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_linsvr():
name = 'LinSVR'
dimension = 'length'
stage, linsvr_reg, scale, features, linsvr_checkpoint_score = stage_meta_init(meta_dimension, name, dimension)
if stage == 0:
linsvr_reg = SVR(kernel = 'linear')
linsvr_checkpoint_score = -np.inf
features = init_feat_selection(X, Y, linsvr_reg)
scale, linsvr_checkpoint_score = test_scaler(linsvr_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = False)
elif stage == 1:
linsvr_checkpoint_score, features = feat_selection(X[features], Y, scale, linsvr_reg, linsvr_checkpoint_score, 10, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = False)
elif stage == 2:
scale, linsvr_checkpoint_score = test_scaler(linsvr_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = False)
elif stage == 3:
linsvr_reg, linsvr_checkpoint_score = C_parameter_tuning(X[features], Y, linsvr_reg, scale, linsvr_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = False)
elif stage == 4:
scale, linsvr_checkpoint_score = test_scaler(linsvr_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = False)
elif stage == 5:
linsvr_checkpoint_score, features = feat_selection(X[features], Y, scale, linsvr_reg, linsvr_checkpoint_score, 10, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, linsvr_reg, scale, linsvr_checkpoint_score, features, final = True)
def tune_rf():
name = 'RFreg'
dimension = 'length'
stage, rf_reg, scale, features, rf_checkpoint_score = stage_meta_init(meta_dimension, name, dimension)
if stage == 0:
rf_reg = RandomForestRegressor(random_state = 1108, n_estimators = 100)
rf_checkpoint_score = -np.inf
scale, rf_checkpoint_score = test_scaler(rf_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, list(X), final = False)
elif stage == 1:
rf_checkpoint_score, features = feat_selection_2(X[features], Y, scale, rf_reg, rf_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = False)
elif stage == 2:
scale, rf_checkpoint_score = test_scaler(rf_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = False)
elif stage == 3:
rf_reg, rf_checkpoint_score = forest_params(X[features], Y, rf_reg, scale, rf_checkpoint_score, iter_ = 1000)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = False)
elif stage == 4:
scale, rf_checkpoint_score = test_scaler(rf_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = False)
elif stage == 5:
rf_checkpoint_score, features = feat_selection(X[features], Y, scale, rf_reg, rf_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = False)
elif stage == 6:
rf_reg, rf_checkpoint_score = rf_trees(X, Y, scale, rf_reg, rf_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, rf_reg, scale, rf_checkpoint_score, features, final = True)
def tune_lasso():
name = 'LassoRegression'
dimension = 'length'
stage, lasso_reg, scale, features, lasso_checkpoint_score = stage_meta_init(meta_dimension, name, dimension)
if stage == 0:
lasso_reg = Lasso(max_iter = 1000, random_state = 1108)
lasso_checkpoint_score = -np.inf
scale, lasso_checkpoint_score = test_scaler(lasso_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, list(X), final = False)
elif stage == 1:
lasso_checkpoint_score, features = feat_selection(X[features], Y, scale, lasso_reg, lasso_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, features, final = False)
elif stage == 2:
scale, lasso_checkpoint_score = test_scaler(lasso_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, features, final = False)
elif stage == 3:
lasso_reg, lasso_checkpoint_score = alpha_parameter_tuning(X[features], Y, lasso_reg, scale, lasso_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, features, final = False)
elif stage == 4:
lasso_checkpoint_score, features = feat_selection(X[features], Y, scale, lasso_reg, lasso_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, features, final = False)
elif stage == 5:
scale, lasso_checkpoint_score = test_scaler(lasso_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, lasso_reg, scale, lasso_checkpoint_score, features, final = True)
def tune_polysvr():
name = 'PolySVR'
dimension = 'length'
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 = poly_hyper_parameter_tuning(X[features], Y, polysvr_reg, scale, polysvr_checkpoint_score, iter_ = 50)
_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_dartr():
name = 'DartrGBM'
dimension = 'length'
stage, dart_reg, scale, features, dartr_checkpoint_score = stage_meta_init(meta_dimension, name, dimension)
if stage == 0:
dart_reg = lgb.LGBMRegressor(random_state = 1108, n_estimators = 100, subsample = .8, verbose=-1)
dartr_checkpoint_score = -np.inf
scale, dartr_checkpoint_score = test_scaler(dart_reg, X, Y)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, list(X), final = False)
elif stage == 1:
dartr_checkpoint_score, features = feat_selection(X[features], Y, scale, dart_reg, dartr_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 2:
scale, dartr_checkpoint_score = test_scaler(dart_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 3:
dart_reg, dartr_checkpoint_score = lgb_find_lr(dart_reg, X[features], Y, scale, dartr_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 4:
scale, dartr_checkpoint_score = test_scaler(dart_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 5:
dartr_checkpoint_score, features = feat_selection(X[features], Y, scale, dart_reg, dartr_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 6:
dart_reg, dartr_checkpoint_score = lgb_tree_params(X[features], Y, dart_reg, scale, dartr_checkpoint_score, iter_ = 1000)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 7:
scale, dartr_checkpoint_score = test_scaler(dart_reg, X[features], Y)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 8:
dartr_checkpoint_score, features = feat_selection(X[features], Y, scale, dart_reg, dartr_checkpoint_score, 24, -1, False)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = False)
elif stage == 9:
dart_reg, dartr_checkpoint_score = lgb_drop_lr(dart_reg, X[features], Y, scale, dartr_checkpoint_score)
_save_meta_model(meta_dimension, stage, dimension, name, dart_reg, scale, dartr_checkpoint_score, features, final = True)
