added random forest classifier

alphamind/api.py

@@ -30,6 +30,7 @@ from alphamind.model import LassoRegression
 from alphamind.model import ConstLinearModel
 from alphamind.model import LogisticRegression
 from alphamind.model import RandomForestRegressor
+from alphamind.model import RandomForestClassifier
 from alphamind.model import XGBRegressor
 from alphamind.model import XGBClassifier
 from alphamind.model import load_model
@@ -70,6 +71,7 @@ __all__ = [
     'ConstLinearModel',
     'LogisticRegression',
     'RandomForestRegressor',
+    'RandomForestClassifier',
     'XGBRegressor',
     'XGBClassifier',
     'load_model',

alphamind/model/__init__.py

@@ -11,6 +11,7 @@ from alphamind.model.linearmodel import ConstLinearModel
 from alphamind.model.linearmodel import LogisticRegression
 
 from alphamind.model.treemodel import RandomForestRegressor
+from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
 
@@ -22,6 +23,7 @@ __all__ = ['LinearRegression',
            'ConstLinearModel',
            'LogisticRegression',
            'RandomForestRegressor',
+           'RandomForestClassifier',
            'XGBRegressor',
            'XGBClassifier',
            'load_model']
\ No newline at end of file

alphamind/model/loader.py

@@ -11,6 +11,7 @@ from alphamind.model.linearmodel import LinearRegression
 from alphamind.model.linearmodel import LassoRegression
 from alphamind.model.linearmodel import LogisticRegression
 from alphamind.model.treemodel import RandomForestRegressor
+from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
 
@@ -30,6 +31,8 @@ def load_model(model_desc: dict) -> ModelBase:
         return LogisticRegression.load(model_desc)
     elif 'RandomForestRegressor' in model_name_parts:
         return RandomForestRegressor.load(model_desc)
+    elif 'RandomForestClassifier' in model_name_parts:
+        return RandomForestClassifier.load(model_desc)
     elif 'XGBRegressor' in model_name_parts:
         return XGBRegressor.load(model_desc)
     elif 'XGBClassifier' in model_name_parts:

alphamind/model/treemodel.py

@@ -9,6 +9,7 @@ from typing import List
 from distutils.version import LooseVersion
 from sklearn import __version__ as sklearn_version
 from sklearn.ensemble import RandomForestRegressor as RandomForestRegressorImpl
+from sklearn.ensemble import RandomForestClassifier as RandomForestClassifierImpl
 from xgboost import __version__ as xgbboot_version
 from xgboost import XGBRegressor as XGBRegressorImpl
 from xgboost import XGBClassifier as XGBClassifierImpl
@@ -18,9 +19,49 @@ from alphamind.utilities import alpha_logger
 
 class RandomForestRegressor(ModelBase):
 
-    def __init__(self, n_estimators: int=100, features: List=None, **kwargs):
+    def __init__(self,
+                 n_estimators: int=100,
+                 max_features: str='auto',
+                 features: List=None,
+                 **kwargs):
+        super().__init__(features)
+        self.impl = RandomForestRegressorImpl(n_estimators=n_estimators,
+                                              max_features=max_features,
+                                              **kwargs)
+        self.trained_time = None
+
+    def save(self) -> dict:
+        model_desc = super().save()
+        model_desc['sklearn_version'] = sklearn_version
+        model_desc['importances'] = self.importances
+        return model_desc
+
+    @classmethod
+    def load(cls, model_desc: dict):
+        obj_layout = super().load(model_desc)
+
+        if LooseVersion(sklearn_version) < LooseVersion(model_desc['sklearn_version']):
+            alpha_logger.warning('Current sklearn version {0} is lower than the model version {1}. '
+                                 'Loaded model may work incorrectly.'.format(sklearn_version,
+                                                                             model_desc['sklearn_version']))
+        return obj_layout
+
+    @property
+    def importances(self):
+        return self.impl.feature_importances_.tolist()
+
+
+class RandomForestClassifier(ModelBase):
+
+    def __init__(self,
+                 n_estimators: int=100,
+                 max_features: str='auto',
+                 features: List = None,
+                 **kwargs):
         super().__init__(features)
-        self.impl = RandomForestRegressorImpl(n_estimators, **kwargs)
+        self.impl = RandomForestClassifierImpl(n_estimators=n_estimators,
+                                               max_features=max_features,
+                                               **kwargs)
         self.trained_time = None
 
     def save(self) -> dict:

alphamind/tests/model/test_treemodel.py

@@ -9,13 +9,14 @@ import unittest
 import numpy as np
 from alphamind.model.loader import load_model
 from alphamind.model.treemodel import RandomForestRegressor
+from alphamind.model.treemodel import RandomForestClassifier
 from alphamind.model.treemodel import XGBRegressor
 from alphamind.model.treemodel import XGBClassifier
 
 
 class TestTreeModel(unittest.TestCase):
 
-    def test_random_forest_regress(self):
+    def test_random_forest_regress_persistence(self):
         model = RandomForestRegressor(features=list(range(10)))
         x = np.random.randn(1000, 10)
         y = np.random.randn(1000)
@@ -29,6 +30,21 @@ 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))
 
+    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)
+
+        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))
+
     def test_xgb_regress_persistence(self):
         model = XGBRegressor(features=list(range(10)))
         x = np.random.randn(1000, 10)