Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
About the Authors
About the Reviewer

PCA with H2O

We can also use the PCA implementation provided by H2O. (We've already seen H2O in the previous chapter and mentioned it along the book.)

With H2O, we first need to turn on the server with the init method. Then, we dump the dataset on a file (precisely, a CSV file) and finally run the PCA analysis. As the last step, we shut down the server.

We're trying this implementation on some of the biggest datasets seen so far—the one with 100K observations and 100 features and the one with 10K observations and 2,500 features:

In: import h2o
from h2o.transforms.decomposition import H2OPCA

def testH2O_pca(nrows, ncols, k=20):
    temp_file = tempfile.NamedTemporaryFile().name
    X, _ = make_blobs(nrows, n_features=ncols, random_state=101)
np.savetxt(temp_file, np.c_[X], delimiter=",")
    del X

pca = H2OPCA(k=k, transform="NONE", pca_method="Power")
    tik = time.time()
    pca.train(x=range(100), \

    print "H2OPCA...