Delete make_training_set.py

Personality_prediction/Personality_prediction/make_training_set.py

-import os
-import collections
-import pandas as pd
-import csv
-import re
-
-
-DATA_DIRECTORY = "data"
-MBTI_UNCLEAN_CSV_PATH = os.path.join(DATA_DIRECTORY, "mbti_unclean.csv")
-DIMENSIONS = ("IE", "NS", "TF", "PJ")
-
-
-df = pd.read_csv(MBTI_UNCLEAN_CSV_PATH)
-
-counts = collections.defaultdict(int)
-for dimension in DIMENSIONS:
-    letter_1, letter_2 = dimension
-    for index, row in df.iterrows():
-        mbti = row["type"]
-        hundred_posts = row["posts"].split("|||")
-        for post in hundred_posts:
-            if (
-                ("http" in post)
-                or (post == "")
-                or (post == None)
-                or (not re.search("[a-zA-Z]", post))
-            ):  # ignore deformed posts
-                continue
-            if letter_1 in mbti:
-                counts[letter_1] += 1
-            if letter_2 in mbti:
-                counts[letter_2] += 1
-
-for dimension in DIMENSIONS:
-    letter_1, letter_2 = dimension
-    if counts[letter_1] < counts[letter_2]:
-        limit = counts[letter_1]
-    else:
-        limit = counts[letter_2]
-
-    for letter in [letter_1, letter_2]:
-        posts = []
-        i = 0
-        for index, row in df.iterrows():
-            if letter in row["type"]:
-                hundred_posts = row["posts"].split("|||")
-                for post in hundred_posts:
-                    if i == limit:
-                        break
-                    if (
-                        ("http" in post)
-                        or (post == "")
-                        or (post == None)
-                        or (not re.search("[a-zA-Z]", post))
-                    ):  # ignore deformed posts
-                        continue
-                    posts.append(post)
-                    i += 1
-
-        train_csv_path = os.path.join(DATA_DIRECTORY, f"train_{letter}.csv")
-        with open(train_csv_path, "w", encoding="utf-8") as f:
-            writer = csv.writer(f)
-            for post in posts:
-                writer.writerow([post])