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)