def tune_dartr(): name = 'DartrGBM' dimension = 'winner' 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_rf(): name = 'RFreg' dimension = 'winner' 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_linsvr(): name = 'LinSVR' dimension = 'winner' 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_lasso(): name = 'LassoRegression' dimension = 'winner' 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 = '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_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)