def gs_BIKE_Ridge(A_list,
yV,
alphas_log=(1, -1, 9),
X_concat=None,
n_folds=5,
n_jobs=-1):
"""
As is a list of A matrices where A is similarity matrix.
X is a concatened linear descriptors.
If no X is used, X can be empty
"""
clf = binary_model.BIKE_Ridge(A_list, X_concat)
parmas = {'alpha': np.logspace(*alphas_log)}
ln = A_list[0].shape[0] # ls is the number of molecules.
kf_n = cross_validation.KFold(ln, n_folds=n_folds, shuffle=True)
gs = grid_search.GridSearchCV(clf,
parmas,
scoring='r2',
cv=kf_n,
n_jobs=n_jobs)
AX_idx = np.array([list(range(ln))]).T
gs.fit(AX_idx, yV)
return gs
def cv_BIKE_Ridge( A_list, yV, alpha = 0.5, XX = None, n_splits = 5, n_jobs = -1, grid_std = None):
clf = binary_model.BIKE_Ridge( A_list, XX, alpha = alpha)
ln = A_list[0].shape[0] # ls is the number of molecules.
kf_n_c = model_selection.KFold( n_splits = n_splits, shuffle=True)
kf_n = kf5_ext_c.split( A_list[0])
AX_idx = np.array([list(range( ln))]).T
yV_pred = model_selection.cross_val_predict( clf, AX_idx, yV, cv = kf_n, n_jobs = n_jobs)
print('The prediction output using cross-validation is given by:')
jutil.cv_show( yV, yV_pred, grid_std = grid_std)
return yV_pred
def gs_Ridge_BIKE( A_list, yV, XX = None, alphas_log = (1, -1, 9), n_splits = 5, n_jobs = -1):
"""
As is a list of A matrices where A is similarity matrix.
X is a concatened linear descriptors.
If no X is used, X can be empty
"""
clf = binary_model.BIKE_Ridge( A_list, XX)
parmas = {'alpha': np.logspace( *alphas_log)}
ln = A_list[0].shape[0] # ls is the number of molecules.
kf_n_c = model_selection.KFold( n_splits = n_splits, shuffle=True)
#kf_n = kf5_ext_c.split( A_list[0])
gs = model_selection.GridSearchCV( clf, parmas, scoring = 'r2', cv = kf_n_c, n_jobs = n_jobs)
AX_idx = np.array([list(range( ln))]).T
gs.fit( AX_idx, yV)
return gs