBox.setValue(svr.cache_size)
self.verboseCheckBox.setChecked(svr.verbose)
self.maxIterationsSpinBox.setValue(svr.max_iter)
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 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 train_svr(x_train: np.ndarray, y_train: np.ndarray,
**kwargs) -> (SVR, dict):
mdl = SVR(**kwargs)
mdl.degree = 2
should_grid_search = False
param_grid = {}
if 'C' not in kwargs:
should_grid_search = True
param_grid['C'] = [0.1, 1, 10, 100, 250]
if 'gamma' not in kwargs:
should_grid_search = True
param_grid['gamma'] = [1e-5, 1e-4, 1e-3, 0.01, 0.1, 0.25]
if 'kernel' not in kwargs:
should_grid_search = True
param_grid['kernel'] = ['rbf', 'linear', 'poly']
if should_grid_search:
param_grid['degree'] = [2]
logger.info(f'Performing grid search. Using param_grid: {param_grid} '
f'This may take a while...')
gs = GridSearchCV(estimator=mdl, param_grid=param_grid)
gs.fit(x_train, y_train)
mdl = gs.best_estimator_
mdl.fit(x_train, y_train)
return mdl, get_model_params(model_name='svr', model=mdl)
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 connectWidgets(self):
svr = SVR()
svr.kernel = 'rbf'
svr.degree = 3
svr.gamma = 'auto'
svr.coef0 = 0.0
svr.tol = 1e-3
svr.C = 1.0
svr.epsilon = 0.1
svr.shrinking = True
svr.cache_size = 200
svr.verbose = False
svr.max_iter = -1
self.cLineEdit.setText(str(svr.C))
self.epsilonLineEdit.setText(str(svr.epsilon))
self.kernel_list.setCurrentItem(self.kernel_list.findItems('Radial Basis Function', QtCore.Qt.MatchExactly)[0])
self.degreeLineEdit.setText(str(svr.degree))
self.coeff0LineEdit.setText(str(svr.coef0))
self.shrinking_list.setCurrentItem(self.shrinking_list.findItems(str(svr.shrinking), QtCore.Qt.MatchExactly)[0])
self.toleranceLineEdit.setText(str(svr.tol))
self.maxIterationsLineEdit.setText(str(svr.max_iter))