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import csv
from random import shuffle
import pandas as pd
NEGATIVE = 0
POSITIVE = 1
ROTTEN = 0
FRESH = 1
def parse_is_top_critic(is_top_critic):
return is_top_critic == "True"
def parse_score_sentiment(score):
if score == "NEGATIVE":
return NEGATIVE
if score == "POSITIVE":
return POSITIVE
raise ValueError(f"Unknown score sentiment: {score}")
def parse_review_state(review_state):
if review_state == "rotten":
return ROTTEN
if review_state == "fresh":
return FRESH
raise ValueError(f"Unknown review state: {review_state}")
def run():
with open("rotten_tomatoes_movie_reviews.csv") as f:
reader = csv.DictReader(f)
rows = list(reader)
positive_rows = []
negative_rows = []
for row in rows:
row["isTopCritic"] = parse_is_top_critic(row["isTopCritic"])
row["scoreSentiment"] = parse_score_sentiment(row["scoreSentiment"])
row["reviewState"] = parse_review_state(row["reviewState"])
if row["scoreSentiment"] == POSITIVE:
positive_rows.append(row)
else:
negative_rows.append(row)
# Save rows to csv file called original.csv
pd.DataFrame(rows).to_csv("original.csv", index=False)
shuffle(positive_rows)
shuffle(negative_rows)
# Generate the balanced datasets
balanced_size = min(len(positive_rows), len(negative_rows))
balanced_rows = []
for i in range(0, balanced_size):
balanced_rows.append(positive_rows[i])
balanced_rows.append(negative_rows[i])
# Save balanced rows to csv file called balanced.csv
pd.DataFrame(balanced_rows).to_csv("balanced.csv", index=False)
if __name__ == "__main__":
run()
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