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))