Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -3,43 +3,61 @@ import json
 import os
 import pandas as pd
 import datasets
+import pickle
+#import cohort
 
 _DESCRIPTION = """\
 Dataset for mimic4 data, by default for the Mortality task.
 Available tasks are: Mortality, Length of Stay, Readmission, Phenotype.
 The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main'
-#TODO ADD DESCRIPTION COHORTS
+mimic path should have this form : 
 """
 
 _HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
 _CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
+_GITHUB = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main"
 
+class Mimic4DatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for Mimic4Dataset."""
+
+    def __init__(
+        self,
+        mimic_path,
+        #config,
+        **kwargs,
+    ):
+        super(Mimic4DatasetConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.mimic_path = mimic_path
+        #self.config = config
+        #cohort.task_cohort(self.task,self.mimic_path)
+
+        
 class Mimic4Dataset(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
+        Mimic4DatasetConfig(
             name="Phenotype",
             version=VERSION,
-            data_dir=os.path.abspath("./data/csv/Phenotype"),
+            data_dir=os.path.abspath("./data/dict"),
             description="Dataset for mimic4 Phenotype task",
         ),
-        datasets.BuilderConfig(
+        Mimic4DatasetConfig(
             name="Readmission",
             version=VERSION,
-            data_dir=os.path.abspath("./data/csv/Readmission"),
+            data_dir=os.path.abspath("./data/dict"),
             description="Dataset for mimic4 Readmission task",
         ),
-        datasets.BuilderConfig(
+        Mimic4DatasetConfig(
             name="Length of Stay",
             version=VERSION,
-            data_dir=os.path.abspath("./data/csv/Lenght_of_Stay"),
+            data_dir=os.path.abspath("./data/dict"),
             description="Dataset for mimic4 Length of Stay task",
         ),
-        datasets.BuilderConfig(
+        Mimic4DatasetConfig(
             name="Mortality",
             version=VERSION,
-            data_dir=os.path.abspath("./data/csv/Mortality"),
+            data_dir=os.path.abspath("./data/dict"),
             description="Dataset for mimic4 Mortality task",
         ),
     ]
@@ -53,33 +71,25 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             {
                 "gender": datasets.Value("string"),
                 "ethnicity": datasets.Value("string"),
-                "insurance": datasets.Value("string"),
                 "age": datasets.Value("int32"),
-                "COND": datasets.Sequence(
-                    {
-                        "cond" : datasets.Value("string"),
-                        "value" : datasets.Value("int32")
-                    }),
+                "COND": datasets.Sequence(datasets.Value("string")),
                 "MEDS": datasets.Sequence(
                     {
-                        "med" : datasets.Value("string"),
-                        "value" : datasets.Sequence(datasets.Value("int32"))
+                        "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
+                        "rate" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
+                        "amount" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }
                     }),
                 "PROC": datasets.Sequence(
-                    {
-                        "proc" : datasets.Value("string"),
-                        "value" : datasets.Sequence(datasets.Value("int32"))
-                    }),
+                        {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))}
+                    ),
                 "CHART": datasets.Sequence(
                     {
-                        "chart" : datasets.Value("string"),
-                        "value" : datasets.Sequence(datasets.Value("int32"))
+                        "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) },
+                        "val" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }
                     }),
                 "OUT": datasets.Sequence(
-                    {
-                        "out" : datasets.Value("string"),
-                        "value" : datasets.Sequence(datasets.Value("int32"))
-                    }),
+                        {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))}
+                    ),
                 "label": datasets.ClassLabel(names=["0", "1"]),
             }
         )
@@ -88,100 +98,45 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             features=features,
             homepage=_HOMEPAGE,
             citation=_CITATION,
+            github=_GITHUB,
         )
 
     def _split_generators(self, dl_manager):
-        data_dir = self.config.data_dir
-
-        # Collect file paths for all CSV files in the subfolders
-        train_files = []
-        for split_name in os.listdir(data_dir):
-            split_dir = os.path.join(data_dir, split_name)
-            if os.path.isdir(split_dir):
-                for file_name in os.listdir(split_dir):
-                    if file_name.endswith(".csv"):
-                        file_path = os.path.join(split_dir, file_name)
-                        train_files.append(file_path)
-
-        # Return a single SplitGenerator for the train split
+        data_dir = self.config.data_dir + "/dataDic"
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": train_files,
-                    "split": datasets.Split.TRAIN,
-                },
-            )
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}),
         ]
 
 
-    def _generate_examples(self, filepaths, split):
-        #each 3 successive files are the same admission (demographic, static, dynamic)
-        labels = pd.read_csv("./data/csv/"+self.config.name +"labels.csv")
-        labels_dict = dict(zip(labels['stay_id'], labels['label']))
-        for i in range(0, len(filepaths), 3):
-            file1, file2, file3 = filepaths[i:i+3]
-            static_file = file1 if "static.csv" in file1 else file2 if "static.csv" in file2 else file3 
-            demographic_file = file1 if "demo.csv" in file1 else file2 if "demo.csv" in file2 else file3
-            dynamic_file = file1 if "dynamic.csv" in file1 else file2 if "dynamic.csv" in file2 else file3 
-            
-            #dynamic
-            dyn = pd.read_csv(dynamic_file, header=[0, 1])
-            meds = dyn['MEDS']
-            proc = dyn['PROC']
-            chart = dyn['CHART']
-            out = dyn['OUT']
-            
-            #static
-            stat = pd.read_csv(static_file, header=[0, 1])
-            stat = stat['COND']
-            
-            #demo
-            demo = pd.read_csv(demographic_file, header=0)
-
-            #dict
-            stat_dict = stat.iloc[0].to_dict()
-            demo_dict = demo.iloc[0].to_dict()
-            meds_dict = meds.to_dict(orient='list')
-            proc_dict = proc.to_dict(orient='list')
-            chart_dict = chart.to_dict(orient='list')
-            out_dict = out.to_dict(orient='list')
-            
-            stat2 = {'cond': list(stat_dict.keys()), 'value': list(stat_dict.values())}
-            meds = {'med': list(meds_dict.keys()), 'value': list(meds_dict.values())}
-            procs = {'proc': list(proc_dict.keys()), 'value': list(proc_dict.values())}
-            charts = {'chart': list(chart_dict.keys()), 'value': list(chart_dict.values())}
-            outs = {'out': list(out_dict.keys()), 'value': list(out_dict.values())}
+    def _generate_examples(self, filepath):
+        with open(filepath, 'rb') as fp:
+            dataDic = pickle.load(fp)
+        for hid, data in dataDic.items():
+            proc_features = data['Proc']
+            chart_features = data['Chart']
+            meds_features = data['Med']
+            out_features = data['Out']
+            cond_features = data['Cond']['fids']
+            eth= data['ethnicity']
+            age = data['age']
+            gender = data['gender']
+            label = data['label']
 
-            #get stay_id which is the name of the folder containing the files
-            stay_id = int(demographic_file.split("/")[-2])
-            #get the label
-            label = int(labels_dict[stay_id])
-
-            yield stay_id, {
-                "gender" : demo_dict['gender'],
-                "ethnicity" : demo_dict['ethnicity'],
-                "insurance" : demo_dict['insurance'],
-                "age" : demo_dict['Age'],
+            yield hid, {
+                "gender" : gender,
+                "ethnicity" : eth,
+                "age" : age,
                 "MEDS" : {
-                    "med" : meds['med'],
-                    "value" : meds['value']
-                },
-                "PROC" : {
-                    "proc" : procs['proc'],
-                    "value" : procs['value']
+                    "signal" : meds_features['signal'],
+                    "rate" : meds_features['rate'],
+                    "amount" : meds_features['amount']
                 },
+                "PROC" : proc_features,
                 "CHART" : {
-                    "chart" : charts['chart'],
-                    "value" : charts['value']
-                },
-                "OUT" : {
-                    "out" : outs['out'],
-                    "value" : outs['value']
-                },
-                "COND" : {
-                    "cond" : stat2['cond'],
-                    "value" : stat2['value']
+                    "signal" : chart_features['signal'],
+                    "val" : chart_features['val']
                 },
+                "OUT" : out_features,
+                "COND" : cond_features,
                 "label" : label
             }