added tests on linear models

alphamind/model/linearmodel.py

@@ -18,7 +18,7 @@ from alphamind.utilities import alpha_logger
 class ConstLinearModel(ModelBase):
 
     def __init__(self,
-                 features: np.ndarray=None,
+                 features: list=None,
                  weights: np.ndarray=None):
         super().__init__(features)
         if features is not None and weights is not None:
@@ -46,7 +46,7 @@ class ConstLinearModel(ModelBase):
 
 class LinearRegression(ModelBase):
 
-    def __init__(self, features, fit_intercept: bool=False):
+    def __init__(self, features: list=None, fit_intercept: bool=False):
         super().__init__(features)
         self.impl = LinearRegressionImpl(fit_intercept=fit_intercept)
 

alphamind/model/modelbase.py

@@ -13,8 +13,9 @@ from alphamind.utilities import alpha_logger
 
 class ModelBase(metaclass=abc.ABCMeta):
 
-    def __init__(self, features: np.ndarray=None):
-        self.features = features
+    def __init__(self, features: list=None):
+        if features is not None:
+            self.features = list(features)
 
     @abc.abstractmethod
     def fit(self, x, y):

alphamind/tests/model/test_linearmodel.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-9-4
+
+@author: cheng.li
+"""
+
+import unittest
+import numpy as np
+from sklearn.linear_model import LinearRegression as LinearRegression2
+from alphamind.model.linearmodel import ConstLinearModel
+from alphamind.model.linearmodel import LinearRegression
+
+
+class TestLinearModel(unittest.TestCase):
+
+    def setUp(self):
+        self.n = 3
+        self.train_x = np.random.randn(1000, self.n)
+        self.train_y = np.random.randn(1000, 1)
+        self.predict_x = np.random.randn(10, self.n)
+
+    def test_const_linear_model(self):
+
+        weights = np.array([1., 2., 3.])
+        model = ConstLinearModel(features=['a', 'b', 'c'],
+                                 weights=weights)
+
+        calculated_y = model.predict(self.predict_x)
+        expected_y = self.predict_x @ weights
+        np.testing.assert_array_almost_equal(calculated_y, expected_y)
+
+    def test_const_linear_model_persistence(self):
+        weights = np.array([1., 2., 3.])
+        model = ConstLinearModel(features=['a', 'b', 'c'],
+                                 weights=weights)
+
+        desc = model.save()
+        new_model = ConstLinearModel.load(desc)
+
+        self.assertEqual(model.features, new_model.features)
+        np.testing.assert_array_almost_equal(model.weights, new_model.weights)
+
+    def test_linear_regression(self):
+        model = LinearRegression(['a', 'b', 'c'], fit_intercept=False)
+        model.fit(self.train_x, self.train_y)
+
+        calculated_y = model.predict(self.predict_x)
+
+        expected_model = LinearRegression2(fit_intercept=False)
+        expected_model.fit(self.train_x, self.train_y)
+        expected_y = expected_model.predict(self.predict_x)
+
+        np.testing.assert_array_almost_equal(calculated_y, expected_y)
+
+    def test_linear_regression_persistence(self):
+        model = LinearRegression(['a', 'b', 'c'], fit_intercept=False)
+        model.fit(self.train_x, self.train_y)
+
+        desc = model.save()
+        new_model = LinearRegression.load(desc)
+
+        calculated_y = new_model.predict(self.predict_x)
+        expected_y = model.predict(self.predict_x)
+
+        np.testing.assert_array_almost_equal(calculated_y, expected_y)

alphamind/tests/test_suite.py

@@ -26,6 +26,7 @@ from alphamind.tests.analysis.test_riskanalysis import TestRiskAnalysis
 from alphamind.tests.analysis.test_perfanalysis import TestPerformanceAnalysis
 from alphamind.tests.analysis.test_factoranalysis import TestFactorAnalysis
 from alphamind.tests.analysis.test_quantilieanalysis import TestQuantileAnalysis
+from alphamind.tests.model.test_linearmodel import TestLinearModel
 
 
 if __name__ == '__main__':
@@ -43,6 +44,7 @@ if __name__ == '__main__':
                          TestRiskAnalysis,
                          TestPerformanceAnalysis,
                          TestFactorAnalysis,
-                         TestQuantileAnalysis],
+                         TestQuantileAnalysis,
+                         TestLinearModel],
                         alpha_logger)
     runner.run()