def main(dict_dataset_options):
options = DatasetOptions(dict_dataset_options)
preproc = PreprocessorNZ(options)
print('grouping: ' + str(options.getGroupingName()))
# preproc.processDischargeFile();
# preproc.processDiagnosisFile();
# preproc.createFeatureSet();
preproc.encodeFeatures()
preproc.fuse()
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()
'data_prefix': 'nz',
'dataset': str(year),
'encoding': 'embedding',
'grouping': 'verylightgrouping',
'newfeatures': None,
'featurereduction': {
'method': 'FUSION'
}
}
options_dataset_year = DatasetOptions(dict_options_dataset)
dataset_year = Dataset(options_dataset_year)
if balanced:
df_year = dataset_year.getBalancedSubSet()
else:
df_year = dataset_year.getDf()
#df_year['main_diag'] = df_year['main_diag'].apply(convertDiagToInd)
print(df_year.shape)
df_all_years = df_all_years.append(df_year)
print('df balanced all years: ' + str(df_all_years.shape))
encoding = options_dataset_year.getEncodingScheme()
grouping = options_dataset_year.getGroupingName()
featureset = options_dataset_year.getFeatureSetStr()
filename_data_years = dirData + 'data_nz_' + str(min(years)) + str(
max(years)
) + '_' + featureset + '_' + encoding + '_' + grouping + '.csv'
df_all_years.to_csv(filename_data_years, line_terminator='\n', index=False)
