# Improving Linear Models Using Explicit Kernel Methods

In this tutorial, we demonstrate how combining (explicit) kernel methods with
linear models can drastically increase the latters' quality of predictions
without significantly increasing training and inference times. Unlike dual
kernel methods, explicit (primal) kernel methods scale well with the size of the
training dataset both in terms of training/inference times and in terms of
memory requirements.

**Intended audience:** Even though we provide a high-level overview of concepts
related to explicit kernel methods, this tutorial primarily targets readers who
already have at least basic knowledge of kernel methods and Support Vector
Machines (SVMs). If you are new to kernel methods, refer to either of the
following sources for an introduction:

* If you have a strong mathematical background:
[Kernel Methods in Machine Learning](http://www.kernel-machines.org/publications/pdfs/0701907.pdf)
* [Kernel method wikipedia page](https://en.wikipedia.org/wiki/Kernel_method)

Currently, TensorFlow supports explicit kernel mappings for dense features only;
TensorFlow will provide support for sparse features at a later release.

This tutorial uses [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn)
(TensorFlow's high-level Machine Learning API) Estimators for our ML models.
If you are not familiar with this API, [tf.estimator Quickstart](https://www.tensorflow.org/get_started/estimator)
is a good place to start. We will use the MNIST dataset. The tutorial consists
of the following steps:

* Load and prepare MNIST data for classification.
* Construct a simple linear model, train it, and evaluate it on the eval data.
* Replace the linear model with a kernelized linear model, re-train, and
re-evaluate.

## Load and prepare MNIST data for classification
Run the following utility command to load the MNIST dataset:

```python
data = tf.contrib.learn.datasets.mnist.load_mnist()
```
The preceding method loads the entire MNIST dataset (containing 70K samples) and
splits it into train, validation, and test data with 55K, 5K, and 10K samples
respectively. Each split contains one numpy array for images (with shape
[sample_size, 784]) and one for labels (with shape [sample_size, 1]). In this
tutorial, we only use the train and validation splits to train and evaluate our
models respectively.

In order to feed data to a tf.contrib.learn Estimator, it is helpful to convert
it to Tensors. For this, we will use an `input function` which adds Ops to the
TensorFlow graph that, when executed, create mini-batches of Tensors to be used
downstream. For more background on input functions, check
@{$get_started/input_fn$Building Input Functions with tf.contrib.learn}. In this
example, we will use the `tf.train.shuffle_batch` Op which, besides converting
numpy arrays to Tensors, allows us to specify the batch_size and whether to
randomize the input every time the input_fn Ops are executed (randomization
typically expedites convergence during training). The full code for loading and
preparing the data is shown in the snippet below. In this example, we use
mini-batches of size 256 for training and the entire sample (5K entries) for
evaluation. Feel free to experiment with different batch sizes.

```python
import numpy as np
import tensorflow as tf

def get_input_fn(dataset_split, batch_size, capacity=10000, min_after_dequeue=3000):

  def _input_fn():
    images_batch, labels_batch = tf.train.shuffle_batch(
        tensors=[dataset_split.images, dataset_split.labels.astype(np.int32)],
        batch_size=batch_size,
        capacity=capacity,
        min_after_dequeue=min_after_dequeue,
        enqueue_many=True,
        num_threads=4)
    features_map = {'images': images_batch}
    return features_map, labels_batch

  return _input_fn

data = tf.contrib.learn.datasets.mnist.load_mnist()

train_input_fn = get_input_fn(data.train, batch_size=256)
eval_input_fn = get_input_fn(data.validation, batch_size=5000)

```

## Training a simple linear model
We can now train a linear model over the MNIST dataset. We will use the
@{tf.contrib.learn.LinearClassifier} estimator with 10 classes representing the
10 digits. The input features form a 784-dimensional dense vector which can
be specified as follows:

```python
image_column = tf.contrib.layers.real_valued_column('images', dimension=784)
```

The full code for constructing, training and evaluating a LinearClassifier
estimator is as follows:

```python
import time

# Specify the feature(s) to be used by the estimator.
image_column = tf.contrib.layers.real_valued_column('images', dimension=784)
estimator = tf.contrib.learn.LinearClassifier(feature_columns=[image_column], n_classes=10)

# Train.
start = time.time()
estimator.fit(input_fn=train_input_fn, steps=2000)
end = time.time()
print('Elapsed time: {} seconds'.format(end - start))

# Evaluate and report metrics.
eval_metrics = estimator.evaluate(input_fn=eval_input_fn, steps=1)
print(eval_metrics)
```
The following table summarizes the results on the eval data.

metric        | value
:------------ | :------------
loss          | 0.25 to 0.30
accuracy      | 92.5%
training time | ~25 seconds on my machine

Note: Metrics will vary depending on various factors.

In addition to experimenting with the (training) batch size and the number of
training steps, there are a couple other parameters that can be tuned as well.
For instance, you can change the optimization method used to minimize the loss
by explicitly selecting another optimizer from the collection of
[available optimizers](https://www.tensorflow.org/code/tensorflow/python/training).
As an example, the following code constructs a LinearClassifier estimator that
uses the Follow-The-Regularized-Leader (FTRL) optimization strategy with a
specific learning rate and L2-regularization.


```python
optimizer = tf.train.FtrlOptimizer(learning_rate=5.0, l2_regularization_strength=1.0)
estimator = tf.contrib.learn.LinearClassifier(
    feature_columns=[image_column], n_classes=10, optimizer=optimizer)
```

Regardless of the values of the parameters, the maximum accuracy a linear model
can achieve on this dataset caps at around **93%**.

## Using explicit kernel mappings with the linear model.
The relatively high error (~7%) of the linear model over MNIST indicates that
the input data is not linearly separable. We will use explicit kernel mappings
to reduce the classification error.

**Intuition:** The high-level idea is to use a non-linear map to transform the
input space to another feature space (of possibly higher dimension) where the
(transformed) features are (almost) linearly separable and then apply a linear
model on the mapped features. This is shown in the following figure:

<div style="text-align:center">
<img src="https://www.tensorflow.org/versions/master/images/kernel_mapping.png" />
</div>


### Technical details
In this example we will use **Random Fourier Features**, introduced in the
["Random Features for Large-Scale Kernel Machines"](https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf)
paper by Rahimi and Recht, to map the input data. Random Fourier Features map a
vector \\(\mathbf{x} \in \mathbb{R}^d\\) to \\(\mathbf{x'} \in \mathbb{R}^D\\)
via the following mapping:

$$
RFFM(\cdot): \mathbb{R}^d \to \mathbb{R}^D, \quad
RFFM(\mathbf{x}) =  \cos(\mathbf{\Omega} \cdot \mathbf{x}+ \mathbf{b})
$$

where \\(\mathbf{\Omega} \in \mathbb{R}^{D \times d}\\),
\\(\mathbf{x} \in \mathbb{R}^d,\\) \\(\mathbf{b} \in \mathbb{R}^D\\) and the
cosine is applied element-wise.

In this example, the entries of \\(\mathbf{\Omega}\\) and \\(\mathbf{b}\\) are
sampled from distributions such that the mapping satisfies the following
property:

$$
RFFM(\mathbf{x})^T \cdot RFFM(\mathbf{y}) \approx
e^{-\frac{\|\mathbf{x} - \mathbf{y}\|^2}{2 \sigma^2}}
$$

The right-hand-side quantity of the expression above is known as the RBF (or
Gaussian) kernel function. This function is one of the most-widely used kernel
functions in Machine Learning and implicitly measures similarity in a different,
much higher dimensional space than the original one. See
[Radial basis function kernel](https://en.wikipedia.org/wiki/Radial_basis_function_kernel)
for more details.

### Kernel classifier
@{tf.contrib.kernel_methods.KernelLinearClassifier} is a pre-packaged
`tf.contrib.learn` estimator that combines the power of explicit kernel mappings
with linear models. Its constructor is almost identical to that of the
LinearClassifier estimator with the additional option to specify a list of
explicit kernel mappings to be applied to each feature the classifier uses. The
following code snippet demonstrates how to replace LinearClassifier with
KernelLinearClassifier.


```python
# Specify the feature(s) to be used by the estimator. This is identical to the
# code used for the LinearClassifier.
image_column = tf.contrib.layers.real_valued_column('images', dimension=784)
optimizer = tf.train.FtrlOptimizer(
   learning_rate=50.0, l2_regularization_strength=0.001)


kernel_mapper = tf.contrib.kernel_methods.RandomFourierFeatureMapper(
  input_dim=784, output_dim=2000, stddev=5.0, name='rffm')
kernel_mappers = {image_column: [kernel_mapper]}
estimator = tf.contrib.kernel_methods.KernelLinearClassifier(
   n_classes=10, optimizer=optimizer, kernel_mappers=kernel_mappers)

# Train.
start = time.time()
estimator.fit(input_fn=train_input_fn, steps=2000)
end = time.time()
print('Elapsed time: {} seconds'.format(end - start))

# Evaluate and report metrics.
eval_metrics = estimator.evaluate(input_fn=eval_input_fn, steps=1)
print(eval_metrics)
```
The only additional parameter passed to `KernelLinearClassifier` is a dictionary
from feature_columns to a list of kernel mappings to be applied to the
corresponding feature column. The following lines instruct the classifier to
first map the initial 784-dimensional images to 2000-dimensional vectors using
random Fourier features and then learn a linear model on the transformed
vectors:

```python
kernel_mapper = tf.contrib.kernel_methods.RandomFourierFeatureMapper(
  input_dim=784, output_dim=2000, stddev=5.0, name='rffm')
kernel_mappers = {image_column: [kernel_mapper]}
estimator = tf.contrib.kernel_methods.KernelLinearClassifier(
   n_classes=10, optimizer=optimizer, kernel_mappers=kernel_mappers)
```
Notice the `stddev` parameter. This is the standard deviation (\\(\sigma\\)) of
the approximated RBF kernel and controls the similarity measure used in
classification. `stddev` is typically determined via hyperparameter tuning.

The results of running the preceding code are summarized in the following table.
We can further increase the accuracy by increasing the output dimension of the
mapping and tuning the standard deviation.

metric        | value
:------------ | :------------
loss          | 0.10
accuracy      | 97%
training time | ~35 seconds on my machine


### stddev
The classification quality is very sensitive to the value of stddev. The
following table shows the accuracy of the classifier on the eval data for
different values of stddev. The optimal value is stddev=5.0. Notice how too
small or too high stddev values can dramatically decrease the accuracy of the
classification.

stddev | eval accuracy
:----- | :------------
1.0    | 0.1362
2.0    | 0.4764
4.0    | 0.9654
5.0    | 0.9766
8.0    | 0.9714
16.0   | 0.8878

### Output dimension
Intuitively, the larger the output dimension of the mapping, the closer the
inner product of two mapped vectors approximates the kernel, which typically
translates to better classification accuracy. Another way to think about this is
that the output dimension equals the number of weights of the linear model; the
larger this dimension, the larger the "degrees of freedom" of the model.
However, after a certain threshold, higher output dimensions increase the
accuracy by very little, while making training take more time. This is shown in
the following two Figures which depict the eval accuracy as a function of the
output dimension and the training time, respectively.

![image](https://www.tensorflow.org/versions/master/images/acc_vs_outdim.png)
![image](https://www.tensorflow.org/versions/master/images/acc-vs-trn_time.png)


## Summary
Explicit kernel mappings combine the predictive power of nonlinear models with
the scalability of linear models. Unlike traditional dual kernel methods,
explicit kernel methods can scale to millions or hundreds of millions of
samples. When using explicit kernel mappings, consider the following tips:

* Random Fourier Features can be particularly effective for datasets with dense
features.
* The parameters of the kernel mapping are often data-dependent. Model quality
can be very sensitive to these parameters. Use hyperparameter tuning to find the
optimal values.
* If you have multiple numerical features, concatenate them into a single
multi-dimensional feature and apply the kernel mapping to the concatenated
vector.
