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 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 test_energy_model(X,
y,
epsilon=0.0841395,
C=0.122,
seed=None,
silent=False):
# best eps = 0.08413951416
# best C = 0.122
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=seed)
svr = SVR()
svr.epsilon = epsilon
svr.C = C
svr.fit(X_train, y_train)
p_train = svr.predict(X_train)
p_test = svr.predict(X_test)
mse_train = np.mean((p_train - y_train)**2)
mse_test = np.mean((p_test - y_test)**2)
mean_abs_err_train = np.mean(np.abs(p_train - y_train))
mean_abs_err_test = np.mean(np.abs(p_test - y_test))
err_rel_train = np.mean(relative_err(p_train, y_train))
err_rel_test = np.mean(relative_err(p_test, y_test))
score_train = r2_score(y_train, p_train)
score_test = r2_score(y_test, p_test)
results = {
'mse_train': mse_train,
'mse_test': mse_test,
'err_rel_train': err_rel_train,
'err_rel_test': err_rel_test,
'mean_abs_err_train': mean_abs_err_train,
'mean_abs_err_test': mean_abs_err_test,
'score_train': score_train,
'score_test': score_test,
'y_train': y_train,
'p_train': p_train,
'y_test': y_test,
'p_test': p_test,
}
if not silent:
print(results)
return results
def test_energy_model_cv(X, y, epsilon=0.0841395, C=0.122, cv=5, silent=False):
svr = SVR()
svr.epsilon = epsilon
svr.C = C
cv_score = cross_val_score(svr, X, y, cv=cv)
y_pred = cross_val_predict(svr, X, y, cv=cv)
cv_mse = mean_squared_error(y, y_pred)
cv_r2 = r2_score(y, y_pred)
if not silent:
print('cv_score', cv_score)
print('cv_r2', cv_r2)
print('cv_mse', cv_mse)
return {'cv_score': cv_score, 'cv_r2': cv_r2, 'cv_mse': cv_mse}
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))
