Fix forgotten intercept in SGDRegressor (#147)

* fix forgotten intercept in SGDRegressor

* update tests

Author: StrikerRUS

lightning/impl/sgd.py

@@ -150,6 +150,7 @@ def __init__(self, loss="hinge", penalty="l2",
         self.n_calls = n_calls
         self.verbose = verbose
         self.coef_ = None
+        self.intercept_ = None
 
     def _get_loss(self):
         if self.multiclass:
@@ -395,6 +396,7 @@ def predict(self, X):
         try:
             assert_all_finite(self.coef_)
             pred = safe_sparse_dot(X, self.coef_.T)
+            pred += self.intercept_
         except ValueError:
             n_samples = X.shape[0]
             n_vectors = self.coef_.shape[0]

lightning/impl/tests/test_sgd.py

@@ -106,7 +106,7 @@ def test_regression_squared_loss():
 
     reg.fit(X, y)
     pred = reg.predict(X)
-    assert_almost_equal(np.mean((pred - y) ** 2), 4.913, 3)
+    assert_almost_equal(np.mean((pred - y) ** 2), 4.749, 3)
 
 
 def test_regression_squared_loss_nn_l1():
@@ -119,7 +119,7 @@ def test_regression_squared_loss_nn_l1():
 
         reg.fit(X, y)
         pred = reg.predict(X)
-        assert_almost_equal(np.mean((pred - y) ** 2), 0.033, 3)
+        assert_almost_equal(np.mean((pred - y) ** 2), 0.016, 3)
         assert_false((reg.coef_ < 0).any())
 
 
@@ -132,7 +132,7 @@ def test_regression_squared_loss_nn_l2():
 
     reg.fit(X, y)
     pred = reg.predict(X)
-    assert_almost_equal(np.mean((pred - y) ** 2), 0.033, 3)
+    assert_almost_equal(np.mean((pred - y) ** 2), 0.016, 3)
     assert_almost_equal(reg.coef_.sum(), 2.131, 3)
     assert_false((reg.coef_ < 0).any())
 
@@ -147,5 +147,5 @@ def test_regression_squared_loss_multiple_output():
     Y[:, 1] = y
     reg.fit(X, Y)
     pred = reg.predict(X)
-    assert_almost_equal(np.mean((pred - Y) ** 2), 4.541, 3)
+    assert_almost_equal(np.mean((pred - Y) ** 2), 4.397, 3)