pca_method¶
Available in: PCA
Hyperparameter: no
Description¶
Use the pca_method parameter to specify the algorithm to use for computing the principal components. Available options include:
GramSVD: Uses a distributed computation of the Gram matrix, followed by a local SVD using the JAMA package
Power: Computes the SVD using the power iteration method (experimental)
Randomized: Uses randomized subspace iteration method
GLRM: Fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental)
Note: For pca_method = Randomized, the algorithm must deal with matrices of size m by k and n by k, where
m is number of rows,
n is expanded column size and
k is the number of eigenvectors desired.
As a result, there is no advantage to be gained by trying to find the eigenvectors of the matrix transpose. In other words, when using PCA with wide datasets, users should not choose Randomize method.
Example¶
library(h2o)
h2o.init()
# Load the Birds dataset
birds.hex <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/birds.csv")
# Train using the Power pca_method
birds.pca <- h2o.prcomp(training_frame = birds.hex, transform = "STANDARDIZE",
k = 3, pca_method="Power", use_all_factor_levels=TRUE,
impute_missing=TRUE)
# View the importance of components
birds.pca@model$importance
Importance of components:
pc1 pc2 pc3
Standard deviation 1.496991 1.351000 1.014182
Proportion of Variance 0.289987 0.236184 0.133098
Cumulative Proportion 0.289987 0.526171 0.659269
# View the eigenvectors
birds.pca@model$eigenvectors
Rotation:
pc1 pc2 pc3
patch.Ref1a 0.007207 0.007449 0.001161
patch.Ref1b -0.003090 0.011257 -0.001066
patch.Ref1c 0.002962 0.008850 -0.000264
patch.Ref1d -0.001295 0.011003 0.000501
patch.Ref1e 0.006559 0.006904 -0.001206
---
pc1 pc2 pc3
S 0.463591 -0.053410 0.184799
year -0.055934 0.009691 -0.968635
area 0.533375 -0.289381 -0.130338
log.area. 0.583966 -0.262287 -0.089582
ENN -0.270615 -0.573900 0.038835
log.ENN. -0.231368 -0.640231 0.026325
# Train again using GLRM pca_method
birds2.pca <- h2o.prcomp(training_frame = birds.hex, transform = "STANDARDIZE",
k = 3, pca_method="GLRM", use_all_factor_levels=TRUE,
impute_missing=TRUE)
# View the importance of components
birds2.pca@model$importance
Importance of components:
pc1 pc2 pc3
Standard deviation 2.659459 0.700971 0.404706
Proportion of Variance 0.915223 0.063583 0.021194
Cumulative Proportion 0.915223 0.978806 1.000000
# View the eigenvectors
birds2.pca@model$eigenvectors
Rotation:
pc1 pc2 pc3
patch.Ref1a -0.092008 0.030110 -0.018916
patch.Ref1b -0.107461 0.040519 0.076546
patch.Ref1c -0.103785 0.059700 0.016164
patch.Ref1d -0.105764 0.044823 0.062234
patch.Ref1e -0.102115 0.058994 -0.037536
---
pc1 pc2 pc3
S 0.003558 0.111264 -0.422437
year 0.000008 -0.004418 0.032813
area 0.004551 0.049496 -0.444745
log.area. 0.002756 0.066183 -0.453866
ENN 0.013259 -0.274711 -0.053960
log.ENN. 0.009517 -0.282830 -0.107461
import(h2o)
h2o.init()
from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
# Load the Birds dataset
birds = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/birds.csv")
# Train with the Power pca_method
birds.pca = H2OPrincipalComponentAnalysisEstimator(k = 3, transform = "STANDARDIZE", pca_method="Power",
use_all_factor_levels=True, impute_missing=True)
birds.pca.train(x=list(range(4)), training_frame=birds)
# View the importance of components
birds.pca.varimp(use_pandas=False)
[(u'Standard deviation', 1.0505993078459912, 0.8950182545325247, 0.5587566783073901),
(u'Proportion of Variance', 0.28699613488673914, 0.20828865401845226, 0.08117966990084355),
(u'Cumulative Proportion', 0.28699613488673914, 0.4952847889051914, 0.5764644588060349)]
# View the eigenvectors
birds.pca.rotation()
Rotation:
pc1 pc2 pc3
----------------- ------------------ ----------------- ----------------
patch.Ref1a 0.00732398141913 -0.0141576160836 0.0294419461081
patch.Ref1b -0.00482860843905 0.00867426840498 0.0330778190153
patch.Ref1c 0.00124768649004 -0.00274167383932 0.0312598825617
patch.Ref1d -0.000370181920761 0.000297923901103 0.0317439245635
patch.Ref1e 0.00223394447742 -0.00459462277502 0.0309648089406
--- --- --- ---
landscape.Bauxite -0.0638494513759 0.136728811833 0.118858152002
landscape.Forest 0.0378085502606 -0.0833578672691 0.969316569884
landscape.Urban -0.0545759062856 0.111309410422 0.0354475756223
S 0.564501605704 -0.767095710638 -0.0466832766991
year -0.814596906726 -0.577331674836 -0.0101626722479
See the whole table with table.as_data_frame()
# Train again with the GLRM pca_method
birds2 = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/pca_test/birds.csv")
birds2.pca = H2OPrincipalComponentAnalysisEstimator(k = 3, transform = "STANDARDIZE",
pca_method="GLRM", use_all_factor_levels=True,
impute_missing=True)
birds2.pca.train(x=list(range(4)), training_frame=birds2)
# View the importance of components
birds2.pca.varimp(use_pandas=False)
[(u'Standard deviation', 1.9286830840160667, 0.2896650415698226, 0.2053712844270903),
(u'Proportion of Variance', 0.9672162180423401, 0.021816948059531167, 0.01096683389812861),
(u'Cumulative Proportion', 0.9672162180423401, 0.9890331661018713, 0.9999999999999999)]
# View the eigenvectors
birds2.pca.rotation()
Rotation:
pc1 pc2 pc3
----------------- ----------------- ----------------- -----------------
patch.Ref1a -0.0973454860413 0.0233748845619 -0.0407839669099
patch.Ref1b -0.0979880717715 -0.0167446302072 -0.0162149496631
patch.Ref1c -0.0971529563124 0.00536661170128 -0.0177009628488
patch.Ref1d -0.100657197505 0.00754923938494 -0.018364320893
patch.Ref1e -0.0982933822825 0.0158116058361 -0.0193764027317
--- --- --- ---
landscape.Bauxite -0.0248166745792 -0.504864083913 0.074374750806
landscape.Forest -0.0296555294277 0.232678445269 -0.537738667852
landscape.Urban -0.0733909967344 -0.112998988851 0.0347355699687
S 0.00878461186804 0.649068763107 -0.130282514102
year -0.000583301909773 -0.0765116904321 -0.69416666169
# See the whole table with table.as_data_frame()