Minor Cleanup of files. Moved to unused folder.

Not_used/extract_wiki_html.py

@@ -0,0 +1,115 @@
+import os
+import re
+import csv
+import pandas as pd
+from bs4 import BeautifulSoup
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+INPUT_DIR = os.path.join(BASE_DIR, "../data/processed/wikipedia_html")
+OUTPUT_TSV = os.path.join(BASE_DIR, "../data/processed/spreadsheet/wikipedia_metadata4.tsv")
+
+WHITELIST = {
+    "slug",
+    "title",
+    "poster_filename",
+    "Directed by",
+    "Produced by",
+    "Written by",
+    "Starring",
+    "Release date",
+    "Running time",
+    "Country",
+    "Language",
+    "Budget",
+    "Box office",
+    "Plot"
+}
+
+def clean(el):
+    if not el:
+        return ""
+    for br in el.find_all("br"):
+        br.replace_with(" | ")
+    return re.sub(r"\s+", " ", el.get_text(" ", strip=True)).strip()
+
+def parse_html(path, slug):
+    with open(path, encoding="utf-8") as f:
+        soup = BeautifulSoup(f, "html.parser")
+    row = {"slug": slug}
+    h1 = soup.select_one("h1.firstHeading")
+    if h1:
+        row["title"] = h1.get_text(strip=True)
+    else:
+        row["title"] = ""
+    # infobox
+    infobox = soup.select_one("table.infobox")
+    if infobox:
+        img = infobox.select_one("img")
+        if img and img.get("src"):
+            row["poster_filename"] = os.path.basename(img["src"])
+        else:
+            row["poster_filename"] = ""
+        for tr in infobox.select("tr"):
+            th = tr.select_one(".infobox-label")
+            td = tr.select_one(".infobox-data")
+            if th and td:
+                row[clean(th)] = clean(td)
+    # sections
+    content = soup.select_one(".mw-parser-output")
+    if not content:
+        return {k: v for k, v in row.items() if k in WHITELIST}
+    skip = {"references", "external links", "see also"}
+    current = None
+    lead = []
+    for el in content.children:
+        if getattr(el, "name", None) == "div" and "mw-heading" in el.get("class", []):
+            h = el.find(["h2", "h3", "h4", "h5", "h6"]) #assuming no more than first 6 headers need to be looked at
+            if h:
+                title = clean(h)
+                if title.lower() in skip:
+                    current = None
+                else:
+                    current = title
+                if current:
+                    row[current] = ""
+            continue
+        if not current:
+            if getattr(el, "name", None) == "p":
+                text = clean(el)
+                if text:
+                    lead.append(text)
+            continue
+        if el.name in ["p", "ul", "ol", "table"]:
+            text = clean(el)
+            if text:
+                row[current] += text
+    if lead:
+        if row.get("Plot"):
+            row["Plot"] = " | ".join(lead) + " | " + row["Plot"]
+        else:
+            row["Plot"] = " | ".join(lead)
+    return {k: v for k, v in row.items() if k in WHITELIST}
+
+def main():
+    rows = []
+    for folder in os.listdir(INPUT_DIR):
+        path = os.path.join(INPUT_DIR, folder)
+        html = next((f for f in os.listdir(path) if f.endswith(".html")), None)
+        if not html:
+            continue
+        try:
+            rows.append(parse_html(os.path.join(path, html), folder))
+        except Exception as e:
+            print("error:", html, e)
+    df = pd.DataFrame(rows).fillna("")
+    if df.empty:
+        print("The folder was empty / None parsed")
+        return
+    cols = ["slug", "poster_filename"] + [c for c in df.columns if c not in ("slug", "poster_filename")]
+    df = df[cols]
+    os.makedirs(os.path.dirname(OUTPUT_TSV), exist_ok=True)
+    df.to_csv(OUTPUT_TSV, sep="\t", index=False, quoting=csv.QUOTE_NONE, escapechar="\\")
+    print(f"Wrote {len(df)} rows -> {OUTPUT_TSV}")
+
+if __name__ == "__main__":
+    main()

Not_used/load.py

@@ -0,0 +1,12 @@
+import pandas as pd
+import dtale
+
+file_path = '../data/raw/imdb_datasets/title.basics.tsv'
+pd.set_option('display.max_columns', None)  # show all columns
+pd.set_option('display.width', 1000) # prevent columns from wrapping
+
+df = pd.read_csv(file_path, sep='\t', nrows=1)
+print(df)
+
+d = dtale.show(df, subprocess=False)
+d.open_browser()

Not_used/preprocessing.py

@@ -0,0 +1,37 @@
+import pandas as pd
+import string, re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+import nltk
+from nltk.stem import PorterStemmer, WordNetLemmatizer
+from sentence_transformers import SentenceTransformer
+
+nltk.download('wordnet')
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+
+stop_words = set(stopwords.words('english'))
+
+stemmer = PorterStemmer()
+lemmatizer = WordNetLemmatizer()
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+df = pd.read_excel('C:\\Users\\ishaa\\OneDrive\\Documents\\MSU\\Spring 2026\\Data mining\\Project\\sample_data.xlsx', engine='openpyxl')
+
+def clean_text(text):
+    text = text.lower()
+    text = text.translate(str.maketrans('', '', string.punctuation))  # Remove punctuation
+    text = re.sub(r'\W', ' ', text)  # Remove special characters
+    text = ([word for word in word_tokenize(text) if word not in stop_words])
+    text = [stemmer.stem(word) for word in text]
+    text = ' '.join(lemmatizer.lemmatize(word) for word in text)
+    return text
+# print(df.columns)
+
+df['preprocessed'] = df['Plot'].apply(clean_text)
+sample_plot = df['preprocessed'][0]
+print(sample_plot)
+
+embeddings = model.encode(sample_plot)
+print(embeddings)

Not_used/rank_cols.py

@@ -0,0 +1,63 @@
+import os
+import csv
+import sys
+from collections import defaultdict
+from tqdm import tqdm
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+TSV_PATH = os.path.join(BASE_DIR, "../data/processed/spreadsheet/wikipedia_metadata3.tsv")
+OUTPUT_PATH = os.path.join(BASE_DIR, "../data/processed/spreadsheet/rank_cols_output.txt")
+
+csv.field_size_limit(min(sys.maxsize, 2**31 - 1)) # try to increase max buffer so it doesn't fail
+#https://stackoverflow.com/questions/53538888/counting-csv-column-occurrences-on-the-fly-in-python
+
+def main():
+    lines = []
+
+    def log(msg=""):
+        print(msg)
+        lines.append(str(msg))
+
+    log(f"Reading: {TSV_PATH}")
+
+    file_size = os.path.getsize(TSV_PATH)
+    col_filled = defaultdict(int)
+    row_count = 0
+
+    with open(TSV_PATH, encoding="utf-8", buffering=4 * 1024 * 1024) as f:
+        reader = csv.reader(f, delimiter="\t")
+        headers = next(reader)
+        num_cols = len(headers)
+
+        with tqdm(total=file_size, unit="B", unit_scale=True, unit_divisor=1024, desc="Processing") as pbar:
+            for row in reader:
+                row_count += 1
+                for i, val in enumerate(row):
+                    if val and val.strip():
+                        col_filled[headers[i]] += 1
+                pbar.update(sum(map(len, row)) + num_cols) #progress bar
+
+    log(f"\nTotal rows: {row_count:,}")
+    log(f"Total columns: {num_cols}\n")
+
+    ranked = sorted(
+        headers,
+        key=lambda c: col_filled.get(c, 0) / row_count,
+        reverse=True,
+    )
+
+    log(f"{'#':<5} {'Column':<40} {'Filled':>10} {'Total':>10} {'Fill %':>8}")
+    log("-" * 75)
+    for i, col in enumerate(ranked, 1):
+        filled = col_filled.get(col, 0)
+        pct = filled / row_count * 100
+        log(f"{i:<5} {col:<40} {filled:>10,} {row_count:>10,} {pct:>7.1f}%")
+
+    with open(OUTPUT_PATH, "w", encoding="utf-8") as out:
+        out.write("\n".join(lines))
+
+    print(f"\nOutput written to: {OUTPUT_PATH}")
+
+
+if __name__ == "__main__":
+    main()

Not_used/scrape_wiki.py

@@ -0,0 +1,69 @@
+import csv
+import os
+import requests
+from time import sleep
+
+HEADERS = {"User-Agent": "cse881"}
+SEARCH_URL = "https://en.wikipedia.org/w/api.php"
+BASE_URL = "https://en.wikipedia.org/api/rest_v1"
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+INPUT_TSV = os.path.abspath(os.path.join(BASE_DIR, "../data/raw/imdb_datasets/title.basics.test.tsv"))
+OUTPUT_DIR = os.path.abspath(os.path.join(BASE_DIR, "../data/raw/wikipedia/wikipedia_html"))
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+def fetch_wikipedia_html(query):
+    params = {
+        "action": "query",
+        "list": "search",
+        "srsearch": query,
+        "format": "json"
+    }
+
+    resp = requests.get(SEARCH_URL, params=params, headers=HEADERS).json()
+    results = resp.get("query", {}).get("search", [])
+
+    if not results:
+        return None
+
+    best_title = results[0]["title"]
+    wiki_title = best_title.replace(" ", "_")
+    html_url = f"{BASE_URL}/page/html/{wiki_title}"
+    r = requests.get(html_url, headers=HEADERS)
+
+    if r.status_code != 200:
+        return None
+    return r.text
+
+
+with open(INPUT_TSV, encoding="utf-8") as f:
+    print("Opened file:", INPUT_TSV)
+    print("First 500 chars:")
+    print(f.read(500))
+    f.seek(0)
+
+    reader = csv.DictReader(f, delimiter="\t")
+    for row in reader:
+        tconst = row["tconst"]
+        title = row["primaryTitle"]
+        year = row["startYear"]
+        outfile = os.path.join(OUTPUT_DIR, f"{tconst}.html")
+        print(outfile)
+
+        if os.path.exists(outfile):
+            print(f"Skipping {tconst}: {query}")
+            continue #if exists, skip
+
+        query = f"{title} {year}" if year != "\\N" else title
+        print(f"Fetching Wikipedia for {tconst}: {query}")
+        html = fetch_wikipedia_html(query)
+        if html:
+            with open(outfile, "w", encoding="utf-8") as out:
+                out.write(html)
+        else:
+            print(f"No Wikipedia page found")
+        sleep(0.5)
+print("Completed")
+
+#https://en.wikipedia.org/w/index.php?api=wmf-restbase&title=Special%3ARestSandbox#/Page%20content/get_page_summary__title_