diff --git a/results/results_accuracy_Bagging_iMac27_2022-01-14_14:00:32_0.json b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:00:32_0.json
new file mode 100644
index 0000000..52bfd1a
--- /dev/null
+++ b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:00:32_0.json
@@ -0,0 +1 @@
+{"score_name": "accuracy", "title": "Test BaggingClassifier with STree", "model": "Bagging", "version": "-", "stratified": false, "folds": 5, "date": "2022-01-14", "time": "14:00:32", "duration": 2.4914512634277344, "seeds": [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1], "platform": "iMac27", "results": [{"dataset": "balance-scale", "samples": 625, "features": 4, "classes": 3, "hyperparameters": {"base_estimator": "Stree(random_state=0)", "max_features": 0.75, "max_samples": 0.4, "n_estimators": 10}, "nodes": 0.0, "leaves": 0.0, "depth": 0.0, "score": NaN, "score_std": NaN, "time": 0.0005263137817382812, "time_std": 7.377665193163519e-05}, {"dataset": "balloons", "samples": 16, "features": 4, "classes": 2, "hyperparameters": {"base_estimator": "Stree(random_state=0)", "max_features": 0.75, "max_samples": 0.4, "n_estimators": 10}, "nodes": 0.0, "leaves": 0.0, "depth": 0.0, "score": NaN, "score_std": NaN, "time": 0.0004933309555053711, "time_std": 2.092596432523415e-05}, {"dataset": 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\ No newline at end of file
diff --git a/results/results_accuracy_Bagging_iMac27_2022-01-14_14:01:48_0.json b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:01:48_0.json
new file mode 100644
index 0000000..d889a68
--- /dev/null
+++ b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:01:48_0.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/results/results_accuracy_Bagging_iMac27_2022-01-14_14:03:24_0.json b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:03:24_0.json
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index 0000000..8bc0463
--- /dev/null
+++ b/results/results_accuracy_Bagging_iMac27_2022-01-14_14:03:24_0.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json b/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json
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index 0000000..714e65a
--- /dev/null
+++ b/results/results_accuracy_RandomForest_iMac27_2022-01-14_12:39:30_0.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/results/results_accuracy_Wodt_iMac27_2022-01-14_12:03:47_0.json b/results/results_accuracy_Wodt_iMac27_2022-01-14_12:03:47_0.json
new file mode 100644
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@@ -0,0 +1 @@
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424.6, "leaves": 212.8, "depth": 50.0, "score": 0.6702379693283977, "score_std": 0.05088784373091166, "time": 0.3186718511581421, "time_std": 0.027290879497068047}, {"dataset": "heart-hungarian", "samples": 294, "features": 12, "classes": 2, "hyperparameters": {}, "nodes": 58.24, "leaves": 29.62, "depth": 9.68, "score": 0.7533722969023963, "score_std": 0.05339145268628328, "time": 0.10717829704284668, "time_std": 0.010609505600479508}, {"dataset": "hepatitis", "samples": 155, "features": 19, "classes": 2, "hyperparameters": {}, "nodes": 20.36, "leaves": 10.68, "depth": 6.2, "score": 0.7748387096774192, "score_std": 0.09558190259697842, "time": 0.03764162063598633, "time_std": 0.0064768788795763225}, {"dataset": "ilpd-indian-liver", "samples": 583, "features": 9, "classes": 2, "hyperparameters": {}, "nodes": 209.56, "leaves": 105.28, "depth": 16.02, "score": 0.6773710580607131, "score_std": 0.03584294141283469, "time": 0.423434944152832, "time_std": 0.021128728522278813}, {"dataset": "ionosphere", "samples": 351, "features": 33, "classes": 2, "hyperparameters": {}, "nodes": 24.84, "leaves": 12.92, "depth": 6.92, "score": 0.8788893360160965, "score_std": 0.030960554046557214, "time": 0.07952766895294189, "time_std": 0.01330600628046465}, {"dataset": "iris", "samples": 150, "features": 4, "classes": 3, "hyperparameters": {}, "nodes": 13.0, "leaves": 7.0, "depth": 5.92, "score": 0.9440000000000001, "score_std": 0.03617242657666809, "time": 0.029955339431762696, "time_std": 0.0064412223307978664}, {"dataset": "led-display", "samples": 1000, "features": 7, "classes": 10, "hyperparameters": {}, "nodes": 4139.64, "leaves": 2070.32, "depth": 50.0, "score": 0.7038, "score_std": 0.028399999999999984, "time": 1.7609633827209472, "time_std": 0.04520911692472444}, {"dataset": "libras", "samples": 360, "features": 90, "classes": 15, "hyperparameters": {}, "nodes": 119.44, "leaves": 60.22, "depth": 11.26, "score": 0.7855555555555556, "score_std": 0.05031996388544112, "time": 0.2919479513168335, "time_std": 0.017065321359632117}, {"dataset": "low-res-spect", "samples": 531, "features": 100, "classes": 9, "hyperparameters": {}, "nodes": 75.08, "leaves": 38.04, "depth": 9.42, "score": 0.8538353024157997, "score_std": 0.030225180924683265, "time": 0.23713892936706543, "time_std": 0.026847420457208965}, {"dataset": "lymphography", "samples": 148, "features": 18, "classes": 4, "hyperparameters": {}, "nodes": 22.88, "leaves": 11.94, "depth": 6.42, "score": 0.7757241379310345, "score_std": 0.06725567781956764, "time": 0.04250402450561523, "time_std": 0.00703877290727569}, {"dataset": "mammographic", "samples": 961, "features": 5, "classes": 2, "hyperparameters": {}, "nodes": 2975.2, "leaves": 1488.1, "depth": 50.0, "score": 0.7567066062176165, "score_std": 0.026136813218176673, "time": 1.2622583723068237, "time_std": 0.051759447495820295}, {"dataset": "molec-biol-promoter", "samples": 106, "features": 57, "classes": 2, "hyperparameters": {}, "nodes": 9.12, "leaves": 5.06, "depth": 3.98, "score": 0.7912987012987013, "score_std": 0.08633959022510546, "time": 0.01750911235809326, "time_std": 0.0036889385653729634}, {"dataset": "musk-1", "samples": 476, "features": 166, "classes": 2, "hyperparameters": {}, "nodes": 45.92, "leaves": 23.46, "depth": 7.86, "score": 0.839282894736842, "score_std": 0.030090802502375974, "time": 0.1498002815246582, "time_std": 0.013965300918253874}, {"dataset": "oocytes_merluccius_nucleus_4d", "samples": 1022, "features": 41, "classes": 2, "hyperparameters": {}, "nodes": 266.88, "leaves": 133.94, "depth": 16.96, "score": 0.74041893830703, "score_std": 0.028693112692103676, "time": 0.6865090465545655, "time_std": 0.036496310515547956}, {"dataset": "oocytes_merluccius_states_2f", "samples": 1022, "features": 25, "classes": 3, "hyperparameters": {}, "nodes": 95.92, "leaves": 48.46, "depth": 11.74, "score": 0.8978421807747491, "score_std": 0.020267256164914475, "time": 0.3044532585144043, "time_std": 0.02397420977193974}, {"dataset": "oocytes_trisopterus_nucleus_2f", "samples": 912, "features": 25, "classes": 2, "hyperparameters": {}, "nodes": 207.0, "leaves": 104.0, "depth": 14.48, "score": 0.7444166216297364, "score_std": 0.03161095033750277, "time": 0.5112469387054444, "time_std": 0.038286965650450824}, {"dataset": "oocytes_trisopterus_states_5b", "samples": 912, "features": 32, "classes": 3, "hyperparameters": {}, "nodes": 100.44, "leaves": 50.72, "depth": 12.36, "score": 0.8916705698672911, "score_std": 0.020702811014803944, "time": 0.3484308385848999, "time_std": 0.027491063443182442}, {"dataset": "parkinsons", "samples": 195, "features": 22, "classes": 2, "hyperparameters": {}, "nodes": 17.28, "leaves": 9.14, "depth": 6.7, "score": 0.8912820512820513, "score_std": 0.04338109945139815, "time": 0.036522345542907717, "time_std": 0.0056772072448939455}, {"dataset": "pima", "samples": 768, "features": 8, "classes": 2, "hyperparameters": {}, "nodes": 217.08, "leaves": 109.04, "depth": 13.6, "score": 0.6842585519056107, "score_std": 0.036954118157522385, "time": 0.43705992221832274, "time_std": 0.025317975798158386}, {"dataset": "pittsburg-bridges-MATERIAL", "samples": 106, "features": 7, "classes": 3, "hyperparameters": {}, "nodes": 76.16, "leaves": 38.58, "depth": 32.76, "score": 0.8118614718614716, "score_std": 0.08062163607707493, "time": 0.04656053066253662, "time_std": 0.01137320258996127}, {"dataset": "pittsburg-bridges-REL-L", "samples": 103, "features": 7, "classes": 3, "hyperparameters": {}, "nodes": 102.92, "leaves": 51.96, "depth": 33.64, "score": 0.6259047619047619, "score_std": 0.10926121729800642, "time": 0.08309615135192872, "time_std": 0.010972621858717564}, {"dataset": "pittsburg-bridges-SPAN", "samples": 92, "features": 7, "classes": 3, "hyperparameters": {}, "nodes": 156.64, "leaves": 78.82, "depth": 47.52, "score": 0.5863157894736841, "score_std": 0.11290675850938181, "time": 0.0840159273147583, "time_std": 0.01233962046954066}, {"dataset": "pittsburg-bridges-T-OR-D", "samples": 102, "features": 7, "classes": 2, "hyperparameters": {}, "nodes": 76.64, "leaves": 38.82, "depth": 33.48, "score": 0.8275238095238096, "score_std": 0.08201979739997459, "time": 0.04265620708465576, "time_std": 0.009771605258376878}, {"dataset": "planning", "samples": 182, "features": 12, "classes": 2, "hyperparameters": {}, "nodes": 66.92, "leaves": 33.96, "depth": 9.56, "score": 0.5784384384384385, "score_std": 0.08440752357130758, "time": 0.10495961666107177, "time_std": 0.007736608520939453}, {"dataset": "post-operative", "samples": 90, "features": 8, "classes": 3, "hyperparameters": {}, "nodes": 396.8, "leaves": 198.9, "depth": 50.0, "score": 0.5477777777777778, "score_std": 0.10830341467206668, "time": 0.11787487030029296, "time_std": 0.018635790491794233}, {"dataset": "seeds", "samples": 210, "features": 7, "classes": 3, "hyperparameters": {}, "nodes": 21.8, "leaves": 11.4, "depth": 6.76, "score": 0.9323809523809523, "score_std": 0.04161152814276852, "time": 0.05017918586730957, "time_std": 0.009351695826926952}, {"dataset": "statlog-australian-credit", "samples": 690, "features": 14, "classes": 2, "hyperparameters": {}, "nodes": 246.72, "leaves": 123.86, "depth": 15.16, "score": 0.5594202898550724, "score_std": 0.03782025608898519, "time": 0.4586418437957764, "time_std": 0.02155917520383023}, {"dataset": "statlog-german-credit", "samples": 1000, "features": 24, "classes": 2, "hyperparameters": {}, "nodes": 166.36, "leaves": 83.68, "depth": 11.76, "score": 0.6850999999999999, "score_std": 0.02579709285946769, "time": 0.4101365089416504, "time_std": 0.023238809047782215}, {"dataset": "statlog-heart", "samples": 270, "features": 13, "classes": 2, "hyperparameters": {}, "nodes": 42.8, "leaves": 21.9, "depth": 7.96, "score": 0.78, "score_std": 0.061154866490519384, "time": 0.08491960525512696, "time_std": 0.009679400932225267}, {"dataset": "statlog-image", "samples": 2310, "features": 18, "classes": 7, "hyperparameters": {}, "nodes": 152.0, "leaves": 76.5, "depth": 15.38, "score": 0.9548917748917749, "score_std": 0.011022906583856079, "time": 0.7529555130004882, "time_std": 0.044649975200733265}, {"dataset": "statlog-vehicle", "samples": 846, "features": 18, "classes": 4, "hyperparameters": {}, "nodes": 210.44, "leaves": 105.72, "depth": 15.82, "score": 0.7293205708318831, "score_std": 0.027854914761975855, "time": 0.5109863376617432, "time_std": 0.028175106084290594}, {"dataset": "synthetic-control", "samples": 600, "features": 60, "classes": 6, "hyperparameters": {}, "nodes": 15.04, "leaves": 8.02, "depth": 5.1, "score": 0.978, "score_std": 0.013412266359153806, "time": 0.09257647514343262, "time_std": 0.008068106679125579}, {"dataset": "tic-tac-toe", "samples": 958, "features": 9, "classes": 2, "hyperparameters": {}, "nodes": 51.48, "leaves": 26.24, "depth": 8.5, "score": 0.9449798211169285, "score_std": 0.029347280139619656, "time": 0.21451024055480958, "time_std": 0.0503618935567868}, {"dataset": "vertebral-column-2clases", "samples": 310, "features": 6, "classes": 2, "hyperparameters": {}, "nodes": 67.08, "leaves": 34.04, "depth": 12.54, "score": 0.8138709677419353, "score_std": 0.04530047675895885, "time": 0.136687536239624, "time_std": 0.013236819997014766}, {"dataset": "wine", "samples": 178, "features": 13, "classes": 3, "hyperparameters": {}, "nodes": 6.6, "leaves": 3.8, "depth": 3.5, "score": 0.964015873015873, "score_std": 0.02991152233765965, "time": 0.020083084106445312, "time_std": 0.0052261067012803935}, {"dataset": "zoo", "samples": 101, "features": 16, "classes": 7, "hyperparameters": {}, "nodes": 13.64, "leaves": 7.32, "depth": 5.22, "score": 0.9484285714285714, "score_std": 0.05254867088510787, "time": 0.02431861400604248, "time_std": 0.0021310197520596217}]}
\ No newline at end of file
diff --git a/src/Experiments.py b/src/Experiments.py
index b8d2d5c..4f5b565 100644
--- a/src/Experiments.py
+++ b/src/Experiments.py
@@ -10,6 +10,9 @@ import pandas as pd
 from sklearn.model_selection import StratifiedKFold, KFold, cross_validate
 from Utils import Folders, Files
 from Models import Models
+from stree import Stree
+from wodt import Wodt
+from sklearn.tree import DecisionTreeClassifier
 
 
 class Randomized:
diff --git a/src/Models.py b/src/Models.py
index 1d091eb..edb962b 100644
--- a/src/Models.py
+++ b/src/Models.py
@@ -1,7 +1,9 @@
+from statistics import mean
 from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, BaggingClassifier
 from sklearn.svm import SVC
 from stree import Stree
-from wodt import TreeClassifier
+from wodt import Wodt
 from odte import Odte
 
 
@@ -15,11 +17,15 @@ class Models:
         if name == "ExtraTree":
             return ExtraTreeClassifier
         if name == "Wodt":
-            return TreeClassifier
+            return Wodt
         if name == "SVC":
             return SVC
         if name == "ODTE":
             return Odte
+        if name == "Bagging":
+            return BaggingClassifier
+        if name == "RandomForest":
+            return RandomForestClassifier
         msg = f"No model recognized {name}"
         if name in ("Stree", "stree"):
             msg += ", did you mean STree?"
@@ -37,6 +43,21 @@ class Models:
             nodes = 0
             leaves = result.get_n_leaves()
             depth = 0
+        elif name=="Bagging":
+            if hasattr(result.base_estimator_, "nodes_leaves"):
+                nodes, leaves = list(zip(*[x.nodes_leaves() for x in result.estimators_]))
+                nodes, leaves = mean(nodes), mean(leaves)
+                depth = mean([x.depth_ for x in result.estimators_])
+            elif hasattr(result.base_estimator_, "tree_"):
+                nodes = mean([x.tree_.node_count for x in result.estimators_])
+                leaves = mean([x.get_n_leaves() for x in result.estimators_])
+                depth = mean([x.get_depth() for x in result.estimators_])
+            else:
+                nodes = leaves=depth=0
+        elif name == "RandomForest":
+            leaves = mean([x.get_n_leaves() for x in result.estimators_])
+            depth = mean([x.get_depth() for x in result.estimators_])
+            nodes = mean([x.tree_.node_count for x in result.estimators_])
         elif name == "SVC":
             nodes = leaves = depth = 0
         else:
diff --git a/src/wodt/WODT.py b/src/wodt/WODT.py
deleted file mode 100644
index f20a9fa..0000000
--- a/src/wodt/WODT.py
+++ /dev/null
@@ -1,318 +0,0 @@
-########################
-"""import"""
-import numpy as np
-import random
-from scipy.optimize import minimize
-from sklearn.base import BaseEstimator, ClassifierMixin
-
-
-"""global var"""
-epsilonepsilon = 1e-220
-epsilon = 1e-50
-
-"""class"""
-
-
-class SplitQuestion(object):
-    """docstring for SplitQuestion"""
-
-    def __init__(self, attrIDs=[0], paras=[0], threshold=0):
-        super(SplitQuestion, self).__init__()
-        self.attrIDs = attrIDs
-        self.paras = paras
-        self.threshold = threshold
-
-    # we only consider continuous attributes for simplicity
-    def test_forOneInstance(self, x):
-        return np.dot(x[self.attrIDs], self.paras) <= self.threshold
-
-    def test(self, X):
-        return np.dot(X[:, self.attrIDs], self.paras) <= self.threshold
-
-
-class Node(object):
-    """docstring for RBNode"""
-
-    def __init__(self, depth, split, sample_ids, X, Y, class_num):
-        super(Node, self).__init__()
-        self.sample_ids = sample_ids
-        self.split = split
-        self.depth = depth
-        self.X = X
-        self.Y = Y
-        self.class_num = class_num
-        self.is_leaf = False
-        # after grow_stump, set the node as an internal node
-
-    def find_best_split(self, max_features="sqrt"):
-        feature_num = self.X.shape[1]
-        subset_feature_num = feature_num
-        if max_features == "sqrt":
-            subset_feature_num = int(np.sqrt(feature_num))
-        if max_features == "all":
-            subset_feature_num = feature_num
-        if max_features == "log":
-            subset_feature_num = int(np.log2(feature_num))
-        if isinstance(max_features, int):
-            subset_feature_num = max_features
-        if isinstance(max_features, float):
-            subset_feature_num = int(feature_num * max_features)
-
-        # ### get random subset of features
-        # ### feature 0 is threshold
-        feature_ids = range(feature_num)
-        subset_feature_ids = random.sample(feature_ids, subset_feature_num)
-        self.split.attrIDs = subset_feature_ids
-        subset_feature_ids = np.array(subset_feature_ids)
-
-        X = self.X
-        subFeatures_X = X[
-            self.sample_ids[:, None], subset_feature_ids[None, :]
-        ]
-        Y = self.Y[self.sample_ids]
-        class_num = self.class_num
-
-        # ##############################
-        # define func and func_gradient for optimization
-        def func(a):
-            paras = a[1:]
-            threshold = a[0]
-            p = sigmoid(np.dot(subFeatures_X, paras) - threshold)
-            w_R = p
-            w_L = 1 - w_R
-            w_R_sum = w_R.sum()
-            w_L_sum = w_L.sum()
-            w_R_eachClass = np.array(
-                [sum(w_R[Y == k]) for k in range(class_num)]
-            )
-            w_L_eachClass = np.array(
-                [sum(w_L[Y == k]) for k in range(class_num)]
-            )
-            fun = (
-                w_L_sum * np.log2(w_L_sum + epsilonepsilon)
-                + w_R_sum * np.log2(w_R_sum + epsilonepsilon)
-                - np.sum(
-                    w_R_eachClass * np.log2(w_R_eachClass + epsilonepsilon)
-                )
-                - np.sum(
-                    w_L_eachClass * np.log2(w_L_eachClass + epsilonepsilon)
-                )
-            )
-            # fun = w_L.sum() * compute_entropy(Y, w_L) + w_R.sum()
-            # * compute_entropy(Y, w_R)
-            return fun
-
-        def func_gradient(a):
-            paras = a[1:]
-            threshold = a[0]
-
-            p = sigmoid(np.dot(subFeatures_X, paras) - threshold)
-            w_R = p
-            w_L = 1 - w_R
-            w_R_eachClass = np.array(
-                [sum(w_R[Y == k]) for k in range(class_num)]
-            )
-            w_L_eachClass = np.array(
-                [sum(w_L[Y == k]) for k in range(class_num)]
-            )
-            la = np.log2(
-                w_L_eachClass[Y] * w_R.sum() + epsilonepsilon
-            ) - np.log2(w_R_eachClass[Y] * w_L.sum() + epsilonepsilon)
-            beta = la * p * (1 - p)
-
-            jac = np.zeros(a.shape)
-            jac[0] = -np.sum(beta)
-            jac[1:] = np.dot(subFeatures_X.T, beta)
-
-            return jac
-
-        ################################################
-        initial_a = np.random.rand(subset_feature_num + 1) - 0.5
-        result = minimize(
-            func,
-            initial_a,
-            method="L-BFGS-B",
-            jac=func_gradient,
-            options={"maxiter": 10, "disp": False},
-        )
-
-        ##########################################
-        self.split.paras = result.x[1:]
-        self.split.threshold = result.x[0]
-
-        return 1
-
-    def grow_stump(self):
-        L_bool = self.split.test(self.X[self.sample_ids])
-        L_sample_ids = self.sample_ids[L_bool]
-        R_sample_ids = self.sample_ids[~L_bool]
-        # if len(R_sample_ids) * len(L_sample_ids) == 0 :
-        # 	print('some branch is 0 sample')
-        LChild = Node(
-            self.depth + 1,
-            SplitQuestion(),
-            L_sample_ids,
-            self.X,
-            self.Y,
-            self.class_num,
-        )
-        RChild = Node(
-            self.depth + 1,
-            SplitQuestion(),
-            R_sample_ids,
-            self.X,
-            self.Y,
-            self.class_num,
-        )
-
-        if len(L_sample_ids) == 0:
-            LChild.is_leaf = True
-            LChild.class_distribution = compute_class_distribution(
-                self.Y[self.sample_ids], self.class_num
-            )
-        if len(R_sample_ids) == 0:
-            RChild.is_leaf = True
-            RChild.class_distribution = compute_class_distribution(
-                self.Y[self.sample_ids], self.class_num
-            )
-
-        self.LChild = LChild
-        self.RChild = RChild
-
-
-class TreeClassifier(BaseEstimator, ClassifierMixin):
-    """docstring for TreeClassifier"""
-
-    def __init__(
-        self,
-        max_depth=50,
-        min_samples_split=2,
-        max_features="all",
-        random_state=None,
-    ):
-        # super(TreeClassifier, self).__init__()
-        self.max_depth = max_depth
-        self.min_samples_split = min_samples_split
-        self.max_features = max_features
-        self.random_state = random_state
-
-    def fit(self, X, Y):
-        self.X = X
-        self.Y = Y
-        self.classNum = self.Y.max() + 1
-        self.sampleNum = self.X.shape[0]
-        if self.random_state is not None:
-            random.seed(self.random_state)
-        ###########
-        self.root_node = Node(
-            1,
-            SplitQuestion(),
-            np.arange(self.sampleNum, dtype=np.uint32),
-            self.X,
-            self.Y,
-            self.classNum,
-        )
-        self.leaf_num = 1
-        self.tree_depth = self.bulid_subtree(self.root_node)
-
-    def nodes_leaves(self):
-        def num_leaves(node):
-            leaves = 0
-            nodes = 0
-            nodes_left = 0
-            nodes_right = 0
-            leaves_left = 0
-            leaves_right = 0
-            if node.is_leaf:
-                leaves += 1
-            else:
-                nodes_left, leaves_left = num_leaves(node.LChild)
-                nodes_right, leaves_right = num_leaves(node.RChild)
-            nodes = nodes_left + nodes_right + 1
-            leaves += leaves_left + leaves_right
-            return nodes, leaves
-
-        def compute_depth(node):
-            if node.is_leaf:
-                return node.depth
-            return max(
-                node.depth,
-                compute_depth(node.LChild),
-                compute_depth(node.RChild),
-            )
-
-        self.depth_ = compute_depth(self.root_node)
-        return num_leaves(self.root_node)
-
-    def bulid_subtree(self, node):
-        if node.is_leaf:
-            return node.depth
-
-        # stopping conditions
-        is_leaf = (
-            node.depth >= self.max_depth
-            or len(node.sample_ids) < self.min_samples_split
-            or is_all_equal(self.Y[node.sample_ids])
-        )
-
-        if is_leaf or node.find_best_split(self.max_features) < 0:
-            node.is_leaf = True
-            node.class_distribution = compute_class_distribution(
-                self.Y[node.sample_ids], self.classNum
-            )
-            return node.depth
-
-        node.grow_stump()
-        node.is_leaf = False
-        self.leaf_num += 1
-        L_subtree_depth = self.bulid_subtree(node.LChild)
-        R_subtree_depth = self.bulid_subtree(node.RChild)
-        return max(L_subtree_depth, R_subtree_depth)
-
-    def predict_forOneInstance(self, x):
-        present_node = self.root_node
-        while not (present_node.is_leaf):
-            if present_node.split.test_forOneInstance(x):
-                present_node = present_node.LChild
-            else:
-                present_node = present_node.RChild
-        return np.argmax(present_node.class_distribution)
-
-    def predict(self, X):
-        m = X.shape[0]
-        Y_predicted = np.zeros((m,), dtype=int)
-        for i in range(m):
-            x = X[i]
-            Y_predicted[i] = self.predict_forOneInstance(x)
-        return Y_predicted
-
-    def score(
-        self, X: np.array, y: np.array, sample_weight: np.array = None
-    ) -> float:
-        y_pred = self.predict(X)
-        return np.mean(y_pred == y)
-
-
-####################
-"""function"""
-
-
-def sigmoid(z):
-    # because that -z is too big will arise runtimeWarning in np.exp()
-    if isinstance(z, float) and (z < -500):
-        z = -500
-    elif not (isinstance(z, float)):
-        z[z < -500] = (-500) * np.ones(sum(z < -500))
-
-    return 1 / (np.exp(-z) + 1)
-
-
-def is_all_equal(x):
-    x_min, x_max = x.min(), x.max()
-    return x_min == x_max
-
-
-def compute_class_distribution(Y, class_num):
-    sample_num = len(Y)
-    ratio_each_class = [sum(Y == k) / sample_num for k in range(class_num)]
-    return np.array(ratio_each_class)
diff --git a/src/wodt/__init__.py b/src/wodt/__init__.py
deleted file mode 100644
index 6a86cb0..0000000
--- a/src/wodt/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .WODT import TreeClassifier
-
-__all__ = [
-    "TreeClassifier",
-]