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  1. adrd/__pycache__/__init__.cpython-311.pyc +0 -0
  2. adrd/model/__pycache__/__init__.cpython-311.pyc +0 -0
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  6. adrd/model/__pycache__/train_resnet.cpython-311.pyc +0 -0
  7. adrd/nn/__pycache__/__init__.cpython-311.pyc +0 -0
  8. adrd/nn/__pycache__/blocks.cpython-311.pyc +0 -0
  9. adrd/nn/__pycache__/c3d.cpython-311.pyc +0 -0
  10. adrd/nn/__pycache__/cnn_resnet3d.cpython-311.pyc +0 -0
  11. adrd/nn/__pycache__/cnn_resnet3d_with_linear_classifier.cpython-311.pyc +0 -0
  12. adrd/nn/__pycache__/dense_net.cpython-311.pyc +0 -0
  13. adrd/nn/__pycache__/focal_loss.cpython-311.pyc +0 -0
  14. adrd/nn/__pycache__/img_model_wrapper.cpython-311.pyc +0 -0
  15. adrd/nn/__pycache__/net_resnet3d.cpython-311.pyc +0 -0
  16. adrd/nn/__pycache__/resnet3d.cpython-311.pyc +0 -0
  17. adrd/nn/__pycache__/resnet_img_model.cpython-311.pyc +0 -0
  18. adrd/nn/__pycache__/selfattention.cpython-311.pyc +0 -0
  19. adrd/nn/__pycache__/transformer.cpython-311.pyc +0 -0
  20. adrd/nn/__pycache__/unet.cpython-311.pyc +0 -0
  21. adrd/nn/__pycache__/unet_3d.cpython-311.pyc +0 -0
  22. adrd/nn/__pycache__/unet_img_model.cpython-311.pyc +0 -0
  23. adrd/nn/__pycache__/vitautoenc.cpython-311.pyc +0 -0
  24. adrd/utils/__pycache__/__init__.cpython-311.pyc +0 -0
  25. adrd/utils/__pycache__/formatter.cpython-311.pyc +0 -0
  26. adrd/utils/__pycache__/imputer.cpython-311.pyc +0 -0
  27. adrd/utils/__pycache__/masker.cpython-311.pyc +0 -0
  28. adrd/utils/__pycache__/misc.cpython-311.pyc +0 -0
  29. adrd/utils/__pycache__/transformer_dataset.cpython-311.pyc +0 -0
  30. app.py +38 -5
  31. data/__pycache__/dataset_csv.cpython-311.pyc +0 -0
  32. data/dataset_csv.py +56 -0
  33. data/default_conf_new.toml +1620 -0
  34. data/nacc_test_with_np_cli.csv +0 -0
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app.py CHANGED
@@ -1,10 +1,12 @@
1
  import streamlit as st
2
  import json
 
3
 
4
  @st.cache_resource
5
  def load_model():
6
  import adrd
7
  ckpt_path = './ckpt_densenet_emb_encoder_2_AUPR.pt'
 
8
  model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
9
  return model
10
 
@@ -14,14 +16,45 @@ def predict_proba(data_dict):
14
  pred_dict = model.predict_proba([data_dict])[1][0]
15
  return pred_dict
16
 
 
 
 
 
 
 
 
 
 
 
 
17
  # Create a form for user input
18
  with st.form("json_input_form"):
19
- st.write("Please enter JSON-formatted dictionary:")
20
- json_input = st.text_area("JSON Input", height=250)
21
- submit_button = st.form_submit_button("Predict")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22
 
23
- # Process the JSON input
24
- if submit_button:
 
 
 
25
  try:
26
  # Parse the JSON input into a Python dictionary
27
  data_dict = json.loads(json_input)
 
1
  import streamlit as st
2
  import json
3
+ import random
4
 
5
  @st.cache_resource
6
  def load_model():
7
  import adrd
8
  ckpt_path = './ckpt_densenet_emb_encoder_2_AUPR.pt'
9
+ # ckpt_path = '../adrd_tool_copied_from_sahana/dev/ckpt/ckpt_densenet_emb_encoder_2_AUPR.pt'
10
  model = adrd.model.ADRDModel.from_ckpt(ckpt_path, device='cpu')
11
  return model
12
 
 
16
  pred_dict = model.predict_proba([data_dict])[1][0]
17
  return pred_dict
18
 
19
+ # load NACC testing data
20
+ from data.dataset_csv import CSVDataset
21
+ dat_tst = CSVDataset(
22
+ dat_file = "./data/nacc_test_with_np_cli.csv",
23
+ cnf_file = "./data/default_conf_new.toml"
24
+ )
25
+
26
+ # initialize session state for the text input if it's not already set
27
+ if 'input_text' not in st.session_state:
28
+ st.session_state.input_text = ""
29
+
30
  # Create a form for user input
31
  with st.form("json_input_form"):
32
+ st.write("Please enter the input features in the textbox below, formatted as a JSON dictionary. Click the \"Random NACC Example\" button to populate the textbox with a randomly selected case from the NACC testing dataset. Use the \"Predict\" button to submit your input to the model, which will then provide probability predictions for mental status and all 10 etiologies.")
33
+ json_input = st.text_area(
34
+ "Please enter JSON-formatted input features:",
35
+ value = st.session_state.input_text,
36
+ height = 250
37
+ )
38
+
39
+ # create three columns
40
+ left_col, middle_col, right_col = st.columns([3, 4, 1])
41
+
42
+ with left_col:
43
+ example_button = st.form_submit_button("Random NACC Example")
44
+
45
+ with right_col:
46
+ submit_button = st.form_submit_button("Predict")
47
+
48
+ if example_button:
49
+ idx = random.randint(0, len(dat_tst) - 1)
50
+ example = dat_tst[idx][0]
51
+ st.session_state.input_text = json.dumps(example)
52
 
53
+ # reset input text after form processing to show updated text in the input box
54
+ if 'input_text' in st.session_state:
55
+ st.experimental_rerun()
56
+
57
+ elif submit_button:
58
  try:
59
  # Parse the JSON input into a Python dictionary
60
  data_dict = json.loads(json_input)
data/__pycache__/dataset_csv.cpython-311.pyc ADDED
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data/dataset_csv.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #%%
2
+ import pandas as pd
3
+ import tomllib
4
+
5
+ value_mapping = {
6
+ 'his_SEX': {'female': 0, 'male': 1},
7
+ 'his_HISPANIC': {'no': 0, 'yes': 1},
8
+ 'his_NACCNIHR': {'whi': 0, 'blk': 1, 'asi': 2, 'ind': 3, 'haw': 4, 'mul': 5},
9
+ 'his_RACE': {'whi': 0, 'blk': 1, 'asi': 2, 'ind': 3, 'haw': 4, 'oth': 5},
10
+ 'his_RACESEC': {'whi': 0, 'blk': 1, 'asi': 2, 'ind': 3, 'haw': 4, 'oth': 5},
11
+ 'his_RACETER': {'whi': 0, 'blk': 1, 'asi': 2, 'ind': 3, 'haw': 4, 'oth': 5},
12
+ }
13
+
14
+ label_names = ['NC', 'MCI', 'DE', 'AD', 'LBD', 'VD', 'PRD', 'FTD', 'NPH', 'SEF', 'PSY', 'TBI', 'ODE']
15
+
16
+ class CSVDataset:
17
+
18
+ def __init__(self, dat_file, cnf_file):
19
+ ''' ... '''
20
+ # load data csv
21
+ df = pd.read_csv(dat_file)
22
+
23
+ # value mapping
24
+ for col, mapping in value_mapping.items():
25
+ df[col] = df[col].replace(mapping)
26
+
27
+ # load toml file to get feature names
28
+ with open(cnf_file, 'rb') as file:
29
+ feature_names = tomllib.load(file)['feature'].keys()
30
+
31
+ self.df = df
32
+ self.df_features = df[feature_names]
33
+ self.df_labels = df[label_names]
34
+
35
+ def __len__(self):
36
+ ''' ... '''
37
+ return len(self.df)
38
+
39
+ def __getitem__(self, idx):
40
+ ''' ... '''
41
+ row = self.df_features.iloc[idx]
42
+ clean_row = row.dropna()
43
+ feature_dict = clean_row.to_dict()
44
+
45
+ row = self.df_labels.iloc[idx]
46
+ clean_row = row.dropna()
47
+ label_dict = clean_row.to_dict()
48
+
49
+ return feature_dict, label_dict
50
+
51
+ if __name__ == '__main__':
52
+ # load dataset
53
+ dset = CSVDataset('./nacc_test_with_np_cli.csv', './default_conf_new.toml')
54
+ print(dset[1])
55
+
56
+ # %%
data/default_conf_new.toml ADDED
@@ -0,0 +1,1620 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [features]
2
+
3
+ [feature.his_NACCREAS]
4
+ type = "categorical"
5
+ num_categories = 3
6
+
7
+ [feature.his_NACCREFR]
8
+ type = "categorical"
9
+ num_categories = 3
10
+
11
+ [feature.his_SEX]
12
+ type = "categorical"
13
+ num_categories = 2
14
+
15
+ [feature.his_HISPANIC]
16
+ type = "categorical"
17
+ num_categories = 2
18
+
19
+ [feature.his_HISPOR]
20
+ type = "categorical"
21
+ num_categories = 8
22
+
23
+ [feature.his_RACE]
24
+ type = "categorical"
25
+ num_categories = 6
26
+
27
+ [feature.his_RACESEC]
28
+ type = "categorical"
29
+ num_categories = 6
30
+
31
+ [feature.his_RACETER]
32
+ type = "categorical"
33
+ num_categories = 6
34
+
35
+ [feature.his_PRIMLANG]
36
+ type = "categorical"
37
+ num_categories = 7
38
+
39
+ [feature.his_MARISTAT]
40
+ type = "categorical"
41
+ num_categories = 6
42
+
43
+ [feature.his_LIVSIT]
44
+ type = "categorical"
45
+ num_categories = 5
46
+
47
+ [feature.his_INDEPEND]
48
+ type = "categorical"
49
+ num_categories = 4
50
+
51
+ [feature.his_RESIDENC]
52
+ type = "categorical"
53
+ num_categories = 4
54
+
55
+ [feature.his_HANDED]
56
+ type = "categorical"
57
+ num_categories = 3
58
+
59
+ [feature.his_NACCNIHR]
60
+ type = "categorical"
61
+ num_categories = 6
62
+
63
+ [feature.his_NACCFAM]
64
+ type = "categorical"
65
+ num_categories = 2
66
+
67
+ [feature.his_NACCMOM]
68
+ type = "categorical"
69
+ num_categories = 2
70
+
71
+ [feature.his_NACCDAD]
72
+ type = "categorical"
73
+ num_categories = 2
74
+
75
+ [feature.his_NACCFADM]
76
+ type = "categorical"
77
+ num_categories = 2
78
+
79
+ [feature.his_NACCAM]
80
+ type = "categorical"
81
+ num_categories = 5
82
+
83
+ [feature.his_NACCAMS]
84
+ type = "categorical"
85
+ num_categories = 4
86
+
87
+ [feature.his_NACCFFTD]
88
+ type = "categorical"
89
+ num_categories = 2
90
+
91
+ [feature.his_NACCFM]
92
+ type = "categorical"
93
+ num_categories = 6
94
+
95
+ [feature.his_NACCFMS]
96
+ type = "categorical"
97
+ num_categories = 4
98
+
99
+ [feature.his_NACCOM]
100
+ type = "categorical"
101
+ num_categories = 2
102
+
103
+ [feature.his_NACCOMS]
104
+ type = "categorical"
105
+ num_categories = 4
106
+
107
+ [feature.his_TOBAC30]
108
+ type = "categorical"
109
+ num_categories = 2
110
+
111
+ [feature.his_TOBAC100]
112
+ type = "categorical"
113
+ num_categories = 2
114
+
115
+ [feature.his_ALCOCCAS]
116
+ type = "categorical"
117
+ num_categories = 2
118
+
119
+ [feature.his_ALCFREQ]
120
+ type = "categorical"
121
+ num_categories = 5
122
+
123
+ [feature.his_CVHATT]
124
+ type = "categorical"
125
+ num_categories = 3
126
+
127
+ [feature.his_HATTMULT]
128
+ type = "categorical"
129
+ num_categories = 2
130
+
131
+ [feature.his_CVAFIB]
132
+ type = "categorical"
133
+ num_categories = 3
134
+
135
+ [feature.his_CVANGIO]
136
+ type = "categorical"
137
+ num_categories = 3
138
+
139
+ [feature.his_CVBYPASS]
140
+ type = "categorical"
141
+ num_categories = 3
142
+
143
+ [feature.his_CVPACDEF]
144
+ type = "categorical"
145
+ num_categories = 3
146
+
147
+ [feature.his_CVPACE]
148
+ type = "categorical"
149
+ num_categories = 3
150
+
151
+ [feature.his_CVCHF]
152
+ type = "categorical"
153
+ num_categories = 3
154
+
155
+ [feature.his_CVANGINA]
156
+ type = "categorical"
157
+ num_categories = 3
158
+
159
+ [feature.his_CVHVALVE]
160
+ type = "categorical"
161
+ num_categories = 3
162
+
163
+ [feature.his_CVOTHR]
164
+ type = "categorical"
165
+ num_categories = 3
166
+
167
+ [feature.his_CBSTROKE]
168
+ type = "categorical"
169
+ num_categories = 3
170
+
171
+ [feature.his_STROKMUL]
172
+ type = "categorical"
173
+ num_categories = 2
174
+
175
+ [feature.his_CBTIA]
176
+ type = "categorical"
177
+ num_categories = 3
178
+
179
+ [feature.his_TIAMULT]
180
+ type = "categorical"
181
+ num_categories = 2
182
+
183
+ [feature.his_PD]
184
+ type = "categorical"
185
+ num_categories = 2
186
+
187
+ [feature.his_PDOTHR]
188
+ type = "categorical"
189
+ num_categories = 2
190
+
191
+ [feature.his_SEIZURES]
192
+ type = "categorical"
193
+ num_categories = 3
194
+
195
+ [feature.his_TBI]
196
+ type = "categorical"
197
+ num_categories = 3
198
+
199
+ [feature.his_TBIBRIEF]
200
+ type = "categorical"
201
+ num_categories = 3
202
+
203
+ [feature.his_TRAUMBRF]
204
+ type = "categorical"
205
+ num_categories = 3
206
+
207
+ [feature.his_TBIEXTEN]
208
+ type = "categorical"
209
+ num_categories = 3
210
+
211
+ [feature.his_TRAUMEXT]
212
+ type = "categorical"
213
+ num_categories = 3
214
+
215
+ [feature.his_TBIWOLOS]
216
+ type = "categorical"
217
+ num_categories = 3
218
+
219
+ [feature.his_TRAUMCHR]
220
+ type = "categorical"
221
+ num_categories = 3
222
+
223
+ [feature.his_NCOTHR]
224
+ type = "categorical"
225
+ num_categories = 3
226
+
227
+ [feature.his_DIABETES]
228
+ type = "categorical"
229
+ num_categories = 3
230
+
231
+ [feature.his_DIABTYPE]
232
+ type = "categorical"
233
+ num_categories = 3
234
+
235
+ [feature.his_HYPERTEN]
236
+ type = "categorical"
237
+ num_categories = 3
238
+
239
+ [feature.his_HYPERCHO]
240
+ type = "categorical"
241
+ num_categories = 3
242
+
243
+ [feature.his_B12DEF]
244
+ type = "categorical"
245
+ num_categories = 3
246
+
247
+ [feature.his_THYROID]
248
+ type = "categorical"
249
+ num_categories = 3
250
+
251
+ [feature.his_ARTHRIT]
252
+ type = "categorical"
253
+ num_categories = 3
254
+
255
+ [feature.his_ARTHTYPE]
256
+ type = "categorical"
257
+ num_categories = 3
258
+
259
+ [feature.his_ARTHUPEX]
260
+ type = "categorical"
261
+ num_categories = 2
262
+
263
+ [feature.his_ARTHLOEX]
264
+ type = "categorical"
265
+ num_categories = 2
266
+
267
+ [feature.his_ARTHSPIN]
268
+ type = "categorical"
269
+ num_categories = 2
270
+
271
+ [feature.his_ARTHUNK]
272
+ type = "categorical"
273
+ num_categories = 2
274
+
275
+ [feature.his_INCONTU]
276
+ type = "categorical"
277
+ num_categories = 3
278
+
279
+ [feature.his_INCONTF]
280
+ type = "categorical"
281
+ num_categories = 3
282
+
283
+ [feature.his_APNEA]
284
+ type = "categorical"
285
+ num_categories = 3
286
+
287
+ [feature.his_RBD]
288
+ type = "categorical"
289
+ num_categories = 3
290
+
291
+ [feature.his_INSOMN]
292
+ type = "categorical"
293
+ num_categories = 3
294
+
295
+ [feature.his_OTHSLEEP]
296
+ type = "categorical"
297
+ num_categories = 3
298
+
299
+ [feature.his_ALCOHOL]
300
+ type = "categorical"
301
+ num_categories = 3
302
+
303
+ [feature.his_ABUSOTHR]
304
+ type = "categorical"
305
+ num_categories = 3
306
+
307
+ [feature.his_PTSD]
308
+ type = "categorical"
309
+ num_categories = 3
310
+
311
+ [feature.his_BIPOLAR]
312
+ type = "categorical"
313
+ num_categories = 3
314
+
315
+ [feature.his_SCHIZ]
316
+ type = "categorical"
317
+ num_categories = 3
318
+
319
+ [feature.his_DEP2YRS]
320
+ type = "categorical"
321
+ num_categories = 2
322
+
323
+ [feature.his_DEPOTHR]
324
+ type = "categorical"
325
+ num_categories = 2
326
+
327
+ [feature.his_ANXIETY]
328
+ type = "categorical"
329
+ num_categories = 3
330
+
331
+ [feature.his_OCD]
332
+ type = "categorical"
333
+ num_categories = 3
334
+
335
+ [feature.his_NPSYDEV]
336
+ type = "categorical"
337
+ num_categories = 3
338
+
339
+ [feature.his_PSYCDIS]
340
+ type = "categorical"
341
+ num_categories = 3
342
+
343
+ [feature.his_NACCTBI]
344
+ type = "categorical"
345
+ num_categories = 2
346
+
347
+ [feature.med_ANYMEDS]
348
+ type = "categorical"
349
+ num_categories = 2
350
+
351
+ [feature.med_NACCAHTN]
352
+ type = "categorical"
353
+ num_categories = 2
354
+
355
+ [feature.med_NACCHTNC]
356
+ type = "categorical"
357
+ num_categories = 2
358
+
359
+ [feature.med_NACCACEI]
360
+ type = "categorical"
361
+ num_categories = 2
362
+
363
+ [feature.med_NACCAAAS]
364
+ type = "categorical"
365
+ num_categories = 2
366
+
367
+ [feature.med_NACCBETA]
368
+ type = "categorical"
369
+ num_categories = 2
370
+
371
+ [feature.med_NACCCCBS]
372
+ type = "categorical"
373
+ num_categories = 2
374
+
375
+ [feature.med_NACCDIUR]
376
+ type = "categorical"
377
+ num_categories = 2
378
+
379
+ [feature.med_NACCVASD]
380
+ type = "categorical"
381
+ num_categories = 2
382
+
383
+ [feature.med_NACCANGI]
384
+ type = "categorical"
385
+ num_categories = 2
386
+
387
+ [feature.med_NACCLIPL]
388
+ type = "categorical"
389
+ num_categories = 2
390
+
391
+ [feature.med_NACCNSD]
392
+ type = "categorical"
393
+ num_categories = 2
394
+
395
+ [feature.med_NACCAC]
396
+ type = "categorical"
397
+ num_categories = 2
398
+
399
+ [feature.med_NACCADEP]
400
+ type = "categorical"
401
+ num_categories = 2
402
+
403
+ [feature.med_NACCAPSY]
404
+ type = "categorical"
405
+ num_categories = 2
406
+
407
+ [feature.med_NACCAANX]
408
+ type = "categorical"
409
+ num_categories = 2
410
+
411
+ [feature.med_NACCADMD]
412
+ type = "categorical"
413
+ num_categories = 2
414
+
415
+ [feature.med_NACCPDMD]
416
+ type = "categorical"
417
+ num_categories = 2
418
+
419
+ [feature.med_NACCEMD]
420
+ type = "categorical"
421
+ num_categories = 2
422
+
423
+ [feature.med_NACCEPMD]
424
+ type = "categorical"
425
+ num_categories = 2
426
+
427
+ [feature.med_NACCDBMD]
428
+ type = "categorical"
429
+ num_categories = 2
430
+
431
+ [feature.ph_VISION]
432
+ type = "categorical"
433
+ num_categories = 2
434
+
435
+ [feature.ph_VISCORR]
436
+ type = "categorical"
437
+ num_categories = 2
438
+
439
+ [feature.ph_VISWCORR]
440
+ type = "categorical"
441
+ num_categories = 2
442
+
443
+ [feature.ph_HEARING]
444
+ type = "categorical"
445
+ num_categories = 2
446
+
447
+ [feature.ph_HEARAID]
448
+ type = "categorical"
449
+ num_categories = 2
450
+
451
+ [feature.ph_HEARWAID]
452
+ type = "categorical"
453
+ num_categories = 2
454
+
455
+ [feature.cvd_ABRUPT]
456
+ type = "categorical"
457
+ num_categories = 2
458
+
459
+ [feature.cvd_STEPWISE]
460
+ type = "categorical"
461
+ num_categories = 2
462
+
463
+ [feature.cvd_SOMATIC]
464
+ type = "categorical"
465
+ num_categories = 2
466
+
467
+ [feature.cvd_EMOT]
468
+ type = "categorical"
469
+ num_categories = 2
470
+
471
+ [feature.cvd_HXHYPER]
472
+ type = "categorical"
473
+ num_categories = 2
474
+
475
+ [feature.cvd_HXSTROKE]
476
+ type = "categorical"
477
+ num_categories = 2
478
+
479
+ [feature.cvd_FOCLSYM]
480
+ type = "categorical"
481
+ num_categories = 2
482
+
483
+ [feature.cvd_FOCLSIGN]
484
+ type = "categorical"
485
+ num_categories = 2
486
+
487
+ [feature.cvd_CVDCOG]
488
+ type = "categorical"
489
+ num_categories = 2
490
+
491
+ [feature.cvd_STROKCOG]
492
+ type = "categorical"
493
+ num_categories = 2
494
+
495
+ [feature.cvd_CVDIMAG]
496
+ type = "categorical"
497
+ num_categories = 2
498
+
499
+ [feature.cvd_CVDIMAG1]
500
+ type = "categorical"
501
+ num_categories = 2
502
+
503
+ [feature.cvd_CVDIMAG2]
504
+ type = "categorical"
505
+ num_categories = 2
506
+
507
+ [feature.cvd_CVDIMAG3]
508
+ type = "categorical"
509
+ num_categories = 2
510
+
511
+ [feature.cvd_CVDIMAG4]
512
+ type = "categorical"
513
+ num_categories = 2
514
+
515
+ [feature.updrs_PDNORMAL]
516
+ type = "categorical"
517
+ num_categories = 2
518
+
519
+ [feature.updrs_SPEECH]
520
+ type = "categorical"
521
+ num_categories = 6
522
+
523
+ [feature.updrs_FACEXP]
524
+ type = "categorical"
525
+ num_categories = 6
526
+
527
+ [feature.updrs_TRESTFAC]
528
+ type = "categorical"
529
+ num_categories = 6
530
+
531
+ [feature.updrs_TRESTRHD]
532
+ type = "categorical"
533
+ num_categories = 6
534
+
535
+ [feature.updrs_TRESTLHD]
536
+ type = "categorical"
537
+ num_categories = 6
538
+
539
+ [feature.updrs_TRESTRFT]
540
+ type = "categorical"
541
+ num_categories = 6
542
+
543
+ [feature.updrs_TRESTLFT]
544
+ type = "categorical"
545
+ num_categories = 6
546
+
547
+ [feature.updrs_TRACTRHD]
548
+ type = "categorical"
549
+ num_categories = 6
550
+
551
+ [feature.updrs_TRACTLHD]
552
+ type = "categorical"
553
+ num_categories = 6
554
+
555
+ [feature.updrs_RIGDNECK]
556
+ type = "categorical"
557
+ num_categories = 6
558
+
559
+ [feature.updrs_RIGDUPRT]
560
+ type = "categorical"
561
+ num_categories = 6
562
+
563
+ [feature.updrs_RIGDUPLF]
564
+ type = "categorical"
565
+ num_categories = 6
566
+
567
+ [feature.updrs_RIGDLORT]
568
+ type = "categorical"
569
+ num_categories = 6
570
+
571
+ [feature.updrs_RIGDLOLF]
572
+ type = "categorical"
573
+ num_categories = 6
574
+
575
+ [feature.updrs_TAPSRT]
576
+ type = "categorical"
577
+ num_categories = 6
578
+
579
+ [feature.updrs_TAPSLF]
580
+ type = "categorical"
581
+ num_categories = 6
582
+
583
+ [feature.updrs_HANDMOVR]
584
+ type = "categorical"
585
+ num_categories = 6
586
+
587
+ [feature.updrs_HANDMOVL]
588
+ type = "categorical"
589
+ num_categories = 6
590
+
591
+ [feature.updrs_HANDALTR]
592
+ type = "categorical"
593
+ num_categories = 6
594
+
595
+ [feature.updrs_HANDALTL]
596
+ type = "categorical"
597
+ num_categories = 6
598
+
599
+ [feature.updrs_LEGRT]
600
+ type = "categorical"
601
+ num_categories = 6
602
+
603
+ [feature.updrs_LEGLF]
604
+ type = "categorical"
605
+ num_categories = 6
606
+
607
+ [feature.updrs_ARISING]
608
+ type = "categorical"
609
+ num_categories = 6
610
+
611
+ [feature.updrs_POSTURE]
612
+ type = "categorical"
613
+ num_categories = 6
614
+
615
+ [feature.updrs_GAIT]
616
+ type = "categorical"
617
+ num_categories = 6
618
+
619
+ [feature.updrs_POSSTAB]
620
+ type = "categorical"
621
+ num_categories = 6
622
+
623
+ [feature.updrs_BRADYKIN]
624
+ type = "categorical"
625
+ num_categories = 6
626
+
627
+ [feature.npiq_NPIQINF]
628
+ type = "categorical"
629
+ num_categories = 3
630
+
631
+ [feature.npiq_DEL]
632
+ type = "categorical"
633
+ num_categories = 4
634
+
635
+ [feature.npiq_HALL]
636
+ type = "categorical"
637
+ num_categories = 4
638
+
639
+ [feature.npiq_AGIT]
640
+ type = "categorical"
641
+ num_categories = 4
642
+
643
+ [feature.npiq_DEPD]
644
+ type = "categorical"
645
+ num_categories = 4
646
+
647
+ [feature.npiq_ANX]
648
+ type = "categorical"
649
+ num_categories = 4
650
+
651
+ [feature.npiq_ELAT]
652
+ type = "categorical"
653
+ num_categories = 4
654
+
655
+ [feature.npiq_APA]
656
+ type = "categorical"
657
+ num_categories = 4
658
+
659
+ [feature.npiq_DISN]
660
+ type = "categorical"
661
+ num_categories = 4
662
+
663
+ [feature.npiq_IRR]
664
+ type = "categorical"
665
+ num_categories = 4
666
+
667
+ [feature.npiq_MOT]
668
+ type = "categorical"
669
+ num_categories = 4
670
+
671
+ [feature.npiq_NITE]
672
+ type = "categorical"
673
+ num_categories = 4
674
+
675
+ [feature.npiq_APP]
676
+ type = "categorical"
677
+ num_categories = 4
678
+
679
+ [feature.gds_NOGDS]
680
+ type = "categorical"
681
+ num_categories = 2
682
+
683
+ [feature.gds_SATIS]
684
+ type = "categorical"
685
+ num_categories = 2
686
+
687
+ [feature.gds_DROPACT]
688
+ type = "categorical"
689
+ num_categories = 2
690
+
691
+ [feature.gds_EMPTY]
692
+ type = "categorical"
693
+ num_categories = 2
694
+
695
+ [feature.gds_BORED]
696
+ type = "categorical"
697
+ num_categories = 2
698
+
699
+ [feature.gds_SPIRITS]
700
+ type = "categorical"
701
+ num_categories = 2
702
+
703
+ [feature.gds_AFRAID]
704
+ type = "categorical"
705
+ num_categories = 2
706
+
707
+ [feature.gds_HAPPY]
708
+ type = "categorical"
709
+ num_categories = 2
710
+
711
+ [feature.gds_HELPLESS]
712
+ type = "categorical"
713
+ num_categories = 2
714
+
715
+ [feature.gds_STAYHOME]
716
+ type = "categorical"
717
+ num_categories = 2
718
+
719
+ [feature.gds_MEMPROB]
720
+ type = "categorical"
721
+ num_categories = 2
722
+
723
+ [feature.gds_WONDRFUL]
724
+ type = "categorical"
725
+ num_categories = 2
726
+
727
+ [feature.gds_WRTHLESS]
728
+ type = "categorical"
729
+ num_categories = 2
730
+
731
+ [feature.gds_ENERGY]
732
+ type = "categorical"
733
+ num_categories = 2
734
+
735
+ [feature.gds_HOPELESS]
736
+ type = "categorical"
737
+ num_categories = 2
738
+
739
+ [feature.gds_BETTER]
740
+ type = "categorical"
741
+ num_categories = 2
742
+
743
+ [feature.faq_BILLS]
744
+ type = "categorical"
745
+ num_categories = 4
746
+
747
+ [feature.faq_TAXES]
748
+ type = "categorical"
749
+ num_categories = 4
750
+
751
+ [feature.faq_SHOPPING]
752
+ type = "categorical"
753
+ num_categories = 4
754
+
755
+ [feature.faq_GAMES]
756
+ type = "categorical"
757
+ num_categories = 4
758
+
759
+ [feature.faq_STOVE]
760
+ type = "categorical"
761
+ num_categories = 4
762
+
763
+ [feature.faq_MEALPREP]
764
+ type = "categorical"
765
+ num_categories = 4
766
+
767
+ [feature.faq_EVENTS]
768
+ type = "categorical"
769
+ num_categories = 4
770
+
771
+ [feature.faq_PAYATTN]
772
+ type = "categorical"
773
+ num_categories = 4
774
+
775
+ [feature.faq_REMDATES]
776
+ type = "categorical"
777
+ num_categories = 4
778
+
779
+ [feature.faq_TRAVEL]
780
+ type = "categorical"
781
+ num_categories = 4
782
+
783
+ [feature.exam_NACCNREX]
784
+ type = "categorical"
785
+ num_categories = 2
786
+
787
+ [feature.exam_NORMEXAM]
788
+ type = "categorical"
789
+ num_categories = 3
790
+
791
+ [feature.exam_FOCLDEF]
792
+ type = "categorical"
793
+ num_categories = 2
794
+
795
+ [feature.exam_GAITDIS]
796
+ type = "categorical"
797
+ num_categories = 2
798
+
799
+ [feature.exam_EYEMOVE]
800
+ type = "categorical"
801
+ num_categories = 2
802
+
803
+ [feature.exam_PARKSIGN]
804
+ type = "categorical"
805
+ num_categories = 2
806
+
807
+ [feature.exam_RESTTRL]
808
+ type = "categorical"
809
+ num_categories = 2
810
+
811
+ [feature.exam_RESTTRR]
812
+ type = "categorical"
813
+ num_categories = 2
814
+
815
+ [feature.exam_SLOWINGL]
816
+ type = "categorical"
817
+ num_categories = 2
818
+
819
+ [feature.exam_SLOWINGR]
820
+ type = "categorical"
821
+ num_categories = 2
822
+
823
+ [feature.exam_RIGIDL]
824
+ type = "categorical"
825
+ num_categories = 2
826
+
827
+ [feature.exam_RIGIDR]
828
+ type = "categorical"
829
+ num_categories = 2
830
+
831
+ [feature.exam_BRADY]
832
+ type = "categorical"
833
+ num_categories = 2
834
+
835
+ [feature.exam_PARKGAIT]
836
+ type = "categorical"
837
+ num_categories = 2
838
+
839
+ [feature.exam_POSTINST]
840
+ type = "categorical"
841
+ num_categories = 2
842
+
843
+ [feature.exam_CVDSIGNS]
844
+ type = "categorical"
845
+ num_categories = 2
846
+
847
+ [feature.exam_CORTDEF]
848
+ type = "categorical"
849
+ num_categories = 2
850
+
851
+ [feature.exam_SIVDFIND]
852
+ type = "categorical"
853
+ num_categories = 2
854
+
855
+ [feature.exam_CVDMOTL]
856
+ type = "categorical"
857
+ num_categories = 2
858
+
859
+ [feature.exam_CVDMOTR]
860
+ type = "categorical"
861
+ num_categories = 2
862
+
863
+ [feature.exam_CORTVISL]
864
+ type = "categorical"
865
+ num_categories = 2
866
+
867
+ [feature.exam_CORTVISR]
868
+ type = "categorical"
869
+ num_categories = 2
870
+
871
+ [feature.exam_SOMATL]
872
+ type = "categorical"
873
+ num_categories = 2
874
+
875
+ [feature.exam_SOMATR]
876
+ type = "categorical"
877
+ num_categories = 2
878
+
879
+ [feature.exam_POSTCORT]
880
+ type = "categorical"
881
+ num_categories = 2
882
+
883
+ [feature.exam_PSPCBS]
884
+ type = "categorical"
885
+ num_categories = 2
886
+
887
+ [feature.exam_EYEPSP]
888
+ type = "categorical"
889
+ num_categories = 2
890
+
891
+ [feature.exam_DYSPSP]
892
+ type = "categorical"
893
+ num_categories = 2
894
+
895
+ [feature.exam_AXIALPSP]
896
+ type = "categorical"
897
+ num_categories = 2
898
+
899
+ [feature.exam_GAITPSP]
900
+ type = "categorical"
901
+ num_categories = 2
902
+
903
+ [feature.exam_APRAXSP]
904
+ type = "categorical"
905
+ num_categories = 2
906
+
907
+ [feature.exam_APRAXL]
908
+ type = "categorical"
909
+ num_categories = 2
910
+
911
+ [feature.exam_APRAXR]
912
+ type = "categorical"
913
+ num_categories = 2
914
+
915
+ [feature.exam_CORTSENL]
916
+ type = "categorical"
917
+ num_categories = 2
918
+
919
+ [feature.exam_CORTSENR]
920
+ type = "categorical"
921
+ num_categories = 2
922
+
923
+ [feature.exam_ATAXL]
924
+ type = "categorical"
925
+ num_categories = 2
926
+
927
+ [feature.exam_ATAXR]
928
+ type = "categorical"
929
+ num_categories = 2
930
+
931
+ [feature.exam_ALIENLML]
932
+ type = "categorical"
933
+ num_categories = 2
934
+
935
+ [feature.exam_ALIENLMR]
936
+ type = "categorical"
937
+ num_categories = 2
938
+
939
+ [feature.exam_DYSTONL]
940
+ type = "categorical"
941
+ num_categories = 2
942
+
943
+ [feature.exam_DYSTONR]
944
+ type = "categorical"
945
+ num_categories = 2
946
+
947
+ [feature.exam_MYOCLLT]
948
+ type = "categorical"
949
+ num_categories = 2
950
+
951
+ [feature.exam_MYOCLRT]
952
+ type = "categorical"
953
+ num_categories = 2
954
+
955
+ [feature.exam_ALSFIND]
956
+ type = "categorical"
957
+ num_categories = 2
958
+
959
+ [feature.exam_GAITNPH]
960
+ type = "categorical"
961
+ num_categories = 2
962
+
963
+ [feature.exam_OTHNEUR]
964
+ type = "categorical"
965
+ num_categories = 2
966
+
967
+ [feature.bat_MMSECOMP]
968
+ type = "categorical"
969
+ num_categories = 2
970
+
971
+ [feature.bat_MMSELOC]
972
+ type = "categorical"
973
+ num_categories = 3
974
+
975
+ [feature.bat_MMSELAN]
976
+ type = "categorical"
977
+ num_categories = 3
978
+
979
+ [feature.bat_MMSEVIS]
980
+ type = "categorical"
981
+ num_categories = 2
982
+
983
+ [feature.bat_MMSEHEAR]
984
+ type = "categorical"
985
+ num_categories = 2
986
+
987
+ [feature.bat_NPSYCLOC]
988
+ type = "categorical"
989
+ num_categories = 3
990
+
991
+ [feature.bat_NPSYLAN]
992
+ type = "categorical"
993
+ num_categories = 3
994
+
995
+ [feature.bat_UDSBENRS]
996
+ type = "categorical"
997
+ num_categories = 2
998
+
999
+ [feature.bat_COGSTAT]
1000
+ type = "categorical"
1001
+ num_categories = 5
1002
+
1003
+ [feature.bat_MODCOMM]
1004
+ type = "categorical"
1005
+ num_categories = 3
1006
+
1007
+ [feature.bat_MOCACOMP]
1008
+ type = "categorical"
1009
+ num_categories = 2
1010
+
1011
+ [feature.bat_MOCAREAS]
1012
+ type = "categorical"
1013
+ num_categories = 4
1014
+
1015
+ [feature.bat_MOCALOC]
1016
+ type = "categorical"
1017
+ num_categories = 3
1018
+
1019
+ [feature.bat_MOCALAN]
1020
+ type = "categorical"
1021
+ num_categories = 3
1022
+
1023
+ [feature.bat_MOCAVIS]
1024
+ type = "categorical"
1025
+ num_categories = 2
1026
+
1027
+ [feature.bat_MOCAHEAR]
1028
+ type = "categorical"
1029
+ num_categories = 2
1030
+
1031
+ [feature.bat_CRAFTCUE]
1032
+ type = "categorical"
1033
+ num_categories = 2
1034
+
1035
+ [feature.bat_RESPVAL]
1036
+ type = "categorical"
1037
+ num_categories = 3
1038
+
1039
+ [feature.bat_RESPHEAR]
1040
+ type = "categorical"
1041
+ num_categories = 2
1042
+
1043
+ [feature.bat_RESPDIST]
1044
+ type = "categorical"
1045
+ num_categories = 2
1046
+
1047
+ [feature.bat_RESPINTR]
1048
+ type = "categorical"
1049
+ num_categories = 2
1050
+
1051
+ [feature.bat_RESPDISN]
1052
+ type = "categorical"
1053
+ num_categories = 2
1054
+
1055
+ [feature.bat_RESPFATG]
1056
+ type = "categorical"
1057
+ num_categories = 2
1058
+
1059
+ [feature.bat_RESPEMOT]
1060
+ type = "categorical"
1061
+ num_categories = 2
1062
+
1063
+ [feature.bat_RESPASST]
1064
+ type = "categorical"
1065
+ num_categories = 2
1066
+
1067
+ [feature.bat_RESPOTH]
1068
+ type = "categorical"
1069
+ num_categories = 2
1070
+
1071
+ [feature.apoe_NACCNE4S]
1072
+ type = "categorical"
1073
+ num_categories = 3
1074
+
1075
+ [feature.his_BIRTHMO]
1076
+ type = "categorical"
1077
+ num_categories = 13
1078
+
1079
+ [feature.his_PACKSPER]
1080
+ type = "categorical"
1081
+ num_categories = 6
1082
+
1083
+ [feature.bat_PENTAGON]
1084
+ type = "categorical"
1085
+ num_categories = 2
1086
+
1087
+ [feature.bat_LOGIMO]
1088
+ type = "categorical"
1089
+ num_categories = 13
1090
+
1091
+ [feature.bat_LOGIDAY]
1092
+ type = "categorical"
1093
+ num_categories = 32
1094
+
1095
+ [feature.bat_MOCATRAI]
1096
+ type = "categorical"
1097
+ num_categories = 2
1098
+
1099
+ [feature.bat_MOCACUBE]
1100
+ type = "categorical"
1101
+ num_categories = 2
1102
+
1103
+ [feature.bat_MOCACLOC]
1104
+ type = "categorical"
1105
+ num_categories = 2
1106
+
1107
+ [feature.bat_MOCACLON]
1108
+ type = "categorical"
1109
+ num_categories = 2
1110
+
1111
+ [feature.bat_MOCACLOH]
1112
+ type = "categorical"
1113
+ num_categories = 2
1114
+
1115
+ [feature.bat_MOCALETT]
1116
+ type = "categorical"
1117
+ num_categories = 2
1118
+
1119
+ [feature.bat_MOCAFLUE]
1120
+ type = "categorical"
1121
+ num_categories = 2
1122
+
1123
+ [feature.bat_MOCAORDT]
1124
+ type = "categorical"
1125
+ num_categories = 2
1126
+
1127
+ [feature.bat_MOCAORMO]
1128
+ type = "categorical"
1129
+ num_categories = 2
1130
+
1131
+ [feature.bat_MOCAORYR]
1132
+ type = "categorical"
1133
+ num_categories = 2
1134
+
1135
+ [feature.bat_MOCAORDY]
1136
+ type = "categorical"
1137
+ num_categories = 2
1138
+
1139
+ [feature.bat_MOCAORPL]
1140
+ type = "categorical"
1141
+ num_categories = 2
1142
+
1143
+ [feature.bat_MOCAORCT]
1144
+ type = "categorical"
1145
+ num_categories = 2
1146
+
1147
+ [feature.his_BIRTHYR]
1148
+ type = "numerical"
1149
+ shape = [1]
1150
+
1151
+ [feature.his_EDUC]
1152
+ type = "numerical"
1153
+ shape = [1]
1154
+
1155
+ [feature.his_NACCAGE]
1156
+ type = "numerical"
1157
+ shape = [1]
1158
+
1159
+ [feature.his_SMOKYRS]
1160
+ type = "numerical"
1161
+ shape = [1]
1162
+
1163
+ [feature.his_QUITSMOK]
1164
+ type = "numerical"
1165
+ shape = [1]
1166
+
1167
+ [feature.his_HATTYEAR]
1168
+ type = "numerical"
1169
+ shape = [1]
1170
+
1171
+ [feature.his_NACCSTYR]
1172
+ type = "numerical"
1173
+ shape = [1]
1174
+
1175
+ [feature.his_NACCTIYR]
1176
+ type = "numerical"
1177
+ shape = [1]
1178
+
1179
+ [feature.his_PDYR]
1180
+ type = "numerical"
1181
+ shape = [1]
1182
+
1183
+ [feature.his_PDOTHRYR]
1184
+ type = "numerical"
1185
+ shape = [1]
1186
+
1187
+ [feature.his_TBIYEAR]
1188
+ type = "numerical"
1189
+ shape = [1]
1190
+
1191
+ [feature.med_NACCAMD]
1192
+ type = "numerical"
1193
+ shape = [1]
1194
+
1195
+ [feature.ph_HEIGHT]
1196
+ type = "numerical"
1197
+ shape = [1]
1198
+
1199
+ [feature.ph_WEIGHT]
1200
+ type = "numerical"
1201
+ shape = [1]
1202
+
1203
+ [feature.ph_NACCBMI]
1204
+ type = "numerical"
1205
+ shape = [1]
1206
+
1207
+ [feature.ph_BPSYS]
1208
+ type = "numerical"
1209
+ shape = [1]
1210
+
1211
+ [feature.ph_BPDIAS]
1212
+ type = "numerical"
1213
+ shape = [1]
1214
+
1215
+ [feature.ph_HRATE]
1216
+ type = "numerical"
1217
+ shape = [1]
1218
+
1219
+ [feature.cvd_HACHIN]
1220
+ type = "numerical"
1221
+ shape = [1]
1222
+
1223
+ [feature.gds_NACCGDS]
1224
+ type = "numerical"
1225
+ shape = [1]
1226
+
1227
+ [feature.bat_MMSEORDA]
1228
+ type = "numerical"
1229
+ shape = [1]
1230
+
1231
+ [feature.bat_MMSEORLO]
1232
+ type = "numerical"
1233
+ shape = [1]
1234
+
1235
+ [feature.bat_NACCMMSE]
1236
+ type = "numerical"
1237
+ shape = [1]
1238
+
1239
+ [feature.bat_LOGIYR]
1240
+ type = "numerical"
1241
+ shape = [1]
1242
+
1243
+ [feature.bat_LOGIPREV]
1244
+ type = "numerical"
1245
+ shape = [1]
1246
+
1247
+ [feature.bat_LOGIMEM]
1248
+ type = "numerical"
1249
+ shape = [1]
1250
+
1251
+ [feature.bat_MEMUNITS]
1252
+ type = "numerical"
1253
+ shape = [1]
1254
+
1255
+ [feature.bat_MEMTIME]
1256
+ type = "numerical"
1257
+ shape = [1]
1258
+
1259
+ [feature.bat_UDSBENTC]
1260
+ type = "numerical"
1261
+ shape = [1]
1262
+
1263
+ [feature.bat_UDSBENTD]
1264
+ type = "numerical"
1265
+ shape = [1]
1266
+
1267
+ [feature.bat_DIGIF]
1268
+ type = "numerical"
1269
+ shape = [1]
1270
+
1271
+ [feature.bat_DIGIFLEN]
1272
+ type = "numerical"
1273
+ shape = [1]
1274
+
1275
+ [feature.bat_DIGIB]
1276
+ type = "numerical"
1277
+ shape = [1]
1278
+
1279
+ [feature.bat_DIGIBLEN]
1280
+ type = "numerical"
1281
+ shape = [1]
1282
+
1283
+ [feature.bat_ANIMALS]
1284
+ type = "numerical"
1285
+ shape = [1]
1286
+
1287
+ [feature.bat_VEG]
1288
+ type = "numerical"
1289
+ shape = [1]
1290
+
1291
+ [feature.bat_TRAILA]
1292
+ type = "numerical"
1293
+ shape = [1]
1294
+
1295
+ [feature.bat_TRAILARR]
1296
+ type = "numerical"
1297
+ shape = [1]
1298
+
1299
+ [feature.bat_TRAILALI]
1300
+ type = "numerical"
1301
+ shape = [1]
1302
+
1303
+ [feature.bat_TRAILB]
1304
+ type = "numerical"
1305
+ shape = [1]
1306
+
1307
+ [feature.bat_TRAILBRR]
1308
+ type = "numerical"
1309
+ shape = [1]
1310
+
1311
+ [feature.bat_TRAILBLI]
1312
+ type = "numerical"
1313
+ shape = [1]
1314
+
1315
+ [feature.bat_WAIS]
1316
+ type = "numerical"
1317
+ shape = [1]
1318
+
1319
+ [feature.bat_BOSTON]
1320
+ type = "numerical"
1321
+ shape = [1]
1322
+
1323
+ [feature.bat_UDSVERFC]
1324
+ type = "numerical"
1325
+ shape = [1]
1326
+
1327
+ [feature.bat_UDSVERFN]
1328
+ type = "numerical"
1329
+ shape = [1]
1330
+
1331
+ [feature.bat_UDSVERNF]
1332
+ type = "numerical"
1333
+ shape = [1]
1334
+
1335
+ [feature.bat_UDSVERLC]
1336
+ type = "numerical"
1337
+ shape = [1]
1338
+
1339
+ [feature.bat_UDSVERLR]
1340
+ type = "numerical"
1341
+ shape = [1]
1342
+
1343
+ [feature.bat_UDSVERLN]
1344
+ type = "numerical"
1345
+ shape = [1]
1346
+
1347
+ [feature.bat_UDSVERTN]
1348
+ type = "numerical"
1349
+ shape = [1]
1350
+
1351
+ [feature.bat_UDSVERTE]
1352
+ type = "numerical"
1353
+ shape = [1]
1354
+
1355
+ [feature.bat_UDSVERTI]
1356
+ type = "numerical"
1357
+ shape = [1]
1358
+
1359
+ [feature.bat_MOCATOTS]
1360
+ type = "numerical"
1361
+ shape = [1]
1362
+
1363
+ [feature.bat_NACCMOCA]
1364
+ type = "numerical"
1365
+ shape = [1]
1366
+
1367
+ [feature.bat_MOCANAMI]
1368
+ type = "numerical"
1369
+ shape = [1]
1370
+
1371
+ [feature.bat_MOCAREGI]
1372
+ type = "numerical"
1373
+ shape = [1]
1374
+
1375
+ [feature.bat_MOCADIGI]
1376
+ type = "numerical"
1377
+ shape = [1]
1378
+
1379
+ [feature.bat_MOCASER7]
1380
+ type = "numerical"
1381
+ shape = [1]
1382
+
1383
+ [feature.bat_MOCAREPE]
1384
+ type = "numerical"
1385
+ shape = [1]
1386
+
1387
+ [feature.bat_MOCAABST]
1388
+ type = "numerical"
1389
+ shape = [1]
1390
+
1391
+ [feature.bat_MOCARECN]
1392
+ type = "numerical"
1393
+ shape = [1]
1394
+
1395
+ [feature.bat_MOCARECC]
1396
+ type = "numerical"
1397
+ shape = [1]
1398
+
1399
+ [feature.bat_MOCARECR]
1400
+ type = "numerical"
1401
+ shape = [1]
1402
+
1403
+ [feature.bat_CRAFTVRS]
1404
+ type = "numerical"
1405
+ shape = [1]
1406
+
1407
+ [feature.bat_CRAFTURS]
1408
+ type = "numerical"
1409
+ shape = [1]
1410
+
1411
+ [feature.bat_DIGFORCT]
1412
+ type = "numerical"
1413
+ shape = [1]
1414
+
1415
+ [feature.bat_DIGFORSL]
1416
+ type = "numerical"
1417
+ shape = [1]
1418
+
1419
+ [feature.bat_DIGBACCT]
1420
+ type = "numerical"
1421
+ shape = [1]
1422
+
1423
+ [feature.bat_DIGBACLS]
1424
+ type = "numerical"
1425
+ shape = [1]
1426
+
1427
+ [feature.bat_CRAFTDVR]
1428
+ type = "numerical"
1429
+ shape = [1]
1430
+
1431
+ [feature.bat_CRAFTDRE]
1432
+ type = "numerical"
1433
+ shape = [1]
1434
+
1435
+ [feature.bat_CRAFTDTI]
1436
+ type = "numerical"
1437
+ shape = [1]
1438
+
1439
+ [feature.bat_MINTTOTS]
1440
+ type = "numerical"
1441
+ shape = [1]
1442
+
1443
+ [feature.bat_MINTTOTW]
1444
+ type = "numerical"
1445
+ shape = [1]
1446
+
1447
+ [feature.bat_MINTSCNG]
1448
+ type = "numerical"
1449
+ shape = [1]
1450
+
1451
+ [feature.bat_MINTSCNC]
1452
+ type = "numerical"
1453
+ shape = [1]
1454
+
1455
+ [feature.bat_MINTPCNG]
1456
+ type = "numerical"
1457
+ shape = [1]
1458
+
1459
+ [feature.bat_MINTPCNC]
1460
+ type = "numerical"
1461
+ shape = [1]
1462
+
1463
+ [feature.bat_MOCBTOTS]
1464
+ type = "numerical"
1465
+ shape = [1]
1466
+
1467
+ [feature.bat_NACCMOCB]
1468
+ type = "numerical"
1469
+ shape = [1]
1470
+
1471
+ [feature.bat_REY1REC]
1472
+ type = "numerical"
1473
+ shape = [1]
1474
+
1475
+ [feature.bat_REY1INT]
1476
+ type = "numerical"
1477
+ shape = [1]
1478
+
1479
+ [feature.bat_REY2REC]
1480
+ type = "numerical"
1481
+ shape = [1]
1482
+
1483
+ [feature.bat_REY2INT]
1484
+ type = "numerical"
1485
+ shape = [1]
1486
+
1487
+ [feature.bat_REY3REC]
1488
+ type = "numerical"
1489
+ shape = [1]
1490
+
1491
+ [feature.bat_REY3INT]
1492
+ type = "numerical"
1493
+ shape = [1]
1494
+
1495
+ [feature.bat_REY4REC]
1496
+ type = "numerical"
1497
+ shape = [1]
1498
+
1499
+ [feature.bat_REY4INT]
1500
+ type = "numerical"
1501
+ shape = [1]
1502
+
1503
+ [feature.bat_REY5REC]
1504
+ type = "numerical"
1505
+ shape = [1]
1506
+
1507
+ [feature.bat_REY5INT]
1508
+ type = "numerical"
1509
+ shape = [1]
1510
+
1511
+ [feature.bat_REY6REC]
1512
+ type = "numerical"
1513
+ shape = [1]
1514
+
1515
+ [feature.bat_REY6INT]
1516
+ type = "numerical"
1517
+ shape = [1]
1518
+
1519
+ [feature.bat_OTRAILA]
1520
+ type = "numerical"
1521
+ shape = [1]
1522
+
1523
+ [feature.bat_OTRLARR]
1524
+ type = "numerical"
1525
+ shape = [1]
1526
+
1527
+ [feature.bat_OTRLALI]
1528
+ type = "numerical"
1529
+ shape = [1]
1530
+
1531
+ [feature.bat_OTRAILB]
1532
+ type = "numerical"
1533
+ shape = [1]
1534
+
1535
+ [feature.bat_OTRLBRR]
1536
+ type = "numerical"
1537
+ shape = [1]
1538
+
1539
+ [feature.bat_OTRLBLI]
1540
+ type = "numerical"
1541
+ shape = [1]
1542
+
1543
+ [feature.bat_REYDREC]
1544
+ type = "numerical"
1545
+ shape = [1]
1546
+
1547
+ [feature.bat_REYDINT]
1548
+ type = "numerical"
1549
+ shape = [1]
1550
+
1551
+ [feature.bat_REYTCOR]
1552
+ type = "numerical"
1553
+ shape = [1]
1554
+
1555
+ [feature.bat_REYFPOS]
1556
+ type = "numerical"
1557
+ shape = [1]
1558
+
1559
+ [feature.bat_VNTTOTW]
1560
+ type = "numerical"
1561
+ shape = [1]
1562
+
1563
+ [feature.bat_VNTPCNC]
1564
+ type = "numerical"
1565
+ shape = [1]
1566
+
1567
+ [labels]
1568
+
1569
+ [label.NC]
1570
+ type = "categorical"
1571
+ num_categories = 2
1572
+
1573
+ [label.MCI]
1574
+ type = "categorical"
1575
+ num_categories = 2
1576
+
1577
+ [label.DE]
1578
+ type = "categorical"
1579
+ num_categories = 2
1580
+
1581
+ [label.AD]
1582
+ type = "categorical"
1583
+ num_categories = 2
1584
+
1585
+ [label.LBD]
1586
+ type = "categorical"
1587
+ num_categories = 2
1588
+
1589
+ [label.VD]
1590
+ type = "categorical"
1591
+ num_categories = 2
1592
+
1593
+ [label.PRD]
1594
+ type = "categorical"
1595
+ num_categories = 2
1596
+
1597
+ [label.FTD]
1598
+ type = "categorical"
1599
+ num_categories = 2
1600
+
1601
+ [label.NPH]
1602
+ type = "categorical"
1603
+ num_categories = 2
1604
+
1605
+ [label.SEF]
1606
+ type = "categorical"
1607
+ num_categories = 2
1608
+
1609
+ [label.PSY]
1610
+ type = "categorical"
1611
+ num_categories = 2
1612
+
1613
+ [label.TBI]
1614
+ type = "categorical"
1615
+ num_categories = 2
1616
+
1617
+ [label.ODE]
1618
+ type = "categorical"
1619
+ num_categories = 2
1620
+
data/nacc_test_with_np_cli.csv ADDED
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