flaskapp / src /create_dataset.py
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import pandas as pd
import ujson as json
import gc
import numpy as np
from concurrent.futures import ProcessPoolExecutor
import multiprocessing as mp
from pymongo import MongoClient
from collections import defaultdict
from pathlib import Path
# def read_json_parallel(file_path, num_workers=None):
# """Read JSON file using parallel processing"""
# if num_workers is None:
# num_workers = max(1, mp.cpu_count() - 1)
# print(f"Reading {file_path}...")
# # Read chunks and concatenate them into a single DataFrame
# df = pd.read_json(file_path, lines=True, dtype_backend="pyarrow", chunksize=100000)
# return next(df)
def read_data_mongo(file_path, num_workers=None):
"""Read JSON file using parallel processing"""
if num_workers is None:
num_workers = max(1, mp.cpu_count() - 1)
print(f"Reading {file_path}...")
conn_str = "mongodb://Mtalha:[email protected]/"
client = MongoClient(conn_str)
databases = client.list_database_names()
db_client=client["Yelp"]
# Read the entire file at once since chunksize isn't needed for parallel reading here
# Use 'records' orient if your JSON was saved with this format
try:
collection = db_client[file_path]
documents = collection.find({}, {"_id": 0})
data = list(documents)
final_dict=defaultdict(list)
for dictt in data:
for k,v in dictt.items():
final_dict[k].append(v)
df=pd.DataFrame(final_dict)
# df = pd.read_json(file_path, orient='records', dtype_backend="pyarrow")
except Exception as e:
# If 'records' doesn't work, try without specifying orient or with 'split'
# This is a fallback for different JSON structures
# df = pd.read_json(file_path, dtype_backend="pyarrow")
print("ERROR WHILE READING FILES FORM MONGODB AS : ",e)
print(f"Finished reading. DataFrame shape: {df.shape}")
return df
def process_datasets(output_path,filename):
# File paths
file_paths = {
'business': "yelp_academic_dataset_business",
'checkin': "yelp_academic_dataset_checkin",
'review': "yelp_academic_dataset_review",
'tip': "yelp_academic_dataset_tip",
'user': "yelp_academic_dataset_user",
'google': "google_review_dataset"
}
# Read datasets with progress tracking
print("Reading datasets...")
dfs = {}
for name, path in file_paths.items():
print(f"Processing {name} dataset...")
dfs[name] = read_data_mongo(path)
print(f"Finished reading {name} dataset. Shape: {dfs[name].shape}")
print("All files read. Starting column renaming...")
# Rename columns to avoid conflicts
# Reviews
dfs['review'] = dfs['review'].rename(columns={
'date': 'review_date',
'stars': 'review_stars',
'text': 'review_text',
'useful': 'review_useful',
'funny': 'review_funny',
'cool': 'review_cool'
})
# print("COLUMNS IN REVIEW DAFRA)
# Tips
dfs['tip'] = dfs['tip'].rename(columns={
'date': 'tip_date',
'text': 'tip_text',
'compliment_count': 'tip_compliment_count'
})
# Checkins
dfs['checkin'] = dfs['checkin'].rename(columns={
'date': 'checkin_date'
})
# Users
dfs['user'] = dfs['user'].rename(columns={
'name': 'user_name',
'review_count': 'user_review_count',
'useful': 'user_useful',
'funny': 'user_funny',
'cool': 'user_cool'
})
# Business
dfs['business'] = dfs['business'].rename(columns={
'name': 'business_name',
'stars': 'business_stars',
'review_count': 'business_review_count'
})
dfs['google'] = dfs['google'].rename(columns={
'name': 'business_name',
'stars': 'business_stars',
'review_count': 'business_review_count'
})
df_business_final= dfs['business']
df_google_final=dfs['google']
df_review_final=dfs['review']
df_tip_final=dfs['tip']
df_checkin_final=dfs['checkin']
df_user_final=dfs['user']
df_business_final=pd.concat([df_business_final,df_google_final],axis=0)
df_business_final.reset_index(drop=True,inplace=True)
print("Starting merge process...")
# Merge process with memory management
print("Step 1: Starting with reviews...")
merged_df = df_review_final
print("Step 2: Merging with business data...")
merged_df = merged_df.merge(
df_business_final,
on='business_id',
how='left'
)
print("Step 3: Merging with user data...")
merged_df = merged_df.merge(
df_user_final,
on='user_id',
how='left'
)
print("Step 4: Merging with checkin data...")
merged_df = merged_df.merge(
df_checkin_final,
on='business_id',
how='left'
)
print("Step 5: Aggregating and merging tip data...")
tip_agg = df_tip_final.groupby('business_id').agg({
'tip_compliment_count': 'sum',
'tip_text': 'count'
}).rename(columns={'tip_text': 'tip_count'})
merged_df = merged_df.merge(
tip_agg,
on='business_id',
how='left'
)
print("Filling NaN values...")
merged_df['tip_count'] = merged_df['tip_count'].fillna(0)
merged_df['tip_compliment_count'] = merged_df['tip_compliment_count'].fillna(0)
merged_df['checkin_date'] = merged_df['checkin_date'].fillna('')
merged_df["friends"].fillna(0,inplace=True)
for col in merged_df.columns:
if merged_df[col].isnull().sum()>0:
print(f" {col} has {merged_df[col].isnull().sum()} null values")
print("Shape of Merged Dataset is : ",merged_df.shape)
output_file = Path(output_path) / filename
print("COLUMNS BEFORE PREPROCESING")
print()
print(merged_df.info())
for col in merged_df.columns:
for v in merged_df[col]:
print(f"Type of values in {col} is {type(v)} and values are like : {v}")
break
merged_df.to_csv(output_file,index=False)
return merged_df
# if __name__ == "__main__":
# process_datasets()