Add report models in benchmark

Add SVC, WODT & ExtraTree models

src/Experiments.py

@@ -8,31 +8,14 @@ from tqdm import tqdm
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import StratifiedKFold, cross_validate
-from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
-from stree import Stree
 from Utils import Folders, Files
+from Models import Models
 
 
 class Randomized:
     seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
 
 
-class Models:
-    @staticmethod
-    def get_model(name):
-        if name == "STree":
-            return Stree
-        elif name == "Cart":
-            return DecisionTreeClassifier
-        elif name == "ExtraTree":
-            return ExtraTreeClassifier
-        else:
-            msg = f"No model recognized {name}"
-            if name == "Stree" or name == "stree":
-                msg += ", did you mean STree?"
-            raise ValueError(msg)
-
-
 class Diterator:
     def __init__(self, data):
         self._stack = data.copy()
@@ -178,20 +161,6 @@ class Experiment:
         self.leaves = []
         self.depths = []
 
-    def _get_complexity(self, result):
-        if self.model_name == "Cart":
-            nodes = result.tree_.node_count
-            depth = result.tree_.max_depth
-            leaves = result.get_n_leaves()
-        if self.model_name == "ExtraTree":
-            nodes = 0
-            leaves = result.get_n_leaves()
-            depth = 0
-        else:
-            nodes, leaves = result.nodes_leaves()
-            depth = result.depth_ if hasattr(result, "depth_") else 0
-        return nodes, leaves, depth
-
     def _n_fold_crossval(self, X, y, hyperparameters):
         if self.scores != []:
             raise ValueError("Must init experiment before!")
@@ -217,8 +186,8 @@ class Experiment:
             self.scores.append(res["test_score"])
             self.times.append(res["fit_time"])
             for result_item in res["estimator"]:
-                nodes_item, leaves_item, depth_item = self._get_complexity(
-                    result_item
+                nodes_item, leaves_item, depth_item = Models.get_complexity(
+                    self.model_name, result_item
                 )
                 self.nodes.append(nodes_item)
                 self.leaves.append(leaves_item)