"eva_100": {
"type": "spectral",
"kernel_width": 100,
"bin_width": 150,
"use_intensity": False,
"spec_type": "ir",
}
}
method = base_config['methods'].keys()[0]
# compute molecules available for all descriptors
cache = {}
for k, config in feat_config.items():
base_config['features'].update(config)
cache[k] = {"features": rl.prepare_features(base_config)}
all_mols = [r['features'].keys() for r in cache.values()]
mol_intersection = set(all_mols[0]).intersection(*all_mols[1:])
res = {n: {g: {} for g in gloms} for n in feat_config}
for glom in gloms:
print('{}\n'.format(glom))
base_config.update({'glomerulus': glom, 'data_path': data_path})
dtm = {}
for name, config in feat_config.items():
base_config['features'].update(config)
data, targets, molids = rl.load_data_targets(base_config, cache[name]['features'])
dtm[name] = {
'data': data,
'targets': targets,
from master.libs import run_lib
from master.libs import utils
reload(run_lib)
plt.close('all')
n_folds_list = [5, 10, 20, 50]
n_repetitions = 5
method = 'svr'
out_path = '/Users/dedan/projects/master/results/validation/gen_score_svr'
base_path = os.path.join(os.path.dirname(__file__), '..')
config = json.load(open(os.path.join(base_path, 'config', 'validate_genscore_svr.json')))
config['data_path'] = os.path.join(base_path, 'data')
# load the features
features = run_lib.prepare_features(config)
used_glomeruli = json.load(open(os.path.join(config['data_path'], 'used_glomeruli.json')))
res = {g: {nf: [] for nf in n_folds_list} for g in used_glomeruli}
for glom in used_glomeruli:
print(glom)
config['glomerulus'] = glom
data, targets, molids = run_lib.load_data_targets(config, features)
config['feature_selection']['k_best'] = data.shape[1]
for i, n_folds in enumerate(n_folds_list):
print(n_folds)
config['methods'][method]['n_folds'] = n_folds
for j in range(n_repetitions):
run_res = run_lib.run_runner(config, data, targets)
"C": 1.0,
"n_folds": 50
}
},
"randomization_test": False
}
used_gloms = json.load(open(os.path.join(config['data_path'], 'used_glomeruli.json')))
alone_haddad, alone_vib, together = [], [], []
for glom in used_gloms:
config['glomerulus'] = glom
# prepare haddad features
features_h = run_lib.prepare_features(config)
data_h, targets_h, molids_h = run_lib.load_data_targets(config, features_h)
config['feature_selection']['k_best'] = data_h.shape[1]
tmp = run_lib.run_runner(config, data_h, targets_h)
print glom, tmp
alone_haddad.append(tmp['svr']['gen_score'])
# prepare vib100
config_spec = copy.deepcopy(config)
config_spec['features']['type'] = 'spectral'
config_spec['features']['kernel_width'] = 100
config_spec['features']['bin_width'] = 150
config_spec['features']['use_intensity'] = False
config_spec['features']['spec_type'] = 'ir'
features_v = run_lib.prepare_features(config_spec)
