Kernel methods and Random features approximation¶

Now let’s step into building a whole pipeline.

Goals:

• learn about the different lightonml modules

• how to load a dataset

• how to encode data

• how to perform random projections with the OPU

• learn why changing the dimensionality of data in machine learning problems is helpful.

[1]:

import warnings
warnings.filterwarnings('ignore')
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

[2]:

random_state = np.random.RandomState(42)


MNIST dataset¶

The MNIST dataset is composed of 60000 labeled training examples and 10000 labeled test examples of handwritten digits. Each image is 28x28 and in grayscale. The lightonml loader offers the images already flattened in arrays of length 784 (28x28). Other datasets are available in lightonml.datasets.

[3]:

from lightonml.datasets import MNIST
from sklearn.model_selection import train_test_split

(X_train, y_train), (X_test, y_test) = MNIST()

[4]:

print('Train examples are arrays of shape {} with elements of type {}.'.format(X_train.shape,
X_train.dtype))
print('Test examples are arrays of shape {} with elements of type {}.'.format(X_test.shape,
X_test.dtype))

Train examples are arrays of shape (60000, 28, 28) with elements of type uint8.
Test examples are arrays of shape (10000, 28, 28) with elements of type uint8.

[5]:

f, axes = plt.subplots(2, 5, sharex='col', sharey='row')

axes[0][0].imshow(X_train[0].reshape(28, 28), cmap='gray')
axes[0][1].imshow(X_train[6000].reshape(28, 28), cmap='gray')
axes[0][2].imshow(X_train[12669].reshape(28, 28), cmap='gray')
axes[0][3].imshow(X_train[19000].reshape(28, 28), cmap='gray')
axes[0][4].imshow(X_train[25020].reshape(28, 28), cmap='gray')
axes[1][0].imshow(X_train[33000].reshape(28, 28), cmap='gray')
axes[1][1].imshow(X_train[39000].reshape(28, 28), cmap='gray')
axes[1][2].imshow(X_train[44000].reshape(28, 28), cmap='gray')
axes[1][3].imshow(X_train[50000].reshape(28, 28), cmap='gray')
axes[1][4].imshow(X_train[55000].reshape(28, 28), cmap='gray')

[5]:

<matplotlib.image.AxesImage at 0x7f8972bc8850>

[6]:

# convert to float and flatten
X_train_fp = X_train.astype('float').reshape(-1, 784)
X_test_fp = X_test.astype('float').reshape(-1, 784)

[7]:

print('Min: {}, {}'.format(X_train.min(), X_test.min()))
print('Max: {}, {}'.format(X_train.max(), X_test.max()))
# scale train data
X_train_fp /= 255.
X_test_fp /= 255.
print('Min: {}, {}'.format(X_train.min(), X_test.min()))
print('Max: {}, {}'.format(X_train.max(), X_test.max()))

Min: 0, 0
Max: 255, 255
Min: 0, 0
Max: 255, 255


Ridge regression is a linear model which solves

$\underset{\beta \in \mathbb{R}^{p \times q}}{\operatorname{argmin}}||\mathbf{X}\beta-\mathbf{Y}||^2_2 + \gamma||\beta||^2_2$

where $$\mathbf{X} \in \mathbb{R}^{n \times p}$$ are the sample images and $$\mathbf{Y} \in \mathbb{R}^{n \times q}$$ are the one-hot encoded labels, while $$\beta$$ are the parameters of the model.

$$n$$ is the number of samples, $$p$$ the number of original features, $$q$$ the number of classes.

[8]:

from sklearn.linear_model import RidgeClassifier

clf = RidgeClassifier()
clf.fit(X_train_fp, y_train)
train_score = clf.score(X_train_fp, y_train)
test_score = clf.score(X_test_fp, y_test)

print("Train accuracy: {:.4f}".format(train_score))
print("Test accuracy: {:.4f}".format(test_score))

Train accuracy: 0.8574
Test accuracy: 0.8604


Kernel Ridge Classification and Approximating Kernels¶

The performance of the standard ridge regression is quite poor, we can improve it using the kernel trick, but this approach has a major drawback: we need to invert an $$n \times n$$ matrix and matrix inversion is an $$O(n^3)$$ operation.

We can follow Random features for large-scale kernel machines, A. Rahimi and B. Recht (2007) and solve a standard ridge regression on a nonlinear mapping of the data to a new feature space of a different dimension.

Our nonlinear mapping is a linear random projection $$\mathbf{R} \in \mathbb{C}^{m \times p}$$ followed by a nonlinear function $$\Phi$$:

$\mathbf{X_{new}} = \Phi(\mathbf{R}\mathbf{X})$

If we choose $$m=10000$$, we have now to invert a matrix of size $$10000^2$$ instead of $$60000^2$$.

When the number of random projections $$m$$ tends to infinity, the inner product between the projected data points approximates a kernel function, due to the concentration of measure (Computation with infinite neural networks, C. Williams, 1998).

Random projections have allowed to deal with large-scale machine learning problem, reaching the point where storing the random matrix and computing the random features has become the bottleneck of the algorithm.

We can use the OPU to overcome these problems and compute the random mapping very quickly and without the need to store a large random matrix. We can then solve the ridge regression problem on the random features.

OPU pipeline¶

Data and data encoding¶

The OPU requires a binary matrix of type uint8 as input, so we need to encode our data in binary format. It can be done by using one of the Encoders provided in lightonml.encoding or by building a custom one.

For grayscale images like MNIST, we can use a very simple BinaryThresholdEncoder, that receives an array of uint8 between 0 and 255 as input and returns an array of zeros and ones of type uint8 according to a threshold. SeparatedBitPlanEncoder and its companion MixingBitPlanDecoder instead work nicely with RGB images.

Another possibility is to train an autoencoder from lightonml.encoding.models on your data.

[9]:

from lightonml.encoding.base import BinaryThresholdEncoder
encoder = BinaryThresholdEncoder(threshold_enc=25)
X_train_encoded = encoder.transform(X_train)
X_test_encoded = encoder.transform(X_test)


Random Mapping on the OPU¶

To use the OPU, we need to initialize an OPUMap object.

[12]:

from lightonml.projections.sklearn import OPUMap


Passing ndims=2 will let the software know that the input is 2D. This parameter will be used in the selection of the optimal display on the input device.

[13]:

n_components = 10000 # number of random projections
random_mapping = OPUMap(n_components=n_components, ndims=2)


When OPUMap is initialized with opu=None (default value), an OPU object attribute is created: random_mapping.opu and the OPU resource is acquired. Resource acquisition takes 3-5 seconds and is a one-time cost. The first time you want to transform data through an OPU, you need to call the fit method, this will set some internal parameters (e.g. how the input is displayed on the input device) to optimize the quality of the operation. Calling fit on different input arrays may affect the result. You can perform the fit and the transform with a single function call using the fit_transform method.

[14]:

train_random_features = random_mapping.fit_transform(X_train_encoded)
test_random_features = random_mapping.transform(X_test_encoded)


The output type is uint8, following operations might be faster converting this to float32.

[15]:

train_random_features.dtype, test_random_features.dtype

[15]:

(dtype('uint8'), dtype('uint8'))

[16]:

train_random_features = train_random_features.astype('float32')
test_random_features = test_random_features.astype('float32')


Decoding¶

Some encoders, like SeparatedBitPlanEncoder, need a specific decoder to decode the random features (MixingBitPlanDecoder). In this case we don’t need one.

Model¶

We will obtain a much better performance than before by using a linear classifier on the non-linear features.

[17]:

classifier = RidgeClassifier()

[18]:

classifier.fit(train_random_features, y_train)
train_accuracy = classifier.score(train_random_features, y_train)
test_accuracy = classifier.score(test_random_features, y_test)

print('Train accuracy: {:.2f} Test accuracy: {:.2f}'.format(train_accuracy * 100, test_accuracy * 100))

Train accuracy: 98.65 Test accuracy: 96.57