Added explained variance

fluents/lib/blmfuncs.py

@@ -328,11 +328,11 @@ class PLS(Model):
                         **options.make_model_options())
         # variance captured
         var_x, exp_var_x = variances(self.model['E0'], self.model['T'], self.model['P'])
-        self.model['var_x'] = var_x
+        self.model['evx'] = var_x
         self.model['exp_var_x'] = exp_var_x
 
         var_y, exp_var_y = variances(self.model['F0'], self.model['T'], self.model['Q'])
-        self.model['var_y'] = var_y
+        self.model['evy'] = var_y
         self.model['exp_var_y'] = exp_var_y
                     
         if options['calc_conf']:
@@ -478,6 +478,14 @@ class LPLS(Model):
         self._data['Z'] = c.asarray()
         self.validation(options)
         self.make_model(options)
+        print self.model['evx']
+        evx_str = [str(i)[:3] for i in self.model['evx']]
+        logger.log('notice', 'Explained variance:X\n\t: ' + str(evx_str))
+        evy_str = [str(i)[:3] for i in self.model['evy']]
+        logger.log('notice', 'Explained variance:Y\n\t: ' + str(evy_str))
+        evz_str = [str(i)[:3] for i in self.model['evz']]
+        logger.log('notice', 'Explained variance:Z\n\t: ' + str(evz_str))
+        
         if options['calc_conf']:
             self.confidence(options)
 
@@ -592,7 +600,7 @@ class PlsOptions(Options):
         opt['center_mth'] = mat_center
         opt['scale'] = 'scores'
         
-        opt['calc_conf'] = False
+        opt['calc_conf'] = True
         opt['n_sets'] = 7
         opt['strict'] = True
         opt['p_center'] = 'med'
@@ -607,7 +615,7 @@ class PlsOptions(Options):
         opt['all_data'] = [('T', 'scores', True),
                            ('P', 'loadings', True),
                            ('E','residuals', False),
-                           ('p_tsq', 't2', False),
+                           ('w_tsq', 't2', True),
                            ('rmsep', 'RMSEP', False)
                            ]
 
@@ -620,10 +628,10 @@ class PlsOptions(Options):
                             (blmplots.PlsCorrelationLoadingPlot, 'Corr. loadings', False)
                             ]
         
-        opt['out_data'] = ['T','P', 'p_tsq']
+        opt['out_data'] = ['T','P', 'w_tsq']
         opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
         
-        opt['out_data'] = None
+        #opt['out_data'] = None
                             
         opt['pack'] = True
         opt['calc_qvals'] = False
@@ -664,17 +672,17 @@ class LplsOptions(Options):
         opt['engine'] = nipals_lpls
         opt['mode'] = 'normal' # how much info to calculate
         opt['amax'] = 10
-        opt['aopt'] = 4
+        opt['aopt'] = 3
-        opt['xz_alpha'] = 0.5
+        opt['xz_alpha'] = 0.4
         opt['auto_aopt'] = False
         opt['center'] = True
         opt['center_mth'] = [2, 2, 1]
         opt['scale'] = 'scores'
-        opt['calc_conf'] = False
+        opt['calc_conf'] = True
         opt['n_sets'] = 75
         opt['strict'] = False
         opt['p_center'] = 'med'
-        opt['alpha'] = .2
+        opt['alpha'] = .3
         opt['cov_center'] = 'med'
         opt['crot'] = True
 
@@ -701,13 +709,14 @@ class LplsOptions(Options):
                             (blmplots.LplsZCorrelationPlot, 'Z corr.', True)
                             ]
         
-        opt['out_data'] = ['T','P','L','K']
+        opt['out_data'] = ['T','P','L','K', 'tsqx', 'tsqz']
-        opt['out_plots'] = [blmplots.PlsScorePlot,
+        opt['out_plots'] = [blmplots.LplsScorePlot,
                             blmplots.LplsXLoadingPlot,
                             blmplots.LplsZLoadingPlot,
                             blmplots.LplsXCorrelationPlot,
                             blmplots.LplsZCorrelationPlot,
-                            blmplots.LineViewXc]
+                            blmplots.LineViewXc,
+                            blmplots.LplsExplainedVariancePlot]
         
         #opt['out_data'] = None
                             

fluents/lib/blmplots.py

@@ -43,6 +43,7 @@ class BlmScatterPlot(plots.ScatterPlot):
     """Scatter plot used for scores and loadings in bilinear models."""
 
     def __init__(self, title, model, absi=0, ordi=1, part_name='T', color_by=None):
+        self.model = model
         if model.model.has_key(part_name)!=True:
             raise ValueError("Model part: %s not found in model" %mod_param)
         self._T = model.model[part_name]
@@ -52,6 +53,7 @@ class BlmScatterPlot(plots.ScatterPlot):
         self._absi = absi
         self._ordi = ordi
         self._cmap = cm.summer
+        
         dataset_1 = model.as_dataset(part_name)
         id_dim = dataset_1.get_dim_name(0)
         sel_dim = dataset_1.get_dim_name(1)
@@ -64,7 +66,7 @@ class BlmScatterPlot(plots.ScatterPlot):
         self._mappable.set_cmap(self._cmap)
         self.sc = self._mappable
         self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
-
+        
     def set_facecolor(self, colors):
         """Set patch facecolors.
         """
@@ -77,6 +79,18 @@ class BlmScatterPlot(plots.ScatterPlot):
     def set_sizes(self, sizes):
         """Set patch sizes."""
         pass
+
+    def set_expvar_axlabels(self, param="evx"):
+        if not self.model.model.has_key(param):
+            self.model.model[param] = None
+        if self.model.model[param]==None:
+            pass #fixme: do expvar calc here if not present
+        else:
+            expvar = self.model.model[param]
+            xstr = "Comp: %s , %.1f " %(self._absi, expvar[self._absi+1])
+            ystr = "Comp: %s , %.1f " %(self._ordi, expvar[self._ordi+1])
+            self.axes.set_xlabel(xstr)
+            self.axes.set_ylabel(ystr)
     
     def add_pc_spin_buttons(self, amax, absi, ordi):    
         sb_a = gtk.SpinButton(climb_rate=1)
@@ -115,6 +129,7 @@ class BlmScatterPlot(plots.ScatterPlot):
         pad = abs(self.xaxis_data.min()-self.xaxis_data.max())*0.05
         new_lims = (self.xaxis_data.min()+pad, self.xaxis_data.max()+pad)
         self.axes.set_xlim(new_lims, emit=True)
+        self.set_expvar_axlabels()
         self.canvas.draw_idle()
         
     def set_ordinate(self, sb):
@@ -127,6 +142,7 @@ class BlmScatterPlot(plots.ScatterPlot):
         pad = abs(self.yaxis_data.min()-self.yaxis_data.max())*0.05
         new_lims = (self.yaxis_data.min()+pad, self.yaxis_data.max()+pad)
         self.axes.set_ylim(new_lims, emit=True)
+        self.set_expvar_axlabels()
         self.canvas.draw_idle()
     
     def show_labels(self, index=None):
@@ -173,22 +189,34 @@ class PlsLoadingPlot(BlmScatterPlot):
     def __init__(self, model, absi=0, ordi=1):
         title = "Pls loadings (%s)" %model._dataset['X'].get_name()
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='w_tsq')
+        #self.set_expvar_axlabels(self, param="expvarx")
 
-
+    
 class PlsCorrelationLoadingPlot(BlmScatterPlot):
     def __init__(self, model, absi=0, ordi=1):
         title = "Pls correlation loadings (%s)" %model._dataset['X'].get_name()
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='CP')
-        
+
+
+class LplsScorePlot(BlmScatterPlot):
+    def __init__(self, model, absi=0, ordi=1):
+        title = "L-pls scores (%s)" %model._dataset['X'].get_name()
+        BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
+        self.set_expvar_axlabels("evx")
+
+
 class LplsXLoadingPlot(BlmScatterPlot):
     def __init__(self, model, absi=0, ordi=1):
         title = "Lpls x-loadings (%s)" %model._dataset['X'].get_name()
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='tsqx')
+        self.set_expvar_axlabels("evx")
+
 
 class LplsZLoadingPlot(BlmScatterPlot, plots.PlotThresholder):
     def __init__(self, model, absi=0, ordi=1):
         title = "Lpls z-loadings (%s)" %model._dataset['Z'].get_name()
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='L', color_by='tsqz')
+        self.set_expvar_axlabels(param="evz")
         plots.PlotThresholder.__init__(self, "IC")
         
 
@@ -204,6 +232,7 @@ class LplsXCorrelationPlot(BlmScatterPlot):
             R = correlation_loadings(model._data['X'], model.model['T'])
             model.model['Rx'] = R
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rx')
+        self.set_expvar_axlabels("evx")
         radius = 1
         center = (0,0)
         c100 = patches.Circle(center,radius=radius,
@@ -229,6 +258,7 @@ class LplsZCorrelationPlot(BlmScatterPlot):
             R = correlation_loadings(model._data['Z'].T, model.model['W'])
             model.model['Rz'] = R
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rz')
+        self.set_expvar_axlabels("evz")
         radius = 1
         center = (0,0)
         c100 = patches.Circle(center,radius=radius,
@@ -254,6 +284,16 @@ class LplsHypoidCorrelationPlot(BlmScatterPlot):
         BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='W')
 
 
+class LplsExplainedVariancePlot(plots.Plot):
+    def __init__(self, model):
+        self.model = model
+        plots.Plot.__init__(self, "Explained variance")
+        xax = scipy.arange(model.model['evx'].shape[0])
+        self.axes.plot(xax, model.model['evx'], 'b-', label='X', linewidth=1.5)
+        self.axes.plot(xax, model.model['evy'], 'k-', label='Y', linewidth=1.5)
+        self.axes.plot(xax, model.model['evz'], 'g-', label='Z', linewidth=1.5)
+        self.canvas.draw()
+    
 class LineViewXc(plots.LineViewPlot):
     """A line view of centered raw data
     """