added xgb trainer models

alphamind/api.py

@@ -33,6 +33,7 @@ from alphamind.model import RandomForestRegressor
 from alphamind.model import RandomForestClassifier
 from alphamind.model import XGBRegressor
 from alphamind.model import XGBClassifier
+from alphamind.model import XGBTrainer
 from alphamind.model import load_model
 from alphamind.model.data_preparing import fetch_data_package
 from alphamind.model.data_preparing import fetch_train_phase
@@ -74,6 +75,7 @@ __all__ = [
     'RandomForestClassifier',
     'XGBRegressor',
     'XGBClassifier',
+    'XGBTrainer',
     'load_model',
     'NaiveExecutor',
     'ThresholdExecutor',

alphamind/data/dbmodel/models.py

@@ -1954,5 +1954,5 @@ class OutrightTmp(Base):
 if __name__ == '__main__':
     from sqlalchemy import create_engine
 
-    engine = create_engine('postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
+    engine = create_engine('postgres+psycopg2://postgres:we083826@101.132.104.118/alpha')
     Base.metadata.create_all(engine)

alphamind/model/__init__.py

@@ -14,6 +14,7 @@ from alphamind.model.treemodel import RandomForestRegressor
 from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
+from alphamind.model.treemodel import XGBTrainer
 
 from alphamind.model.loader import load_model
 
@@ -26,4 +27,5 @@ __all__ = ['LinearRegression',
            'RandomForestClassifier',
            'XGBRegressor',
            'XGBClassifier',
+           'XGBTrainer',
            'load_model']
\ No newline at end of file

alphamind/model/loader.py

@@ -14,6 +14,7 @@ from alphamind.model.treemodel import RandomForestRegressor
 from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
+from alphamind.model.treemodel import XGBTrainer
 
 
 def load_model(model_desc: dict) -> ModelBase:
@@ -37,5 +38,7 @@ def load_model(model_desc: dict) -> ModelBase:
         return XGBRegressor.load(model_desc)
     elif 'XGBClassifier' in model_name_parts:
         return XGBClassifier.load(model_desc)
+    elif 'XGBTrainer' in model_name_parts:
+        return XGBTrainer.load(model_desc)
     else:
         raise ValueError('{0} is not currently supported in model loader.'.format(model_name))

alphamind/model/treemodel.py

@@ -162,7 +162,7 @@ class XGBClassifier(ModelBase):
 class XGBTrainer(ModelBase):
 
     def __init__(self,
-                 objective,
+                 objective='binary:logistic',
                  booster='gbtree',
                  tree_method='hist',
                  n_estimators: int=100,

alphamind/tests/model/test_treemodel.py

@@ -12,16 +12,18 @@ from alphamind.model.treemodel import RandomForestRegressor
 from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
+from alphamind.model.treemodel import XGBTrainer
 
 
 class TestTreeModel(unittest.TestCase):
 
+    def setUp(self):
+        self.x = np.random.randn(1000, 10)
+        self.y = np.random.randn(1000)
+
     def test_random_forest_regress_persistence(self):
         model = RandomForestRegressor(features=list(range(10)))
-        x = np.random.randn(1000, 10)
-        y = np.random.randn(1000)
-
-        model.fit(x, y)
+        model.fit(self.x, self.y)
 
         desc = model.save()
         new_model = load_model(desc)
@@ -32,11 +34,8 @@ class TestTreeModel(unittest.TestCase):
 
     def test_random_forest_classify_persistence(self):
         model = RandomForestClassifier(features=list(range(10)))
-        x = np.random.randn(1000, 10)
-        y = np.random.randn(1000)
-        y = np.where(y > 0, 1, 0)
-
-        model.fit(x, y)
+        y = np.where(self.y > 0, 1, 0)
+        model.fit(self.x, y)
 
         desc = model.save()
         new_model = load_model(desc)
@@ -47,10 +46,7 @@ class TestTreeModel(unittest.TestCase):
 
     def test_xgb_regress_persistence(self):
         model = XGBRegressor(features=list(range(10)))
-        x = np.random.randn(1000, 10)
-        y = np.random.randn(1000)
-
-        model.fit(x, y)
+        model.fit(self.x, self.y)
 
         desc = model.save()
         new_model = load_model(desc)
@@ -61,11 +57,24 @@ class TestTreeModel(unittest.TestCase):
 
     def test_xgb_classify_persistence(self):
         model = XGBClassifier(features=list(range(10)))
-        x = np.random.randn(1000, 10)
-        y = np.random.randn(1000)
-        y = np.where(y > 0, 1, 0)
+        y = np.where(self.y > 0, 1, 0)
+        model.fit(self.x, y)
+
+        desc = model.save()
+        new_model = load_model(desc)
+        self.assertEqual(model.features, new_model.features)
+
+        sample_x = np.random.randn(100, 10)
+        np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
 
-        model.fit(x, y)
+    def test_xgb_trainer_persisence(self):
+        model = XGBTrainer(features=list(range(10)),
+                           objective='binary:logistic',
+                           booster='gbtree',
+                           tree_method='hist',
+                           n_estimators=200)
+        y = np.where(self.y > 0, 1, 0)
+        model.fit(self.x, y)
 
         desc = model.save()
         new_model = load_model(desc)