955 lines
34 KiB
Python
955 lines
34 KiB
Python
"""This module contains bilinear models(Functions)
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"""
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import os
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import pygtk
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import gtk
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import gtk.glade
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import fluents
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from fluents.workflow import Function, OptionsDialog, Options
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from fluents.dataset import Dataset
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from fluents import plots, dataset, workflow, logger
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import scipy
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from engines import *
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from cx_stats import leverage, variances, hotelling
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from cx_utils import mat_center
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from validation import *
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import blmplots
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import engines
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import copy
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class Model(Function):
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"""Base class of bilinear models.
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"""
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def __init__(self,id='johndoe',name='JohnDoe'):
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Function.__init__(self,id,name)
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self.name = name
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self._options = None
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self._data = {}
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self._dataset = {}
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self._packers = {}
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self.model = {}
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def clear(self):
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""" Clears model paramters
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"""
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self.model = {}
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self._data = {}
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self._packers = {}
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class PCA(Model):
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def __init__(self,id='pca',name='PCA'):
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Model.__init__(self,id,name)
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self._options = PcaOptions()
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def validation(self, amax, cv_val_sets, pert_val_sets, cv_val_method, pert_val_method):
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"""Model validation and estimate of optimal numer of components.
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"""
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if self._options['calc_cv']:
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if cv_val_method == 'random':
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sep, aopt = pca_cv_val(self.model['E0'], amax, cv_val_sets)
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self.model['sep'] = sep
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if self._options['calc_pert']:
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if pert_val_method == 'random_diag':
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sep, aopt = pca_alter_val(self.model['E0'], amax, pert_val_sets)
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self.model['sep'] = sep
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if self._options['calc_cv']==False and self._options['calc_pert']==False:
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self.model['sep'] = None
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aopt = self._options['amax']
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if self._options['auto_aopt']:
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logger.log("notice", "Auto aopt: " + str(aopt))
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self._options['aopt'] = aopt
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if aopt==1:
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logger.log('notice', 'Aopt at first component!')
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def confidence(self, aopt, n_sets, alpha, p_center,
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crot, strict, cov_center ):
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"""Returns a confidence measure for model parameters.
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Based on aopt.
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"""
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if aopt<2:
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aopt = 2
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logger.log('notice','Hotellings T2 needs more than 1 comp.\n switching to 2!!')
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jk_segments = pca_jkP(self.model['E0'], aopt, n_sets)
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Pcal = self.model['P'][:,:aopt]
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# add the scale to P
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tnorm = scipy.apply_along_axis(norm, 0, self.model['T'][:,:aopt])
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Pcal = Pcal*tnorm
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tsq = hotelling(jk_segments, Pcal, p_center,
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cov_center, alpha, crot, strict)
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self.model['p_tsq'] = tsq
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def make_model(self, amax, mode, scale):
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"""Model on optimal number of components.
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"""
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dat = pca(self.model['E0'], amax, scale, mode)
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# explained variance
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var_x, exp_var_x = variances(self.model['E0'], dat['T'], dat['P'])
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dat['var_x'] = var_x
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dat['exp_var_x'] = exp_var_x
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#fixme###
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do_lev_s = False
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do_lev_v = False
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#####
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if do_lev_s:
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# sample leverages
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tnorm = scipy.apply_along_axis(norm, 0, dat['T']) # norm of Ts
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s_lev = leverage(amax, tnorm)
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dat['s_lev'] = s_lev
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if do_lev_v:
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# variable leverages
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v_lev = leverage(amax, dat['P'])
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dat['v_lev'] = v_lev
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self.model.update(dat)
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def as_dataset(self, param, dtype='dataset'):
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"""Return model parameter as Dataset.
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"""
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if not param in self.model.keys():
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return
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DX = self._dataset['X'] #input dataset
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dim_name_0, dim_name_1 = DX.get_dim_name()
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# samples
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ids_0 = [dim_name_0, DX.get_identifiers(dim_name_0, sorted=True)]
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# vars
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ids_1 = [dim_name_1, DX.get_identifiers(dim_name_1, sorted=True)]
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# components (hidden)
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pc_ids = ['_amax', map(str,range(self._options['amax'])) ]
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pc_ids_opt = ['_aopt', map(str, range(self._options['aopt'])) ]
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zero_dim = ['_doe', ['0']] # null dim, vector (hidden)
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match_ids = {'E':[ids_0, ids_1],
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'E0':[ids_0, ids_1],
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'P':[ids_1, pc_ids],
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'T':[ids_0, pc_ids],
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'W':[ids_1, pc_ids],
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'p_tsq':[ids_1, zero_dim],
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'rmsep':[pc_ids, zero_dim],
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'var_leverages':[ids_1, zero_dim],
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'sample_leverages':[pc_ids, zero_dim],
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'exp_var_x': [pc_ids, zero_dim],
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'var_x': [pc_ids, zero_dim],
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}
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out = Dataset(self.model[param], match_ids[param], name=param)
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return out
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def get_out_plots(self, options):
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out=[]
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for plt in options['out_plots']:
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#try:
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out.append(plt(self))
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#except:
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# logger.log('debug', 'Plot: %s failed') %str(plt)
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return out
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def run_o(self, data):
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"""Run pca with present options.
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"""
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self.clear()
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options = self._options
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for item in options.items():
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print item
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self._dataset['X'] = data
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self._data['X'] = data.asarray().astype('<f8')
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if options['center']:
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center = options['center_mth']
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self.model['E0'] = center(self._data['X'])
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else:
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self.model['E0'] = data.asarray()
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self.validation(**options.validation_options())
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self.model['aopt'] = self._options['aopt']
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self.make_model(**options.make_model_options())
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if options['calc_conf']:
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self.confidence(**options.confidence_options())
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out = [self.as_dataset(p) for p in options['out_data']]
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for plt in self.get_out_plots(options):
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out.append(plt)
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return out
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def run(self, data):
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"""Run Pca with option gui.
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"""
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dialog = PcaOptionsDialog([data], self._options)
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dialog.show_all()
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response = dialog.run()
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dialog.hide()
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if response == gtk.RESPONSE_OK:
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# set output data and plots
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dialog.set_output()
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#run with current data and options
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return self.run_o(data)
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class PLS(Model):
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def __init__(self, id='pls', name='PLS'):
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Model.__init__(self, id, name)
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self._options = PlsOptions()
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def validation(self, amax, n_sets, cv_val_method):
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"""Returns rmsec,rmsep for model.
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"""
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m, n = self.model['E0'].shape
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if m>n:
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val_engine = w_pls_cv_val
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else:
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val_engine = pls_val
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if self._options['calc_cv']==True:
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rmsep, aopt = val_engine(self.model['E0'], self.model['F0'],
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amax, n_sets)
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self.model['rmsep'] = rmsep[:,:-1]
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self.model['aopt'] = aopt
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else:
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self.model['rmsep'] = None
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self.model['aopt'] = self._options['aopt']
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def confidence(self, aopt, n_sets, alpha, p_center,
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crot, strict, cov_center ):
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"""Returns a confidence measure for model parameters
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Supported parameters: W
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"""
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aopt = self.model['aopt']
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if self._options['calc_conf']:
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jk_segments = pls_jkW(self.model['E0'], self.model['F0'],
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aopt, n_sets)
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Wcal = self.model['W'][:,:aopt]
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# ensure that Wcal is scaled
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tnorm = scipy.apply_along_axis(norm, 0, self.model['T'][:,:aopt])
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Wcal = Wcal*tnorm
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tsq = hotelling(jk_segments, Wcal, p_center,
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alpha, crot, strict, cov_center)
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self.model['w_tsq'] = tsq
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else:
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self.model['w_tsq'] = None
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def permutation_confidence(self, a, b, aopt, reg, n_iter, algo,
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sim_method):
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"""Estimates cut off on significant vars by controlling fdr."""
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if self._options['calc_qvals']==True:
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qvals_sorted, qvals = pls_qvals(a, b,
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aopt=None,
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alpha=reg,
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n_iter=n_iter,
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algo='pls',
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sim_method=sim_method)
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self.model['qval'] = qvals
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self.model['qval_sorted'] = qvals_sorted
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else:
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self.model['qval'] = None
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self.model['qval_sorted'] = None
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def make_model(self, a, b, amax, scale, mode, engine):
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"""Make model on amax components.
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"""
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dat = engine(a, b, amax, scale, mode)
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self.model.update(dat)
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def as_dataset(self, name, dtype='Dataset'):
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"""Return any model parameter as Dataset
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No ids matching
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"""
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if name not in self.model.keys():
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return
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DX, DY = self._dataset['X'], self._dataset['Y']
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dim_name_0, dim_name_1 = DX.get_dim_name()
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dim_name_2, dim_name_3 = DY.get_dim_name()
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#samples
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ids_0 = [dim_name_0, DX.get_identifiers(dim_name_0, sorted=True)]
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# x vars
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ids_1 = [dim_name_1, DX.get_identifiers(dim_name_1, sorted=True)]
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# y vars
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ids_3 = [dim_name_3, DY.get_identifiers(dim_name_3, sorted=True)]
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# components (hidden)
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pc_ids = ['_comp', map(str, range(self._options['amax']))]
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pc_ids_opt = ['_comp', map(str, range(self.model['aopt']))]
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zero_dim = ['_doe',['0']] # null dim, vector (hidden)
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match_ids = {'E':[ids_0, ids_1],
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'P':[ids_1, pc_ids],
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'T':[ids_0, pc_ids],
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'W': [ids_1, pc_ids],
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'R': [ids_1, pc_ids],
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'Q':[ids_3, pc_ids],
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'F':[ids_0, ids_3],
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'B':[ids_1, ids_3],
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'qval':[ids_1, zero_dim],
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'qval_sorted':[ids_1, zero_dim],
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'w_tsq':[ids_1, zero_dim],
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'rmsep':[ids_3, pc_ids],
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}
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array = self.model[name]
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M = Dataset(array, identifiers=match_ids[name], name=name)
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return M
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def get_out_plots(self, options):
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out=[]
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for plt in options['out_plots']:
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#try:
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out.append(plt(self))
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#except:
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# logger.log('debug', 'Plot: %s failed' %plt)
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return out
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def run_o(self, a, b):
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"""Run PLS with present options."""
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options = self._options
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self._dataset['X'] = a
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self._dataset['Y'] = b
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self._data['X'] = a.asarray()
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self._data['Y'] = b.asarray()
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if options['center']:
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self.model['E0'] = options['center_mth'](self._data['X'])
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self.model['F0'] = options['center_mth'](self._data['Y'])
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else:
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self.model['E0'] = self._data['X']
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self.model['F0'] = self._data['Y']
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self.validation(**options.validation_options())
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self.make_model(self.model['E0'], self.model['F0'],
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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['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['exp_var_y'] = exp_var_y
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if options['calc_conf']:
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self.confidence(**options.confidence_options())
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out = [self.as_dataset(p) for p in options['out_data']]
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for plt in self.get_out_plots(options):
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out.append(plt)
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return out
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def run(self, a, b):
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"""Run PLS with option gui.
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"""
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dialog = PlsOptionsDialog([a, b], self._options)
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dialog.show_all()
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response = dialog.run()
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dialog.hide()
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if response == gtk.RESPONSE_OK:
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# set output data and plots
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dialog.set_output()
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#run with current data and options
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for key, val in self._options.items():
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print (key, val)
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return self.run_o(a, b)
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class Packer:
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"""A compression object used to speed up model calculations.
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Often used in conjunction with crossvalidation and perturbations
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analysis.
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"""
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def __init__(self,array):
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self._shape = array.shape
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self._array = array
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self._packed_data = None
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def expand(self,a):
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if self._inflater!=None:
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return dot(self._inflater,a)
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def collapse(self,axis=None,mode='svd'):
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if not axis:
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axis = argmin(self._array.shape) # default is the smallest dim
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if axis == 1:
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self._array = self._array.T
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u, s, vt = svd(self._array,full_matrices=0)
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self._inflater = vt.T
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self._packed_data = u*s
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return self._packed_data
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def get_packed_data(self):
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return self._packed_data
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class PcaOptions(Options):
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"""Options for Principal Component Analysis.
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"""
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def __init__(self):
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Options.__init__(self)
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self._set_default()
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def _set_default(self):
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opt = {}
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opt['algo'] = 'pca'
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opt['engine'] = engines.pca
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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'] = 100
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opt['auto_aopt'] = False
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opt['center'] = True
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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['n_sets'] = 5
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opt['strict'] = True
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opt['p_center'] = 'med'
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opt['alpha'] = .8
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opt['cov_center'] = 'med'
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opt['crot'] = True
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opt['calc_cv'] = False
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opt['calc_pert'] = True
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opt['pert_val_method'] = 'random_diag'
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opt['cv_val_method'] = 'random'
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opt['cv_val_sets'] = 10
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opt['pert_val_sets'] = 10
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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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('rmsep', 'RMSEP', False)
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]
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# (class, name, sensitive, ticked)
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opt['all_plots'] = [(blmplots.PcaScorePlot, 'Scores', True),
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(blmplots.PcaLoadingPlot, 'Loadings', True),
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(blmplots.LineViewXc, 'Line view', True),
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(blmplots.PredictionErrorPlot, 'Residual Error', False)
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]
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opt['out_data'] = ['T','P', 'p_tsq']
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opt['out_plots'] = [blmplots.PcaScorePlot,blmplots.PcaLoadingPlot,blmplots.LineViewXc]
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self.update(opt)
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def make_model_options(self):
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"""Options for make_model method."""
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opt_list = ['scale', 'mode', 'amax']
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return self._copy_from_list(opt_list)
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def confidence_options(self):
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"""Options for confidence method."""
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opt_list = ['n_sets', 'aopt', 'alpha', 'p_center',
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'strict', 'crot', 'cov_center']
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return self._copy_from_list(opt_list)
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def validation_options(self):
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"""Options for pre_validation method."""
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opt_list = ['amax', 'cv_val_sets', 'pert_val_sets',
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'cv_val_method', 'pert_val_method']
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return self._copy_from_list(opt_list)
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class PlsOptions(Options):
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"""Options for Partial Least Squares Regression.
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"""
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def __init__(self):
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Options.__init__(self)
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self._set_default()
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def _set_default(self):
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opt = {}
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opt['algo'] = 'pls'
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opt['engine'] = engines.pls
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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'] = 10
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opt['auto_aopt'] = False
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opt['center'] = True
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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['n_sets'] = 5
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opt['strict'] = True
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opt['p_center'] = 'med'
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opt['alpha'] = .8
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opt['cov_center'] = 'med'
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opt['crot'] = True
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opt['calc_cv'] = False
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opt['cv_val_method'] = 'random'
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opt['cv_val_sets'] = opt['n_sets']
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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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('rmsep', 'RMSEP', False)
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]
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# (class, name, sensitive, ticked)
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opt['all_plots'] = [(blmplots.PlsScorePlot, 'Scores', True),
|
|
(blmplots.PlsLoadingPlot, 'Loadings', True),
|
|
(blmplots.LineViewXc, 'Line view', True),
|
|
(blmplots.PredictionErrorPlot, 'Residual Error', False),
|
|
(blmplots.RMSEPPlot, 'RMSEP', False)
|
|
]
|
|
|
|
opt['out_data'] = ['T','P', 'p_tsq']
|
|
opt['out_plots'] = [blmplots.PlsScorePlot,blmplots.PlsLoadingPlot,blmplots.LineViewXc]
|
|
|
|
opt['out_data'] = None
|
|
|
|
opt['pack'] = False
|
|
opt['calc_qvals'] = False
|
|
opt['q_pert_method'] = 'shuffle_rows'
|
|
opt['q_iter'] = 20
|
|
|
|
self.update(opt)
|
|
|
|
def make_model_options(self):
|
|
"""Options for make_model method."""
|
|
opt_list = ['scale','mode', 'amax', 'engine']
|
|
return self._copy_from_list(opt_list)
|
|
|
|
def confidence_options(self):
|
|
"""Options for confidence method."""
|
|
opt_list = ['n_sets', 'aopt', 'alpha', 'p_center',
|
|
'strict', 'crot', 'cov_center']
|
|
return self._copy_from_list(opt_list)
|
|
|
|
def validation_options(self):
|
|
"""Options for pre_validation method."""
|
|
opt_list = ['amax', 'n_sets', 'cv_val_method']
|
|
return self._copy_from_list(opt_list)
|
|
|
|
def permutation_confidence(self):
|
|
opt_list = ['q_pert_method', 'q_iter']
|
|
return self._copy_from_list(opt_list)
|
|
|
|
class PcaOptionsDialog(OptionsDialog):
|
|
"""Options dialog for Principal Component Analysis.
|
|
"""
|
|
def __init__(self, data, options, input_names=['X']):
|
|
OptionsDialog.__init__(self, data, options, input_names)
|
|
#glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
|
|
glade_file = os.path.join("/home/flatberg/fluents/fluents/", 'pca_options.glade')
|
|
|
|
notebook_name = "vbox1"
|
|
page_name = "Options"
|
|
self.add_page_from_glade(glade_file, notebook_name, page_name)
|
|
# connect signals to handlers
|
|
dic = {"on_amax_value_changed" : self.on_amax_changed,
|
|
"on_aopt_value_changed" : self.on_aopt_changed,
|
|
"auto_aopt_toggled" : self.auto_aopt_toggled,
|
|
"center_toggled" : self.center_toggled,
|
|
#"on_scale_changed" : self.on_scale_changed,
|
|
"on_val_none" : self.val_toggled,
|
|
"on_val_cv" : self.cv_toggled,
|
|
"on_val_pert" : self.pert_toggled,
|
|
"on_cv_method_changed" : self.on_cv_method_changed,
|
|
"on_cv_sets_changed" : self.on_cv_sets_changed,
|
|
"on_pert_sets_changed" : self.on_pert_sets_changed,
|
|
"on_conf_toggled" : self.on_conf_toggled,
|
|
"on_subset_loc_changed" : self.on_subset_loc_changed,
|
|
"on_cov_loc_changed" : self.on_cov_loc_changed,
|
|
"on_alpha_changed" : self.on_alpha_changed,
|
|
"on_rot_changed" : self.on_rot_changed
|
|
}
|
|
|
|
self.wTree.signal_autoconnect(dic)
|
|
|
|
# set/ensure valid default values/ranges
|
|
#
|
|
amax_sb = self.wTree.get_widget("amax_spinbutton")
|
|
max_comp = min(data[0].shape) # max num of components
|
|
if self._options['amax']>max_comp:
|
|
logger.log('debug', 'amax default too large ... adjusting')
|
|
self._options['amax'] = max_comp
|
|
amax_sb.get_adjustment().set_all(self._options['amax'], 1, max_comp, 1, 0, 0)
|
|
# aopt spin button
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
if self._options['aopt']>self._options['amax']:
|
|
self._options['aopt'] = self._options['amax'] + 1 - 1
|
|
aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, self._options['amax'], 1, 0, 0)
|
|
|
|
# scale
|
|
# scale_cb = self.wTree.get_widget("scale_combobox")
|
|
# scale_cb.set_active(0)
|
|
|
|
# validation frames
|
|
if self._options['calc_cv']==False:
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
cv_frame.set_sensitive(False)
|
|
if self._options['calc_pert']==False:
|
|
pert_frame = self.wTree.get_widget("pert_frame")
|
|
pert_frame.set_sensitive(False)
|
|
|
|
cv = self.wTree.get_widget("cv_method").set_active(0)
|
|
pm = self.wTree.get_widget("pert_method").set_active(0)
|
|
|
|
# confidence
|
|
if self._options['calc_conf']==True:
|
|
self.wTree.get_widget("subset_expander").set_sensitive(True)
|
|
else:
|
|
self.wTree.get_widget("subset_expander").set_sensitive(False)
|
|
|
|
cb = self.wTree.get_widget("subset_loc")
|
|
_m = {'med': 0, 'mean': 1, 'full_model': 2}
|
|
cb.set_active(_m.get(self._options['p_center']))
|
|
|
|
cb = self.wTree.get_widget("cov_loc")
|
|
_m = {'med': 0, 'mean': 1}
|
|
cb.set_active(_m.get(self._options['cov_center']))
|
|
|
|
hs = self.wTree.get_widget("alpha_scale")
|
|
hs.set_value(self._options['alpha'])
|
|
|
|
|
|
def on_amax_changed(self, sb):
|
|
logger.log("debug", "amax changed: new value: %s" %sb.get_value_as_int())
|
|
amax = sb.get_value_as_int()
|
|
# update aopt if needed
|
|
if amax<self._options['aopt']:
|
|
self._options['aopt'] = amax
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, amax, 1, 0, 0)
|
|
self._options['amax'] = sb.get_value_as_int()
|
|
|
|
def on_aopt_changed(self, sb):
|
|
aopt = sb.get_value_as_int()
|
|
self._options['aopt'] = aopt
|
|
|
|
def auto_aopt_toggled(self, tb):
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
if tb.get_active():
|
|
self._options['auto_aopt'] = True
|
|
aopt_sb.set_sensitive(False)
|
|
else:
|
|
self._options['auto_aopt'] = False
|
|
aopt_sb.set_sensitive(True)
|
|
|
|
def center_toggled(self, tb):
|
|
if tb.get_active():
|
|
self._options['center'] = True
|
|
else:
|
|
logger.log("debug", "centering set to False")
|
|
self._options['center'] = False
|
|
|
|
#def on_scale_changed(self, cb):
|
|
# scale = cb.get_active_text()
|
|
# if scale=='Scores':
|
|
# self._options['scale'] = 'scores'
|
|
# elif scale=='Loadings':
|
|
# self._options['scale'] = 'loads'
|
|
# else:
|
|
# raise IOError
|
|
|
|
def val_toggled(self, tb):
|
|
"""Callback for validation: None. """
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
pert_frame = self.wTree.get_widget("pert_frame")
|
|
cv_tb = self.wTree.get_widget("cv_toggle")
|
|
p_tb = self.wTree.get_widget("pert_toggle")
|
|
if tb.get_active():
|
|
self._options['calc_cv'] = False
|
|
self._options['calc_pert'] = False
|
|
cv_frame.set_sensitive(False)
|
|
pert_frame.set_sensitive(False)
|
|
cv_tb.set_sensitive(False)
|
|
p_tb.set_sensitive(False)
|
|
else:
|
|
p_tb.set_sensitive(True)
|
|
cv_tb.set_sensitive(True)
|
|
if p_tb.get_active():
|
|
pert_frame.set_sensitive(True)
|
|
self._options['calc_pert'] = True
|
|
if cv_tb.get_active():
|
|
cv_frame.set_sensitive(True)
|
|
self._options['calc_cv'] = True
|
|
|
|
def cv_toggled(self, tb):
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
if tb.get_active():
|
|
cv_frame.set_sensitive(True)
|
|
self._options['calc_cv'] = True
|
|
else:
|
|
cv_frame.set_sensitive(False)
|
|
self._options['calc_cv'] = False
|
|
|
|
def pert_toggled(self, tb):
|
|
pert_frame = self.wTree.get_widget("pert_frame")
|
|
if tb.get_active():
|
|
pert_frame.set_sensitive(True)
|
|
self._options['calc_pert'] = True
|
|
else:
|
|
pert_frame.set_sensitive(False)
|
|
self._options['calc_pert'] = False
|
|
|
|
|
|
def on_cv_method_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method == 'Random':
|
|
self._options['cv_val_method'] = 'random'
|
|
|
|
def on_pert_method_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method == 'Random diags':
|
|
self._options['pert_val_method'] = 'random_diag'
|
|
|
|
def on_cv_sets_changed(self, sb):
|
|
val = sb.get_value_as_int()
|
|
self._options['cv_val_sets'] = val
|
|
|
|
def on_pert_sets_changed(self, sb):
|
|
val = sb.get_value_as_int()
|
|
self._options['pert_val_sets'] = val
|
|
|
|
def on_conf_toggled(self, tb):
|
|
if tb.get_active():
|
|
self._options['calc_conf'] = False
|
|
self.wTree.get_widget("subset_expander").set_sensitive(False)
|
|
else:
|
|
self._options['calc_conf'] = True
|
|
self.wTree.get_widget("subset_expander").set_sensitive(True)
|
|
|
|
def on_subset_loc_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method=='Full model':
|
|
self._options['p_center'] = 'full_model'
|
|
elif method=='Median':
|
|
self._options['p_center'] = 'med'
|
|
elif method=='Mean':
|
|
self._options['p_center'] = 'mean'
|
|
|
|
def on_cov_loc_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method=='Median':
|
|
self._options['cov_center'] = 'med'
|
|
elif method=='Mean':
|
|
self._options['cov_center'] = 'mean'
|
|
|
|
def on_alpha_changed(self, hs):
|
|
self._options['alpha'] = hs.get_value()
|
|
|
|
def on_rot_changed(self, rg):
|
|
proc, strict = rg
|
|
if proc.get_active():
|
|
self._options['crot'] = True
|
|
else:
|
|
self._options['crot'] = True
|
|
self._options['strict'] = True
|
|
|
|
|
|
class PlsOptionsDialog(OptionsDialog):
|
|
"""Options dialog for Partial Least squares regression.
|
|
"""
|
|
def __init__(self, data, options, input_names=['X', 'Y']):
|
|
OptionsDialog.__init__(self, data, options, input_names)
|
|
#glade_file = os.path.join(fluents.DATADIR, 'pca_options.glade')
|
|
glade_file = os.path.join("/home/flatberg/fluents/fluents/", 'pls_options.glade')
|
|
|
|
notebook_name = "vbox1"
|
|
page_name = "Options"
|
|
self.add_page_from_glade(glade_file, notebook_name, page_name)
|
|
# connect signals to handlers
|
|
dic = {"on_amax_value_changed" : self.on_amax_changed,
|
|
"on_aopt_value_changed" : self.on_aopt_changed,
|
|
"auto_aopt_toggled" : self.auto_aopt_toggled,
|
|
"center_toggled" : self.center_toggled,
|
|
#"on_scale_changed" : self.on_scale_changed,
|
|
"on_val_none" : self.val_toggled,
|
|
"on_val_cv" : self.cv_toggled,
|
|
"on_cv_method_changed" : self.on_cv_method_changed,
|
|
"on_cv_sets_changed" : self.on_cv_sets_changed,
|
|
"on_conf_toggled" : self.conf_toggled,
|
|
"on_subset_loc_changed" : self.on_subset_loc_changed,
|
|
"on_cov_loc_changed" : self.on_cov_loc_changed,
|
|
"on_alpha_changed" : self.on_alpha_changed,
|
|
"on_rot_changed" : self.on_rot_changed,
|
|
"on__toggled" : self.conf_toggled,
|
|
"on_qval_changed" : self.on_qval_changed,
|
|
"on_iter_changed" : self.on_iter_changed
|
|
}
|
|
|
|
self.wTree.signal_autoconnect(dic)
|
|
|
|
# set/ensure valid default values/ranges
|
|
#
|
|
amax_sb = self.wTree.get_widget("amax_spinbutton")
|
|
max_comp = min(data[0].shape) # max num of components
|
|
if self._options['amax']>max_comp:
|
|
logger.log('debug', 'amax default too large ... adjusting')
|
|
self._options['amax'] = max_comp
|
|
amax_sb.get_adjustment().set_all(self._options['amax'], 1, max_comp, 1, 0, 0)
|
|
# aopt spin button
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
if self._options['aopt']>self._options['amax']:
|
|
self._options['aopt'] = self._options['amax'] + 1 - 1
|
|
aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, self._options['amax'], 1, 0, 0)
|
|
|
|
# scale
|
|
# scale_cb = self.wTree.get_widget("scale_combobox")
|
|
# scale_cb.set_active(0)
|
|
|
|
# validation frames
|
|
if self._options['calc_cv']==False:
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
cv_frame.set_sensitive(False)
|
|
|
|
cv = self.wTree.get_widget("cv_method").set_active(0)
|
|
|
|
# confidence
|
|
if self._options['calc_conf']==True:
|
|
self.wTree.get_widget("subset_expander").set_sensitive(True)
|
|
else:
|
|
self.wTree.get_widget("subset_expander").set_sensitive(False)
|
|
|
|
cb = self.wTree.get_widget("subset_loc")
|
|
_m = {'med': 0, 'mean': 1, 'full_model': 2}
|
|
cb.set_active(_m.get(self._options['p_center']))
|
|
|
|
cb = self.wTree.get_widget("cov_loc")
|
|
_m = {'med': 0, 'mean': 1}
|
|
cb.set_active(_m.get(self._options['cov_center']))
|
|
|
|
hs = self.wTree.get_widget("alpha_scale")
|
|
hs.set_value(self._options['alpha'])
|
|
|
|
tb = self.wTree.get_widget("qvals")
|
|
tb.set_sensitive(True)
|
|
|
|
|
|
def on_amax_changed(self, sb):
|
|
logger.log("debug", "amax changed: new value: %s" %sb.get_value_as_int())
|
|
amax = sb.get_value_as_int()
|
|
# update aopt if needed
|
|
if amax<self._options['aopt']:
|
|
self._options['aopt'] = amax
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
aopt_sb.get_adjustment().set_all(self._options['aopt'], 1, amax, 1, 0, 0)
|
|
self._options['amax'] = sb.get_value_as_int()
|
|
|
|
def on_aopt_changed(self, sb):
|
|
aopt = sb.get_value_as_int()
|
|
self._options['aopt'] = aopt
|
|
|
|
def auto_aopt_toggled(self, tb):
|
|
aopt_sb = self.wTree.get_widget("aopt_spinbutton")
|
|
if tb.get_active():
|
|
self._options['auto_aopt'] = True
|
|
aopt_sb.set_sensitive(False)
|
|
else:
|
|
self._options['auto_aopt'] = False
|
|
aopt_sb.set_sensitive(True)
|
|
|
|
def center_toggled(self, tb):
|
|
if tb.get_active():
|
|
self._options['center'] = True
|
|
else:
|
|
logger.log("debug", "centering set to False")
|
|
self._options['center'] = False
|
|
|
|
#def on_scale_changed(self, cb):
|
|
# scale = cb.get_active_text()
|
|
# if scale=='Scores':
|
|
# self._options['scale'] = 'scores'
|
|
# elif scale=='Loadings':
|
|
# self._options['scale'] = 'loads'
|
|
# else:
|
|
# raise IOError
|
|
|
|
def val_toggled(self, tb):
|
|
"""Callback for validation: None. """
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
cv_tb = self.wTree.get_widget("cv_toggle")
|
|
if tb.get_active():
|
|
self._options['calc_cv'] = False
|
|
cv_frame.set_sensitive(False)
|
|
cv_tb.set_sensitive(False)
|
|
else:
|
|
cv_tb.set_sensitive(True)
|
|
if cv_tb.get_active():
|
|
cv_frame.set_sensitive(True)
|
|
self._options['calc_cv'] = True
|
|
|
|
def cv_toggled(self, tb):
|
|
cv_frame = self.wTree.get_widget("cv_frame")
|
|
val_tb = self.wTree.get_widget("val_none_toggle")
|
|
if tb.get_active():
|
|
cv_frame.set_sensitive(True)
|
|
self._options['calc_cv'] = True
|
|
else:
|
|
cv_frame.set_sensitive(False)
|
|
self._options['calc_cv'] = False
|
|
|
|
def on_cv_method_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method == 'Random':
|
|
self._options['cv_val_method'] = 'random'
|
|
|
|
def on_cv_sets_changed(self, sb):
|
|
val = sb.get_value_as_int()
|
|
self._options['cv_val_sets'] = val
|
|
|
|
def conf_toggled(self, tb):
|
|
if tb.get_active():
|
|
self._options['calc_conf'] = False
|
|
self.wTree.get_widget("subset_expander").set_sensitive(False)
|
|
else:
|
|
self._options['calc_conf'] = True
|
|
self.wTree.get_widget("subset_expander").set_sensitive(True)
|
|
|
|
def on_subset_loc_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method=='Full model':
|
|
self._options['p_center'] = 'full_model'
|
|
elif method=='Median':
|
|
self._options['p_center'] = 'med'
|
|
elif method=='Mean':
|
|
self._options['p_center'] = 'mean'
|
|
|
|
def on_cov_loc_changed(self, cb):
|
|
method = cb.get_active_text()
|
|
if method=='Median':
|
|
self._options['cov_center'] = 'med'
|
|
elif method=='Mean':
|
|
self._options['cov_center'] = 'mean'
|
|
|
|
def on_alpha_changed(self, hs):
|
|
self._options['alpha'] = hs.get_value()
|
|
|
|
def on_rot_changed(self, rg):
|
|
proc, strict = rg
|
|
if proc.get_active():
|
|
self._options['crot'] = True
|
|
else:
|
|
self._options['crot'] = True
|
|
self._options['strict'] = True
|
|
|
|
def qval_toggled(self, tb):
|
|
if tb.get_active():
|
|
self._options['calc_qval'] = False
|
|
print "Setting not sens"
|
|
self.wTree.get_widget("qval_method").set_sensitive(False)
|
|
self.wTree.get_widget("q_iter").set_sensitive(False)
|
|
else:
|
|
self._options['calc_qval'] = True
|
|
self.wTree.get_widget("qval_method").set_sensitive(True)
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self.wTree.get_widget("q_iter").set_sensitive(True)
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def on_iter_changed(self, sb):
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self._options['q_iter'] = sb.get_value()
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def on_qval_changed(self, cb):
|
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q_method = cb.get_active_text()
|
|
if method=='Shuffle rows':
|
|
self._options['q_pert_method'] = 'shuffle'
|
|
|
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|