Merge pull request #6 from perib/dev

allow survival selector to be none
EpistasisLab · Apr 20, 2023 · 388ce58 · 388ce58
2 parents d0129f0 + 00819dd
commit 388ce58
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Showing 5 changed files with 19 additions and 64 deletions.
diff --git a/tpot2/base_evolver.py b/tpot2/base_evolver.py
@@ -526,11 +526,12 @@ def step(self,):
 
     def one_generation_step(self, ): #your EA Algorithm goes here
 
-        n_survivors = max(1,int(self.cur_population_size*self.survival_percentage)) #always keep at least one individual
-        #Get survivors from current population
-        weighted_scores = self.population.get_column(self.population.population, column_names=self.objective_names) * self.objective_function_weights
-        new_population_index = np.ravel(self.survival_selector(weighted_scores, k=n_survivors)) #TODO make it clear that we are concatenating scores...
-        self.population.set_population(np.array(self.population.population)[new_population_index])
+        if self.survival_selector is not None:
+            n_survivors = max(1,int(self.cur_population_size*self.survival_percentage)) #always keep at least one individual
+            #Get survivors from current population
+            weighted_scores = self.population.get_column(self.population.population, column_names=self.objective_names) * self.objective_function_weights
+            new_population_index = np.ravel(self.survival_selector(weighted_scores, k=n_survivors)) #TODO make it clear that we are concatenating scores...
+            self.population.set_population(np.array(self.population.population)[new_population_index])
         weighted_scores = self.population.get_column(self.population.population, column_names=self.objective_names) * self.objective_function_weights
 
         #number of crossover pairs and mutation only parent to generate

diff --git a/tpot2/evolutionary_algorithms/__init__.py b/tpot2/evolutionary_algorithms/__init__.py
@@ -1,2 +1,2 @@
-from .simple_evolver import SimpleEvolver
+
 
diff --git a/tpot2/evolutionary_algorithms/simple_evolver.py b/tpot2/evolutionary_algorithms/simple_evolver.py
diff --git a/tpot2/tpot_estimator/estimator.py b/tpot2/tpot_estimator/estimator.py
@@ -83,6 +83,7 @@ def __init__(self,  scorers,
                         verbose = 0,
                         periodic_checkpoint_folder = None, 
                         callback: tpot2.CallBackInterface = None,
+                        processes = True,
                         ):
 
         '''
@@ -376,6 +377,10 @@ def __init__(self,  scorers,
         callback : tpot2.CallBackInterface, default=None
             Callback object. Not implemented
 
+        processes : bool, default=True
+            If True, will use multiprocessing to parallelize the optimization process. If False, will use threading.
+            True seems to perform better. However, False is required for interactive debugging.
+            
         '''
 
         # sklearn BaseEstimator must have a corresponding attribute for each parameter.
@@ -443,6 +448,8 @@ def __init__(self,  scorers,
 
         self.objective_function_names = objective_function_names
 
+        self.processes = processes
+
         #Initialize other used params
 
 
@@ -508,7 +515,7 @@ def fit(self, X, y):
                 silence_logs = 50
             cluster = LocalCluster(n_workers=self.n_jobs, #if no client is passed in and no global client exists, create our own
                     threads_per_worker=1,
-                    processes=True,
+                    processes=self.processes,
                     silence_logs=silence_logs,
                     memory_limit=self.memory_limit)
             _client = Client(cluster)

diff --git a/tpot2/tpot_estimator/templates/tpottemplates.py b/tpot2/tpot_estimator/templates/tpottemplates.py
@@ -66,6 +66,7 @@ def __init__(       self,
                         verbose=0,
                         periodic_checkpoint_folder=None, 
                         callback: tpot2.CallBackInterface=None,
+                        processes = True,
         ):
         super(TPOTRegressor,self).__init__(
             scorers=scorers, 
@@ -127,6 +128,7 @@ def __init__(       self,
             verbose=verbose,
             periodic_checkpoint_folder=periodic_checkpoint_folder, 
             callback=callback,
+            processes=processes,
 )
 
 
@@ -191,6 +193,7 @@ def __init__(       self,
                         verbose=0,
                         periodic_checkpoint_folder=None, 
                         callback: tpot2.CallBackInterface=None,
+                        processes = True,
         ):
         super(TPOTClassifier,self).__init__(
                         scorers=scorers, 
@@ -252,6 +255,7 @@ def __init__(       self,
             verbose=verbose,
             periodic_checkpoint_folder=periodic_checkpoint_folder, 
             callback=callback,
+            processes = processes,
         )