def setUp(self):
self.feature_count = 50
self.number_docs = 1000
self.docs = range(self.number_docs)
self.features = np.random.rand(self.number_docs, self.feature_count)
self.linear_model = Linear(self.feature_count)
self.linear_w = self.linear_model.initialize_weights("random")
self.hidden_model = OneHiddenLayer(self.feature_count)
self.hidden_w = self.hidden_model.initialize_weights("random")
def setUp(self):
self.feature_count = 50
self.number_docs = 1000
self.docs = range(self.number_docs)
self.features = np.random.rand(self.number_docs, self.feature_count)
self.linear_model = Linear(self.feature_count)
self.linear_w = self.linear_model.initialize_weights("random")
self.hidden_model = OneHiddenLayer(self.feature_count)
self.hidden_w = self.hidden_model.initialize_weights("random")
class TestRankers(unittest.TestCase):
def setUp(self):
self.feature_count = 50
self.number_docs = 1000
self.docs = range(self.number_docs)
self.features = np.random.rand(self.number_docs, self.feature_count)
self.linear_model = Linear(self.feature_count)
self.linear_w = self.linear_model.initialize_weights("random")
self.hidden_model = OneHiddenLayer(self.feature_count)
self.hidden_w = self.hidden_model.initialize_weights("random")
def testLinear(self):
scores = self.linear_model.score(self.features, self.linear_w)
docs1 = [d for _, d in sorted(zip(scores, self.docs))]
scores = self.linear_model.score(self.features, self.linear_w * 200)
docs2 = [d for _, d in sorted(zip(scores, self.docs))]
self.assertListEqual(docs1, docs2, "Linear Ranker should be magnitude"
"independent")
def testOneHiddenLayer(self):
scores = self.hidden_model.score(self.features, self.hidden_w)
docs1 = [d for _, d in sorted(zip(scores, self.docs))]
scores = self.hidden_model.score(self.features, self.hidden_w * 10)
docs2 = [d for _, d in sorted(zip(scores, self.docs))]
self.assertNotEqual(
docs1, docs2, "Hidden Layer Ranker should be "
"magnitude dependent")
def testInitOneHiddenLayer(self):
orderings = set()
reps = 1000
for _ in range(reps):
w = self.hidden_model.initialize_weights("random")
scores = self.hidden_model.score(self.features, w)
ordering = tuple([d
for _, d in sorted(zip(scores, self.docs))][:10])
orderings.add(ordering)
self.assertEqual(reps, len(orderings))
class TestRankers(unittest.TestCase):
def setUp(self):
self.feature_count = 50
self.number_docs = 1000
self.docs = range(self.number_docs)
self.features = np.random.rand(self.number_docs, self.feature_count)
self.linear_model = Linear(self.feature_count)
self.linear_w = self.linear_model.initialize_weights("random")
self.hidden_model = OneHiddenLayer(self.feature_count)
self.hidden_w = self.hidden_model.initialize_weights("random")
def testLinear(self):
scores = self.linear_model.score(self.features, self.linear_w)
docs1 = [d for _, d in sorted(zip(scores, self.docs))]
scores = self.linear_model.score(self.features, self.linear_w * 200)
docs2 = [d for _, d in sorted(zip(scores, self.docs))]
self.assertListEqual(docs1, docs2, "Linear Ranker should be magnitude"
"independent")
def testOneHiddenLayer(self):
scores = self.hidden_model.score(self.features, self.hidden_w)
docs1 = [d for _, d in sorted(zip(scores, self.docs))]
scores = self.hidden_model.score(self.features, self.hidden_w * 10)
docs2 = [d for _, d in sorted(zip(scores, self.docs))]
self.assertNotEqual(docs1, docs2, "Hidden Layer Ranker should be "
"magnitude dependent")
def testInitOneHiddenLayer(self):
orderings = set()
reps = 1000
for _ in range(reps):
w = self.hidden_model.initialize_weights("random")
scores = self.hidden_model.score(self.features, w)
ordering = tuple([d for _, d in
sorted(zip(scores, self.docs))][:10])
orderings.add(ordering)
self.assertEqual(reps, len(orderings))
