def testNoWeightedExamples(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] # Zeroed out example weights. example_weights = [0.0, 0.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) lr.minimize().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) with self.assertRaisesOpError( 'No examples found or all examples have zero weight.'): lr.approximate_duality_gap().eval()
def testNoWeightedExamples(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto( {'age': [0], 'gender': [0]}, 0), make_example_proto( {'age': [1], 'gender': [1]}, 1), ] # Zeroed out example weights. example_weights = [0.0, 0.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) lr.minimize().run() self.assertAllClose([0.5, 0.5], lr.predictions(examples).eval()) with self.assertRaisesOpError( 'No examples found or all examples have zero weight.'): lr.approximate_duality_gap().eval()
def testSimple(self): # Setup test data example_protos = [ make_example_proto( {'age': [0], 'gender': [0]}, -10.0), make_example_proto( {'age': [1], 'gender': [1]}, 14.0), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='squared_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() predictions = lr.predictions(examples) for _ in xrange(20): lr.minimize().run() # Predictions should be 2/3 of label due to minimizing regularized loss: # (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2 self.assertAllClose([-20.0 / 3.0, 28.0 / 3.0], predictions.eval(), rtol=0.005) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimple(self): # Setup test data example_protos = [ make_example_proto({"age": [0], "gender": [0]}, -10.0), make_example_proto({"age": [1], "gender": [1]}, 14.0), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="squared_loss") lr = SdcaModel(examples, variables, options) tf.initialize_all_variables().run() predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() # Predictions should be 2/3 of label due to minimizing regularized loss: # (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2 self.assertAllClose([-20.0 / 3.0, 28.0 / 3.0], predictions.eval(), rtol=0.005) # Approximate gap should be very close to 0.0. (In fact, because the gap # is only approximate, it is likely that upon convergence the duality gap # can have a tiny negative value). self.assertAllClose(0.0, lr.approximate_duality_gap().eval(), atol=1e-2)
def testSomeUnweightedExamples(self): # Setup test data with 4 examples, but should produce the same # results as testSimple. example_protos = [ # Will be used. make_example_proto({"age": [0], "gender": [0]}, 0), # Will be ignored. make_example_proto({"age": [1], "gender": [0]}, 0), # Will be used. make_example_proto({"age": [1], "gender": [1]}, 1), # Will be ignored. make_example_proto({"age": [1], "gender": [0]}, 1), ] example_weights = [1.0, 0.0, 1.0, 0.0] with self._single_threaded_test_session(): # Only use examples 0 and 2 examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss") lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in xrange(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllClose([0, 1, 1, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalanced(self): # Setup test data with 1 positive, and 3 negative examples. example_protos = [ make_example_proto({"age": [0], "gender": [0]}, 0), make_example_proto({"age": [2], "gender": [0]}, 0), make_example_proto({"age": [3], "gender": [0]}, 0), make_example_proto({"age": [1], "gender": [1]}, 1), ] example_weights = [1.0, 1.0, 1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(3, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss") lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in xrange(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.226487 + 0.102902, unregularized_loss.eval(), atol=0.08) self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimpleNoL2(self): # Same as test above (so comments from above apply) but without an L2. # The algorithm should behave as if we have an L2 of 1 in optimization but # 0 in regularized_loss. example_protos = [ make_example_proto({"age": [0], "gender": [0]}, 0), make_example_proto({"age": [1], "gender": [1]}, 1), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type="logistic_loss") lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() for _ in xrange(_MAX_ITERATIONS): train_op.run() # There is neither L1 nor L2 loss, so regularized and unregularized losses # should be exactly the same. self.assertAllClose(0.40244, unregularized_loss.eval(), atol=0.01) self.assertAllClose(0.40244, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalancedWithExampleWeights(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [3.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.284860, unregularized_loss.eval(), rtol=0.08) self.assertAllClose(0.408044, loss.eval(), atol=0.012) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimple(self): # Setup test data example_protos = [ make_example_proto({"age": [0], "gender": [0]}, 0), make_example_proto({"age": [1], "gender": [1]}, 1), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss") lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() for _ in xrange(_MAX_ITERATIONS): train_op.run() # The high tolerance in unregularized_loss comparisons is due to the # fact that it's possible to trade off unregularized_loss vs. # regularization and still have a sum that is quite close to the # optimal regularized_loss value. SDCA's duality gap only ensures that # the regularized_loss is within 0.01 of optimal. # 0.525457 is the optimal regularized_loss. # 0.411608 is the unregularized_loss at that optimum. self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSparseRandom(self): dim = 20 num_examples = 1000 # Number of non-zero features per example. non_zeros = 10 # Setup test data. with self._single_threaded_test_session(): examples, variables = make_random_examples_and_variables_dicts( num_examples, dim, non_zeros) options = dict( symmetric_l2_regularization=.1, symmetric_l1_regularization=0, num_table_shards=1, adaptive=False, loss_type='logistic_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() train_op = lr.minimize() for _ in range(4): train_op.run() lr.update_weights(train_op).run() # Duality gap is 1.4e-5. # It would be 0.01 without shuffling and 0.02 with adaptive sampling. self.assertNear(0.0, lr.approximate_duality_gap().eval(), err=1e-3)
def testInstancesOfOneClassOnly(self): # Setup test data with 1 positive (ignored), and 1 negative example. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [0] }, 1), # Shares gender with the instance above. ] example_weights = [1.0, 0.0] # Second example "omitted" from training. with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.12) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 0], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalancedWithExampleWeights(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto({"age": [0], "gender": [0]}, 0), make_example_proto({"age": [1], "gender": [1]}, 1), ] example_weights = [3.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type="logistic_loss", ) lr = SdcaModel(examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.284860, unregularized_loss.eval(), atol=0.08) self.assertAllClose(0.408044, loss.eval(), atol=0.012) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.0, lr.approximate_duality_gap().eval(), rtol=2e-2, atol=1e-2)
def testInstancesOfOneClassOnly(self): # Setup test data with 1 positive (ignored), and 1 negative example. example_protos = [ make_example_proto( {'age': [0], 'gender': [0]}, 0), make_example_proto( {'age': [1], 'gender': [0]}, 1), # Shares gender with the instance above. ] example_weights = [1.0, 0.0] # Second example "omitted" from training. with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.12) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 0], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalancedWithExampleWeights(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto( {'age': [0], 'gender': [0]}, 0), make_example_proto( {'age': [1], 'gender': [1]}, 1), ] example_weights = [3.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.284860, unregularized_loss.eval(), rtol=0.08) self.assertAllClose(0.408044, loss.eval(), atol=0.012) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testInstancesOfOneClassOnly(self): # Setup test data with 1 positive (ignored), and 1 negative example. example_protos = [ make_example_proto({"age": [0], "gender": [0]}, 0), make_example_proto({"age": [1], "gender": [0]}, 1), # Shares gender with the instance above. ] example_weights = [1.0, 0.0] # Second example "omitted" from training. for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type="logistic_loss", ) lr = SdcaModel(examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 0], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSomeUnweightedExamples(self): # Setup test data with 4 examples, but should produce the same # results as testSimple. example_protos = [ # Will be used. make_example_proto({ 'age': [0], 'gender': [0] }, 0), # Will be ignored. make_example_proto({ 'age': [1], 'gender': [0] }, 0), # Will be used. make_example_proto({ 'age': [1], 'gender': [1] }, 1), # Will be ignored. make_example_proto({ 'age': [1], 'gender': [0] }, 1), ] example_weights = [1.0, 0.0, 1.0, 0.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): # Only use examples 0 and 2 examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options, num_table_shards=num_shards) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic( predictions) self.assertAllClose([0, 1, 1, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testDistributedSimple(self): # Setup test data example_protos = [ make_example_proto({'age': [0], 'gender': [0]}, 0), make_example_proto({'age': [1], 'gender': [1]}, 1), ] example_weights = [1.0, 1.0] for num_shards in _SHARD_NUMBERS: for num_loss_partitions in _NUM_LOSS_PARTITIONS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss', num_table_shards=num_shards, num_loss_partitions=num_loss_partitions) lr = SdcaModel(examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() def Minimize(): with self._single_threaded_test_session(): for _ in range(_MAX_ITERATIONS): train_op.run() threads = [] for _ in range(num_loss_partitions): threads.append(Thread(target=Minimize)) threads[-1].start() for t in threads: t.join() lr.update_weights(train_op).run() # The high tolerance in unregularized_loss comparisons is due to the # fact that it's possible to trade off unregularized_loss vs. # regularization and still have a sum that is quite close to the # optimal regularized_loss value. SDCA's duality gap only ensures # that the regularized_loss is within 0.01 of optimal. # 0.525457 is the optimal regularized_loss. # 0.411608 is the unregularized_loss at that optimum. self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertTrue(lr.approximate_duality_gap().eval() < 0.02)
def testDistributedSimple(self): # Setup test data example_protos = [ make_example_proto({'age': [0], 'gender': [0]}, 0), make_example_proto({'age': [1], 'gender': [1]}, 1), ] example_weights = [1.0, 1.0] for num_shards in _SHARD_NUMBERS: for num_loss_partitions in _NUM_LOSS_PARTITIONS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss', num_table_shards=num_shards, num_loss_partitions=num_loss_partitions) lr = SdcaModel(examples, variables, options) tf.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() def Minimize(): with self._single_threaded_test_session(): for _ in range(_MAX_ITERATIONS): train_op.run() threads = [] for _ in range(num_loss_partitions): threads.append(Thread(target=Minimize)) threads[-1].start() for t in threads: t.join() lr.update_weights(train_op).run() # The high tolerance in unregularized_loss comparisons is due to the # fact that it's possible to trade off unregularized_loss vs. # regularization and still have a sum that is quite close to the # optimal regularized_loss value. SDCA's duality gap only ensures # that the regularized_loss is within 0.01 of optimal. # 0.525457 is the optimal regularized_loss. # 0.411608 is the unregularized_loss at that optimum. self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertTrue(lr.approximate_duality_gap().eval() < 0.02)
def testImbalanced(self): # Setup test data with 1 positive, and 3 negative examples. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [2], 'gender': [0] }, 0), make_example_proto({ 'age': [3], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0, 1.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(3, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options, num_table_shards=num_shards) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() self.assertAllClose(0.226487 + 0.102902, unregularized_loss.eval(), atol=0.08) self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic( predictions) self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimpleNoL2(self): # Same as test above (so comments from above apply) but without an L2. # The algorithm should behave as if we have an L2 of 1 in optimization but # 0 in regularized_loss. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options, num_table_shards=num_shards) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() # There is neither L1 nor L2 loss, so regularized and unregularized # losses should be exactly the same. self.assertAllClose(0.40244, unregularized_loss.eval(), atol=0.01) self.assertAllClose(0.40244, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic( predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalanced(self): # Setup test data with 1 positive, and 3 negative examples. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [2], 'gender': [0] }, 0), make_example_proto({ 'age': [3], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0, 1.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(3, 1) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type='logistic_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() lr.update_weights(train_op).run() self.assertAllClose( 0.226487 + 0.102902, unregularized_loss.eval(), atol=0.08) self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval()) self.assertAllClose( 0.0, lr.approximate_duality_gap().eval(), rtol=2e-2, atol=1e-2)
def testPartitionedPrimals(self): # Setup test data example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1, partitioned=True) options = dict( symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type='logistic_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() lr.update_weights(train_op).run() # The high tolerance in unregularized_loss comparisons is due to the # fact that it's possible to trade off unregularized_loss vs. # regularization and still have a sum that is quite close to the # optimal regularized_loss value. SDCA's duality gap only ensures that # the regularized_loss is within 0.01 of optimal. # 0.525457 is the optimal regularized_loss. # 0.411608 is the unregularized_loss at that optimum. self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose( 0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimple(self): # Setup test data example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 2), ] example_weights = [100.0, 100.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict( symmetric_l2_regularization=1.0, symmetric_l1_regularization=0, loss_type='poisson_loss') model = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() # Before minimization, the weights default to zero. There is no loss due # to regularization, only unregularized loss which is 1 for each example. predictions = model.predictions(examples) self.assertAllClose([1.0, 1.0], predictions.eval()) unregularized_loss = model.unregularized_loss(examples) regularized_loss = model.regularized_loss(examples) approximate_duality_gap = model.approximate_duality_gap() self.assertAllClose(1.0, unregularized_loss.eval()) self.assertAllClose(1.0, regularized_loss.eval()) # There are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender # (say w3 and w4). The minimization leads to: # w1=w3=-1.96487, argmin of 100*(exp(2*w)-2*w*0)+w**2. # w2=w4=0.345708, argmin of 100*(exp(2*w)-2*w*2)+w**2. # This gives an unregularized loss of .3167 and .3366 with regularization. train_op = model.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() model.update_weights(train_op).run() self.assertAllClose([0.0196, 1.9965], predictions.eval(), atol=1e-4) self.assertAllClose(0.3167, unregularized_loss.eval(), atol=1e-4) self.assertAllClose(0.3366, regularized_loss.eval(), atol=1e-4) self.assertAllClose(0., approximate_duality_gap.eval(), atol=1e-6)
def testSimpleLogistic(self): # Setup test data example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) for _ in xrange(5): lr.minimize().run() # The high tolerance in unregularized_loss comparisons is due to the # fact that it's possible to trade off unregularized_loss vs. # regularization and still have a sum that is quite close to the # optimal regularized_loss value. SDCA's duality gap only ensures that # the regularized_loss is within 0.01 of optimal. # 0.525457 is the optimal regularized_loss. # 0.411608 is the unregularized_loss at that optimum. self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalancedWithExampleWeights(self): # Setup test data with 1 positive, and 1 negative example. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [3.0, 1.0] for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type='logistic_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() lr.update_weights(train_op).run() self.assertAllClose(0.284860, unregularized_loss.eval(), atol=0.08) self.assertAllClose(0.408044, loss.eval(), atol=0.012) predicted_labels = get_binary_predictions_for_logistic( predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.0, lr.approximate_duality_gap().eval(), rtol=2e-2, atol=1e-2)
def testInstancesOfOneClassOnly(self): # Setup test data with 1 positive (ignored), and 1 negative example. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [0] }, 1), # Shares gender with the instance above. ] example_weights = [1.0, 0.0] # Second example "omitted" from training. for num_shards in _SHARD_NUMBERS: with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, num_table_shards=num_shards, loss_type='logistic_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() lr.update_weights(train_op).run() self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05) self.assertAllClose(0.525457, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic( predictions) self.assertAllEqual([0, 0], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimpleLogisticNoL2(self): # Same as test above (so comments from above apply) but without an L2. # The algorithm should behave as if we have an L2 of 1 in optimization but # 0 in regularized_loss. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11) self.assertAllClose(0.371705, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimple(self): # Setup test data example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, -10.0), make_example_proto({ 'age': [1], 'gender': [1] }, 14.0), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type='squared_loss') lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() predictions = lr.predictions(examples) train_op = lr.minimize() for _ in range(_MAX_ITERATIONS): train_op.run() lr.update_weights(train_op).run() # Predictions should be 2/3 of label due to minimizing regularized loss: # (label - 2 * weight)^2 / 2 + L2 * 2 * weight^2 self.assertAllClose([-20.0 / 3.0, 28.0 / 3.0], predictions.eval(), rtol=0.005) # Approximate gap should be very close to 0.0. (In fact, because the gap # is only approximate, it is likely that upon convergence the duality gap # can have a tiny negative value). self.assertAllClose(0.0, lr.approximate_duality_gap().eval(), atol=1e-2)
def testSimpleNoL2(self): # Same as test above (so comments from above apply) but without an L2. # The algorithm should behave as if we have an L2 of 1 in optimization but # 0 in regularized_loss. example_protos = [ make_example_proto( {'age': [0], 'gender': [0]}, 0), make_example_proto( {'age': [1], 'gender': [1]}, 1), ] example_weights = [1.0, 1.0] with self._single_threaded_test_session(): examples = make_example_dict(example_protos, example_weights) variables = make_variable_dict(1, 1) options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type='logistic_loss') lr = SdcaModel(CONTAINER, examples, variables, options) tf.initialize_all_variables().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) for _ in xrange(5): lr.minimize().run() self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11) self.assertAllClose(0.371705, loss.eval(), atol=0.01) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testDistributedSimple(self): # Distributed SDCA may not converge if the workers update concurrently the # same example. In this test the examples are partitioned across workers. # The examples are the same for all workers, just the example_ids are # different. example_protos = [ make_example_proto({ 'age': [0], 'gender': [0] }, 0), make_example_proto({ 'age': [1], 'gender': [1] }, 1), ] example_weights = [1.0, 1.0] examples = make_example_dict(example_protos, example_weights) example_ids = array_ops.placeholder( dtypes.string, shape=(len(example_weights),)) examples['example_ids'] = example_ids variables = make_variable_dict(1, 1) for num_shards in _SHARD_NUMBERS: for num_loss_partitions in _NUM_LOSS_PARTITIONS: with self._single_threaded_test_session(): options = dict( # Keep the same solution as for TestSimple: since the number of # examples is multplied by num_loss_partitions, multiply also # L2 by the same value. symmetric_l2_regularization=num_loss_partitions, symmetric_l1_regularization=0, loss_type='logistic_loss', num_table_shards=num_shards, num_loss_partitions=num_loss_partitions) lr = SdcaModel(examples, variables, options) variables_lib.global_variables_initializer().run() unregularized_loss = lr.unregularized_loss(examples) loss = lr.regularized_loss(examples) predictions = lr.predictions(examples) self.assertAllClose(0.693147, unregularized_loss.eval()) self.assertAllClose(0.693147, loss.eval()) train_op = lr.minimize() def minimize(worker_id): with self._single_threaded_test_session(): feed_dict = {example_ids: [ str(i + worker_id*len(example_weights)) for i in range( len(example_weights))]} for _ in range(_MAX_ITERATIONS): train_op.run(feed_dict=feed_dict) # pylint: disable=cell-var-from-loop threads = [] for worker_id in range(num_loss_partitions): threads.append(threading.Thread(target=minimize, args=(worker_id,))) threads[-1].start() for t in threads: t.join() lr.update_weights(train_op).run(feed_dict={ example_ids: [str(i) for i in range(len(example_weights))]}) # Test only the unregularized loss because the optimal value of the # regularized loss depends on num_loss_partitions. self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.02) predicted_labels = get_binary_predictions_for_logistic(predictions) self.assertAllEqual([0, 1], predicted_labels.eval()) self.assertNear(0.0, lr.approximate_duality_gap().eval(), 0.02)