def tune_rbfsvr():
name = 'RbfSVR'
dimension = 'length'
stage, rbfsvr_reg, scale, features, rbfsvr_checkpoint_score = stage_meta_init(meta_dimension, name, dimension)
if stage == 0:
rbfsvr_reg = SVR(kernel = 'rbf')
rbfsvr_checkpoint_score = -np.inf
# features = init_feat_selection(X, Y, rbfsvr_reg)
scale, rbfsvr_checkpoint_score = test_scaler(rbfsvr_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, list(X), final = False)
elif stage == 1:
rbfsvr_checkpoint_score, features = feat_selection_2(X[features], Y, scale, rbfsvr_reg, rbfsvr_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, features, final = False)
elif stage == 2:
scale, linsvc_checkpoint_score = test_scaler(rbfsvr_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, features, final = False)
elif stage == 3:
rbfsvr_reg, rbfsvr_checkpoint_score = svc_hyper_parameter_tuning(X[features], Y, rbfsvr_reg, scale, rbfsvr_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, features, final = False)
elif stage == 4:
scale, rbfsvr_checkpoint_score = test_scaler(rbfsvr_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, features, final = False)
elif stage == 5:
rbfsvr_checkpoint_score, features = feat_selection_2(X[features], Y, scale, rbfsvr_reg, rbfsvr_checkpoint_score, 10, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, rbfsvr_reg, scale, rbfsvr_checkpoint_score, features, final = True)
def tune_polysvr():
name = 'PolySVR'
dimension = 'winner'
stage, polysvr_reg, scale, features, polysvr_checkpoint_score = stage_meta_init(
meta_dimension, name, dimension)
if stage == 0:
polysvr_reg = SVR(kernel='poly')
polysvr_checkpoint_score = -np.inf
# features = init_feat_selection(X, Y, rbfsvc_clf)
scale, polysvr_checkpoint_score = test_scaler(polysvr_reg, X, Y)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
list(X),
final=False)
elif stage == 1:
polysvr_checkpoint_score, features = feat_selection_2(
X[features], Y, scale, polysvr_reg, polysvr_checkpoint_score, 24,
-1, False)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
features,
final=False)
elif stage == 2:
scale, polysvr_checkpoint_score = test_scaler(polysvr_reg, X[features],
Y)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
features,
final=False)
elif stage == 3:
polysvr_reg, polysvr_checkpoint_score = svc_hyper_parameter_tuning(
X[features], Y, polysvr_reg, scale, polysvr_checkpoint_score)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
features,
final=False)
elif stage == 4:
scale, polysvr_checkpoint_score = test_scaler(polysvr_reg, X[features],
Y)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
features,
final=False)
elif stage == 5:
polysvr_checkpoint_score, features = feat_selection_2(
X[features], Y, scale, polysvr_reg, polysvr_checkpoint_score, 10,
-1, False)
_save_meta_model(meta_dimension,
stage,
dimension,
name,
polysvr_reg,
scale,
polysvr_checkpoint_score,
features,
final=True)
def tune_polysvc():
name = 'PolySVC'
dimension = 'winner'
stage, polysvc_clf, polysvc_checkpoint_score = stage_init(
name, dimension, extension=EXTENSION)
if stage == 0:
polysvc_clf = SVC(random_state=1108,
class_weight='balanced',
kernel='poly',
probability=True)
polysvc_clf, polysvc_checkpoint_score = pipe_init(X, Y, polysvc_clf)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
polysvc_clf, polysvc_checkpoint_score = test_scaler(
polysvc_clf, polysvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
polysvc_clf, polysvc_checkpoint_score = feat_selection_2(
X, Y, polysvc_clf, polysvc_checkpoint_score)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
polysvc_clf, polysvc_checkpoint_score = test_scaler(
polysvc_clf, polysvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
polysvc_clf, polysvc_checkpoint_score = pca_tune(
X, Y, polysvc_clf, polysvc_checkpoint_score)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
polysvc_clf, polysvc_checkpoint_score = feat_selection_2(
X, Y, polysvc_clf, polysvc_checkpoint_score)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
polysvc_clf, polysvc_checkpoint_score = test_scaler(
polysvc_clf, polysvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
polysvc_clf, polysvc_checkpoint_score = svc_hyper_parameter_tuning(
X, Y, polysvc_clf, polysvc_checkpoint_score)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
polysvc_clf, polysvc_checkpoint_score = pca_tune(
X, Y, polysvc_clf, polysvc_checkpoint_score, iter_=10)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 9:
polysvc_clf, polysvc_checkpoint_score = feat_selection_2(
X, Y, polysvc_clf, polysvc_checkpoint_score)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 10:
polysvc_clf, polysvc_checkpoint_score = test_scaler(
polysvc_clf, polysvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
polysvc_clf,
polysvc_checkpoint_score,
final=True,
extension=EXTENSION)