def cv_BIKE_Ridge( A_list, yV, alpha = 0.5, XX = None, n_folds = 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 = cross_validation.KFold( ln, n_folds=n_folds, shuffle=True)
AX_idx = np.array([list(range( ln))]).T
yV_pred = cross_validation.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 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 cv( method, xM, yV, alpha, n_folds = 5, n_jobs = -1, grid_std = None, graph = True, shuffle = True):
"""
method can be 'Ridge', 'Lasso'
cross validation is performed so as to generate prediction output for all input molecules
"""
print(xM.shape, yV.shape)
clf = getattr( linear_model, method)( alpha = alpha)
kf_n = cross_validation.KFold( xM.shape[0], n_folds=n_folds, shuffle=shuffle)
yV_pred = cross_validation.cross_val_predict( clf, xM, yV, cv = kf_n, n_jobs = n_jobs)
if graph:
print('The prediction output using cross-validation is given by:')
jutil.cv_show( yV, yV_pred, grid_std = grid_std)
return yV_pred
def cv_SVR( xM, yV, svr_params, n_splits = 5, n_jobs = -1, grid_std = None, graph = True, shuffle = True):
"""
method can be 'Ridge', 'Lasso'
cross validation is performed so as to generate prediction output for all input molecules
"""
print(xM.shape, yV.shape)
clf = svm.SVR( **svr_params)
kf_n_c = model_selection.KFold( n_splits=n_splits, shuffle=shuffle)
kf_n = kf5_ext_c.split( xM)
yV_pred = model_selection.cross_val_predict( clf, xM, yV, cv = kf_n, n_jobs = n_jobs)
if graph:
print('The prediction output using cross-validation is given by:')
jutil.cv_show( yV, yV_pred, grid_std = grid_std)
return yV_pred
def _cv_r0( method, xM, yV, alpha, n_splits = 5, n_jobs = -1, grid_std = None, graph = True):
"""
method can be 'Ridge', 'Lasso'
cross validation is performed so as to generate prediction output for all input molecules
"""
print(xM.shape, yV.shape)
clf = getattr( linear_model, method)( alpha = alpha)
kf_n_c = model_selection.KFold( n_splits = n_splits, shuffle=True)
kf_n = kf5_ext_c.split( xM)
yV_pred = model_selection.cross_val_predict( clf, xM, yV, cv = kf_n, n_jobs = n_jobs)
if graph:
print('The prediction output using cross-validation is given by:')
jutil.cv_show( yV, yV_pred, grid_std = grid_std)
return yV_pred
def cv_SVR( xM, yV, svr_params, n_splits = 5, n_jobs = -1, grid_std = None, graph = True, shuffle = True):
"""
method can be 'Ridge', 'Lasso'
cross validation is performed so as to generate prediction output for all input molecules
"""
print(xM.shape, yV.shape)
clf = svm.SVR( **svr_params)
kf_n_c = model_selection.KFold( n_splits=n_splits, shuffle=shuffle)
kf_n = kf5_ext_c.split( xM)
yV_pred = model_selection.cross_val_predict( clf, xM, yV, cv = kf_n, n_jobs = n_jobs)
if graph:
print('The prediction output using cross-validation is given by:')
jutil.cv_show( yV, yV_pred, grid_std = grid_std)
return yV_pred
def _cv_LOO_r0(method, xM, yV, alpha, n_jobs=-1, grid_std=None, graph=True):
"""
method can be 'Ridge', 'Lasso'
cross validation is performed so as to generate prediction output for all input molecules
"""
n_folds = xM.shape[0]
print(xM.shape, yV.shape)
clf = getattr(linear_model, method)(alpha=alpha)
# print("Note - shuffling is not applied because of LOO.")
kf_n_c = model_selection.KFold(n_splits=n_folds)
kf_n = kf_n_c.split(xM)
yV_pred = model_selection.cross_val_predict(
clf, xM, yV, cv=kf_n, n_jobs=n_jobs)
if graph:
print('The prediction output using cross-validation is given by:')
jutil.cv_show(yV, yV_pred, grid_std=grid_std)
return yV_pred
