if len(sys.argv) > 2:
threshold_epoch = int(sys.argv[2])
dict_data_train = {
'dir_data': DIRPROJECT + 'data/',
'data_prefix': 'nz',
'dataset': '20122016',
'encoding': 'embedding',
'newfeatures': None,
'featurereduction': {
'method': 'FUSION'
},
'grouping': 'verylightgrouping'
}
dataset_options_train = DatasetOptions(dict_data_train)
diag_group_names = dataset_options_train.getDiagGroupNames()
indices_diag_codes = getDiagCodesIndices(diag_group_names)
main_groups = icd10_chapters.getMainGroups()
num_colors = len(main_groups)
colors = plt.cm.rainbow(np.linspace(0, 1, num_colors))
num_diags = len(indices_diag_codes)
filenames_encodings = glob.glob(dir_model + 'basic_encodings_*')
var_encodings = []
for l, f in enumerate(sorted(filenames_encodings)):
print(f)
epoch = int(f.split('/')[-1].split('.')[0].split('_')[-1])
print('epoch: ' + str(epoch))
basic_encodings = np.load(f)
def analyze(flags_obj):
"""Run Wide-Deep training and eval loop.
Args:
flags_obj: An object containing parsed flag values.
"""
dict_data_train = {
'dir_data': DIRPROJECT + 'data/',
'data_prefix': 'nz',
'dataset': '20072016',
'encoding': 'embedding',
'newfeatures': None,
'featurereduction': {
'method': 'FUSION'
},
'grouping': 'verylightgrouping'
}
dataset_options_train = DatasetOptions(dict_data_train)
dataset_options_eval = None
if dict_data_train['data_prefix'] == 'nz':
feature_columns = FeatureColumnsAutoEncoderNZ(
dataset_options=dataset_options_train)
else:
print('unknown data prefix..exit')
sys.exit()
dict_dataset_options = {
'train': dataset_options_train,
'eval': dataset_options_eval,
'test': None
}
nn = AutoEncoderModel('analysis', dict_dataset_options, feature_columns,
flags_obj)
basic_encodings = nn.analyze()
num_colors = 26
colors = plt.cm.rainbow(np.linspace(0, 1, num_colors))
pca = PCA(n_components=2)
weights_2d_pca = pca.fit_transform(basic_encodings)
tsne = TSNE(n_components=2)
weights_2d_tsne = tsne.fit_transform(basic_encodings)
diag_group_names = dataset_options_train.getDiagGroupNames()
num_diags = len(diag_group_names)
if dataset_options_train.getGroupingName() == 'verylightgrouping':
num_subcategories = 100
elif dataset_options_train.getGroupingName() == 'lightgrouping':
num_subcategories = 10
elif dataset_options_train.getGroupingName() == 'grouping':
num_subcategories = 1
else:
print('grouping scheme is unknown...exit')
sys.exit()
plt.figure()
for k in range(0, num_colors):
c = colors[k]
plt.scatter(
weights_2d_pca[k * num_subcategories:(k * num_subcategories +
num_subcategories), 0],
weights_2d_pca[k * num_subcategories:(k * num_subcategories +
num_subcategories), 1],
label=string.ascii_uppercase[k],
alpha=0.5,
s=100,
c=c)
plt.legend()
plt.title('pca')
plt.draw()
plt.figure()
for k in range(0, num_colors):
c = colors[k]
plt.scatter(
weights_2d_tsne[k * num_subcategories:(k * num_subcategories +
num_subcategories), 0],
weights_2d_tsne[k * num_subcategories:(k * num_subcategories +
num_subcategories), 1],
label=string.ascii_uppercase[k],
alpha=0.5,
s=100,
c=c)
plt.legend()
plt.title('t-sne')
plt.draw()
plt.show()
