Update dataset_utils.py
Browse files- dataset_utils.py +4 -37
dataset_utils.py
CHANGED
@@ -75,39 +75,8 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
|
|
75 |
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
|
76 |
labVocabDict = pickle.load(fp)
|
77 |
|
78 |
-
return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),
|
79 |
-
|
80 |
-
def open_dict(task,cond, proc, out, chart, lab, med):
|
81 |
-
if cond:
|
82 |
-
with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
|
83 |
-
condDict = pickle.load(fp)
|
84 |
-
else:
|
85 |
-
condDict = None
|
86 |
-
if proc:
|
87 |
-
with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
|
88 |
-
procDict = pickle.load(fp)
|
89 |
-
else:
|
90 |
-
procDict = None
|
91 |
-
if out:
|
92 |
-
with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
|
93 |
-
outDict = pickle.load(fp)
|
94 |
-
else:
|
95 |
-
outDict = None
|
96 |
-
if chart:
|
97 |
-
with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
|
98 |
-
chartDict = pickle.load(fp)
|
99 |
-
elif lab:
|
100 |
-
with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
|
101 |
-
chartDict = pickle.load(fp)
|
102 |
-
else:
|
103 |
-
chartDict = None
|
104 |
-
if med:
|
105 |
-
with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
|
106 |
-
medDict = pickle.load(fp)
|
107 |
-
else:
|
108 |
-
medDict = None
|
109 |
-
|
110 |
-
return condDict, procDict, outDict, chartDict, medDict
|
111 |
|
112 |
def concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
|
113 |
meds=data['Med']
|
@@ -136,11 +105,11 @@ def concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,
|
|
136 |
for p,v in zip(feat,proc_val):
|
137 |
proc_df[p]=v
|
138 |
proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
|
139 |
-
print(proc_df)
|
140 |
else:
|
141 |
procedures=pd.DataFrame(procDict,columns=['PROC'])
|
142 |
features=pd.DataFrame(np.zeros([interval,len(procedures)]),columns=procedures['PROC'])
|
143 |
features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
|
|
|
144 |
|
145 |
##########OUT#########
|
146 |
if (feat_out):
|
@@ -207,7 +176,7 @@ def concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,
|
|
207 |
|
208 |
|
209 |
|
210 |
-
def generate_deep(data,interval,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
|
211 |
meds = []
|
212 |
charts = []
|
213 |
proc = []
|
@@ -215,8 +184,6 @@ def generate_deep(data,interval,task,feat_cond,feat_proc,feat_out,feat_chart,fea
|
|
215 |
lab = []
|
216 |
stat = []
|
217 |
demo = []
|
218 |
-
|
219 |
-
size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
|
220 |
dyn,cond_df,demo=concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict)
|
221 |
if feat_chart:
|
222 |
charts = dyn['CHART'].fillna(0).values
|
|
|
75 |
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
|
76 |
labVocabDict = pickle.load(fp)
|
77 |
|
78 |
+
return (len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),
|
79 |
+
ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict,condVocabDict,procVocabDict,medVocabDict,outVocabDict,chartVocabDict,labVocabDict)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
80 |
|
81 |
def concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
|
82 |
meds=data['Med']
|
|
|
105 |
for p,v in zip(feat,proc_val):
|
106 |
proc_df[p]=v
|
107 |
proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
|
|
|
108 |
else:
|
109 |
procedures=pd.DataFrame(procDict,columns=['PROC'])
|
110 |
features=pd.DataFrame(np.zeros([interval,len(procedures)]),columns=procedures['PROC'])
|
111 |
features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
|
112 |
+
proc_df=features.fillna(0)
|
113 |
|
114 |
##########OUT#########
|
115 |
if (feat_out):
|
|
|
176 |
|
177 |
|
178 |
|
179 |
+
def generate_deep(data,interval,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict, eth_vocab,gender_vocab,age_vocab,ins_vocab):
|
180 |
meds = []
|
181 |
charts = []
|
182 |
proc = []
|
|
|
184 |
lab = []
|
185 |
stat = []
|
186 |
demo = []
|
|
|
|
|
187 |
dyn,cond_df,demo=concat_data(data,interval,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict)
|
188 |
if feat_chart:
|
189 |
charts = dyn['CHART'].fillna(0).values
|