def append_suffix_to_files(dict, suffix, legend_suffix):
d = OrderedDict()
for key,value in dict.iteritems():
f = helper_functions.remove_suffix(key, '.pkl')
f += suffix + '.pkl'
d[f] = value + legend_suffix
return d
def _run_experiment_args(self, results_file, data_and_splits, method_results, i_labels, split):
num_labels = self.configs.num_labels[i_labels]
s = str(num_labels) + '-' + str(split)
curr_results = _load_temp_split_file(results_file, num_labels, split)
if curr_results:
return curr_results
if mpi_utility.is_master():
timer.tic()
#print 'num_labels-split: ' + s
temp_file_name = _temp_split_file_name(results_file, num_labels, split)
temp_dir_root = helper_functions.remove_suffix(temp_file_name, '.pkl')
temp_dir = temp_dir_root + '/CV-temp/'
curr_data = data_and_splits.get_split(split, num_labels)
learner = self.configs.learner
curr_learner = copy.deepcopy(learner)
curr_learner.split_idx_str = s
curr_learner.temp_dir = temp_dir
curr_results = curr_learner.train_and_test(curr_data)
if mpi_utility.is_group_master():
helper_functions.save_object(_temp_split_file_name(results_file,num_labels,split),curr_results)
helper_functions.delete_dir_if_exists(temp_dir_root)
instance_subset = learner.configs.instance_subset
results_features = learner.configs.results_features
test_error_to_print = 'is_train'
if mpi_utility.is_group_master():
if hasattr(curr_learner, 'best_params'):
print s + '-' + str(curr_learner.best_params) + ' Error: ' + \
str(curr_results.compute_error(self.configs.loss_function, results_features, test_error_to_print))
else:
print s + ' Done'
if mpi_utility.is_master():
timer.toc()
return curr_results
def _run_experiment_args(self, results_file, data_and_splits, method_results, i_labels, split):
num_labels = self.configs.num_labels[i_labels]
s = str(num_labels) + '-' + str(split)
curr_results = _load_temp_split_file(results_file, num_labels, split)
if curr_results:
return curr_results
#print 'num_labels-split: ' + s
temp_file_name = _temp_split_file_name(results_file, num_labels, split)
temp_dir_root = helper_functions.remove_suffix(temp_file_name, '.pkl')
temp_dir = temp_dir_root + '/CV-temp/'
curr_data = data_and_splits.get_split(split, num_labels)
learner = self.configs.learner
curr_learner = copy.deepcopy(learner)
curr_learner.split_idx_str = s
curr_learner.temp_dir = temp_dir
curr_results = curr_learner.train_and_test(curr_data)
if mpi_utility.is_group_master():
helper_functions.save_object(_temp_split_file_name(results_file,num_labels,split),curr_results)
helper_functions.delete_dir_if_exists(temp_dir_root)
if mpi_utility.is_group_master():
if hasattr(curr_learner, 'best_params'):
print s + '-' + str(curr_learner.best_params) + ' Error: ' + str(curr_results.compute_error(self.configs.loss_function))
else:
print s + ' Done'
return curr_results
def append_suffix_to_files(dict, suffix, legend_suffix):
d = OrderedDict()
for key, value in dict.iteritems():
f = helper_functions.remove_suffix(key, '.pkl')
f += suffix + '.pkl'
d[f] = value + legend_suffix
return d
def append_suffix_to_files(dict, suffix, legend_suffix, keys_to_skip=None):
d = OrderedDict()
for key, value in dict.iteritems():
if keys_to_skip is not None and key in keys_to_skip:
f = key
else:
f = helper_functions.remove_suffix(key, '.pkl')
f += suffix + '.pkl'
d[f] = value + legend_suffix
return d
def get_sized_results(file_name):
file_name_no_suffix = os.path.basename(helper_functions.remove_suffix(file_name, '.pkl'))
dir_name = os.path.dirname(file_name)
all_files = os.listdir(dir_name)
sized_file_name = file_name_no_suffix + '-num_labels='
files = []
results = []
for s in all_files:
if sized_file_name in s:
files.append(dir_name + '/' + s)
results.append(helper_functions.load_object(dir_name + '/' + s))
return results
def __init__(self, data_set=None, **kwargs):
super(VisualizationConfigs, self).__init__()
pc = ProjectConfigs(data_set, **kwargs)
self.copy_fields(pc, pc_fields_to_copy)
self.max_rows = 2
self.vis_table = vis_table
self.size_to_vis = size_to_vis
self.sizes_to_use = sizes_to_use
if pc.data_set in data_set_sizes_to_use:
self.sizes_to_use = data_set_sizes_to_use[pc.data_set]
for key, value in kwargs.iteritems():
setattr(self, key, value)
self.fontsize = fontsize
'''
self.files = [
'TargetTransfer+NW.pkl',
'HypothesisTransfer.pkl',
'LocalTransferDelta_C3=0_radius.pkl',
'LocalTransferDelta_C3=0.pkl',
'LocalTransferDelta_radius.pkl',
'LocalTransferDelta.pkl',
'LocalTransferDeltaSMS.pkl',
]
'''
self.files = {
'TargetTransfer+NW.pkl': 'Target Only',
'LocalTransferDelta_C3=0.pkl': 'Our Method, alpha=0',
'LocalTransferDelta.pkl': 'Our Method',
'LocalTransferDelta_C3=0_cons.pkl': None,
'LocalTransferDelta_cons.pkl': None,
'LocalTransferDelta_C3=0_radius.pkl':
'Our Method, ball graph, alpha=0',
'LocalTransferDelta_radius.pkl': 'Our Method, ball graph'
}
self.files = OrderedDict()
if plot_idx == PLOT_PARAMETRIC:
self.files = OrderedDict()
self.files['TargetTransfer+NW.pkl'] = 'Target Only'
#self.files['StackTransfer+SKL-RidgeReg.pkl'] = 'Stacking'
#self.files['SLL-NW.pkl'] = 'LLGC'
#self.files['CovShift.pkl'] = 'Reweighting'
#self.files['LocalTransferDeltaSMS_scale'] = 'SMS'
#
#self.files['OffsetTransfer-jointCV.pkl'] = 'Offset Transfer'
self.files['LocalTransferNew-grad-bounds.pkl'] = 'Ours'
self.files[
'LocalTransferNew-grad-bounds-opt_ft.pkl'] = 'Our Method: Optimize Target'
#self.files['LocalTransferNew-grad-bounds-loo.pkl'] = 'Local Transfer New: LOO'
self.files[
'LocalTransferNew-grad-bounds-scaleB.pkl'] = 'Our Method: Scale'
#self.files['LocalTransferNew-grad-bounds-loo-noTransform.pkl'] = 'Local Transfer New: LOO, no Transform'
#self.files['LocalTransferNew-grad.pkl'] = 'Local Transfer New: No Bounds'
self.files[
'LocalTransferNew-bounds-linearB.pkl'] = 'Our Method: Linear'
elif plot_idx == PLOT_VALIDATION:
self.files = OrderedDict()
self.files['TargetTransfer+NW.pkl'] = 'Target Only'
self.files['SLL-NW.pkl'] = 'LLGC'
self.files[
'LocalTransferDelta_radius_l2_linear-b_clip-b_use-val.pkl'] = 'Ours: Linear, validation'
self.files[
'LocalTransferDelta_radius_l2_use-val_lap-reg.pkl'] = 'Ours: Nonparametric, validation'
elif plot_idx == PLOT_CONSTRAINED:
self.files = OrderedDict()
self.files['TargetTransfer+NW.pkl'] = 'Target Only'
self.files['LocalTransferNew-grad-bounds.pkl'] = 'Our Method'
#self.files['LocalTransferNew-grad-bounds-boundB'] = 'Ours Method: Bounds'
#self.files['LocalTransferNew-grad-bounds-boundPerc=80'] = 'Our Method: Bounds 80%'
#self.files['LocalTransferNew-grad-bounds-boundUpper=80'] = 'Our Method: Bound Upper 80%'
self.files[
'LocalTransferNew-grad-bounds-boundPerc=[10, 90].pkl'] = 'Our Method: Bound Constraints'
#self.files['LocalTransferNew-grad-bounds-boundPerc=[0, 100].pkl'] = 'Our Method: Bound Constraints [0, 100]'
elif plot_idx == PLOT_SMS:
self.files = OrderedDict()
self.files['TargetTransfer+NW.pkl'] = 'Target Only'
self.files['SLL-NW.pkl'] = 'LLGC'
self.files['LocalTransferDeltaSMS_scale.pkl'] = 'SMS scale'
self.files['LocalTransferDeltaSMS.pkl'] = 'SMS no scale'
#self.files['LocalTransferDelta_C3=0_radius_l2_constant-b.pkl'] = 'Constant b, alpha=0'
elif plot_idx == PLOT_COMPETING_METHODS:
self.files = OrderedDict()
self.files['LocalTransferNew-grad-bounds-opt_ft.pkl'] = 'Ours'
#self.files['TargetTransfer+NW.pkl'] = 'Target Only'
self.files['StackTransfer+SKL-RidgeReg-jointCV.pkl'] = 'Stacking'
#self.files['SLL-NW.pkl'] = 'LLGC'
#self.files['CovShift.pkl'] = 'Reweighting'
self.files['OffsetTransfer-jointCV.pkl'] = 'Offset'
#self.files['LocalTransferDeltaSMS_scale'] = 'SMS'
elif plot_idx == PLOT_TABLE_COMPETING_METHODS:
self.baseline_idx = 1
self.data_names_for_table = names_for_table
self.method_names_for_table = [
'Ours', 'Target Only', 'Stacking', 'LLGC', 'Reweighting',
'Offset', 'SMS'
]
self.files = OrderedDict()
#self.files['LocalTransferNew-grad-bounds.pkl'] = 'Local Transfer New'
self.files[
'LocalTransferNew-grad-bounds-opt_ft.pkl'] = 'Local Transfer New: Opt f_t'
#self.files['LocalTransferNew-grad-bounds-scaleB.pkl'] = 'Local Transfer New: Scale'
#self.files['LocalTransferNew-bounds-linearB.pkl'] = 'Local Transfer New: Linear B'
self.files['TargetTransfer+NW.pkl'] = 'Target Only'
self.files['StackTransfer+SKL-RidgeReg-jointCV.pkl'] = 'Stacking'
self.files['SLL-NW.pkl'] = 'LLGC'
self.files['CovShift.pkl'] = 'Reweighting'
self.files['OffsetTransfer-jointCV.pkl'] = 'Offset Transfer'
self.files['LocalTransferDeltaSMS_scale'] = 'SMS'
elif plot_idx == PLOT_TABLE_OUR_METHODS:
self.baseline_idx = 0
self.data_names_for_table = names_for_table
self.method_names_for_table = [
'Ours', 'Ours: Fixed $f_T$', 'Ours: Fixed $f_T$+Scaling',
'Ours: Fixed $f_T$+linear $b$', 'Ours: Bound Constraints'
]
self.files = OrderedDict()
self.files[
'LocalTransferNew-grad-bounds-opt_ft.pkl'] = 'Local Transfer New: Opt f_t'
self.files[
'LocalTransferNew-grad-bounds.pkl'] = 'Local Transfer New'
self.files[
'LocalTransferNew-grad-bounds-scaleB.pkl'] = 'Local Transfer New: Scale'
self.files[
'LocalTransferNew-bounds-linearB.pkl'] = 'Local Transfer New: Linear B'
self.files[
'LocalTransferNew-grad-bounds-boundPerc=[10, 90].pkl'] = 'Our Method: Bound Constraints'
elif plot_idx == PLOT_TABLE_VAL:
self.files = OrderedDict()
self.files[
'LocalTransferDelta_radius_l2_linear-b_clip-b_use-val-stacking.pkl'] = 'Ours: Linear, Stacking, VAL'
self.files[
'LocalTransferDelta_radius_l2_linear-b_clip-b_use-val-stacking-sourceLOO.pkl'] = 'Ours: Linear, Stacking, LOO, VAL'
self.files['StackTransfer+SKL-RidgeReg-VAL.pkl'] = 'Stacking, VAL'
elif plot_idx == PLOT_ALPHA:
self.files = OrderedDict()
self.files[
'LocalTransferDelta_radius_l2_linear-b_clip-b.pkl'] = 'Ours: Linear'
self.files[
'LocalTransferDelta_C3=0_radius_l2_linear-b.pkl'] = 'Ours: Linear, alpha=0'
elif plot_idx == PLOT_TABLE_CONSTRAINED:
self.baseline_idx = 1
self.data_names_for_table = names_for_table
self.method_names_for_table = [
'Ours: Fixed $f_T$', 'Ours: Fixed $F_T$, Constraints'
]
self.files = OrderedDict()
self.files['LocalTransferNew-grad-bounds.pkl'] = 'Our Method'
self.files[
'LocalTransferNew-grad-bounds-boundPerc=[10, 90].pkl'] = 'Our Method: Bound Constraints'
if use_validation:
test_files = OrderedDict()
for f, leg in self.files.iteritems():
f = helper_functions.remove_suffix(f, '.pkl')
if f == 'OffsetTransfer-jointCV' or f.find(
'LocalTransferNew') == 0:
f += '-VAL.pkl'
elif f == 'LocalTransferDelta_l2_lap-reg':
f = 'LocalTransferDelta_l2_use-val_lap-reg.pkl'
elif f == 'TargetTransfer+NW':
f += '.pkl'
elif f == 'StackTransfer+SKL-RidgeReg':
f += '-VAL.pkl'
else:
#f += '_use-val.pkl'
f += '-VAL.pkl'
#leg = 'VALIDATION: ' + leg
test_files[f] = leg
self.files = test_files
if use_sms_plot_data_sets:
if max_rows == 3:
self.figsize = (4, 10)
self.borders = (.15, .9, .95, .05)
else:
self.figsize = (6, 6)
self.borders = (.1, .95, .95, .1)
if data_set == bc.DATA_SYNTHETIC_STEP_LINEAR_TRANSFER:
self.ylims = (0, 10)
elif data_set == bc.DATA_SYNTHETIC_DELTA_LINEAR:
self.ylims = (0, 200)
elif data_set == bc.DATA_SYNTHETIC_CROSS:
self.ylims = (0, 20)
elif use_1d_data:
self.borders = (.05, .95, .95, .05)
if plot_idx in {PLOT_CONSTRAINED, PLOT_VALIDATION}:
self.borders = (.1, .95, .95, .05)
if max_rows == 3:
self.figsize = (12, 10)
else:
self.figsize = (14, 6)
self.borders = (.05, .95, .95, .1)
else:
if max_rows == 3:
self.figsize = (4, 4)
self.borders = (.15, .9, .95, .05)
else:
self.figsize = (14, 6)
self.borders = (.05, .95, .95, .08)
self.data_set_to_use = pc.data_set
self.title = bc.data_name_dict.get(self.data_set_to_use,
'Unknown Data Set')
self.show_legend_on_all = show_legend_on_all
self.show_legend_on_missing_files = False
self.crash_on_missing_files = crash_on_missing_files
if self.use_1d_data and self.data_set_to_use < bc.DATA_SYNTHETIC_START:
self.title += ' 1D'
self.x_axis_string = 'Number of labeled target instances'
def generate_file_names(self, pc):
self.files = OrderedDict()
base_file_name = 'RelReg-cvx-constraints-%s=%s'
use_test = other_method_configs['use_test_error_for_model_selection']
use_val = other_method_configs['use_validation']
#self.files['Mixed-feats_QuadFeatsFew_method=HardConstraints_signs=20.pkl'] = 'Mixed: Hard Constraints, Quad Feats Few, 20 signs'
#self.files['SKL-RidgeReg-QuadFeatsFew.pkl'] = 'SKL Ridge Regression, Quad Feats Few'
#self.files['SLL-NW.pkl'] = 'LLGC'
#self.files['NW.pkl'] = 'NW'
if other_method_configs['use_nonneg']:
self.files['Mixed-feats_method=Ridge_nonneg.pkl'] = 'Mixed: Ridge, nonneg'
self.files['Mixed-feats_method=Rel_pairs=10_corr_nonneg.pkl'] = 'Mixed: Relative, corr, 10 pairs, nonneg'
self.files['Mixed-feats_method=Rel_pairs=1000_corr_nonneg.pkl'] = 'Mixed: Relative, corr, 1000 pairs, nonneg'
self.files['Mixed-feats_method=Ridge_nonneg_stacked.pkl'] = 'Mixed: Ridge, nonneg, stacked'
self.files['Mixed-feats_method=Rel_pairs=10_corr_nonneg_stacked.pkl'] = 'Mixed: Relative, corr, nonneg, stacked, 10 pairs'
else:
'''
self.files['SKL-RidgeReg.pkl'] = 'SKL Ridge Regression'
self.files['Mixed-feats_method=Ridge.pkl'] = 'Mixed: Ridge'
self.files['Mixed-feats_method=Rel_signs=10_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 10 signs'
self.files['Mixed-feats_method=Rel_pairs=10_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 10 pairs'
self.files['Mixed-feats_method=Rel_signs=1000_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 1000 signs'
'''
self.files['Mixed-feats_method=Ridge_CVXOPT.pkl'] = 'Mixed: Ridge'
self.files['Mixed-feats_method=Rel_signs=10_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 10 signs'
self.files['Mixed-feats_method=Rel_signs=50_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 50 signs'
#self.files['Mixed-feats_method=Ridge_stacked.pkl'] = 'Mixed: Ridge, stacked'
self.files['Mixed-feats_method=Rel_pairs=10_corr_CVXOPT.pkl'] = 'Mixed: Relative, corr, 10 pairs'
#self.files['Mixed-feats_method=Rel_pairs=10_corr_CVXOPT_l1.pkl'] = 'Mixed: Relative, corr, 10 pairs, l1'
#self.files['Mixed-feats_method=Rel_signs=10_corr_CVXOPT_l1.pkl'] = 'Mixed: Relative, corr, 10 signs, l1'
#self.files['Mixed-feats_method=Rel_signs=10_corr_stacked.pkl'] = 'Mixed: Relative, corr, stacked, 10 signs'
'''
self.files['Mixed-feats_method=Ridge.pkl'] = 'Mixed: Ridge'
self.files['Mixed-feats_method=HardConstraints_pairs=5.pkl'] = 'Mixed: Hard Constraints, 5 pairs'
self.files['Mixed-feats_method=HardConstraints_pairs=10.pkl'] = 'Mixed: Hard Constraints, 10 pairs'
self.files['Mixed-feats_method=HardConstraints_pairs=20.pkl'] = 'Mixed: Hard Constraints, 20 pairs'
self.files['Mixed-feats_method=HardConstraints_signs=5.pkl'] = 'Mixed: Hard Constraints, 5 signs'
self.files['Mixed-feats_method=HardConstraints_signs=10.pkl'] = 'Mixed: Hard Constraints, 10 signs'
self.files['Mixed-feats_method=HardConstraints_signs=25.pkl'] = 'Mixed: Hard Constraints, 25 signs'
'''
#self.files['SKL-DumReg.pkl'] = 'Predict Mean'
sizes = []
suffixes = OrderedDict()
#suffixes['mixedCV'] = [None,'']
if not use_test:
suffixes['nCV'] = [None, '10']
#suffixes['numFeats'] = [str(num_feat)]
ordered_keys = [
'nCV',
]
if use_test or use_val:
test_files = {}
for f, leg in self.files.iteritems():
f = helper_functions.remove_suffix(f, '.pkl')
if data_set_to_use == bc.DATA_DROSOPHILIA:
f += '_50'
if use_test:
f += '-TEST.pkl'
leg = 'TEST: ' + leg
else:
f += '-VAL.pkl'
leg = 'VALIDATION: ' + leg
test_files[f] = leg
self.files = test_files
methods = []
#methods.append(('numRandPairs','RelReg, %s pairs', 'Our Method: %s relative'))
self.title = 'Test'
all_params = list(grid_search.ParameterGrid(suffixes))
for file_suffix, legend_name, legend_name_paper in methods:
for size in sizes:
for params in all_params:
file_name = base_file_name
file_name = file_name % (file_suffix, str(size))
legend = legend_name
if viz_for_paper:
legend = legend_name_paper
legend %= str(size)
for key in ordered_keys:
if not params.has_key(key):
continue
value = params[key]
if value is None:
continue
if value == '':
file_name += '-' + key
if not viz_for_paper:
legend += ', ' + key
else:
file_name += '-' + key + '=' + str(value)
if not viz_for_paper:
legend += ', ' + str(value) + ' ' + key
if use_test:
file_name += '-TEST'
legend = 'TEST: ' + legend
file_name += '.pkl'
self.files[file_name] = legend
self.files['SKL-DumReg.pkl'] = 'Predict Mean'
def _delete_temp_folder(file):
folder = helper_functions.remove_suffix(file, '.pkl')
helper_functions.delete_dir_if_empty(folder)
def _temp_experiment_file_name(final_file_name, num_labels):
directory = helper_functions.remove_suffix(final_file_name, '.pkl')
return directory + '/num_labels=' + str(num_labels) + '.pkl'
def _temp_split_file_name(final_file_name, num_labels, split):
directory = helper_functions.remove_suffix(final_file_name, '.pkl')
return directory + '/num_labels=' + str(num_labels) + '_split=' + str(split) + '.pkl'
def _delete_temp_folder(file):
folder = helper_functions.remove_suffix(file, '.pkl')
helper_functions.delete_dir_if_empty(folder)
def _temp_experiment_file_name(final_file_name, num_labels):
directory = helper_functions.remove_suffix(final_file_name, '.pkl')
return directory + '/num_labels=' + str(num_labels) + '.pkl'
def _temp_split_file_name(final_file_name, num_labels, split):
directory = helper_functions.remove_suffix(final_file_name, '.pkl')
return directory + '/num_labels=' + str(num_labels) + '_split=' + str(split) + '.pkl'
def generate_file_names(self, pc):
self.files = OrderedDict()
base_file_name = 'RelReg-cvx-constraints-%s=%s'
use_test = other_method_configs['use_test_error_for_model_selection']
use_val = other_method_configs['use_validation']
#self.files['Mixed-feats_QuadFeatsFew_method=HardConstraints_signs=20.pkl'] = 'Mixed: Hard Constraints, Quad Feats Few, 20 signs'
#self.files['SKL-RidgeReg-QuadFeatsFew.pkl'] = 'SKL Ridge Regression, Quad Feats Few'
#self.files['SLL-NW.pkl'] = 'LLGC'
#self.files['NW.pkl'] = 'NW'
if viz_type == VIZ_SAME_SIGN:
self.files['Mixed-feats_method=Ridge.pkl'] = 'Ridge'
self.files[
'Mixed-feats_method=Rel_meanB-pairsSameSignPrimalHinge=1-use_sign_corr_l1.pkl'] = 'Our Method: 100% pairwise signs'
elif viz_type == VIZ_DUAL:
self.files['Mixed-feats_method=Ridge.pkl'] = 'Ridge'
#self.files['Mixed-feats_method=Rel_meanB_signs=1-use_sign_corr_l1.pkl'] = 'Primal: Signs, meanB'
##self.files['Mixed-feats_method=Rel_dual_meanB_signs=1-use_sign_corr_l1.pkl'] = 'Dual: Signs, meanB'
self.files[
'Mixed-feats_method=Rel_dual_signs=1-use_sign_corr_l1.pkl'] = 'Dual: Signs'
#self.files['Mixed-feats_method=Rel_meanB_pairsSameSignPrimal=0.25-use_sign_corr_l1.pkl'] = 'Same Sign Primal: meanB, 25% pairs'
#self.files['Mixed-feats_method=Rel_meanB_pairsSameSignPrimal=0.5-use_sign_corr_l1.pkl'] = 'Same Sign Primal: meanB, 50% pairs'
#self.files['Mixed-feats_method=Rel_meanB_pairsSameSignPrimal=1-use_sign_corr_l1.pkl'] = 'Same Sign Primal: meanB, 100% pairs'
#self.files['Mixed-feats_method=Rel_meanB_pairsSameSignPrimal=2-use_sign_corr_l1.pkl'] = 'Same Sign Primal: meanB, 200% pairs'
#self.files['Mixed-feats_method=Rel_meanB-pairsSameSignQCQP=1-use_sign_corr_l1.pkl'] = 'Same sign QCQP'
self.files[
'Mixed-feats_method=Rel_meanB-pairsSameSignPrimalHinge=1-use_sign_corr_l1.pkl'] = 'Same Sign Hinge'
self.files[
'Mixed-feats_method=Rel_meanB-pairsSameSignPrimalHinge=1-use_sign_corr_l1_fixC3'] = 'Same Sign Hinge: Fix C2'
elif viz_type == VIZ_EXPERIMENT:
self.files['Mixed-feats_method=Ridge.pkl'] = 'Mixed: Ridge'
self.files['Mixed-feats_method=Lasso.pkl'] = 'Mixed: Lasso'
'''
self.files['Mixed-feats_method=Rel_nonneg_l1.pkl'] = 'Mixed: Nonneg'
self.files['Mixed-feats_method=Rel_nonneg_not-relaxed_l1.pkl'] = 'Mixed: Nonneg, not relaxed'
'''
self.files[
'Mixed-feats_method=Rel_signs=0.25-use_sign_corr_l1.pkl'] = 'Mixed: 25% signs'
self.files[
'Mixed-feats_method=Rel_signs=0.5-use_sign_corr_l1.pkl'] = 'Mixed: 50% signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'Mixed: 100% signs'
'''
self.files['Mixed-feats_method=Rel_pairs=0.25-use_sign_corr_l1.pkl'] = 'Mixed: 25% pairs'
self.files['Mixed-feats_method=Rel_pairs=0.5-use_sign_corr_l1.pkl'] = 'Mixed: 50% pairs'
self.files['Mixed-feats_method=Rel_pairs=1-use_sign_corr_l1.pkl'] = 'Mixed: 100% pairs'
'''
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_trainCorr_l1.pkl'] = 'Mixed: 100% signs, train correlation'
elif viz_type == VIZ_PAPER_ERROR:
self.files[
'Mixed-feats_method=Rel-use_sign_nonneg_not-relaxed_l1.pkl'] = 'Nonnegative'
self.files['Mixed-feats_method=Ridge.pkl'] = 'Ridge'
self.files['Mixed-feats_method=Lasso.pkl'] = 'Lasso'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'PSCR: $p$ signs'
self.files[
'Mixed-feats_method=Rel_pairs=1-use_sign_corr_l1.pkl'] = 'PRCR: $p$ pairs'
self.files[
'Mixed-feats_method=Rel_meanB-pairsSameSignPrimalHinge=1-use_sign_corr_l1.pkl'] = 'PPSCR: $p$ sign pairs'
elif viz_type == VIZ_PAPER_SIGNS:
self.files[
'Mixed-feats_method=Rel_signs=0.25-use_sign_corr_l1.pkl'] = 'PSCR: $.25p$ signs'
self.files[
'Mixed-feats_method=Rel_signs=0.5-use_sign_corr_l1.pkl'] = 'PSCR: $.5p$ signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'PSCR: $p$ signs'
elif viz_type == VIZ_PAPER_RELATIVE:
self.files[
'Mixed-feats_method=Rel_pairs=0.25-use_sign_corr_l1.pkl'] = 'PRCR: $.25p$ pairs'
self.files[
'Mixed-feats_method=Rel_pairs=0.5-use_sign_corr_l1.pkl'] = 'PRCR: $.5p$ pairs'
self.files[
'Mixed-feats_method=Rel_pairs=1-use_sign_corr_l1.pkl'] = 'PRCR: $p$ pairs'
elif viz_type == VIZ_PAPER_TRAINING_CORR:
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'PSCR: $p$ signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_trainCorr_l1.pkl'] = 'PSCR: $p$ signs, Training Guidance'
elif viz_type == VIZ_PAPER_TRANSFER:
self.files['Mixed-feats_method=Ridge_transfer'] = 'Ridge: Transfer'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'PSCR: $p$ signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1_transfer.pkl'] = 'Transfer PSCR: $p$ signs'
elif viz_type == VIZ_PAPER_TRAINING_SINGLE:
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'With Outside Guidance'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_trainCorr_l1.pkl'] = 'Without Outside Guidance'
elif viz_type == VIZ_PAPER_HARD_CONSTRAINTS:
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'PSCR: $p$ signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_not-relaxed_l1.pkl'] = 'Signed Ridge: $p$'
elif viz_type == VIZ_PAPER_TABLE:
self.vis_table = True
self.data_names_for_table = [
'Synthetic', 'BH', 'Wine', 'Concrete', 'Housing', 'ITS',
'Heart'
]
self.method_names_for_table = [
'Nonnegative', 'Ridge', 'Lasso', 'PSCR: $p$ Signs',
'PRCR: $p$ Pairs', 'PPSCR: $p$ pairs'
]
self.files = OrderedDict()
self.files[
'Mixed-feats_method=Rel-use_sign_nonneg_not-relaxed_l1.pkl'] = 'Nonnegative'
self.files['Mixed-feats_method=Ridge.pkl'] = 'Ridge'
self.files['Mixed-feats_method=Lasso.pkl'] = 'Lasso'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'Our Method: 100% signs'
self.files[
'Mixed-feats_method=Rel_pairs=1-use_sign_corr_l1.pkl'] = 'Our Method: 100% pairs'
self.files[
'Mixed-feats_method=Rel_meanB-pairsSameSignPrimalHinge=1-use_sign_corr_l1.pkl'] = 'Our Method: 100% pairwise signs'
elif viz_type == VIZ_PAPER_TABLE_TRANSFER:
self.vis_table = True
self.data_names_for_table = [
'Synthetic', 'BH', 'Wine', 'Concrete', 'Housing', 'ITS',
'Heart'
]
self.method_names_for_table = [
'Ridge', 'Lasso', 'Transfer Ridge', 'PSCR: $p$ Signs',
'Transfer PSCR: $p$ Signs'
]
self.files['Mixed-feats_method=Ridge.pkl'] = 'Ridge'
self.files['Mixed-feats_method=Lasso.pkl'] = 'Lasso'
self.files['Mixed-feats_method=Ridge_transfer'] = 'Ridge: Transfer'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'Our Method'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1_transfer.pkl'] = 'Our Method: Transfer'
elif viz_type == VIZ_PAPER_TABLE_TRAINING_CORR:
self.vis_table = True
self.data_names_for_table = [
'Synthetic', 'BH', 'Wine', 'Concrete', 'Housing', 'ITS',
'Heart'
]
self.method_names_for_table = [
'PSCR: $p$ Signs', 'PSCR: Training Set Guidance, $p$ Signs'
]
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'Our Method'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_trainCorr_l1.pkl'] = 'Our Method: Training Set Signs'
elif viz_type == VIZ_PAPER_TABLE_HARD_CONSTRAINTS:
self.vis_table = True
self.data_names_for_table = [
'Synthetic', 'BH', 'Wine', 'Concrete', 'Housing', 'ITS',
'Heart'
]
self.method_names_for_table = ['PSCR: $p$ Signs', 'Signed Ridge']
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_l1.pkl'] = 'Our Method: 100% signs'
self.files[
'Mixed-feats_method=Rel_signs=1-use_sign_corr_not-relaxed_l1.pkl'] = 'Signed Ridge'
#self.files['SKL-DumReg.pkl'] = 'Predict Mean'
if use_test or use_val:
test_files = OrderedDict()
for f, leg in self.files.iteritems():
f = helper_functions.remove_suffix(f, '.pkl')
if data_set_to_use == bc.DATA_DROSOPHILIA:
f += '_50'
if use_test:
f += '-TEST.pkl'
leg = 'TEST: ' + leg
else:
f += '-VAL.pkl'
#leg = 'VALIDATION: ' + leg
test_files[f] = leg
self.files = test_files
self.title = self.results_dir
if viz_type > VIZ_EXPERIMENT:
self.title = bc.data_name_dict[pc.data_set]
