Update test_dataset.py

test_dataset.py

@@ -15,17 +15,10 @@
 """TODO: Add a description here."""
 
 
-import csv
-import json
-import os
+import datasets
 import pandas as pd
 import numpy as np
 
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
 @InProceedings{huggingface:dataset,
 title = {A great new dataset},
@@ -35,29 +28,20 @@ year={2020}
 }
 """
 
-# TODO: Add description of the dataset here
-# You can copy an official description
 _DESCRIPTION = """\
 This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
 """
 
-# TODO: Add a link to an official homepage for the dataset here
 _HOMEPAGE = ""
 
-# TODO: Add the licence for the dataset here if you can find it
 _LICENSE = ""
 
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
 }
 
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
+class HealthStatisticsDataset(datasets.GeneratorBasedBuilder):
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -74,7 +58,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                     "Break_Out_Details": datasets.Value("string"),
                     "Break_Out_Type": datasets.Value("int32"),
                     "Life_Expectancy": datasets.Value("float32")
-                    # These are the features of your dataset like images, labels ...
                 }
             ), 
             supervised_keys=None,
@@ -84,48 +67,42 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # dl_paths = dl_manager.download_and_extract({
-        #     'train_csv': 'https://drive.google.com/file/d/1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi/view?usp=sharing'
-        # })
-        processed_filepath = self.preprocess_data("/content/drive/MyDrive/my_processed_data.csv")
+        data = pd.read_csv(dl_manager.download_and_extract(_URLS["first_domain"]))
+        processed_data = self.preprocess_data(data)
         return [
-        datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "csvpath": processed_filepath,
-            },
-        ),
-    ]
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data": processed_data},
+            ),
+        ]
+
+    def _generate_examples(self, data):
+        for key, row in data.iterrows():
+            year = int(row['Year']) if 'Year' in row else None
+            if 'Geolocation' in row and isinstance(row['Geolocation'], str):
+                geo_str = row['Geolocation'].replace('(', '').replace(')', '')
+                latitude, longitude = map(float, geo_str.split(', '))
+            else:
+                latitude, longitude = None, None 
+            yield key, {
+                "Year": year,
+                "Location_Abbr": row.get('LocationAbbr', None),
+                "Location_Desc": row.get('LocationDesc', None), 
+                "Geolocation": {
+                    "latitude": latitude,
+                    "longitude": longitude
+                },
+                "Disease_Type": int(row["Disease_Type"]) if "Disease_Type" in row else None,
+                "Data_Value_Type": int(row["Data_Value_Type"]) if "Data_Value_Type" in row else None,
+                "Data_Value": float(row["Data_Value"]) if "Data_Value" in row else None,
+                "Break_Out_Category": row.get("Break_Out_Category", None),
+                "Break_Out_Details": row.get("Break_Out_Details", None),
+                "Break_Out_Type": int(row["Break_Out_Type"]) if 'Break_Out_Type' in row else None,
+                "Life_Expectancy": float(row["Life_Expectancy"]) if row.get("Life_Expectancy") else None
+            }
 
-    def _generate_examples(self, csvpath):
-        with open(csvpath, encoding="utf-8") as f:
-            reader = csv.DictReader(f)  
-            for key, row in enumerate(reader):
-                year = int(row['Year']) if 'Year' in row else None
-                if 'Geolocation' in row and isinstance(row['Geolocation'], str):
-                    geo_str = row['Geolocation'].replace('(', '').replace(')', '')
-                    latitude, longitude = map(float, geo_str.split(', '))
-                else:
-                    latitude, longitude = None, None 
-                yield key, {
-                    "Year": year,
-                    "Location_Abbr": row.get('LocationAbbr', None),
-                    "Location_Desc": row.get('LocationDesc', None), 
-                    "Geolocation": {
-                        "latitude": latitude,
-                        "longitude": longitude
-                    },
-                    "Disease_Type": int(row["Disease_Type"]) if "Disease_Type" in row else None,
-                    "Data_Value_Type": int(row["Data_Value_Type"]) if "Data_Value_Type" in row else None,
-                    "Data_Value": float(row["Data_Value"]) if "Data_Value" in row else None,
-                    "Break_Out_Category": row.get("Break_Out_Category", None),
-                    "Break_Out_Details": row.get("Break_Out_Details", None),
-                    "Break_Out_Type": int(row["Break_Out_Type"]) if 'Break_Out_Type' in row else None,
-                    "Life_Expectancy": float(row["Life_Expectancy"]) if row.get("Life_Expectancy") else None
-                }
-      
     @staticmethod
-    def preprocess_data(filepath):
+    def preprocess_data(data):
         data = pd.read_csv("https://drive.google.com/file/d/1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-/view?usp=drive_link")
         data = data[['YearStart', 'LocationAbbr', 'LocationDesc', 'Geolocation', 'Topic', 'Question', 'Data_Value_Type', 'Data_Value', 'Data_Value_Alt', 
                      'Low_Confidence_Limit', 'High_Confidence_Limit', 'Break_Out_Category', 'Break_Out']]
@@ -206,5 +183,4 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         data2.rename(columns={'Question':'Disease_Type'}, inplace=True)
         data2['Life_Expectancy'] = np.where(data2['Break_Out_Type'] == 0, data2['Life_Expectancy'], np.nan)
         data2 = data2.reset_index(drop=True)
-        processed_filepath = '/content/drive/MyDrive/my_processed_data.csv'
-        return processed_filepath
+        return data2