cluster max with features and not samples

EpistasisLab · Oct 8, 2024 · 38fe6af · 38fe6af
1 parent bfb40d9
commit 38fe6af
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Showing 2 changed files with 9 additions and 6 deletions.
diff --git a/tpot2/config/get_configspace.py b/tpot2/config/get_configspace.py
@@ -36,6 +36,7 @@
 from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
 from sklearn.svm import SVC, SVR, LinearSVR, LinearSVC
 from lightgbm import LGBMClassifier, LGBMRegressor
+import sklearn
 from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
 from sklearn.decomposition import FastICA, PCA
 from sklearn.cluster import FeatureAgglomeration
@@ -117,7 +118,7 @@
         "selectors": ["SelectFwe", "SelectPercentile", "VarianceThreshold",],
         "selectors_classification": ["SelectFwe", "SelectPercentile", "VarianceThreshold", "RFE_classification", "SelectFromModel_classification"],
         "selectors_regression": ["SelectFwe", "SelectPercentile", "VarianceThreshold", "RFE_regression", "SelectFromModel_regression"],
-        "classifiers" :  ["LGBMClassifier", "BaggingClassifier", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', "LinearSVC", "SVC", 'MLPClassifier', 'MultinomialNB',  "QuadraticDiscriminantAnalysis", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'],
+        "classifiers" :  ["LGBMClassifier", "BaggingClassifier", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', "LinearSVC_wrapped", "SVC", 'MLPClassifier', 'MultinomialNB',  "QuadraticDiscriminantAnalysis", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'],
         "regressors" : ["LGBMRegressor", 'AdaBoostRegressor', "ARDRegression", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor',  'LinearSVR', "MLPRegressor", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'],
 
 
@@ -298,7 +299,7 @@ def get_configspace(name, n_classes=3, n_samples=1000, n_features=100, random_st
         case "FastICA":
             return transformers.get_FastICA_configspace(n_features=n_features, random_state=random_state)
         case "FeatureAgglomeration":
-            return transformers.get_FeatureAgglomeration_configspace(n_samples=n_samples)
+            return transformers.get_FeatureAgglomeration_configspace(n_features=n_features)
         case "Nystroem":
             return transformers.get_Nystroem_configspace(n_features=n_features, random_state=random_state)
         case "RBFSampler":
@@ -522,7 +523,9 @@ def get_node(name, n_classes=3, n_samples=100, n_features=100, random_state=None
     
     """
 
-
+    if name == "LinearSVC_wrapped":
+        ext = get_node("LinearSVC", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
+        return WrapperPipeline(estimator_search_space=ext, method=sklearn.calibration.CalibratedClassifierCV, space={})
     if name == "RFE_classification":
         rfe_sp = get_configspace(name="RFE", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         ext = get_node("ExtraTreesClassifier", n_classes=n_classes, n_samples=n_samples, random_state=random_state)
@@ -577,7 +580,7 @@ def get_node(name, n_classes=3, n_samples=100, n_features=100, random_state=None
         configspace = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         return base_node(STRING_TO_CLASS[name], configspace, hyperparameter_parser=classifiers.GaussianProcessClassifier_hyperparameter_parser)
     if name == "FeatureAgglomeration":
-        configspace = get_configspace(name, n_features=n_features)
+        configspace = get_configspace(name, n_classes=n_classes, n_samples=n_samples, random_state=random_state)
         return base_node(STRING_TO_CLASS[name], configspace, hyperparameter_parser=transformers.FeatureAgglomeration_hyperparameter_parser)
 
     configspace = get_configspace(name, n_classes=n_classes, n_samples=n_samples, n_features=n_features, random_state=random_state)

diff --git a/tpot2/config/transformers.py b/tpot2/config/transformers.py
@@ -44,11 +44,11 @@ def get_FastICA_configspace(n_features=100, random_state=None):
 
     )
 
-def get_FeatureAgglomeration_configspace(n_samples):
+def get_FeatureAgglomeration_configspace(n_features):
 
     linkage = Categorical('linkage', ['ward', 'complete', 'average'])
     metric = Categorical('metric', ['euclidean', 'l1', 'l2', 'manhattan', 'cosine'])
-    n_clusters = Integer('n_clusters', bounds=(2, min(n_samples,400)))
+    n_clusters = Integer('n_clusters', bounds=(2, min(n_features,400)))
     pooling_func = Categorical('pooling_func', ['mean', 'median', 'max'])
 
     metric_condition = NotEqualsCondition(metric, linkage, 'ward')