Added explained variance
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@ -328,11 +328,11 @@ class PLS(Model):
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**options.make_model_options())
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# variance captured
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var_x, exp_var_x = variances(self.model['E0'], self.model['T'], self.model['P'])
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self.model['var_x'] = var_x
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self.model['evx'] = var_x
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self.model['exp_var_x'] = exp_var_x
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var_y, exp_var_y = variances(self.model['F0'], self.model['T'], self.model['Q'])
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self.model['var_y'] = var_y
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self.model['evy'] = var_y
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self.model['exp_var_y'] = exp_var_y
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if options['calc_conf']:
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@ -478,6 +478,14 @@ class LPLS(Model):
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self._data['Z'] = c.asarray()
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self.validation(options)
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self.make_model(options)
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print self.model['evx']
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evx_str = [str(i)[:3] for i in self.model['evx']]
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logger.log('notice', 'Explained variance:X\n\t: ' + str(evx_str))
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evy_str = [str(i)[:3] for i in self.model['evy']]
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logger.log('notice', 'Explained variance:Y\n\t: ' + str(evy_str))
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evz_str = [str(i)[:3] for i in self.model['evz']]
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logger.log('notice', 'Explained variance:Z\n\t: ' + str(evz_str))
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if options['calc_conf']:
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self.confidence(options)
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@ -592,7 +600,7 @@ class PlsOptions(Options):
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opt['center_mth'] = mat_center
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opt['scale'] = 'scores'
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opt['calc_conf'] = False
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opt['calc_conf'] = True
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opt['n_sets'] = 7
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opt['strict'] = True
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opt['p_center'] = 'med'
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@ -607,7 +615,7 @@ class PlsOptions(Options):
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opt['all_data'] = [('T', 'scores', True),
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('P', 'loadings', True),
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('E','residuals', False),
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('p_tsq', 't2', False),
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('w_tsq', 't2', True),
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('rmsep', 'RMSEP', False)
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]
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@ -620,10 +628,10 @@ class PlsOptions(Options):
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(blmplots.PlsCorrelationLoadingPlot, 'Corr. loadings', False)
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]
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opt['out_data'] = ['T','P', 'p_tsq']
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opt['out_data'] = ['T','P', 'w_tsq']
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opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
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opt['out_data'] = None
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#opt['out_data'] = None
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opt['pack'] = True
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opt['calc_qvals'] = False
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@ -664,17 +672,17 @@ class LplsOptions(Options):
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opt['engine'] = nipals_lpls
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opt['mode'] = 'normal' # how much info to calculate
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opt['amax'] = 10
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opt['aopt'] = 4
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opt['xz_alpha'] = 0.5
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opt['aopt'] = 3
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opt['xz_alpha'] = 0.4
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opt['auto_aopt'] = False
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opt['center'] = True
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opt['center_mth'] = [2, 2, 1]
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opt['scale'] = 'scores'
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opt['calc_conf'] = False
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opt['calc_conf'] = True
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opt['n_sets'] = 75
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opt['strict'] = False
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opt['p_center'] = 'med'
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opt['alpha'] = .2
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opt['alpha'] = .3
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opt['cov_center'] = 'med'
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opt['crot'] = True
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@ -701,13 +709,14 @@ class LplsOptions(Options):
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(blmplots.LplsZCorrelationPlot, 'Z corr.', True)
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]
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opt['out_data'] = ['T','P','L','K']
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opt['out_plots'] = [blmplots.PlsScorePlot,
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opt['out_data'] = ['T','P','L','K', 'tsqx', 'tsqz']
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opt['out_plots'] = [blmplots.LplsScorePlot,
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blmplots.LplsXLoadingPlot,
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blmplots.LplsZLoadingPlot,
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blmplots.LplsXCorrelationPlot,
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blmplots.LplsZCorrelationPlot,
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blmplots.LineViewXc]
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blmplots.LineViewXc,
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blmplots.LplsExplainedVariancePlot]
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#opt['out_data'] = None
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@ -43,6 +43,7 @@ class BlmScatterPlot(plots.ScatterPlot):
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"""Scatter plot used for scores and loadings in bilinear models."""
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def __init__(self, title, model, absi=0, ordi=1, part_name='T', color_by=None):
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self.model = model
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if model.model.has_key(part_name)!=True:
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raise ValueError("Model part: %s not found in model" %mod_param)
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self._T = model.model[part_name]
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@ -52,6 +53,7 @@ class BlmScatterPlot(plots.ScatterPlot):
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self._absi = absi
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self._ordi = ordi
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self._cmap = cm.summer
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dataset_1 = model.as_dataset(part_name)
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id_dim = dataset_1.get_dim_name(0)
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sel_dim = dataset_1.get_dim_name(1)
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@ -78,6 +80,18 @@ class BlmScatterPlot(plots.ScatterPlot):
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"""Set patch sizes."""
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pass
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def set_expvar_axlabels(self, param="evx"):
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if not self.model.model.has_key(param):
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self.model.model[param] = None
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if self.model.model[param]==None:
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pass #fixme: do expvar calc here if not present
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else:
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expvar = self.model.model[param]
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xstr = "Comp: %s , %.1f " %(self._absi, expvar[self._absi+1])
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ystr = "Comp: %s , %.1f " %(self._ordi, expvar[self._ordi+1])
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self.axes.set_xlabel(xstr)
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self.axes.set_ylabel(ystr)
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def add_pc_spin_buttons(self, amax, absi, ordi):
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sb_a = gtk.SpinButton(climb_rate=1)
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sb_a.set_range(1, amax)
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@ -115,6 +129,7 @@ class BlmScatterPlot(plots.ScatterPlot):
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pad = abs(self.xaxis_data.min()-self.xaxis_data.max())*0.05
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new_lims = (self.xaxis_data.min()+pad, self.xaxis_data.max()+pad)
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self.axes.set_xlim(new_lims, emit=True)
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self.set_expvar_axlabels()
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self.canvas.draw_idle()
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def set_ordinate(self, sb):
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@ -127,6 +142,7 @@ class BlmScatterPlot(plots.ScatterPlot):
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pad = abs(self.yaxis_data.min()-self.yaxis_data.max())*0.05
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new_lims = (self.yaxis_data.min()+pad, self.yaxis_data.max()+pad)
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self.axes.set_ylim(new_lims, emit=True)
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self.set_expvar_axlabels()
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self.canvas.draw_idle()
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def show_labels(self, index=None):
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@ -173,6 +189,7 @@ class PlsLoadingPlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "Pls loadings (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='w_tsq')
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#self.set_expvar_axlabels(self, param="expvarx")
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class PlsCorrelationLoadingPlot(BlmScatterPlot):
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@ -180,15 +197,26 @@ 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 LplsScorePlot(BlmScatterPlot):
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def __init__(self, model, absi=0, ordi=1):
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title = "L-pls scores (%s)" %model._dataset['X'].get_name()
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BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
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self.set_expvar_axlabels("evx")
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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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self.set_expvar_axlabels("evx")
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class LplsZLoadingPlot(BlmScatterPlot, plots.PlotThresholder):
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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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self.set_expvar_axlabels(param="evz")
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plots.PlotThresholder.__init__(self, "IC")
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@ -204,6 +232,7 @@ class LplsXCorrelationPlot(BlmScatterPlot):
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R = correlation_loadings(model._data['X'], model.model['T'])
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model.model['Rx'] = R
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rx')
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self.set_expvar_axlabels("evx")
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radius = 1
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center = (0,0)
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c100 = patches.Circle(center,radius=radius,
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@ -229,6 +258,7 @@ class LplsZCorrelationPlot(BlmScatterPlot):
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R = correlation_loadings(model._data['Z'].T, model.model['W'])
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model.model['Rz'] = R
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rz')
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self.set_expvar_axlabels("evz")
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radius = 1
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center = (0,0)
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c100 = patches.Circle(center,radius=radius,
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@ -254,6 +284,16 @@ class LplsHypoidCorrelationPlot(BlmScatterPlot):
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BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='W')
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class LplsExplainedVariancePlot(plots.Plot):
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def __init__(self, model):
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self.model = model
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plots.Plot.__init__(self, "Explained variance")
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xax = scipy.arange(model.model['evx'].shape[0])
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self.axes.plot(xax, model.model['evx'], 'b-', label='X', linewidth=1.5)
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self.axes.plot(xax, model.model['evy'], 'k-', label='Y', linewidth=1.5)
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self.axes.plot(xax, model.model['evz'], 'g-', label='Z', linewidth=1.5)
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self.canvas.draw()
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class LineViewXc(plots.LineViewPlot):
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"""A line view of centered raw data
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"""
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