Update NC_Crime.py

NC_Crime.py

@@ -22,8 +22,6 @@ from typing import List
 import datasets
 import logging
 import pandas as pd
-from pyproj import Transformer
-
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -80,138 +78,35 @@ class NCCrimeDataset(datasets.GeneratorBasedBuilder):
         )
 
     
-    
     def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
         # Use the raw GitHub link to download the CSV file
-        cary_path = dl_manager.download_and_extract("https://data.townofcary.org/api/explore/v2.1/catalog/datasets/cpd-incidents/exports/csv?lang=en&timezone=US%2FEastern&use_labels=true&delimiter=%2C")
-        # durham_path = dl_manager.download_and_extract("https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data")
-        chapel_hill_path = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI")
-        # raleigh_path = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=1SZi4e01TxwuDDb6k9EU_7i-qTP1Xq2sm")
-        # Cary
-        # "https://data.townofcary.org/api/explore/v2.1/catalog/datasets/cpd-incidents/exports/csv?lang=en&timezone=US%2FEastern&use_labels=true&delimiter=%2C",
-        # Durham
-        # "https://www.arcgis.com/sharing/rest/content/items/7132216432df4957830593359b0c4030/data",
-        # Raleigh
-        # "https://drive.google.com/uc?export=download&id=19cZzyedCLUtQt9Ko4bcOixWIJHBn9CfI",
-        # Chapel Hill
-        # "https://drive.google.com/uc?export=download&id=1SZi4e01TxwuDDb6k9EU_7i-qTP1Xq2sm"
-
-        cary_df = self._preprocess_cary(cary_path)
-        # durham_df = self._preprocess_durham(durham_path)
-        # raleigh_df = self._preprocess_raleigh(raleigh_path)
-        chapel_hill_df = self._preprocess_chapel_hill(chapel_hill_path)
-        
-        # combined_df = pd.concat([cary_df, durham_df, raleigh_df, chapel_hill_df], ignore_index=True)
-
-        combined_df = pd.concat([cary_df, chapel_hill_df], ignore_index=True)
-
-        
-        combined_file_path = os.path.join(dl_manager.download_dir, "combined_dataset.csv")
-        combined_df.to_csv(combined_file_path, index=False)
+        downloaded_file_path = dl_manager.download_and_extract(
+            # https://drive.google.com/file/d/109KUBevJNNC_aKcTmGhjh53c3JrlgTW4/view?usp=drive_link
+        # "https://drive.google.com/uc?export=download&id=109KUBevJNNC_aKcTmGhjh53c3JrlgTW4"
+            # "https://drive.google.com/uc?export=download&id=1SdnSc-e3OwzfXgpCZVdZuq2Fq9iCrd21"
+            # "https://drive.google.com/uc?export=download&id=1C1vwAe4nVTdu6P8lHsmyLbJHUsHfT72h"
+
+            #"https://drive.google.com/uc?export=download&id=1Se-B8Y-SdU0caZzGJyX_0YW44TZwaq3l"
+            
+        # "https://raw.githubusercontent.com/znw2024/NC-Crime/main/DCCR.csv"
+        "https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/DCCR.csv.zip"
+            # "https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/NC_dataset.csv.zip"
 
+        )
+        unzipped_file_path = os.path.join(downloaded_file_path, "DCCR.csv")
+        # Return a list of split generators
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": combined_file_path})
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": unzipped_file_path})
         ]
 
 
-    def _preprocess_chapel_hill(self, file_path):
-         # Load the dataset
-        Chapel = pd.read_csv(file_path, low_memory=False)
-
-        # Replace specified values with None
-        replace_values = {'<Null>': None, 'NONE': None}
-        Chapel['Weapon_Description'] = Chapel['Weapon_Description'].replace(replace_values)
-
-        # Define the category mapping
-        category_mapping = {
-        'Theft': ['THEFT/LARCENY', 'LARCENY FROM AU', 'LARCENY FROM PE', 'LARCENY OF OTHE', 'LARCENY FROM BU', 'LARCENY OF BIKE', 'LARCENY FROM RE', 'LARCENY OF AUTO'],
-        'Assault': ['ASSAULT/SEXUAL', 'ASSAULT', 'STAB GUNSHOT PE', 'ACTIVE ASSAILAN'],
-        'Burglary': ['BURGLARY', 'BURGLARY ATTEMP', 'STRUCTURE COLLAPSE', 'ROBBERY/CARJACK'],
-        'Drugs': ['DRUGS'],
-        'Traffic Violations': ['TRAFFIC STOP', 'TRAFFIC/TRANSPO', 'TRAFFIC VIOLATI', 'MVC', 'MVC W INJURY', 'MVC W INJURY AB', 'MVC W INJURY DE', 'MVC ENTRAPMENT'],
-        'Disorderly Conduct': ['DISTURBANCE/NUI', 'DOMESTIC DISTUR', 'DISPUTE', 'DISTURBANCE', 'LOST PROPERTY', 'TRESPASSING/UNW', 'REFUSAL TO LEAV', 'SUSPICIOUS COND', 'STRUCTURE FIRE'],
-        'Fraud': ['FRAUD OR DECEPT'],
-        'Sexual Offenses': ['SEXUAL OFFENSE'],
-        'Homicide': ['SUICIDE ATTEMPT', 'ABUSE/ABANDOMEN', 'DECEASED PERSON'],
-        'Weapons Violations': ['WEAPON/FIREARMS'],
-        'Animal-related Offenses': ['ANIMAL BITE', 'ANIMAL', 'ANIMAL CALL'],
-        'Missing Person': ['MISSING PERSON'],
-        'Public Service': ['PUBLIC SERVICE', 'PUBLICE SERVICE'],
-        'Miscellaneous': ['<Null>', 'SUSPICIOUS/WANT', 'MISC OFFICER IN', 'INDECENCY/LEWDN', 'PUBLIC SERVICE', 'TRESPASSING', 'UNKNOWN PROBLEM', 'LOUD NOISE', 'ESCORT', 'ABDUCTION/CUSTO', 'THREATS', 'BURGLAR ALARM', 'DOMESTIC', 'PROPERTY FOUND', 'FIREWORKS', 'MISSING/RUNAWAY', 'MENTAL DISORDER', 'CHECK WELL BEIN', 'PSYCHIATRIC', 'OPEN DOOR', 'ABANDONED AUTO', 'HARASSMENT THRE', 'JUVENILE RELATE', 'ASSIST MOTORIST', 'HAZARDOUS DRIVI', 'MVC', 'GAS LEAK FIRE', 'ASSIST OTHER AG', 'DOMESTIC ASSIST', 'SUSPICIOUS VEHI', 'UNKNOWN LE', 'ALARMS', '911 HANGUP', 'BOMB/CBRN/PRODU', 'STATIONARY PATR', 'LITTERING', 'HOUSE CHECK', 'CARDIAC', 'CLOSE PATROL', 'BOMB FOUND/SUSP', 'INFO FOR ALL UN', 'UNCONCIOUS OR F', 'LIFTING ASSISTA', 'ATTEMPT TO LOCA', 'SICK PERSON', 'HEAT OR COLD EX', 'CONFINED SPACE', 'TRAUMATIC INJUR', 'DROWNING', 'CITY ORDINANCE']
-        }
-         # Function to categorize crime
-        def categorize_crime(crime):
-            for category, crimes in category_mapping.items():
-                if crime in crimes:
-                    return category
-            return 'Miscellaneous'
-
-        # Create a new DataFrame with simplified crime categories
-        Chapel_new = pd.DataFrame({
-            "year": pd.to_datetime(Chapel['Date_of_Occurrence']).dt.year,
-            "city": "Chapel Hill",
-            "crime_major_category": Chapel['Reported_As'].apply(categorize_crime),
-            "crime_detail": Chapel['Offense'].str.title(),
-            "latitude": Chapel['X'].round(5).fillna(0),
-            "longitude": Chapel['Y'].round(5).fillna(0),
-            "occurance_time": pd.to_datetime(Chapel['Date_of_Occurrence'].str.replace(r'\+\d{2}$', '', regex=True)).dt.strftime('%Y/%m/%d %H:%M:%S'),
-            "clear_status": None,
-            "incident_address": Chapel['Street'].str.replace("@", " "),
-            "notes": Chapel['Weapon_Description'].apply(lambda x: f"Weapon: {x}" if pd.notnull(x) else "Weapon: None").str.title()
-        }).fillna("No Data")
-
-        # Correct the latitude and longitude if necessary
-        Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['latitude', 'longitude']] = Chapel_new.loc[(Chapel_new['latitude'].between(-80, -70)) & (Chapel_new['longitude'].between(30, 40)), ['longitude', 'latitude']].values
-
-        # Ensure latitude and longitude are in the expected range
-        Chapel_new = Chapel_new.loc[(Chapel_new['latitude'].between(30, 40)) & (Chapel_new['longitude'].between(-80, -70))]
-
-        # Filter for years 2015 and onwards
-        Chapel_new = Chapel_new[Chapel_new['year'] >= 2015]
-
-        return Chapel_new
-
-    def _preprocess_cary(self, file_path):
-        # Load the dataset
-        df = pd.read_csv(file_path, low_memory=False).dropna(subset=['Year'])
-
-        # Define the crime categorization function
-        def categorize_crime(crime):
-            crime_mapping = {
-                'Theft': ['BURGLARY', 'MOTOR VEHICLE THEFT', 'LARCENY'],
-                'Arson': ['ARSON'],
-                'Assault': ['AGGRAVATED ASSAULT'],
-                'Homicide': ['MURDER'],
-                'Robbery': ['ROBBERY']
-            }
-            for category, crimes in crime_mapping.items():
-                if crime in crimes:
-                    return category
-            return 'Miscellaneous'
-
-        # Apply the crime categorization function and preprocess the dataset
-        processed_df = pd.DataFrame({
-            "year": df["Year"].astype(int),
-            "city": "Cary",
-            "crime_major_category": df['Crime Category'].apply(categorize_crime).str.title(),
-            "crime_detail": df['Crime Type'].str.title(),
-            "latitude": df['Lat'].fillna(0).round(5).fillna(0),
-            "longitude": df['Lon'].fillna(0).round(5).fillna(0),
-            "occurance_time": pd.to_datetime(df['Begin Date Of Occurrence'] + ' ' + df['Begin Time Of Occurrence']).dt.strftime('%Y/%m/%d %H:%M:%S'),
-            "clear_status": None,
-            "incident_address": df['Geo Code'],
-            "notes": 'District: '+ df['District'].str.title() + ' Violent Property: ' + df['Violent Property'].str.title()
-        }).fillna("No Data")
-
-        # Filter the dataset for records from 2015 onwards
-        processed_df = processed_df[processed_df['year'] >= 2015]
-        
-        return processed_df
+
+
 
 
     def _generate_examples(self, filepath):
         # Read the CSV file
-        df = pd.read_csv(filepath)  
+        df = pd.read_csv(filepath)  ## just for test
         # Iterate over the rows and yield examples
         for i, row in df.iterrows():
             yield i, {