def testInvalidInputShape(self): x = constant_op.constant([[2.0, 1.0]]) with self.cached_session(): rffm = RandomFourierFeatureMapper(3, 10) with self.assertRaisesWithPredicateMatch( dense_kernel_mapper.InvalidShapeError, r'Invalid dimension: expected 3 input features, got 2 instead.' ): rffm.map(x)
def testMappedShape(self): x1 = constant_op.constant([[2.0, 1.0, 0.0]]) x2 = constant_op.constant([[1.0, -1.0, 2.0], [-1.0, 10.0, 1.0], [4.0, -2.0, -1.0]]) with self.cached_session(): rffm = RandomFourierFeatureMapper(3, 10, 1.0) mapped_x1 = rffm.map(x1) mapped_x2 = rffm.map(x2) self.assertEqual([1, 10], mapped_x1.get_shape()) self.assertEqual([3, 10], mapped_x2.get_shape())
def testSameOmegaReused(self): x = constant_op.constant([[2.0, 1.0, 0.0]]) with self.cached_session(): rffm = RandomFourierFeatureMapper(3, 100) mapped_x = rffm.map(x) mapped_x_copy = rffm.map(x) # Two different evaluations of tensors output by map on the same input # are identical because the same parameters are used for the mappings. self.assertAllClose(mapped_x.eval(), mapped_x_copy.eval(), atol=0.001)
def testMulticlassDataWithAndWithoutKernels(self): """Tests classifier w/ and w/o kernels on multiclass data.""" feature_column = layers.real_valued_column('feature', dimension=4) # Metrics for linear classifier (no kernels). linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[feature_column], n_classes=3) linear_classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=50) linear_metrics = linear_classifier.evaluate( input_fn=test_data.iris_input_multiclass_fn, steps=1) linear_loss = linear_metrics['loss'] linear_accuracy = linear_metrics['accuracy'] # Using kernel mappers allows to discover non-linearities in data (via RBF # kernel approximation), reduces loss and increases accuracy. kernel_mappers = { feature_column: [ RandomFourierFeatureMapper(input_dim=4, output_dim=50, stddev=1.0, name='rffm') ] } kernel_linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[], n_classes=3, kernel_mappers=kernel_mappers) kernel_linear_classifier.fit( input_fn=test_data.iris_input_multiclass_fn, steps=50) kernel_linear_metrics = kernel_linear_classifier.evaluate( input_fn=test_data.iris_input_multiclass_fn, steps=1) kernel_linear_loss = kernel_linear_metrics['loss'] kernel_linear_accuracy = kernel_linear_metrics['accuracy'] self.assertLess(kernel_linear_loss, linear_loss) self.assertGreater(kernel_linear_accuracy, linear_accuracy)
def testBadKernelApproximation(self): x = constant_op.constant([[2.0, 1.0, 0.0]]) y = constant_op.constant([[1.0, -1.0, 2.0]]) stddev = 3.0 with self.cached_session(): # The mapped dimension is fairly small, so the kernel approximation is # very rough. rffm = RandomFourierFeatureMapper(3, 100, stddev, seed=0) mapped_x = rffm.map(x) mapped_y = rffm.map(y) exact_kernel_value = _compute_exact_rbf_kernel(x, y, stddev) approx_kernel_value = _inner_product(mapped_x, mapped_y) self.assertAllClose(exact_kernel_value.eval(), approx_kernel_value.eval(), atol=0.2)
def testGoodKernelApproximationAmortized(self): # Parameters. num_points = 20 input_dim = 5 mapped_dim = 5000 stddev = 5.0 points_shape = [1, input_dim] points = [ random_ops.random_uniform(shape=points_shape, maxval=1.0) for _ in xrange(num_points) ] normalized_points = [nn.l2_normalize(point, dim=1) for point in points] total_absolute_error = 0.0 with self.cached_session(): rffm = RandomFourierFeatureMapper(input_dim, mapped_dim, stddev, seed=0) # Cache mappings so that they are not computed multiple times. cached_mappings = dict( (point, rffm.map(point)) for point in normalized_points) for x in normalized_points: mapped_x = cached_mappings[x] for y in normalized_points: mapped_y = cached_mappings[y] exact_kernel_value = _compute_exact_rbf_kernel( x, y, stddev) approx_kernel_value = _inner_product(mapped_x, mapped_y) abs_error = math_ops.abs(exact_kernel_value - approx_kernel_value) total_absolute_error += abs_error self.assertAllClose([[0.0]], total_absolute_error.eval() / (num_points * num_points), atol=0.02)
def testClassifierWithAndWithoutKernelsNoRealValuedColumns(self): """Tests kernels have no effect for non-real valued columns .""" def input_fn(): return { 'price': constant_op.constant([[0.4], [0.6], [0.3]]), 'country': sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'], indices=[[0, 0], [1, 3], [2, 1]], dense_shape=[3, 5]), }, constant_op.constant([[1], [0], [1]]) price = layers.real_valued_column('price') country = layers.sparse_column_with_hash_bucket('country', hash_bucket_size=5) linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[price, country]) linear_classifier.fit(input_fn=input_fn, steps=100) linear_metrics = linear_classifier.evaluate(input_fn=input_fn, steps=1) linear_loss = linear_metrics['loss'] linear_accuracy = linear_metrics['accuracy'] kernel_mappers = { country: [RandomFourierFeatureMapper(2, 30, 0.6, 1, 'rffm')] } kernel_linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[price, country], kernel_mappers=kernel_mappers) kernel_linear_classifier.fit(input_fn=input_fn, steps=100) kernel_linear_metrics = kernel_linear_classifier.evaluate( input_fn=input_fn, steps=1) kernel_linear_loss = kernel_linear_metrics['loss'] kernel_linear_accuracy = kernel_linear_metrics['accuracy'] # The kernel mapping is applied to a non-real-valued feature column and so # it should have no effect on the model. The loss and accuracy of the # "kernelized" model should match the loss and accuracy of the initial model # (without kernels). self.assertAlmostEqual(linear_loss, kernel_linear_loss, delta=0.01) self.assertAlmostEqual(linear_accuracy, kernel_linear_accuracy, delta=0.01)
def testVariablesWithAndWithoutKernels(self): """Tests variables w/ and w/o kernel.""" multi_dim_feature = layers.real_valued_column('multi_dim_feature', dimension=2) linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[multi_dim_feature]) linear_classifier.fit(input_fn=_linearly_inseparable_binary_input_fn, steps=50) linear_variables = linear_classifier.get_variable_names() self.assertIn('linear/multi_dim_feature/weight', linear_variables) self.assertIn('linear/bias_weight', linear_variables) linear_weights = linear_classifier.get_variable_value( 'linear/multi_dim_feature/weight') linear_bias = linear_classifier.get_variable_value( 'linear/bias_weight') kernel_mappers = { multi_dim_feature: [RandomFourierFeatureMapper(2, 30, 0.6, 1, 'rffm')] } kernel_linear_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[], kernel_mappers=kernel_mappers) kernel_linear_classifier.fit( input_fn=_linearly_inseparable_binary_input_fn, steps=50) kernel_linear_variables = kernel_linear_classifier.get_variable_names() self.assertIn('linear/multi_dim_feature_MAPPED/weight', kernel_linear_variables) self.assertIn('linear/bias_weight', kernel_linear_variables) kernel_linear_weights = kernel_linear_classifier.get_variable_value( 'linear/multi_dim_feature_MAPPED/weight') kernel_linear_bias = kernel_linear_classifier.get_variable_value( 'linear/bias_weight') # The feature column used for linear classification (no kernels) has # dimension 2 so the model will learn a 2-dimension weights vector (and a # scalar for the bias). In the kernelized model, the features are mapped to # a 30-dimensional feature space and so the weights variable will also have # dimension 30. self.assertEqual(2, len(linear_weights)) self.assertEqual(1, len(linear_bias)) self.assertEqual(30, len(kernel_linear_weights)) self.assertEqual(1, len(kernel_linear_bias))
def testLinearlyInseparableBinaryDataWithAndWithoutKernels(self): """Tests classifier w/ and w/o kernels on non-linearly-separable data.""" multi_dim_feature = layers.real_valued_column('multi_dim_feature', dimension=2) # Data points are non-linearly separable so there will be at least one # mis-classified sample (accuracy < 0.8). In fact, the loss is minimized for # w1=w2=0.0, in which case each example incurs a loss of ln(2). The overall # (average) loss should then be ln(2) and the logits should be approximately # 0.0 for each sample. logreg_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[multi_dim_feature]) logreg_classifier.fit(input_fn=_linearly_inseparable_binary_input_fn, steps=50) logreg_metrics = logreg_classifier.evaluate( input_fn=_linearly_inseparable_binary_input_fn, steps=1) logreg_loss = logreg_metrics['loss'] logreg_accuracy = logreg_metrics['accuracy'] logreg_predictions = logreg_classifier.predict( input_fn=_linearly_inseparable_binary_input_fn, as_iterable=False) self.assertAlmostEqual(logreg_loss, np.log(2), places=3) self.assertLess(logreg_accuracy, 0.8) self.assertAllClose(logreg_predictions['logits'], [[0.0], [0.0], [0.0], [0.0]]) # Using kernel mappers allows to discover non-linearities in data. Mapping # the data to a higher dimensional feature space using approx RBF kernels, # substantially reduces the loss and leads to perfect classification # accuracy. kernel_mappers = { multi_dim_feature: [RandomFourierFeatureMapper(2, 30, 0.6, 1, 'rffm')] } kernelized_logreg_classifier = kernel_estimators.KernelLinearClassifier( feature_columns=[], kernel_mappers=kernel_mappers) kernelized_logreg_classifier.fit( input_fn=_linearly_inseparable_binary_input_fn, steps=50) kernelized_logreg_metrics = kernelized_logreg_classifier.evaluate( input_fn=_linearly_inseparable_binary_input_fn, steps=1) kernelized_logreg_loss = kernelized_logreg_metrics['loss'] kernelized_logreg_accuracy = kernelized_logreg_metrics['accuracy'] self.assertLess(kernelized_logreg_loss, 0.2) self.assertEqual(kernelized_logreg_accuracy, 1.0)
def testTwoMapperObjects(self): x = constant_op.constant([[2.0, 1.0, 0.0]]) y = constant_op.constant([[1.0, -1.0, 2.0]]) stddev = 3.0 with self.cached_session(): # The mapped dimension is fairly small, so the kernel approximation is # very rough. rffm1 = RandomFourierFeatureMapper(3, 100, stddev) rffm2 = RandomFourierFeatureMapper(3, 100, stddev) mapped_x1 = rffm1.map(x) mapped_y1 = rffm1.map(y) mapped_x2 = rffm2.map(x) mapped_y2 = rffm2.map(y) approx_kernel_value1 = _inner_product(mapped_x1, mapped_y1) approx_kernel_value2 = _inner_product(mapped_x2, mapped_y2) self.assertAllClose(approx_kernel_value1.eval(), approx_kernel_value2.eval(), atol=0.01)