first commit

convert.py

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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()