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 testLinearlySeparableBinaryDataNoKernels(self):
"""Tests classifier w/o kernels (log. regression) for lin-separable data."""
feature1 = layers.real_valued_column('feature1')
feature2 = layers.real_valued_column('feature2')
logreg_classifier = kernel_estimators.KernelLinearClassifier(
feature_columns=[feature1, feature2])
logreg_classifier.fit(input_fn=_linearly_separable_binary_input_fn,
steps=100)
metrics = logreg_classifier.evaluate(
input_fn=_linearly_separable_binary_input_fn, steps=1)
# Since the data is linearly separable, the classifier should have small
# loss and perfect accuracy.
self.assertLess(metrics['loss'], 0.1)
self.assertEqual(metrics['accuracy'], 1.0)
# As a result, it should assign higher probability to class 1 for the 1st
# and 3rd example and higher probability to class 0 for the second example.
logreg_prob_predictions = list(
logreg_classifier.predict_proba(
input_fn=_linearly_separable_binary_input_fn))
self.assertGreater(logreg_prob_predictions[0][1], 0.5)
self.assertGreater(logreg_prob_predictions[1][0], 0.5)
self.assertGreater(logreg_prob_predictions[2][1], 0.5)
def testInvalidNumberOfClasses(self):
"""ValueError raised when the kernel mappers provided have invalid type."""
feature = layers.real_valued_column('feature')
with self.assertRaises(ValueError):
_ = kernel_estimators.KernelLinearClassifier(
feature_columns=[feature], n_classes=1)
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 testInvalidKernelMapper(self):
"""ValueError raised when the kernel mappers provided have invalid type."""
class DummyKernelMapper(object):
def __init__(self):
pass
feature = layers.real_valued_column('feature')
kernel_mappers = {feature: [DummyKernelMapper()]}
with self.assertRaises(ValueError):
_ = kernel_estimators.KernelLinearClassifier(
feature_columns=[feature], kernel_mappers=kernel_mappers)
def testNoFeatureColumnsOrKernelMappers(self):
"""Tests that at least one of feature columns or kernels is provided."""
with self.assertRaises(ValueError):
_ = kernel_estimators.KernelLinearClassifier()
