confidence
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@ -390,7 +390,7 @@ class LPLS(Model):
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"""
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aopt = self.model['aopt']
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if opt['calc_conf']:
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Wx, Wz = lpls_jk(self.model['X'], self.model['Y'], self.model['Z'], aopt, n_sets)
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Wx, Wz = lpls_jk(self._data['X'], self._data['Y'], self._data['Z'], aopt, opt['n_sets'], opt['xz_alpha'])
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Wcal = self.model['W'][:,:aopt]
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Lcal = self.model['L'][:,:aopt]
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# ensure that Wcal is scaled
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@ -669,8 +669,8 @@ class LplsOptions(Options):
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opt['center'] = True
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opt['center_mth'] = [2, 0, 1]
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opt['scale'] = 'scores'
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opt['calc_conf'] = False
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opt['n_sets'] = 7
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opt['calc_conf'] = True
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opt['n_sets'] = 20
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opt['strict'] = False
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opt['p_center'] = 'med'
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opt['alpha'] = .3
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@ -698,8 +698,8 @@ class LplsOptions(Options):
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(blmplots.LplsHypoidCorrelationPlot, 'Hypoid corr.', False)
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]
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opt['out_data'] = ['T','P']
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opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
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opt['out_data'] = ['T','P', 'tsqx']
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opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.LplsXLoadingPlot,blmplots.LplsZLoadingPlot, blmplots.LineViewXc]
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#opt['out_data'] = None
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@ -190,6 +190,15 @@ class PlsCorrelationLoadingPlot(BlmScatterPlot):
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title = "Pls correlation loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='CP')
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class LplsXLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Lpls x-loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='tsqx')
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class LplsZLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Lpls z-loadings (%s)" %model._dataset['Z'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='L', color_by='tsqz')
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class LplsHypoidCorrelationPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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@ -219,7 +219,7 @@ def bridge(a, b, aopt, scale='scores', mode='normal', r=0):
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def nipals_lpls(X, Y, Z, amax, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', scale='scores', verbose=False):
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def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', scale='scores', verbose=False):
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""" L-shaped Partial Least Sqaures Regression by the nipals algorithm.
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(X!Z)->Y
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@ -260,18 +260,18 @@ def nipals_lpls(X, Y, Z, amax, alpha=.7, mean_ctr=[2, 0, 1], mode='normal', scal
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u, o = Z.shape
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# initialize
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U = empty((k, amax))
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Q = empty((l, amax))
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T = empty((m, amax))
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W = empty((n, amax))
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P = empty((n, amax))
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K = empty((o, amax))
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L = empty((u, amax))
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var_x = empty((amax,))
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var_y = empty((amax,))
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var_z = empty((amax,))
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U = empty((k, a_max))
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Q = empty((l, a_max))
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T = empty((m, a_max))
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W = empty((n, a_max))
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P = empty((n, a_max))
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K = empty((o, a_max))
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L = empty((u, a_max))
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var_x = empty((a_max,))
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var_y = empty((a_max,))
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var_z = empty((a_max,))
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for a in range(amax):
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for a in range(a_max):
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if verbose:
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print "\n Working on comp. %s" %a
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u = Y[:,:1]
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@ -6,7 +6,7 @@ from scipy.stats import median
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from scipy.linalg import triu,inv,svd,norm
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from select_generators import w_pls_gen,w_pls_gen_jk,pls_gen,pca_gen,diag_pert
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from engines import w_simpls,pls,bridge,pca
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from engines import w_simpls,pls,bridge,pca,nipals_lpls
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from cx_utils import m_shape
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def w_pls_cv_val(X, Y, amax, n_blocks=None, algo='simpls'):
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@ -111,15 +111,17 @@ def pls_val(X, Y, amax=2, n_blocks=10, algo='pls', metric=None):
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def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5):
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"""Performs crossvalidation to get generalisation error in lpls"""
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cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=True)
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cv_iter = pls_gen(X, Y, n_blocks=nsets,center=False,index_out=True)
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k, l = Y.shape
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Yhat = empty((a_max,k,l), 'd')
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for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
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T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
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dat = nipals_lpls(xcal,ycal,Z,
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a_max=a_max,
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alpha=alpha,
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mean_ctr=[2,0,1],
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verbose=False)
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B = dat['B']
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b0 = dat['b0']
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for a in range(a_max):
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Yhat[a,ind,:] = b0[a][0][0] + dot(xi, B[a])
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Yhat_class = zeros_like(Yhat)
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@ -135,9 +137,6 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5):
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def pca_alter_val(a, amax, n_sets=10, method='diag'):
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"""Pca validation by altering elements in X.
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comments:
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-- may do all jk estimates in this loop
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"""
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@ -250,7 +249,7 @@ def pca_jkP(a, aopt, n_blocks=None, metric=None):
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def lpls_jk(X, Y, Z, a_max, nsets=None, alpha=.5):
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cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
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cv_iter = pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
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m, n = X.shape
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k, l = Y.shape
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o, p = Z.shape
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@ -260,15 +259,11 @@ def lpls_jk(X, Y, Z, a_max, nsets=None, alpha=.5):
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WWz = empty((nsets, o, a_max), 'd')
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#WWy = empty((nsets, l, a_max), 'd')
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for i, (xcal,xi,ycal,yi) in enumerate(cv_iter):
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T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
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a_max=a_max,
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alpha=alpha,
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mean_ctr=[2,0,1],
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scale='loads',
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verbose=False)
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WWx[i,:,:] = W
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WWz[i,:,:] = L
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#WWy[i,:,:] = Q
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dat = nipals_lpls(xcal,ycal,Z,a_max=a_max,alpha=alpha,
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mean_ctr=[2,0,1],scale='loads',verbose=False)
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WWx[i,:,:] = dat['W']
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WWz[i,:,:] = dat['L']
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#WWy[i,:,:] = dat['Q']
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return WWx, WWz
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@ -1,44 +0,0 @@
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def smdb_annot(orflist=None, input_fname='registry.genenames.tab', output_fname='yeast.annot'):
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"""Reads registry.genenames.tab from the Stanford yeast
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microarray database.
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Available from:
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ftp://genome-ftp.stanford.edu/pub/yeast/data_download/gene_registry/registry.genenames.tab
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input: orf -- list of orfs (open reading frames)
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file -- (optional) file to fetch info from
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registry.genames contains:
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0 = Locus name
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1 = Other name
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2 = Description
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3 = Gene product
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4 = Phenotype
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5 = ORF name
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6 = SGDID
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NB! Other name, Gene product and Phenotype may have more
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than one mapping. These are separated by |
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Output: writes an annotation file
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"""
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outfile = open(output_fname, 'w')
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header = "Orf\tLocus_id\tOther_name\tDescription\tGene_product\tPhenotype\tSGD_ID\n"
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outfile.write(header)
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text = open(input_fname, 'r').read().splitlines()
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for line in text:
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els = line.split('\t')
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orf_name = els.pop(5)
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if orf_name!='': # we dont care about non-named orfs
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if orflist and orf_name not in orflist:
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break
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for e in els:
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if e !='':
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outfile.write(str(e) + "\t")
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else:
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outfile.write("NA")
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f.write("\n")
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