diff --git a/LGBM_Tuning.py b/LGBM_Tuning.py new file mode 100644 index 0000000..ff7fec0 --- /dev/null +++ b/LGBM_Tuning.py @@ -0,0 +1,280 @@ +import itertools +import os +import random + +import pandas as pd +import tqdm +from imblearn.over_sampling import KMeansSMOTE +from lightgbm import LGBMClassifier +from sklearn.metrics import ( + accuracy_score, + f1_score, + fbeta_score, + precision_score, + recall_score, +) +from sklearn.model_selection import StratifiedKFold, train_test_split + +from utils import scaling_handler + +RESULTS_FILENAME = "results_lgbm_kmeans_smote_tuning_grid.csv" + + +def get_metrics(y_true, y_pred, prefix=""): + metrics = {} + + metrics[f"{prefix}accuracy"] = accuracy_score(y_true, y_pred) + metrics[f"{prefix}f1_macro"] = f1_score(y_true, y_pred, average="macro") + metrics[f"{prefix}f2_macro"] = fbeta_score(y_true, y_pred, beta=2, average="macro") + metrics[f"{prefix}recall_macro"] = recall_score(y_true, y_pred, average="macro") + metrics[f"{prefix}precision_macro"] = precision_score( + y_true, y_pred, average="macro" + ) + + f1_scores = f1_score(y_true, y_pred, average=None, zero_division=0) + f2_scores = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=0) + recall_scores = recall_score(y_true, y_pred, average=None, zero_division=0) + precision_scores = precision_score(y_true, y_pred, average=None, zero_division=0) + + metrics[f"{prefix}f1_class0"] = f1_scores[0] + metrics[f"{prefix}f1_class1"] = f1_scores[1] + metrics[f"{prefix}f2_class0"] = f2_scores[0] + metrics[f"{prefix}f2_class1"] = f2_scores[1] + metrics[f"{prefix}recall_class0"] = recall_scores[0] + metrics[f"{prefix}recall_class1"] = recall_scores[1] + metrics[f"{prefix}precision_class0"] = precision_scores[0] + metrics[f"{prefix}precision_class1"] = precision_scores[1] + + TP = sum((y_true == 1) & (y_pred == 1)) + TN = sum((y_true == 0) & (y_pred == 0)) + FP = sum((y_true == 0) & (y_pred == 1)) + FN = sum((y_true == 1) & (y_pred == 0)) + + metrics[f"{prefix}TP"] = TP + metrics[f"{prefix}TN"] = TN + metrics[f"{prefix}FP"] = FP + metrics[f"{prefix}FN"] = FN + + return metrics + + +try: + data_frame = pd.read_csv("./data/Ketamine_icp_no_missing.csv") +except FileNotFoundError: + print("Please ensure the data file exists at './data/Ketamine_icp_no_missing.csv'") + exit() + +random_state = 42 +n_split_kfold = 5 + +scaling_methods_list = [ + "standard_scaling", + "robust_scaling", + "minmax_scaling", + "yeo_johnson", +] + +boosting_type_list = ["gbdt", "dart"] +learning_rate_list = [0.03, 0.05, 0.1] +number_of_leaves_list = [100] +l2_regularization_lambda_list = [0.1, 0.5] +l1_regularization_alpha_list = [0.1, 0.5] +tree_subsample_tree_list = [0.8, 1.0] +subsample_list = [0.8, 1.0] +is_balanced_list = [True, False] + +kmeans_smote_k_neighbors_list = [10] +kmeans_smote_n_clusters_list = [5] + +param_combinations = list( + itertools.product( + scaling_methods_list, + boosting_type_list, + learning_rate_list, + number_of_leaves_list, + l2_regularization_lambda_list, + l1_regularization_alpha_list, + tree_subsample_tree_list, + subsample_list, + is_balanced_list, + kmeans_smote_k_neighbors_list, + kmeans_smote_n_clusters_list, + ) +) + +template_metrics = get_metrics( + pd.Series([0, 1, 0, 1]), + pd.Series([0, 1, 0, 1]), +) + +template_cols = ["iteration", "model", "params"] +for k in template_metrics.keys(): + template_cols.append(f"avg_val_{k}") + template_cols.append(f"test_{k}") +empty_df = pd.DataFrame(columns=template_cols) +if not os.path.exists(RESULTS_FILENAME): + empty_df.to_csv(RESULTS_FILENAME, index=False) + print(f"Initialized {RESULTS_FILENAME} with headers.") +else: + print(f"File {RESULTS_FILENAME} already exists. Appending to it.") + +iteration = 0 +for ( + scaling_method, + boosting_type, + learning_rate, + num_leaves, + reg_lambda, + reg_alpha, + colsample_bytree, + subsample, + is_balanced, + k_neighbors, + kmeans_estimator, +) in tqdm.tqdm(param_combinations): + skf = StratifiedKFold( + n_splits=n_split_kfold, shuffle=True, random_state=random_state + ) + + data_frame_scaled = scaling_handler(data_frame, scaling_method) + y = data_frame_scaled["label"] + X = data_frame_scaled.drop(columns=["label"]) + + x_train_val, x_test, y_train_val, y_test = train_test_split( + X, y, test_size=0.15, stratify=y, random_state=random_state + ) + + fold_results = [] + lgbm_classifier_params = None + + sampling_method = "none" + + for fold_idx, (train_index, val_index) in enumerate( + skf.split(x_train_val, y_train_val) + ): + x_train_fold, x_val = x_train_val.iloc[train_index], x_train_val.iloc[val_index] + y_train_fold, y_val = y_train_val.iloc[train_index], y_train_val.iloc[val_index] + + x_train = x_train_fold + y_train = y_train_fold + lgbm_classifier_params = None + + lgbm_base_params = { + "boosting_type": boosting_type, + "objective": "binary", + "learning_rate": learning_rate, + "n_jobs": -1, + "num_leaves": num_leaves, + "reg_lambda": reg_lambda, + "reg_alpha": reg_alpha, + "colsample_bytree": colsample_bytree, + "subsample": subsample, + } + + if is_balanced: + sampling_method = "KMeansSMOTE" + + smote_params = { + "sampling_strategy": "minority", + "k_neighbors": k_neighbors, + "kmeans_estimator": kmeans_estimator, + "cluster_balance_threshold": 0.001, + "random_state": random_state, + "n_jobs": -1, + } + + try: + smote = KMeansSMOTE(**smote_params) + x_train, y_train = smote.fit_resample(x_train_fold, y_train_fold) + lgbm_classifier_params = lgbm_base_params.copy() + + except RuntimeError as e: + print( + f"KMeansSMOTE failed with RuntimeError in fold {fold_idx} of iteration {iteration}: {e}. Skipping fold." + ) + continue + except ValueError as e: + print( + f"KMeansSMOTE failed with ValueError in fold {fold_idx} of iteration {iteration}: {e}. Skipping fold." + ) + continue + + else: + sampling_method = "class_weight" + + class_1_weight = int( + (y_train_fold.shape[0] - y_train_fold.sum()) / y_train_fold.sum() + ) + + lgbm_classifier_params = lgbm_base_params.copy() + lgbm_classifier_params["class_weight"] = {0: 1, 1: class_1_weight} + + if lgbm_classifier_params: + model = LGBMClassifier( + **lgbm_classifier_params, random_state=random_state, verbose=-1 + ) + model.fit(x_train, y_train) + y_pred_val = model.predict(x_val) + + val_metrics = get_metrics(y_val, y_pred_val) + fold_results.append(val_metrics) + + avg_val_metrics = {} + if fold_results: + val_df = pd.DataFrame(fold_results) + avg_val_metrics = val_df.mean().to_dict() + + test_metrics = {} + if lgbm_classifier_params: + x_train_final = x_train_val + y_train_final = y_train_val + + if is_balanced and sampling_method == "KMeansSMOTE": + try: + smote = KMeansSMOTE(**smote_params) + x_train_final, y_train_final = smote.fit_resample( + x_train_val, y_train_val + ) + except (RuntimeError, ValueError) as e: + print( + f"Final KMeansSMOTE failed for iteration {iteration}: {e}. Skipping test evaluation." + ) + lgbm_classifier_params = None + + if lgbm_classifier_params: + final_lgbm_params = lgbm_base_params.copy() + + test_model = LGBMClassifier( + **final_lgbm_params, random_state=random_state, verbose=-1 + ) + + test_model.fit(x_train_final, y_train_final) + y_pred_test = test_model.predict(x_test) + + test_metrics = get_metrics(y_test, y_pred_test, prefix="test_") + + if lgbm_classifier_params: + params_str = str(lgbm_base_params).replace("}", "") + if is_balanced: + params_str += f", 'smote_k_neighbors': {k_neighbors}, 'smote_n_clusters': {kmeans_estimator}" + + final_result_dict = { + "iteration": iteration, + "model": "LGBMClassifier", + "params": params_str + + f", 'sampling_method': '{sampling_method}', 'scaling_method': '{scaling_method}'}}", + } + + for k in template_metrics.keys(): + final_result_dict[f"avg_val_{k}"] = avg_val_metrics.get(k, float("nan")) + final_result_dict[f"test_{k}"] = test_metrics.get(f"test_{k}", float("nan")) + + result_row_df = pd.DataFrame([final_result_dict]) + + result_row_df = result_row_df.reindex(columns=template_cols, fill_value=None) + + result_row_df.to_csv(RESULTS_FILENAME, mode="a", header=False, index=False) + + iteration += 1 + +print(f"Finished: check {RESULTS_FILENAME}") diff --git a/__pycache__/train.cpython-312.pyc b/__pycache__/train.cpython-312.pyc index 4013c78..b809b9b 100644 Binary files a/__pycache__/train.cpython-312.pyc and b/__pycache__/train.cpython-312.pyc differ diff --git a/__pycache__/utils.cpython-312.pyc b/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000..bda65e1 Binary files /dev/null and b/__pycache__/utils.cpython-312.pyc differ diff --git a/catboost_info/catboost_training.json b/catboost_info/catboost_training.json deleted file mode 100644 index e2fb048..0000000 --- a/catboost_info/catboost_training.json +++ /dev/null @@ -1,504 +0,0 @@ -{ 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import RandomForestClassifier +from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split -# from xgboost import XGBClassifier +from xgboost import XGBClassifier -# from custom_models.LGBMFocalWrapper import LGBMFocalWrapper +from custom_models.LGBMFocalWrapper import LGBMFocalWrapper from train import test_model, train_model_with_kfold data_frame = pandas.read_csv("./data/Ketamin_icp_cleaned.csv") @@ -28,265 +28,33 @@ pos = sum(y_train == 1) scale_pos = neg / pos if pos > 0 else 1.0 models = [ - # { - # "name": "LGBM_FOCAL_LOSS", - # "model": LGBMFocalWrapper( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "LGBM_SMOTE", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "smote", - # }, - # { - # "name": "LGBM_KMEANS_SMOTE", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "LGBM_SVM_SMOTE", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "svm", - # }, - # { - # "name": "LGBM_BORDERLINE_SMOTE", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "borderline", - # }, - # { - # "name": "LGBM_ADASYN_SMOTE", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "adasyn", - # }, - # { - # "name": "LGBM_Balanced", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # class_weight="balanced", - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": False, - # }, - # { - # "name": "LGBM_DART", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # boosting_type="dart", - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "LGBM_GOSS", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # boosting_type="goss", - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "LGBM_RF", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # boosting_type="rf", - # subsample=0.8, - # colsample_bytree=0.8, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "LGBM_scale_pos_weight", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # scale_pos_weight=scale_pos, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": False, - # }, - # { - # "name": "LGBM_is_unbalance", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # is_unbalance=True, - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": False, - # }, - # { - # "name": "LGBM_DART", - # "model": LGBMClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=-1, - # subsample=0.8, - # colsample_bytree=0.8, - # boosting_type="dart", - # random_state=42, - # verbose=-1, - # n_jobs=-1, - # ), - # "smote": True, - # "smote_method": "kmeans", - # }, - # { - # "name": "XGB_scale_pos_weight", - # "model": XGBClassifier( - # n_estimators=500, - # learning_rate=0.05, - # max_depth=6, - # scale_pos_weight=scale_pos, - # random_state=42, - # n_jobs=-1, - # use_label_encoder=False, - # eval_metric="logloss", - # ), - # "smote": False, - # }, - # { - # "name": "CatBoost_balanced", - # "model": CatBoostClassifier( - # iterations=500, - # learning_rate=0.05, - # depth=6, - # class_weights=[1, scale_pos], - # random_state=42, - # verbose=0, - # ), - # "smote": False, - # }, - # { - # "name": "RandomForest_balanced", - # "model": RandomForestClassifier( - # n_estimators=500, - # max_depth=None, - # class_weight="balanced", - # random_state=42, - # n_jobs=-1, - # ), - # "smote": False, - # }, - # { - # "name": "BalancedRandomForest", - # "model": BalancedRandomForestClassifier( - # n_estimators=500, - # max_depth=None, - # random_state=42, - # n_jobs=-1, - # ), - # "smote": False, - # }, - # { - # "name": "LogisticRegression_balanced", - # "model": LogisticRegression( - # max_iter=1000, - # class_weight="balanced", - # solver="liblinear", - # random_state=42, - # ), - # "smote": False, - # }, { - "name": "CatBoost_balanced", - "model": CatBoostClassifier( - iterations=500, + "name": "LGBM_FOCAL_LOSS", + "model": LGBMFocalWrapper( + n_estimators=500, learning_rate=0.05, - depth=6, - class_weights=[1, scale_pos], + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, random_state=42, - verbose=0, ), - "smote": False, + "smote": True, + "smote_method": "kmeans", + }, + { + "name": "LGBM_SMOTE", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "smote", }, { "name": "LGBM_KMEANS_SMOTE", @@ -302,8 +70,212 @@ models = [ ), "smote": True, "smote_method": "kmeans", - } - + }, + { + "name": "LGBM_SVM_SMOTE", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "svm", + }, + { + "name": "LGBM_BORDERLINE_SMOTE", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "borderline", + }, + { + "name": "LGBM_ADASYN_SMOTE", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "adasyn", + }, + { + "name": "LGBM_Balanced", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + class_weight="balanced", + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": False, + }, + { + "name": "LGBM_DART", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + boosting_type="dart", + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "kmeans", + }, + { + "name": "LGBM_GOSS", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + boosting_type="goss", + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "kmeans", + }, + { + "name": "LGBM_RF", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + boosting_type="rf", + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "kmeans", + }, + { + "name": "LGBM_scale_pos_weight", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + scale_pos_weight=scale_pos, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": False, + }, + { + "name": "LGBM_is_unbalance", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + is_unbalance=True, + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": False, + }, + { + "name": "LGBM_DART", + "model": LGBMClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=-1, + subsample=0.8, + colsample_bytree=0.8, + boosting_type="dart", + random_state=42, + verbose=-1, + n_jobs=-1, + ), + "smote": True, + "smote_method": "kmeans", + }, + { + "name": "XGB_scale_pos_weight", + "model": XGBClassifier( + n_estimators=500, + learning_rate=0.05, + max_depth=6, + scale_pos_weight=scale_pos, + random_state=42, + n_jobs=-1, + use_label_encoder=False, + eval_metric="logloss", + ), + "smote": False, + }, + { + "name": "CatBoost_balanced", + "model": CatBoostClassifier( + iterations=500, + learning_rate=0.05, + depth=6, + class_weights=[1, scale_pos], + random_state=42, + verbose=0, + ), + "smote": False, + }, + { + "name": "RandomForest_balanced", + "model": RandomForestClassifier( + n_estimators=500, + max_depth=None, + class_weight="balanced", + random_state=42, + n_jobs=-1, + ), + "smote": False, + }, + { + "name": "BalancedRandomForest", + "model": BalancedRandomForestClassifier( + n_estimators=500, + max_depth=None, + random_state=42, + n_jobs=-1, + ), + "smote": False, + }, + { + "name": "LogisticRegression_balanced", + "model": LogisticRegression( + max_iter=1000, + class_weight="balanced", + solver="liblinear", + random_state=42, + ), + "smote": False, + }, ] @@ -336,7 +308,7 @@ for m in models: ) results_df = pandas.DataFrame(results_to_save) -csv_file = "lgbm_vs_cat_kmeans_smote_k10_results.csv" +csv_file = "lightgbm_results.csv" try: results_df.to_csv(csv_file, mode="a", index=False, header=False) diff --git a/train.py b/train.py index ecb25bb..33bf10e 100644 --- a/train.py +++ b/train.py @@ -42,13 +42,8 @@ def train_model_with_kfold( if smote: if smote_method.lower() == "kmeans": - from collections import Counter - minority = Counter(y_train)[1] - - k_neighbors = min(10, max(2, minority // 10)) - sampler = KMeansSMOTE( - k_neighbors=k_neighbors, + k_neighbors=15, cluster_balance_threshold=0.1, random_state=random_state, ) diff --git a/utils.py b/utils.py index 6f9dea6..da1d0c0 100644 --- a/utils.py +++ b/utils.py @@ -40,18 +40,62 @@ def missing_value_handler(data_path): missing_value_counts = data_frame_imputed.isna().sum() write_textfile(f"{data_directory}/no_missing.txt", missing_value_counts) + + data_frame_imputed.to_csv("./data/Ketamine_icp_no_missing.csv", index=False) + return data_frame_imputed def scaling_handler(data_frame, method="robust_scaling"): - if method == "robust_scaling": - import pandas - from sklearn.preprocessing import RobustScaler + import pandas + from sklearn.preprocessing import ( + MaxAbsScaler, + MinMaxScaler, + PowerTransformer, + QuantileTransformer, + RobustScaler, + StandardScaler, + ) - labels = data_frame["label"] + # Separate features and label + labels = data_frame["label"] + X = data_frame.drop("label", axis=1) + + # Choose scaler/transformer + if method == "robust_scaling": scaler = RobustScaler() - x = data_frame.drop("label", axis=1) - x_scale = scaler.fit_transform(x) - data_frame_scaled = pandas.DataFrame(x_scale, columns=x.columns) - data_frame_scaled["label"] = labels.values - return data_frame_scaled + elif method == "standard_scaling": + scaler = StandardScaler() + elif method == "minmax_scaling": + scaler = MinMaxScaler() + elif method == "maxabs_scaling": + scaler = MaxAbsScaler() + elif method == "quantile_normal": + scaler = QuantileTransformer(output_distribution="normal", random_state=42) + elif method == "quantile_uniform": + scaler = QuantileTransformer(output_distribution="uniform", random_state=42) + elif method == "yeo_johnson": + scaler = PowerTransformer(method="yeo-johnson") + elif method == "box_cox": + # Box-Cox requires all positive values + scaler = PowerTransformer( + method="box-cox", + ) + X_pos = X.copy() + + min_per_column = X_pos.min() + + for col in X_pos.columns: + if min_per_column[col] <= 0: + X_pos[col] = X_pos[col] + abs(min_per_column[col]) + 1e-6 # tiny offset + + X = X_pos + else: + raise ValueError(f"Unknown scaling method: {method}") + + # Fit and transform + X_scaled = scaler.fit_transform(X) + data_frame_scaled = pandas.DataFrame(X_scaled, columns=X.columns) + data_frame_scaled["label"] = labels.values + + return data_frame_scaled