Doctorado-ML/benchmark
1
0
Fork 0
mirror of https://github.com/Doctorado-ML/benchmark.git synced 2025-08-16 16:05:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add BaggingStree and BaggingWodt models

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2022-01-15 10:41:12 +01:00
parent 2c901d761c
commit d34d71fdef
4 changed files with 20 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
results/results_accuracy_SVC_macbook-pro_2022-01-15_10:19:36_0.json Normal file
View File

File diff suppressed because one or more lines are too long

1
results/results_accuracy_SVC_macbook-pro_2022-01-15_10:38:07_0.json Normal file
View File

File diff suppressed because one or more lines are too long

5
src/Experiments.py
View File

@@ -159,7 +159,6 @@ class Experiment:
self.title = title self.title = title
self.stratified = stratified == "1" self.stratified = stratified == "1"
self.stratified_class = StratifiedKFold if self.stratified else KFold self.stratified_class = StratifiedKFold if self.stratified else KFold
self.model = Models.get_model(model_name)
self.datasets = datasets self.datasets = datasets
dictionary = json.loads(hyperparams_dict) dictionary = json.loads(hyperparams_dict)
hyper = BestResults( hyper = BestResults(
@@ -185,8 +184,10 @@ class Experiment:
return self.output_file return self.output_file
def _build_classifier(self, random_state, hyperparameters): def _build_classifier(self, random_state, hyperparameters):
clf = self.model(random_state=random_state) self.model = Models.get_model(self.model_name, random_state)
clf = self.model
clf.set_params(**hyperparameters) clf.set_params(**hyperparameters)
clf.set_params(random_state=random_state)
return clf return clf
def _init_experiment(self): def _init_experiment(self):

26
src/Models.py
View File

@@ -9,23 +9,27 @@ from odte import Odte
class Models: class Models:
@staticmethod @staticmethod
def get_model(name): def get_model(name, random_state=None):
if name == "STree": if name == "STree":
return Stree return Stree()
if name == "Cart": if name == "Cart":
return DecisionTreeClassifier return DecisionTreeClassifier()
if name == "ExtraTree": if name == "ExtraTree":
return ExtraTreeClassifier return ExtraTreeClassifier()
if name == "Wodt": if name == "Wodt":
return Wodt return Wodt()
if name == "SVC": if name == "SVC":
return SVC return SVC()
if name == "ODTE": if name == "ODTE":
return Odte return Odte()
if name == "Bagging": if name == "BaggingStree":
return BaggingClassifier clf = Stree(random_state=random_state)
return BaggingClassifier(base_estimator=clf)
if name == "BaggingWodt":
clf = Wodt(random_state=random_state)
return BaggingClassifier(base_estimator=clf)
if name == "RandomForest": if name == "RandomForest":
return RandomForestClassifier return RandomForestClassifier()
msg = f"No model recognized {name}" msg = f"No model recognized {name}"
if name in ("Stree", "stree"): if name in ("Stree", "stree"):
msg += ", did you mean STree?" msg += ", did you mean STree?"
@@ -43,7 +47,7 @@ class Models:
nodes = 0 nodes = 0
leaves = result.get_n_leaves() leaves = result.get_n_leaves()
depth = 0 depth = 0
elif name == "Bagging": elif name.startswith("Bagging"):
if hasattr(result.base_estimator_, "nodes_leaves"): if hasattr(result.base_estimator_, "nodes_leaves"):
nodes, leaves = list( nodes, leaves = list(
zip(*[x.nodes_leaves() for x in result.estimators_]) zip(*[x.nodes_leaves() for x in result.estimators_])