def getPCAProjections(input_filename):
""" Run PCA on the Kushmerick ad data
Stop when there are sufficient PCA components to explain threshold_variance
Project input data onto the top PCA components that explain threshold_variance
Normalize this data
Sort attributes by their correlation with output
- input_filename : prepocessed data
- Output 'pca': data projected onto PCA components
'norm': pca data normalized to std deviation 1
'corr': normalized data sorted by correlation with output
'index':
"""
print 'getPCAProjections:', input_filename
explained_variance = 0.99
root_name = 'pca%03d' % round(explained_variance * 100.0)
pca_filename = csv.makeCsvPath(root_name)
pca_norm_filename = csv.makeCsvPath(root_name + '.norm')
pca_norm_corr_filename = csv.makeCsvPath(root_name + '.norm.corr')
corr_index_filename = csv.makeCsvPath(root_name + '.corr.idx')
pca.pcaAdData(explained_variance, input_filename, pca_filename)
pca.normalizeData(pca_filename, pca_norm_filename)
sort_order, corr_index = pca.rankByCorrelationWithOutcomes(pca_norm_filename)
def reorder(in_cells):
return pca.reorderMatrix(in_cells, sort_order)
csv.modifyCsvRaw(pca_norm_filename, pca_norm_corr_filename, reorder)
csv.writeCsv(corr_index_filename, corr_index)
return {'pca': pca_filename, 'norm':pca_norm_filename, 'corr':pca_norm_corr_filename, 'index':corr_index_filename}
describe(numpy)
describe(scipy)
describe(mdp)
describe(bimdp)
if False:
covTest()
#doTests()
explained_variance = 0.99
ev = str(int(explained_variance*100.0))
# pca_filename = csv.headered_name_pca
pca_filename = csv.makeCsvPath('pca' + ev)
#pca_norm_filename = csv.headered_name_pca_norm
pca_norm_filename = csv.makeCsvPath('pca' + ev + '.norm')
#pca_norm_corr_filename = csv.headered_name_pca_corr
pca_norm_corr_filename = csv.makeCsvPath('pca' + ev + '.norm.corr')
if True:
pcaAdData(explained_variance, csv.headered_name_pp, pca_filename)
if True:
normalizeData(pca_filename, pca_norm_filename)
if True:
sort_order = rankByCorrelationWithOutcomes(pca_norm_filename)
def reorder(in_cells):
return reorderMatrix(in_cells, sort_order)
csv.modifyCsvRaw(pca_norm_filename, pca_norm_corr_filename, reorder)
