def test_regressor_modifications(self):
regressor = KernelRidge(alpha=1e-8, kernel="rbf", gamma=0.1)
kpcovr = self.model(mixing=0.5,
regressor=regressor,
kernel="rbf",
gamma=0.1)
# KPCovR regressor matches the original
self.assertTrue(
regressor.get_params() == kpcovr.regressor.get_params())
# KPCovR regressor updates its parameters
# to match the original regressor
regressor.set_params(gamma=0.2)
self.assertTrue(
regressor.get_params() == kpcovr.regressor.get_params())
# Fitting regressor outside KPCovR fits the KPCovR regressor
regressor.fit(self.X, self.Y)
self.assertTrue(hasattr(kpcovr.regressor, "dual_coef_"))
# Raise error during KPCovR fit since regressor and KPCovR
# kernel parameters now inconsistent
with self.assertRaises(ValueError) as cm:
kpcovr.fit(self.X, self.Y)
self.assertTrue(
str(cm.message),
"Kernel parameter mismatch: the regressor has kernel parameters "
"{kernel: linear, gamma: 0.2, degree: 3, coef0: 1, kernel_params: None}"
" and KernelPCovR was initialized with kernel parameters "
"{kernel: linear, gamma: 0.1, degree: 3, coef0: 1, kernel_params: None}",
)
def fun_krr_fs(x, *args):
X, y, flag, n_splits, random_seed = args
clf = KernelRidge(kernel='rbf', )
n_samples, n_var = X.shape
kernel = {
2: 'linear',
3: 'poly',
0: 'rbf',
1: 'sigmoid',
4: 'laplacian',
5: 'chi2'
}
p = {
'alpha': x[0],
'kernel': kernel[int(round(x[1]))],
'gamma': x[2],
'degree': int(round(x[3])),
'coef0': x[4],
'kernel_params': {
'C': x[5],
},
}
clf.set_params(**p)
n_param = len(p)
if len(x) <= n_param:
ft = np.array([1 for i in range(n_var)])
ft = np.where(ft > 0.5)
else:
ft = np.array([1 if k > 0.5 else 0 for k in x[2::]])
ft = np.where(ft > 0.5)
n_splits = n_splits
try:
#cv=KFold(n_splits=n_splits, shuffle=True, random_state=random_seed)
cv = KFold(n_splits=n_splits,
shuffle=True,
random_state=int(random_seed))
#cv=StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=int(random_seed))
y_p = cross_val_predict(clf, X, y, cv=cv, n_jobs=1)
#r = mean_squared_error(y,y_p)**0.5
r = RMSE(y_p, y)
r2 = MAPE(y_p, y)
r3 = RRMSE(y_p, y)
r4 = -r2_score(y_p, y)
#r = -accuracy_score(y,y_p)
#r = -precision_score(y,y_p)
#r = -f1_score(y,y_p,average='weighted')
except:
y_p = [None]
r = 1e12
# print (r,'\t',p,'\t',ft)
if flag == 'eval':
return r
else:
clf.fit(X[:, ft].squeeze(), y)
return {
'Y_TRUE': y,
'Y_PRED': y_p,
'EST_PARAMS': p,
'PARAMS': x,
'EST_NAME': 'KRR',
'ESTIMATOR': clf,
'ACTIVE_VAR': ft,
'DATA': X,
'SEED': random_seed,
'ERROR_TRAIN': {
'RMSE': r,
'MAPE': r2,
'RRMSE': r3,
'R2_SCORE': r4
}
}
n_alphas = 50
alphas = np.logspace(-1, 8, n_alphas)
ridge = Ridge(fit_intercept=True)
kernel_ridge = KernelRidge(kernel='poly', gamma=1, degree=3, coef0=1)
test_scores_ridge = []
test_scores_kernel = []
for alpha in alphas:
ridge.set_params(alpha=alpha)
ridge.fit(X_train_sc, y_train_sc)
test_mse = mean_squared_error_scorer(ridge, X_test_sc, y_test_sc)
test_scores_ridge.append(test_mse)
kernel_ridge.set_params(alpha=alpha)
kernel_ridge.fit(X_train_sc, y_train_sc)
test_mse = mean_squared_error_scorer(kernel_ridge, X_test_sc, y_test_sc)
test_scores_kernel.append(test_mse)
poly = PolynomialNetworkRegressor(degree=3,
n_components=2,
tol=1e-3,
warm_start=True,
random_state=0)
test_scores_poly = []
for alpha in alphas:
poly.set_params(beta=alpha)
poly.fit(X_train_sc, y_train_sc)
n_alphas = 50
alphas = np.logspace(-1, 8, n_alphas)
ridge = Ridge(fit_intercept=True)
kernel_ridge = KernelRidge(kernel='poly', gamma=1, degree=3, coef0=1)
test_scores_ridge = []
test_scores_kernel = []
for alpha in alphas:
ridge.set_params(alpha=alpha)
ridge.fit(X_train_sc, y_train_sc)
test_mse = mean_squared_error_scorer(ridge, X_test_sc, y_test_sc)
test_scores_ridge.append(test_mse)
kernel_ridge.set_params(alpha=alpha)
kernel_ridge.fit(X_train_sc, y_train_sc)
test_mse = mean_squared_error_scorer(kernel_ridge, X_test_sc, y_test_sc)
test_scores_kernel.append(test_mse)
poly = PolynomialNetworkRegressor(degree=3, n_components=2, tol=1e-3,
warm_start=True, random_state=0)
test_scores_poly = []
for alpha in alphas:
poly.set_params(beta=alpha)
poly.fit(X_train_sc, y_train_sc)
test_mse = mean_squared_error_scorer(poly, X_test_sc, y_test_sc)
test_scores_poly.append(test_mse)
