remove train predictions

docs/api.md

@@ -27,9 +27,8 @@ import substratools as tools
 
 class DummyAlgo(tools.Algo):
     def train(self, X, y, models, rank):
-        predictions = 0
         new_model = None
-        return predictions, new_model
+        return new_model
 
     def predict(self, X, model):
         predictions = 0
@@ -84,7 +83,7 @@ __Arguments__
 __Returns__
 
 
-`tuple`: (predictions, model).
+`model`: model object.
 
 ## predict
 ```python
@@ -171,10 +170,9 @@ import substratools as tools
 
 class DummyCompositeAlgo(tools.CompositeAlgo):
     def train(self, X, y, head_model, trunk_model, rank):
-        predictions = 0
         new_head_model = None
         new_trunk_model = None
-        return predictions, new_head_model, new_trunk_model
+        return new_head_model, new_trunk_model
 
     def predict(self, X, head_model, trunk_model):
         predictions = 0
@@ -218,7 +216,7 @@ __Arguments__
 __Returns__
 
 
-`tuple`: (predictions, head_model, trunk_model).
+`tuple`: (head_model, trunk_model).
 
 ## predict
 ```python

substratools/algo.py

@@ -40,9 +40,8 @@ class Algo(abc.ABC):
 
     class DummyAlgo(tools.Algo):
         def train(self, X, y, models, rank):
-            predictions = 0
             new_model = None
-            return predictions, new_model
+            return new_model
 
         def predict(self, X, model):
             predictions = 0
@@ -93,7 +92,7 @@ def train(self, X, y, models, rank):
 
         # Returns
 
-        tuple: (predictions, model).
+        model: model object.
         """
         raise NotImplementedError
 
@@ -123,7 +122,7 @@ def _train_fake_data(self, *args, **kwargs):
         replaced by the opener fake data.
 
         By default, it only calls directly `Algo.train()` method. Override this
-        method if you want to implement a different behaviour.
+        method if you want to implement a different behavior.
         """
         return self.train(*args, **kwargs)
 
@@ -135,7 +134,7 @@ def _predict_fake_data(self, *args, **kwargs):
         the opener fake data.
 
         By default, it only calls directly `Algo.predict()` method. Override
-        this method if you want to implement a different behaviour.
+        this method if you want to implement a different behavior.
         """
         return self.predict(*args, **kwargs)
 
@@ -208,17 +207,14 @@ def train(self, model_names, rank=0, fake_data=False):
         logger.info("launching training task")
         method = (self._interface.train if not fake_data else
                   self._interface._train_fake_data)
-        pred, model = method(X, y, models, rank)
+        model = method(X, y, models, rank)
 
         # serialize output model and save it to workspace
         logger.info("saving output model to '{}'".format(
             self._workspace.output_model_path))
         self._interface.save_model(model, self._workspace.output_model_path)
 
-        # save predictions
-        self._opener_wrapper.save_predictions(pred)
-
-        return pred, model
+        return model
 
     def predict(self, model_name, fake_data=False):
         """Predict method wrapper."""
@@ -362,10 +358,9 @@ class CompositeAlgo(abc.ABC):
 
     class DummyCompositeAlgo(tools.CompositeAlgo):
         def train(self, X, y, head_model, trunk_model, rank):
-            predictions = 0
             new_head_model = None
             new_trunk_model = None
-            return predictions, new_head_model, new_trunk_model
+            return new_head_model, new_trunk_model
 
         def predict(self, X, head_model, trunk_model):
             predictions = 0
@@ -406,7 +401,7 @@ def train(self, X, y, head_model, trunk_model, rank):
 
         # Returns
 
-        tuple: (predictions, head_model, trunk_model).
+        tuple: (head_model, trunk_model).
         """
         raise NotImplementedError
 
@@ -437,7 +432,7 @@ def _train_fake_data(self, *args, **kwargs):
         replaced by the opener fake data.
 
         By default, it only calls directly `Algo.train()` method. Override this
-        method if you want to implement a different behaviour.
+        method if you want to implement a different behavior.
         """
         return self.train(*args, **kwargs)
 
@@ -449,7 +444,7 @@ def _predict_fake_data(self, *args, **kwargs):
         the opener fake data.
 
         By default, it only calls directly `Algo.predict()` method. Override
-        this method if you want to implement a different behaviour.
+        this method if you want to implement a different behavior.
         """
         return self.predict(*args, **kwargs)
 
@@ -550,7 +545,7 @@ def train(self, input_head_model_filename=None, input_trunk_model_filename=None,
         logger.info("launching training task")
         method = (self._interface.train if not fake_data else
                   self._interface._train_fake_data)
-        pred, head_model, trunk_model = method(X, y, head_model, trunk_model, rank)
+        head_model, trunk_model = method(X, y, head_model, trunk_model, rank)
 
         # serialize output head and trunk models and save them to workspace
         output_head_model_path = self._workspace.output_head_model_path
@@ -561,10 +556,7 @@ def train(self, input_head_model_filename=None, input_trunk_model_filename=None,
         logger.info("saving output trunk model to '{}'".format(output_trunk_model_path))
         self._interface.save_trunk_model(trunk_model, output_trunk_model_path)
 
-        # save predictions
-        self._opener_wrapper.save_predictions(pred)
-
-        return pred, head_model, trunk_model
+        return head_model, trunk_model
 
     def predict(self, input_head_model_filename, input_trunk_model_filename,
                 fake_data=False):

tests/test_algo.py

@@ -19,9 +19,7 @@ def train(self, X, y, models, rank):
             assert isinstance(m, dict)
             assert 'value' in m
             new_model['value'] += m['value']
-        new_value = new_model['value']
-        pred = list(range(new_value, new_value + 3))
-        return pred, new_model
+        return new_model
 
     def predict(self, X, model):
         pred = model['value']
@@ -65,8 +63,7 @@ def test_create():
 def test_train_no_model():
     a = DummyAlgo()
     wp = algo.AlgoWrapper(a)
-    pred, model = wp.train([])
-    assert pred == [0, 1, 2]
+    model = wp.train([])
     assert model['value'] == 0
 
 
@@ -76,16 +73,14 @@ def test_train_multiple_models(workdir, create_models):
     a = DummyAlgo()
     wp = algo.AlgoWrapper(a)
 
-    pred, model = wp.train(model_filenames)
-    assert pred == [3, 4, 5]
+    model = wp.train(model_filenames)
     assert model['value'] == 3
 
 
 def test_train_fake_data():
     a = DummyAlgo()
     wp = algo.AlgoWrapper(a)
-    pred, model = wp.train([], fake_data=True)
-    assert pred == [0, 1, 2]
+    model = wp.train([], fake_data=True)
     assert model['value'] == 0
 
 
@@ -134,10 +129,7 @@ def test_execute_train_multiple_models(workdir, create_models):
         model = json.load(f)
     assert model['value'] == 3
 
-    assert pred_path.exists()
-    with open(pred_path, 'r') as f:
-        pred = json.load(f)
-    assert pred == [3, 4, 5]
+    assert not pred_path.exists()
 
 
 def test_execute_predict(workdir, create_models):
@@ -152,11 +144,6 @@ def test_execute_predict(workdir, create_models):
     command.extend(model_filenames)
     algo.execute(DummyAlgo(), sysargs=command)
     assert output_model_path.exists()
-    assert pred_path.exists()
-    with open(pred_path, 'r') as f:
-        pred = json.load(f)
-    assert pred == [3, 4, 5]
-    pred_path.unlink()
 
     # do predict on output model
     assert not pred_path.exists()

tests/test_compositealgo.py

@@ -27,10 +27,7 @@ def train(self, X, y, head_model, trunk_model, rank):
         new_head_model['value'] += 1
         new_trunk_model['value'] -= 1
 
-        # get predictions
-        pred = list(range(new_head_model['value'], new_trunk_model['value']))
-
-        return pred, new_head_model, new_trunk_model
+        return new_head_model, new_trunk_model
 
     def predict(self, X, head_model, trunk_model):
         pred = list(range(head_model['value'], trunk_model['value']))
@@ -97,24 +94,21 @@ def test_create():
 
 
 def test_train_no_model(dummy_wrapper):
-    pred, head_model, trunk_model = dummy_wrapper.train()
-    assert pred == []
+    head_model, trunk_model = dummy_wrapper.train()
     assert head_model['value'] == 1
     assert trunk_model['value'] == -1
 
 
 def test_train_input_head_trunk_models(create_models, dummy_wrapper):
     _, _, head_filename, trunk_filename = create_models
 
-    pred, head_model, trunk_model = dummy_wrapper.train(head_filename, trunk_filename)
-    assert pred == []
+    head_model, trunk_model = dummy_wrapper.train(head_filename, trunk_filename)
     assert head_model['value'] == 2
     assert trunk_model['value'] == -2
 
 
 def test_train_fake_data(dummy_wrapper):
-    pred, head_model, trunk_model = dummy_wrapper.train(fake_data=True)
-    assert pred == []
+    head_model, trunk_model = dummy_wrapper.train(fake_data=True)
     assert head_model['value'] == 1
     assert trunk_model['value'] == -1
 
@@ -188,10 +182,7 @@ def test_execute_train_multiple_models(workdir, create_models):
         trunk_model = json.load(f)
     assert trunk_model['value'] == -2
 
-    assert pred_path.exists()
-    with open(pred_path, 'r') as f:
-        pred = json.load(f)
-    assert pred == []
+    assert not pred_path.exists()
 
 
 def test_execute_predict(workdir, create_models):

tests/test_workflow.py

@@ -43,9 +43,8 @@ def save_predictions(self, pred, path):
 class DummyAlgo(Algo):
     def train(self, X, y, models, rank):
         total = sum([m['i'] for m in models])
-        pred = {'sum': len(models)}
         new_model = {'i': len(models) + 1, 'total': total}
-        return pred, new_model
+        return new_model
 
     def predict(self, X, model):
         return {'sum': model['i']}
@@ -70,26 +69,23 @@ def test_workflow(workdir, dummy_opener):
     models_path = algo_wp._workspace.input_models_folder_path
 
     # loop 1 (no input)
-    pred, model = algo_wp.train([])
-    assert pred == {'sum': 0}
+    model = algo_wp.train([])
     assert model == {'i': 1, 'total': 0}
     output_model_path = os.path.join(models_path, 'model')
     assert os.path.exists(output_model_path)
 
     # loop 2 (one model as input)
     model_1_name = 'model1'
     shutil.move(output_model_path, os.path.join(models_path, model_1_name))
-    pred, model = algo_wp.train([model_1_name])
-    assert pred == {'sum': 1}
+    model = algo_wp.train([model_1_name])
     assert model == {'i': 2, 'total': 1}
     output_model_path = os.path.join(models_path, 'model')
     assert os.path.exists(output_model_path)
 
     # loop 3 (two models as input)
     model_2_name = 'model2'
     shutil.move(output_model_path, os.path.join(models_path, model_2_name))
-    pred, model = algo_wp.train([model_1_name, model_2_name])
-    assert pred == {'sum': 2}
+    model = algo_wp.train([model_1_name, model_2_name])
     assert model == {'i': 3, 'total': 3}
     output_model_path = os.path.join(models_path, 'model')
     assert os.path.exists(output_model_path)