Avoid repeated classifiers

alpha_automl/pipeline_search/pipeline/PipelineLogic.py

@@ -95,17 +95,20 @@ def has_legal_moves(self):
 
     def next_state(self, action):
         s = self.valid_moves[action]
-        nt = self.non_terminals[s[:s.index('-')].strip()]
+        nt = self.non_terminals[s[:s.index('->')].strip()]
         r = [self.non_terminals[p] if p in self.non_terminals.keys() else
-             self.terminals[p] for p in s[s.index('-')+2:].strip().split(' ')]
+             self.terminals[p] for p in s[s.index('->')+2:].strip().split(' ')]
         r = [x for x in r if x != 0]
         s = []
+        not_used = True
+
         for p in self.pieces_p:
             if p == 0:
                 continue
 
-            if p == nt:
+            if p == nt and not_used:  # Chose one primitive at the time
                 s += r
+                not_used = False
             else:
                 s.append(p)
 

alpha_automl/pipeline_synthesis/pipeline_builder.py

@@ -104,7 +104,7 @@ def make_primitive_objects(self, primitives):
 
             change_default_hyperparams(primitive_object)
 
-            if primitive_type in nonnumeric_columns:  # Create a  new transformer and add it to the list
+            if primitive_type in nonnumeric_columns:  # Create a new transformer and add it to the list
                 transformers += self.create_transformers(primitive_object, primitive_name, primitive_type)
             else:
                 if len(transformers) > 0:  # Add previous transformers to the pipeline

alpha_automl/pipeline_synthesis/setup_search.py

@@ -48,14 +48,57 @@ def signal_handler(queue, signum):
     sys.exit(0)
 
 
+def check_repeated_classifiers(pipeline_primitives, all_primitives, ensemble_pipelines_hash):
+    # Verify if the classifiers are repeated in the ensembles (regardless of the order)
+    classifiers = []
+    pipeline_hash = ''
+    has_ensemble_primitive = False
+    has_repeated_classifiers = False
+
+    for primitive_name in pipeline_primitives:
+        primitive_type = all_primitives[primitive_name]['type']
+
+        if primitive_type == 'CLASSIFIER':
+            classifiers.append(primitive_name)
+        elif primitive_type == 'MULTI_ENSEMBLER':
+            has_ensemble_primitive = True
+            pipeline_hash += primitive_name
+            if len(classifiers) != len(set(classifiers)):  # All classifiers should be different
+                has_repeated_classifiers = True
+        else:
+            pipeline_hash += primitive_name
+
+    if not has_ensemble_primitive:
+        return False
+
+    if has_repeated_classifiers:
+        return True
+
+    pipeline_hash += ''.join(sorted(classifiers))
+
+    if pipeline_hash in ensemble_pipelines_hash:
+        return True
+    else:
+        ensemble_pipelines_hash.add(pipeline_hash)
+        return False
+
+
 def search_pipelines(X, y, scoring, splitting_strategy, task_name, automl_hyperparams, metadata, output_folder, verbose,
                      queue):
     signal.signal(signal.SIGTERM, lambda signum, frame: signal_handler(queue, signum))
     hide_logs(verbose)  # Hide logs here too, since multiprocessing has some issues with loggers
 
     builder = BaseBuilder(metadata, automl_hyperparams)
+    all_primitives = builder.all_primitives
+    ensemble_pipelines_hash = set()
 
     def evaluate_pipeline(primitives, origin):
+        has_repeated_classifiers = check_repeated_classifiers(primitives, all_primitives, ensemble_pipelines_hash)
+
+        if has_repeated_classifiers:
+            logger.debug('Repeated classifiers detected in ensembles, ignoring pipeline')
+            return None
+
         pipeline = builder.make_pipeline(primitives)
         score = None
 

alpha_automl/resource/base_grammar.bnf

@@ -7,7 +7,7 @@ SEMISUPERVISED_TASK -> IMPUTER ENCODERS FEATURE_SCALER SEMISUPERVISED_CLASSIFIER
 NA_TASK -> CLASSIFICATION_TASK | REGRESSION_TASK | SEMISUPERVISED_TASK
 ENCODERS -> TEXT_ENCODER DATETIME_ENCODER CATEGORICAL_ENCODER IMAGE_ENCODER
 ENSEMBLER -> SINGLE_ENSEMBLER | CLASSIFIER CLASSIFIER MULTI_ENSEMBLER | E
-SEMISUPERVISED_CLASSIFIER -> CLASSIFIER SEMISUPERVISED_SELFTRAINER| SEMISUPERVISED_LABELPROPAGATOR
+SEMISUPERVISED_CLASSIFIER -> CLASSIFIER SEMISUPERVISED_SELFTRAINER | SEMISUPERVISED_LABELPROPAGATOR
 IMPUTER -> 'primitive_terminal'
 FEATURE_SCALER -> 'primitive_terminal' | 'E'
 FEATURE_SELECTOR -> 'primitive_terminal' | 'E'
@@ -22,4 +22,4 @@ REGRESSOR -> 'primitive_terminal'
 CLUSTERER -> 'primitive_terminal'
 TIME_SERIES_FORECASTER -> 'primitive_terminal'
 SEMISUPERVISED_SELFTRAINER -> 'primitive_terminal'
-SEMISUPERVISED_LABELPROPAGATOR -> 'primitive_terminal'
+SEMISUPERVISED_LABELPROPAGATOR -> 'primitive_terminal'