Reduced the space #24

johnantonn · Mar 19, 2022 · 3353c3b · 3353c3b
1 parent 08ecc40
commit 3353c3b
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Showing 6 changed files with 35 additions and 44 deletions.
diff --git a/src/pyod_models/cblof.py b/src/pyod_models/cblof.py
@@ -1,6 +1,6 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
-    UniformFloatHyperparameter, CategoricalHyperparameter
+    UniformFloatHyperparameter, Constant
 
 from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
 from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS
@@ -67,7 +67,8 @@ def get_hyperparameter_search_space(dataset_properties=None):
         n_clusters  = UniformIntegerHyperparameter(
             name = "n_clusters",
             lower = 2,
-            upper = 20, # ad-hoc
+            upper = 16, # ad-hoc
+            q = 2,
             default_value = 8
         )
         contamination = UniformFloatHyperparameter(
@@ -80,20 +81,19 @@ def get_hyperparameter_search_space(dataset_properties=None):
         alpha = UniformFloatHyperparameter(
             name = "alpha",
             lower = 0.5,
-            upper = 0.99,
-            q = 0.01,
+            upper = 1.0,
+            q = 0.1,
             default_value = 0.9
         )
         beta = UniformIntegerHyperparameter(
             name = "beta",
-            lower = 1,
-            upper = 100, # ad-hoc
+            lower = 2,
+            upper = 10, # ad-hoc
             default_value = 5
         )
-        use_weights = CategoricalHyperparameter(
+        use_weights = Constant(
             name = "use_weights",
-            choices = [True, False],
-            default_value = False
+            value = "False"
         )
         cs.add_hyperparameters([n_clusters, contamination, alpha, beta, use_weights])
 

diff --git a/src/pyod_models/hbos.py b/src/pyod_models/hbos.py
@@ -1,6 +1,6 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
-    UniformFloatHyperparameter
+    UniformFloatHyperparameter, Constant
 
 from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
 from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS
@@ -61,24 +61,22 @@ def get_properties(dataset_properties=None):
     def get_hyperparameter_search_space(dataset_properties=None):
         cs = ConfigurationSpace()
 
-        n_bins = UniformIntegerHyperparameter(
+        n_bins = Constant(
             name = "n_bins", 
-            lower = 2, # ad-hoc
-            upper = 200, # ad-hoc
-            default_value = 10
+            value = "auto"
         )
         alpha = UniformFloatHyperparameter(
             name = "alpha",
-            lower = 0.0,
+            lower = 0.1,
             upper = 1.0,
-            q = 0.01,
+            q = 0.1,
             default_value = 0.1
         )
         tol = UniformFloatHyperparameter(
             name = "tol",
-            lower = 0.01,
+            lower = 0.1,
             upper = 1.0,
-            q = 0.01,
+            q = 0.1,
             default_value = 0.5
         )
         contamination = UniformFloatHyperparameter(

diff --git a/src/pyod_models/iforest.py b/src/pyod_models/iforest.py
@@ -1,6 +1,6 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
-    UniformFloatHyperparameter, CategoricalHyperparameter
+    UniformFloatHyperparameter, Constant
 
 from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
 from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS
@@ -66,23 +66,21 @@ def get_hyperparameter_search_space(dataset_properties=None):
 
         n_estimators = UniformIntegerHyperparameter(
             name = "n_estimators",
-            lower = 2, # ad-hoc
+            lower = 5, # ad-hoc
             upper = 200, # ad-hoc
+            q = 5, # step
             default_value = 100
         )
         max_samples = UniformFloatHyperparameter(
             name = "max_samples",
-            lower = 0.1, # ad-hoc
+            lower = 0.2, # ad-hoc
             upper = 1.0,
-            q = 0.01,
+            q = 0.2,
             default_value = 1.0
         )
-        max_features = UniformFloatHyperparameter(
+        max_features = Constant(
             name = "max_features",
-            lower = 0.1, # ad-hoc
-            upper = 1.0,
-            q = 0.01,
-            default_value = 1.0
+            value = 1.0,
         )
         contamination = UniformFloatHyperparameter(
             name = "contamination",
@@ -91,10 +89,9 @@ def get_hyperparameter_search_space(dataset_properties=None):
             q = 0.01,
             default_value = 0.1
         )
-        bootstrap = CategoricalHyperparameter(
+        bootstrap = Constant(
             name = "bootstrap", 
-            choices = [True, False],
-            default_value = False
+            value = "False"
         )
         cs.add_hyperparameters([n_estimators, max_samples, max_features, contamination, bootstrap])
 

diff --git a/src/pyod_models/knn.py b/src/pyod_models/knn.py
@@ -1,6 +1,6 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
-    UniformFloatHyperparameter, CategoricalHyperparameter
+    UniformFloatHyperparameter, Constant
 
 from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
 from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS
@@ -67,17 +67,14 @@ def get_hyperparameter_search_space(dataset_properties=None):
             upper = 100, # ad-hoc
             default_value = 5
         )
-        method = CategoricalHyperparameter(
+        method = Constant(
             name = "method",
-            choices = ['largest', 'mean', 'median'],
-            default_value = 'largest'
+            value = 'largest'
         )
         # order of minkowski distance metric (used by default)
-        p = UniformIntegerHyperparameter(
+        p = Constant(
             name = "p",
-            lower = 1, # manhattan
-            upper = 2, # euclidean
-            default_value = 2
+            value = 2 # euclidean
         )
         contamination = UniformFloatHyperparameter(
             name = "contamination",

diff --git a/src/pyod_models/lmdd.py b/src/pyod_models/lmdd.py
@@ -68,8 +68,9 @@ def get_hyperparameter_search_space(dataset_properties=None):
         )
         n_iter = UniformIntegerHyperparameter(
             name = "n_iter",
-            lower = 1,
+            lower = 5,
             upper = 200, # ad-hoc
+            q = 5,
             default_value = 50
         )
         dis_measure = CategoricalHyperparameter(

diff --git a/src/pyod_models/lof.py b/src/pyod_models/lof.py
@@ -1,6 +1,6 @@
 from ConfigSpace.configuration_space import ConfigurationSpace
 from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, \
-    UniformFloatHyperparameter
+    UniformFloatHyperparameter, Constant
 
 from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
 from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, PREDICTIONS
@@ -66,11 +66,9 @@ def get_hyperparameter_search_space(dataset_properties=None):
             default_value = 20
         )
         # order of minkowski distance metric (used by default)
-        p = UniformIntegerHyperparameter(
+        p = Constant(
             name = "p",
-            lower = 1, # manhattan
-            upper = 2, # euclidean
-            default_value = 2
+            value = 2 # euclidean
         )
         contamination = UniformFloatHyperparameter(
             name = "contamination",