def run(self,X_exp,y_exp):
'''
given input data X_exp labels y_exp \in {-1,0,1} (0=unlabeled),
define input/output state files and serialize input data
'''
if not pathexists(self.exp_dir):
os.makedirs(self.exp_dir)
input_statefile = pathjoin(self.exp_dir,self.exp_name+'_input.pkl')
output_statefile = pathjoin(self.exp_dir,self.exp_name+'_output.pkl')
jldump({'X_exp':X_exp,'y_exp':y_exp,'exp_name':self.exp_name},
input_statefile,compress=jlcompress,cache_size=jlcache)
input_state, output_state = self._collect_state(input_statefile,
output_statefile)
return input_state, output_state
def _collect_state(self,input_statefile,output_statefile,
output_exists='overwrite'):
'''
run _cv for an experiment (input_statefile) and serialize the output
(output_statefile), scales features using scaling_method, and excludes
unlabeled (y=0) samples
if output_exists=='overwrite': overwrite existing output dump
output_exists=='noupdate': read existing output dump and return without updating
'''
input_state = jlload(input_statefile)
output_state = {}
if output_exists != 'overwrite' and pathexists(output_statefile):
print('Loading existing state from', output_statefile)
output_state = jlload(output_statefile)
if output_exists == 'noupdate':
return input_state, output_state
X = input_state['X_exp'].copy()
y = input_state['y_exp'].copy()
multi_output = y.size != max(y.shape)
if multi_output:
# error out if incompatible models in model_eval
for model in self.model_eval:
if model in model_nomulti:
print('Error: model %s not compatible with multi-output labels'%model)
return input_state,output_state
if self.scaling_method=='Normalize':
scale_fn = Normalizer(norm='l2').fit_transform
elif self.scaling_method=='MinMax':
scale_fn = MinMaxScaler().fit_transform
elif self.scaling_method=='Standard':
scale_fn = StandardScaler().fit_transform
elif self.scaling_method==None:
scale_fn = lambda X: X
else:
print('Error: unknown scaling method "%s"'%self.scaling_method)
return input_state,output_state
print('Scaling features using method "%s"'%self.scaling_method)
X = scale_fn(X)
# remove unlabeled samples
if multi_output:
labmask = (y!=0).any(axis=1)
y = y[labmask,:]
else:
labmask = y!=0
y = y[labmask].ravel()
X = X[labmask,:]
# make sure X,y are valid after scaling/masking operations
check_X_y(X,y,multi_output=multi_output)
# get number of *labeled* samples
N = len(y)
if self.cv_id == 'loo':
cv = LeaveOneOut(N)
elif self.pred_mode == 'clf':
cv = StratifiedKFold(y,n_folds=self.train_folds,random_state=train_state)
elif self.pred_mode == 'reg':
cv = KFold(n=N,n_folds=self.train_folds,random_state=train_state,shuffle=True)
output_state = self._run_cv(X,y,cv,output_state)
output_state.update({'cv':cv,'cv_id':self.cv_id,'labmask':labmask,
'scaling_method':self.scaling_method,
'model_eval':self.model_eval,'model_classes':2,
'model_features':X.shape[1]})
jldump(output_state,output_statefile,compress=jlcompress,
cache_size=jlcache)
return input_state, output_state
