add tests

Author: antoinedemathelin

adapt/feature_based/_mdd.py

@@ -73,7 +73,7 @@ class MDD(BaseDeepFeature):
     task_ : tensorflow Model
         Principal task network.
         
-    task_adv_ : tensorflow Model
+    discriminator_ : tensorflow Model
         Adversarial task network.
     
     model_ : tensorflow Model

adapt/instance_based/_kliep.py

@@ -290,6 +290,7 @@ def fit_estimator(self, X, y,
                 sample_weight /= (sample_weight.sum() + EPS)
             if sample_weight.sum() <= 0:
                 sample_weight = np.ones(len(sample_weight))
+                sample_weight /= (sample_weight.sum() + EPS)
             bootstrap_index = np.random.choice(
             len(X), size=len(X), replace=True,
             p=sample_weight)

adapt/utils.py

@@ -364,7 +364,7 @@ class GradientHandler(Layer):
     """
     def __init__(self, lambda_init=1., name="g_handler"):
         super().__init__(name=name)
-        self.lambda_init=1.
+        self.lambda_init=lambda_init
         self.lambda_ = tf.Variable(lambda_init,
                                    trainable=False,
                                    dtype="float32")

tests/test_coral.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 from sklearn.linear_model import LogisticRegression
+from scipy import linalg
 import tensorflow as tf
 from tensorflow.keras import Sequential, Model
 from tensorflow.keras.layers import Dense
@@ -62,6 +63,17 @@ def test_fit_coral():
     assert np.abs(model.estimator_.coef_[0][0] /
            model.estimator_.coef_[0][1] + 0.5) < 0.1
     assert (model.predict(Xt) == yt).sum() / len(Xt) >= 0.99
+    
+    
+def test_fit_coral_complex():
+    np.random.seed(0)
+    model = CORAL(LogisticRegression(), lambda_=10000.)
+    Xs_ = np.random.randn(10, 100)
+    Xt_ = np.random.randn(10, 100)
+    model.fit(Xs_, ys[:10], Xt_)
+    assert np.iscomplexobj(linalg.inv(linalg.sqrtm(model.Cs_)))
+    assert np.iscomplexobj(linalg.sqrtm(model.Ct_))
+    model.predict(Xs_)
 
 
 def test_fit_deepcoral():

tests/test_kliep.py

@@ -4,9 +4,21 @@
 
 import numpy as np
 from sklearn.linear_model import LinearRegression
+from sklearn.base import BaseEstimator
 
 from adapt.instance_based import KLIEP
 
+
+class DummyEstimator(BaseEstimator):
+    
+    def __init__(self):
+        pass
+    
+    def fit(self, X, y):
+        self.y = y
+        return self
+
+
 np.random.seed(0)
 Xs = np.concatenate((
     np.random.randn(50)*0.1,
@@ -34,3 +46,21 @@ def test_fit():
     assert model.weights_[:50].sum() > 90
     assert model.weights_[50:].sum() < 0.5
     assert np.abs(model.predict(Xt) - yt).sum() < 20
+    
+
+def test_fit_estimator_bootstrap_index():
+    np.random.seed(0)
+    ys_ = np.random.randn(100)
+    model = KLIEP(DummyEstimator(),
+                  sigmas=[10, 100])
+    model.fit_estimator(Xs, ys_, sample_weight=np.random.random(len(ys)))
+    assert len(set(list(model.estimator_.y.ravel())) & set(list(ys_.ravel()))) > 50
+    
+    
+def test_fit_estimator_sample_weight_zeros():
+    np.random.seed(0)
+    ys_ = np.random.randn(100)
+    model = KLIEP(DummyEstimator(),
+                  sigmas=[10, 100])
+    model.fit_estimator(Xs, ys_, sample_weight=np.zeros(len(ys)))
+    assert len(set(list(model.estimator_.y.ravel())) & set(list(ys_.ravel()))) > 50

tests/test_utils.py

@@ -433,6 +433,9 @@ def test_gradienthandler(lambda_):
         gradient = tape.gradient(grad_handler(inputs),
                                  inputs)
     assert np.all(gradient == lambda_ * np.ones(3))
+    config = grad_handler.get_config()
+    assert config['lambda_init'] == lambda_
+    
 
 
 def test_make_classification_da():