def runPrintResults(X, y, alpha, name):
print(name+":\n=========")
if (alpha is not None):
X_new = np.divide(X, alpha)
else:
X_new = X
enetCV = ElasticNetCV(l1_ratio=0.8, fit_intercept=False) # cv=nCV, max_iter=5000
# enetCV = LassoCV(fit_intercept=False) # cv=nCV, max_iter=5000
enetCV.fit(X_new, y)
y_pred_enet = enetCV.predict(X_new)
r2_score_enet = r2_score(y, y_pred_enet)
print("R2= ", r2_score_enet)
if (alpha is not None):
enetCV_coef = np.divide(enetCV.coef_, alpha)
else:
enetCV_coef = enetCV.coef_
print("Best Alpha: {}".format(enetCV.alpha_))
# print("coefs_: {}".format(enetCV.coef_))
print("coefs_/alpha: {}".format(enetCV_coef))
return enetCV.alpha_, enetCV_coef
class ElasticNetCVImpl():
def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000, tol=0.0001, cv=3, copy_X=True, verbose=0, n_jobs=None, positive=False, random_state=None, selection='cyclic'):
self._hyperparams = {
'l1_ratio': l1_ratio,
'eps': eps,
'n_alphas': n_alphas,
'alphas': alphas,
'fit_intercept': fit_intercept,
'normalize': normalize,
'precompute': precompute,
'max_iter': max_iter,
'tol': tol,
'cv': cv,
'copy_X': copy_X,
'verbose': verbose,
'n_jobs': n_jobs,
'positive': positive,
'random_state': random_state,
'selection': selection}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def predict(self, X):
return self._sklearn_model.predict(X)
def runPrintResults(X, y, alpha, name):
print(name + ":\n=========")
if (alpha is not None):
X_new = np.divide(X, alpha)
else:
X_new = X
enetCV = ElasticNetCV(l1_ratio=0.8,
fit_intercept=False) # cv=nCV, max_iter=5000
# enetCV = LassoCV(fit_intercept=False) # cv=nCV, max_iter=5000
enetCV.fit(X_new, y)
y_pred_enet = enetCV.predict(X_new)
r2_score_enet = r2_score(y, y_pred_enet)
print("R2= ", r2_score_enet)
if (alpha is not None):
enetCV_coef = np.divide(enetCV.coef_, alpha)
else:
enetCV_coef = enetCV.coef_
print("Best Alpha: {}".format(enetCV.alpha_))
# print("coefs_: {}".format(enetCV.coef_))
print("coefs_/alpha: {}".format(enetCV_coef))
return enetCV.alpha_, enetCV_coef
