ENH boosting early_termination control (fix #7)

imbalanced_ensemble/ensemble/_boost.py

@@ -76,10 +76,12 @@ def __init__(self,
                 sampling_type:str,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         self._sampling_type = sampling_type
         self.base_sampler = base_sampler
+        self.early_termination = early_termination
 
         super(ResampleBoostClassifier, self).__init__(
             base_estimator=base_estimator,
@@ -319,6 +321,9 @@ def _fit(self, X, y,
 
         self.sampler_kwargs_ = check_type(
             sampler_kwargs, 'sampler_kwargs', dict)
+
+        early_termination_ = check_type(
+            self.early_termination, 'early_termination', bool)
 
         # Check that algorithm is supported.
         if self.algorithm not in ('SAMME', 'SAMME.R'):
@@ -435,14 +440,14 @@ def _fit(self, X, y,
             self._training_log_to_console(iboost, y_resampled)
 
             # Early termination.
-            if sample_weight is None:
+            if sample_weight is None and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight is None).")
                 break
 
             # Stop if error is zero.
-            if estimator_error == 0:
+            if estimator_error == 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (training error is 0).")
@@ -451,7 +456,7 @@ def _fit(self, X, y,
             sample_weight_sum = np.sum(sample_weight)
 
             # Stop if the sum of sample weights has become non-positive.
-            if sample_weight_sum <= 0:
+            if sample_weight_sum <= 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight_sum <= 0).")
@@ -521,8 +526,11 @@ def __init__(self,
                 n_estimators:int,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
+        self.early_termination = early_termination
+
         super(ReweightBoostClassifier, self).__init__(
             base_estimator=base_estimator,
             n_estimators=n_estimators,
@@ -737,6 +745,9 @@ def _fit(self, X, y,
             eval_metrics:dict,
             train_verbose:bool or int or dict,
             ):
+
+        early_termination_ = check_type(
+            self.early_termination, 'early_termination', bool)
 
         # Check that algorithm is supported.
         if self.algorithm not in ('SAMME', 'SAMME.R'):
@@ -834,14 +845,14 @@ def _fit(self, X, y,
             self._training_log_to_console(iboost, y)
 
             # Early termination.
-            if sample_weight is None:
+            if sample_weight is None and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight is None).")
                 break
 
             # Stop if error is zero.
-            if estimator_error == 0:
+            if estimator_error == 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (training error is 0).")
@@ -850,7 +861,7 @@ def _fit(self, X, y,
             sample_weight_sum = np.sum(sample_weight)
 
             # Stop if the sum of sample weights has become non-positive.
-            if sample_weight_sum <= 0:
+            if sample_weight_sum <= 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight_sum <= 0).")

imbalanced_ensemble/ensemble/compatible/adaboost_compatible.py

@@ -17,7 +17,8 @@
 from ..base import ImbalancedEnsembleClassifierMixin, MAX_INT
 from ...utils._validation_data import check_eval_datasets
 from ...utils._validation_param import (check_train_verbose, 
-                                        check_eval_metrics)
+                                        check_eval_metrics, 
+                                        check_type)
 from ...utils._validation import _deprecate_positional_args
 from ...utils._docstring import (Substitution, FuncSubstitution, 
                                  FuncGlossarySubstitution,
@@ -49,6 +50,7 @@
 
 
 @Substitution(
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     example=_get_example_docstring(_method_name)
 )
 class CompatibleAdaBoostClassifier(ImbalancedEnsembleClassifierMixin, 
@@ -70,7 +72,6 @@ class CompatibleAdaBoostClassifier(ImbalancedEnsembleClassifierMixin,
         Support for sample weighting is required, as well as proper
         ``classes_`` and ``n_classes_`` attributes. If ``None``, then
         the base estimator is :class:`~sklearn.tree.DecisionTreeClassifier`
-        initialized with `max_depth=1`.
 
     n_estimators : int, default=50
         The maximum number of estimators at which boosting is terminated.
@@ -87,6 +88,8 @@ class CompatibleAdaBoostClassifier(ImbalancedEnsembleClassifierMixin,
         If 'SAMME' then use the SAMME discrete boosting algorithm.
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
+    
+    {early_termination}
 
     random_state : int, RandomState instance or None, default=None
         Controls the random seed given at each `base_estimator` at each
@@ -148,8 +151,11 @@ def __init__(self,
                 n_estimators:int=50,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
+        self.early_termination = early_termination
+
         super(CompatibleAdaBoostClassifier, self).__init__(
             base_estimator=base_estimator,
             n_estimators=n_estimators,
@@ -200,6 +206,9 @@ def fit(self, X, y,
         self : object
         """
 
+        early_termination_ = check_type(
+            self.early_termination, 'early_termination', bool)
+
         # Check that algorithm is supported.
         if self.algorithm not in ('SAMME', 'SAMME.R'):
             raise ValueError("algorithm %s is not supported" % self.algorithm)
@@ -281,14 +290,14 @@ def fit(self, X, y,
             self._training_log_to_console(iboost, y)
 
             # Early termination.
-            if sample_weight is None:
+            if sample_weight is None and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight is None).")
                 break
 
             # Stop if error is zero.
-            if estimator_error == 0:
+            if estimator_error == 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (training error is 0).")
@@ -297,7 +306,7 @@ def fit(self, X, y,
             sample_weight_sum = np.sum(sample_weight)
 
             # Stop if the sum of sample weights has become non-positive.
-            if sample_weight_sum <= 0:
+            if sample_weight_sum <= 0 and early_termination_:
                 print (f"Training early-stop at iteration"
                        f" {iboost+1}/{self.n_estimators}"
                        f" (sample_weight_sum <= 0).")

imbalanced_ensemble/ensemble/over_sampling/kmeans_smote_boost.py

@@ -50,6 +50,7 @@
 
 @Substitution(
     n_jobs_sampler=_get_parameter_docstring('n_jobs_sampler', **_properties),
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -108,6 +109,8 @@ class KmeansSMOTEBoostClassifier(ResampleBoostClassifier):
         If 'SAMME' then use the SAMME discrete boosting algorithm.
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
+    
+    {early_termination}
 
     {random_state}
 
@@ -177,6 +180,7 @@ def __init__(self,
                 density_exponent="auto",
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         base_sampler = _sampler_class()
@@ -189,6 +193,7 @@ def __init__(self,
             sampling_type=sampling_type,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
         self.__name__ = _method_name

imbalanced_ensemble/ensemble/over_sampling/over_boost.py

@@ -44,7 +44,7 @@
 
 
 @Substitution(
-    n_jobs_sampler=_get_parameter_docstring('n_jobs_sampler', **_properties),
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -81,6 +81,8 @@ class OverBoostClassifier(ResampleBoostClassifier):
         If 'SAMME' then use the SAMME discrete boosting algorithm.
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
+    
+    {early_termination}
 
     {random_state}
 
@@ -145,6 +147,7 @@ def __init__(self,
                 *,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         base_sampler = _sampler_class()
@@ -157,6 +160,7 @@ def __init__(self,
             sampling_type=sampling_type,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
         self.__name__ = _method_name

imbalanced_ensemble/ensemble/over_sampling/smote_boost.py

@@ -49,7 +49,7 @@
 
 
 @Substitution(
-    n_jobs_sampler=_get_parameter_docstring('n_jobs_sampler', **_properties),
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -91,6 +91,8 @@ class balancing by SMOTE over-sampling the sample at each
         If 'SAMME' then use the SAMME discrete boosting algorithm.
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
+    
+    {early_termination}
 
     {random_state}
 
@@ -156,6 +158,7 @@ def __init__(self,
                 k_neighbors:int=5,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         base_sampler = _sampler_class()
@@ -168,6 +171,7 @@ def __init__(self,
             sampling_type=sampling_type,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
         self.__name__ = _method_name

imbalanced_ensemble/ensemble/reweighting/adacost.py

@@ -42,6 +42,7 @@
 
 
 @Substitution(
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -82,6 +83,8 @@ class AdaCostClassifier(ReweightBoostClassifier):
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
     
+    {early_termination}
+    
     {random_state}
     
     Attributes
@@ -143,13 +146,15 @@ def __init__(self,
                 *,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         super(AdaCostClassifier, self).__init__(
             base_estimator=base_estimator,
             n_estimators=n_estimators,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
         self.__name__ = _method_name

imbalanced_ensemble/ensemble/reweighting/adauboost.py

@@ -46,6 +46,7 @@
 
 
 @Substitution(
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -83,6 +84,8 @@ class AdaUBoostClassifier(ReweightBoostClassifier):
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
     
+    {early_termination}
+    
     {random_state}
     
     Attributes
@@ -147,6 +150,7 @@ def __init__(self,
                 *,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         self.__name__ = 'AdaUBoostClassifier'
@@ -156,6 +160,7 @@ def __init__(self,
             n_estimators=n_estimators,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
 

imbalanced_ensemble/ensemble/reweighting/asymmetric_boost.py

@@ -42,6 +42,7 @@
 
 
 @Substitution(
+    early_termination=_get_parameter_docstring('early_termination', **_properties),
     random_state=_get_parameter_docstring('random_state', **_properties),
     example=_get_example_docstring(_method_name)
 )
@@ -78,6 +79,8 @@ class AsymBoostClassifier(ReweightBoostClassifier):
         The SAMME.R algorithm typically converges faster than SAMME,
         achieving a lower test error with fewer boosting iterations.
     
+    {early_termination}
+    
     {random_state}
     
     Attributes
@@ -137,13 +140,15 @@ def __init__(self,
                 *,
                 learning_rate:float=1.,
                 algorithm:str='SAMME.R',
+                early_termination:bool=False,
                 random_state=None):
 
         super(AsymBoostClassifier, self).__init__(
             base_estimator=base_estimator,
             n_estimators=n_estimators,
             learning_rate=learning_rate,
             algorithm=algorithm,
+            early_termination=early_termination,
             random_state=random_state)
 
         self.__name__ = _method_name