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baseline.py
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baseline.py
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"""Code for baseline linear model"""
import json
import pandas as pd
# import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import sys
from collections import Counter
from dataprocessing import parse_json, business_path, review_path, user_path
businessDF = parse_json(business_path)
reviewDF = parse_json(review_path)
stateList = businessDF['state'].to_numpy()
businessIDList = businessDF['business_id'].to_numpy()
businessIDList = businessIDList[np.where(stateList == 'GA')]
businessIDList = businessIDList.tolist()
reviewDF = reviewDF[reviewDF['business_id'].isin(businessIDList)]
allRatings = reviewDF['stars'].to_numpy()
# userID -> ratings, businessID -> ratings
userIDCount = {}
businessIDCount = {}
for index in reviewDF.index:
rating = reviewDF['stars'][index]
businessID = reviewDF['business_id'][index]
userID = reviewDF['user_id'][index]
if businessID not in businessIDCount:
businessIDCount[businessID] = []
businessIDCount[businessID].append(rating)
if userID not in userIDCount:
userIDCount[userID] = []
userIDCount[userID].append(rating)
for key in businessIDCount:
ratings = businessIDCount[key]
businessIDCount[key] = sum(ratings) / len(ratings)
for key in list(userIDCount):
ratings = userIDCount[key]
if len(ratings) < 10:
userIDCount.pop(key, None)
else:
userIDCount[key] = sum(ratings) / len(ratings)
mu = np.mean(allRatings)
original_stdout = sys.stdout
with open("baseline_results_new.txt", 'w') as f:
sys.stdout = f
print("mean ratings in GA ", mu)
print("==== businessID to avg rating ===")
print(businessIDCount)
print("==== userIDCount to avg rating ===")
print(userIDCount)
print("==== userID that has more than or equal to 10 reviews ====")
print(userIDCount.keys())
print("==== all business id in GA ====")
print(businessIDCount.keys())
sys.stdout = original_stdout