pca_impl

  • Available in: PCA

  • Hyperparameter: no

Description

The pca_impl parameter allows you to specify PCA implementations for Singular-Value Decomposition (SVD) or Eigenvalue Decomposition (EVD), using either the Matrix Toolkit Java (MTJ) libary or the Java Matrix (JAMA) library.

Available options include:

  • mtj_evd_densematrix: Eigenvalue decompositions for dense matrix using MTJ

  • mtj_evd_symmmatrix: Eigenvalue decompositions for symmetric matrix using MTJ (default)

  • mtj_svd_densematrix: Singular-value decompositions for dense matrix using MTJ

  • jama: Eigenvalue decompositions for dense matrix using JAMA

Example

R

Python

library(h2o)
h2o.init()

# Load the US Arrests dataset
arrestsH2O = h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")

# Train using the JAMA PCA implementation option
model <- h2o.prcomp(training_frame=arrestsH2O, k=4, pca_impl="JAMA", seed=1234)

# View the importance of components
model@model$importance
Importance of components:
                              pc1       pc2      pc3      pc4
Standard deviation     202.723056 27.832264 6.523048 2.581365
Proportion of Variance   0.980347  0.018479 0.001015 0.000159
Cumulative Proportion    0.980347  0.998826 0.999841 1.000000

# View the eigenvectors
model@model$eigenvectors
Rotation:
               pc1       pc2       pc3       pc4
Murder   -0.042392 -0.016163  0.065884  0.996795
Assault  -0.943957 -0.320686 -0.066552 -0.040946
UrbanPop -0.308428  0.938459 -0.154967  0.012343
Rape     -0.109637  0.127257  0.983471 -0.067603

import(h2o)
h2o.init()
from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator as H2OPCA

# Load the US Arrests dataset
arrestsH2O = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/USArrests.csv")

# Train using the jama PCA implementation option
impl = "jama"
model = H2OPCA(k = 4, pca_impl=impl, seed=1234)
model.train(x=list(range(4)), training_frame=arrestsH2O)

# View the importance of components
model.varimp(use_pandas=False)
[(u'Standard deviation', 202.7230564425318, 27.832263730019577, 6.523048232982174, 2.5813652317810947),
 (u'Proportion of Variance', 0.980347353161874, 0.0184786717900806, 0.0010150206303792286, 0.00015895441766549314),
 (u'Cumulative Proportion', 0.980347353161874, 0.9988260249519546, 0.9998410455823339, 0.9999999999999993)]

# View the eigenvectors
model.rotation()
Rotation:
          pc1         pc2         pc3         pc4
--------  ----------  ----------  ----------  ----------
Murder    -0.0423918  -0.0161626  0.0658843   0.996795
Assault   -0.943957   -0.320686   -0.0665517  -0.0409457
UrbanPop  -0.308428   0.938459    -0.154967   0.0123426
Rape      -0.109637   0.127257    0.983471    -0.0676028