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_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_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_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)
def tune_dartr():
name = 'DartrGBM'
dimension = 'length'
stage, dart_reg, scale, features, dartr_checkpoint_score = stage_init(
name, dimension)
if stage == 0:
# import time
# start = time.time()
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)
# done = time.time()
# done - start
#ubuntu
#sinlge: 50.78195834159851
#multi: 64.57199811935425
_save_model(stage,
dimension,
name,
dart_reg,
scale,
dartr_checkpoint_score,
list(X),
final=False)
elif stage == 1:
dartr_checkpoint_score, features = feat_selection_2(
X[features], Y, scale, dart_reg, dartr_checkpoint_score)
_save_model(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_model(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_model(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_model(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, _iter=25)
_save_model(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)
_save_model(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_model(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)
_save_model(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_model(stage,
dimension,
name,
dart_reg,
scale,
dartr_checkpoint_score,
features,
final=True)
def tune_lgr():
name = 'LightGBR'
dimension = 'length'
stage, lgb_reg, scale, features, lgbr_checkpoint_score = stage_init(
name, dimension)
if stage == 0:
lgb_reg = lgb.LGBMRegressor(random_state=1108,
n_estimators=100,
subsample=.8,
verbose=-1)
lgbr_checkpoint_score = -np.inf
scale, lgbr_checkpoint_score = test_scaler(lgb_reg, X, Y)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
list(X),
final=False)
elif stage == 1:
lgbr_checkpoint_score, features = feat_selection_2(
X[features], Y, scale, lgb_reg, lgbr_checkpoint_score)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 2:
scale, lgbr_checkpoint_score = test_scaler(lgb_reg, X[features], Y)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 3:
lgb_reg, lgbr_checkpoint_score = lgb_find_lr(lgb_reg, X[features], Y,
scale,
lgbr_checkpoint_score)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 4:
scale, lgbr_checkpoint_score = test_scaler(lgb_reg, X[features], Y)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 5:
lgbr_checkpoint_score, features = feat_selection(
X[features], Y, scale, lgb_reg, lgbr_checkpoint_score)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 6:
lgb_reg, lgbr_checkpoint_score = lgb_tree_params(
X[features], Y, lgb_reg, scale, lgbr_checkpoint_score)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 7:
scale, lgbr_checkpoint_score = test_scaler(lgb_reg, X[features], Y)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 8:
lgbr_checkpoint_score, features = feat_selection(X[features],
Y,
scale,
lgb_reg,
lgbr_checkpoint_score,
_iter=25)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=False)
elif stage == 9:
lgb_reg, lgbr_checkpoint_score = lgb_drop_lr(lgb_reg, X[features], Y,
scale,
lgbr_checkpoint_score)
_save_model(stage,
dimension,
name,
lgb_reg,
scale,
lgbr_checkpoint_score,
features,
final=True)
def tune_dart():
name = 'DartGBM'
dimension = 'winner'
stage, dart_clf, dart_checkpoint_score = stage_init(name,
dimension,
extension=EXTENSION)
if stage == 0:
dart_clf = lgb.LGBMClassifier(random_state=1108,
n_estimators=100,
subsample=.8,
verbose=-1,
is_unbalance=True)
dart_clf, dart_checkpoint_score = pipe_init(X, Y, dart_clf)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
dart_clf, dart_checkpoint_score = test_scaler(dart_clf,
dart_checkpoint_score, X,
Y)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
dart_clf, dart_checkpoint_score = feat_selection(
X, Y, dart_clf, dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
dart_clf, dart_checkpoint_score = test_scaler(dart_clf,
dart_checkpoint_score, X,
Y)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
dart_clf, dart_checkpoint_score = pca_tune(X, Y, dart_clf,
dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
dart_clf, dart_checkpoint_score = feat_selection(
X, Y, dart_clf, dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
dart_clf, dart_checkpoint_score = test_scaler(dart_clf,
dart_checkpoint_score, X,
Y)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
dart_clf, dart_checkpoint_score = lgb_find_lr(dart_clf, X, Y,
dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
dart_clf, dart_checkpoint_score = pca_tune(X,
Y,
dart_clf,
dart_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 9:
dart_clf, dart_checkpoint_score = feat_selection(
X, Y, dart_clf, dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 10:
dart_clf, dart_checkpoint_score = test_scaler(dart_clf,
dart_checkpoint_score, X,
Y)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 11:
dart_clf, dart_checkpoint_score = lgb_tree_params(
X, Y, dart_clf, dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 12:
dart_clf, dart_checkpoint_score = pca_tune(X,
Y,
dart_clf,
dart_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 13:
dart_clf, dart_checkpoint_score = feat_selection(
X, Y, dart_clf, dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 14:
dart_clf, dart_checkpoint_score = test_scaler(dart_clf,
dart_checkpoint_score, X,
Y)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 15:
dart_clf, dart_checkpoint_score = lgb_drop_lr(dart_clf, X, Y,
dart_checkpoint_score)
_save_model(stage,
'winner',
name,
dart_clf,
dart_checkpoint_score,
final=True,
extension=EXTENSION)
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)
def tune_log():
name = 'LogRegression'
dimension = 'winner'
stage, log_clf, log_checkpoint_score = stage_init(name,
dimension,
extension=EXTENSION)
if stage == 0:
log_clf = LogisticRegression(max_iter=1000,
random_state=1108,
class_weight='balanced',
solver='lbfgs')
log_clf, log_checkpoint_score = pipe_init(X, Y, log_clf)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
log_clf, log_checkpoint_score = test_scaler(log_clf,
log_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
log_clf, log_checkpoint_score = feat_selection(
X, Y, log_clf, log_checkpoint_score) #, _iter = 5)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
log_clf, log_checkpoint_score = test_scaler(log_clf,
log_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
log_clf, log_checkpoint_score = pca_tune(X, Y, log_clf,
log_checkpoint_score)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
log_clf, log_checkpoint_score = feat_selection(
X, Y, log_clf, log_checkpoint_score) #, _iter = 5)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
log_clf, log_checkpoint_score = test_scaler(log_clf,
log_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
log_clf, log_checkpoint_score = C_parameter_tuning(
X, Y, log_clf, log_checkpoint_score)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
log_clf, log_checkpoint_score = pca_tune(X,
Y,
log_clf,
log_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 9:
log_clf, log_checkpoint_score = feat_selection(
X, Y, log_clf, log_checkpoint_score) #, _iter = 5)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 10:
log_clf, log_checkpoint_score = test_scaler(log_clf,
log_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 11:
log_clf, log_checkpoint_score = test_solver(X, Y, log_clf,
log_checkpoint_score)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 12:
log_clf, log_checkpoint_score = pca_tune(X,
Y,
log_clf,
log_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 13:
log_clf, log_checkpoint_score = feat_selection(
X, Y, log_clf, log_checkpoint_score) #, _iter = 5)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 14:
log_clf, log_checkpoint_score = test_scaler(log_clf,
log_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
log_clf,
log_checkpoint_score,
final=True,
extension=EXTENSION)
def tune_linsvc():
name = 'LinSVC'
dimension = 'winner'
stage, linsvc_clf, linsvc_checkpoint_score = stage_init(
name, dimension, extension=EXTENSION)
if stage == 0:
linsvc_clf = SVC(random_state=1108,
class_weight='balanced',
kernel='linear',
probability=True)
linsvc_clf, linsvc_checkpoint_score = pipe_init(X, Y, linsvc_clf)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
linsvc_clf, linsvc_checkpoint_score = test_scaler(
linsvc_clf, linsvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
linsvc_clf, linsvc_checkpoint_score = feat_selection(
X, Y, linsvc_clf, linsvc_checkpoint_score)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
linsvc_clf, linsvc_checkpoint_score = test_scaler(
linsvc_clf, linsvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
linsvc_clf, linsvc_checkpoint_score = pca_tune(
X, Y, linsvc_clf, linsvc_checkpoint_score)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
linsvc_clf, linsvc_checkpoint_score = feat_selection(
X, Y, linsvc_clf, linsvc_checkpoint_score)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
linsvc_clf, linsvc_checkpoint_score = test_scaler(
linsvc_clf, linsvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
linsvc_clf, linsvc_checkpoint_score = C_parameter_tuning(
X, Y, linsvc_clf, linsvc_checkpoint_score)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
linsvc_clf, linsvc_checkpoint_score = pca_tune(X,
Y,
linsvc_clf,
linsvc_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 9:
linsvc_clf, linsvc_checkpoint_score = feat_selection(
X, Y, linsvc_clf, linsvc_checkpoint_score)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 10:
linsvc_clf, linsvc_checkpoint_score = test_scaler(
linsvc_clf, linsvc_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
linsvc_clf,
linsvc_checkpoint_score,
final=True,
extension=EXTENSION)
def tune_knn():
name = 'KNN'
dimension = 'winner'
stage, knn_clf, knn_checkpoint_score = stage_init(name,
dimension,
extension=EXTENSION)
if stage == 0:
knn_clf = KNeighborsClassifier()
knn_clf, knn_checkpoint_score = pipe_init(X, Y, knn_clf)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
knn_clf, knn_checkpoint_score = test_scaler(knn_clf,
knn_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
knn_clf, knn_checkpoint_score = feat_selection_2(
X, Y, knn_clf, knn_checkpoint_score)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
knn_clf, knn_checkpoint_score = test_scaler(knn_clf,
knn_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
knn_clf, knn_checkpoint_score = pca_tune(X, Y, knn_clf,
knn_checkpoint_score)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
knn_clf, knn_checkpoint_score = knn_hyper_parameter_tuning(
X, Y, knn_clf, knn_checkpoint_score)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
knn_clf, knn_checkpoint_score = feat_selection_2(
X, Y, knn_clf, knn_checkpoint_score)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
knn_clf, knn_checkpoint_score = test_scaler(knn_clf,
knn_checkpoint_score, X, Y)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
knn_clf, knn_checkpoint_score = pca_tune(X,
Y,
knn_clf,
knn_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
knn_clf,
knn_checkpoint_score,
final=True,
extension=EXTENSION)
def tune_rf():
name = 'RFclass'
dimension = 'winner'
stage, rf_clf, rf_checkpoint_score = stage_init(name,
dimension,
extension=EXTENSION)
if stage == 0:
rf_clf = RandomForestClassifier(random_state=1108, n_estimators=100)
rf_clf, rf_checkpoint_score = pipe_init(X, Y, rf_clf)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 1:
rf_clf, rf_checkpoint_score = test_scaler(rf_clf, rf_checkpoint_score,
X, Y)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 2:
rf_clf, rf_checkpoint_score = feat_selection(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 3:
rf_clf, rf_checkpoint_score = test_scaler(rf_clf, rf_checkpoint_score,
X, Y)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 4:
rf_clf, rf_checkpoint_score = pca_tune(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 5:
rf_clf, rf_checkpoint_score = feat_selection(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 6:
rf_clf, rf_checkpoint_score = test_scaler(rf_clf, rf_checkpoint_score,
X, Y)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 7:
rf_clf, rf_checkpoint_score = forest_params(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 8:
rf_clf, rf_checkpoint_score = pca_tune(X,
Y,
rf_clf,
rf_checkpoint_score,
iter_=10)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 9:
rf_clf, rf_checkpoint_score = feat_selection(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 10:
rf_clf, rf_checkpoint_score = test_scaler(rf_clf, rf_checkpoint_score,
X, Y)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=False,
extension=EXTENSION)
elif stage == 11:
rf_clf, rf_checkpoint_score = rf_trees(X, Y, rf_clf,
rf_checkpoint_score)
_save_model(stage,
'winner',
name,
rf_clf,
rf_checkpoint_score,
final=True,
extension=EXTENSION)
