Training ivis on Out-of-memory Datasets

Introduction

Out-of-memory Datasets

Some datasets are so large that it becomes infeasible to load them into memory all at the same time. Other visualisation techniques might only be able to run on a smaller subset of the data; however, this runs the risk of potentially missing out on important smaller patterns in the data.

ivis was developed to address the issue of dimensionality reduction in very large datasets through batch-wise training of the neural network on data stored HDF5 format. Since training occurs in batches, the whole dataset does not need to be loaded into memory at once, and can instead be loaded from disk in chunks. In this example, we will show how ivis can scale up and be used to visualize massive datasets that don’t fit into memory.

Example

Data Selection

In this example we will make use of the KDD Cup 1999 dataset. Although the dataset can be easily read-in to RAM, it provides a toy example for a general use case. The KDD99 dataset contains network traffic, with the competition task being to detect network intruders. The dataset is unbalanced, with the majority of traffic being normal.

Data Preparation

To train ivis on an out-of-memory dataset, the dataset must first be converted into the h5 file format. There are numerous methods of doing this using various external tools such as Apache Spark. In this example, we will assume that the dataset has already been preprocessed and converted to .h5 format.

Dimensionality Reduction

To train on a h5 file that exists on disk, we can use a Keras utility class, the HDF5Matrix class. This will allow us to run ivis on the HDF5Matrix object using the fit method. We will train ivis in unsupervised mode for 5 epochs to speed up training; other hyperparameters are left at their default values.

Note

When training on a h5 dataset, we recommend to use the shuffle_mode='batch' option in the fit method. This will speed up the training process by pulling a batch of data from disk and shuffling tethat batch, rather than shuffling across the whole dataset.

from tensorflow.keras.utils import HDF5Matrix

X = HDF5Matrix(h5_filepath, 'data')
y = HDF5Matrix(h5_filepath, 'labels')

model = Ivis(epochs=5)
model.fit(X, shuffle_mode='batch') # Shuffle within batches when using h5 files

y_pred = model.transform(X)

Visualisations

plt.figure()
plt.scatter(x=y_pred[:, 0], y=y_pred[:, 1], c=y)
plt.set_xlabel('ivis 1')
plt.set_ylabel('ivis 2')
plt.show()
_images/kdd99-ivis-demo.png

With anomalies being shown in yellow, we can see that ivis is able to pin point anomalous observations.

Conclusions

ivis is able to scale and deal with the massive, out-of-memory datasets found in the real world by training directly on h5 files. Additionally, it can effectively learn embeddings in an unbalanced dataset without labels.