update models

alphamind/model/treemodel.py

@@ -173,6 +173,7 @@ class XGBTrainer(ModelBase):
                  subsample=1.,
                  colsample_bytree=1.,
                  features: List = None,
+                 random_state=0,
                  **kwargs):
         super().__init__(features)
         self.params = {
@@ -183,13 +184,15 @@ class XGBTrainer(ModelBase):
             'booster': booster,
             'tree_method': tree_method,
             'subsample': subsample,
-            'colsample_bytree': colsample_bytree
+            'colsample_bytree': colsample_bytree,
+            'seed': random_state
         }
 
         self.eval_sample = eval_sample
         self.num_boost_round = n_estimators
         self.early_stopping_rounds = early_stopping_rounds
         self.impl = None
+        self.kwargs = kwargs
 
     def fit(self, x, y):
         if self.eval_sample:
@@ -203,12 +206,14 @@ class XGBTrainer(ModelBase):
                                   dtrain=d_train,
                                   num_boost_round=self.num_boost_round,
                                   evals=[(d_eval, 'eval')],
-                                  verbose_eval=False)
+                                  verbose_eval=False,
+                                  **self.kwargs)
         else:
             d_train = xgb.DMatrix(x, y)
             self.impl = xgb.train(params=self.params,
                                   dtrain=d_train,
-                                  num_boost_round=self.num_boost_round)
+                                  num_boost_round=self.num_boost_round,
+                                  **self.kwargs)
 
         self.trained_time = arrow.now().format("YYYY-MM-DD HH:mm:ss")
 

alphamind/tests/model/test_treemodel.py

@@ -31,6 +31,7 @@ class TestTreeModel(unittest.TestCase):
 
         sample_x = np.random.randn(100, 10)
         np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
+        np.testing.assert_array_almost_equal(model.importances, new_model.importances)
 
     def test_random_forest_classify_persistence(self):
         model = RandomForestClassifier(features=list(range(10)))
@@ -43,6 +44,7 @@ class TestTreeModel(unittest.TestCase):
 
         sample_x = np.random.randn(100, 10)
         np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
+        np.testing.assert_array_almost_equal(model.importances, new_model.importances)
 
     def test_xgb_regress_persistence(self):
         model = XGBRegressor(features=list(range(10)))
@@ -54,6 +56,7 @@ class TestTreeModel(unittest.TestCase):
 
         sample_x = np.random.randn(100, 10)
         np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
+        np.testing.assert_array_almost_equal(model.importances, new_model.importances)
 
     def test_xgb_classify_persistence(self):
         model = XGBClassifier(features=list(range(10)))
@@ -66,8 +69,36 @@ class TestTreeModel(unittest.TestCase):
 
         sample_x = np.random.randn(100, 10)
         np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
+        np.testing.assert_array_almost_equal(model.importances, new_model.importances)
 
-    def test_xgb_trainer_persisence(self):
+    def test_xgb_trainer_equal_classifier(self):
+        sample_x = np.random.randn(100, 10)
+
+        model1 = XGBClassifier(n_estimators=100,
+                               learning_rate=0.1,
+                               max_depth=3,
+                               features=list(range(10)),
+                               random_state=42)
+
+        model2 = XGBTrainer(features=list(range(10)),
+                            objective='reg:logistic',
+                            booster='gbtree',
+                            tree_method='exact',
+                            n_estimators=100,
+                            learning_rate=0.1,
+                            max_depth=3,
+                            random_state=42)
+
+        y = np.where(self.y > 0, 1, 0)
+        model1.fit(self.x, y)
+        model2.fit(self.x, y)
+
+        predict1 = model1.predict(sample_x)
+        predict2 = model2.predict(sample_x)
+        predict2 = np.where(predict2 > 0.5, 1., 0.)
+        np.testing.assert_array_almost_equal(predict1, predict2)
+
+    def test_xgb_trainer_persistence(self):
         model = XGBTrainer(features=list(range(10)),
                            objective='binary:logistic',
                            booster='gbtree',
@@ -82,3 +113,4 @@ class TestTreeModel(unittest.TestCase):
 
         sample_x = np.random.randn(100, 10)
         np.testing.assert_array_almost_equal(model.predict(sample_x), new_model.predict(sample_x))
+        np.testing.assert_array_almost_equal(model.importances, new_model.importances)

build_linux_dependencies.sh

@@ -4,7 +4,12 @@ cd xgboost
 git submodule init
 git submodule update
 
+mkdir build
+cd build
+cmake ..
 make -j4
+cd ..
+
 cd python-package
 python setup.py install
 
@@ -22,4 +27,4 @@ if [ $? -ne 0 ] ; then
     exit 1
 fi
 
-cd ../..
\ No newline at end of file
+cd ../..

xgboost @ a187ed6c

-Subproject commit bf4367184164e593cd2856ef38f8dd4f8cc76999
+Subproject commit a187ed6c8f3aa40b47d5be80667cbbe6a6fd563d