tag analysis
This commit is contained in:
bmachado
410
scripts/analyze_tags.py
Executable file
410
scripts/analyze_tags.py
Executable file
@ -0,0 +1,410 @@
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#!/usr/bin/env python3
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"""
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Analyze arc_puzzles_tags table for patterns, correlations, and duplicates.
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"""
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import os
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import sys
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import pymysql
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from dotenv import load_dotenv
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from collections import defaultdict
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def load_env_config():
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"""Load database configuration from .env file"""
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load_dotenv()
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return {
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'host': os.getenv('DB_HOST'),
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'user': os.getenv('DB_USER'),
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'password': os.getenv('DB_PASSWORD'),
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'database': os.getenv('DB_NAME'),
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'port': int(os.getenv('DB_PORT', 3306)),
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'charset': 'utf8mb4'
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}
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def print_section(title):
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print(f"\n{'=' * 70}")
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print(f" {title}")
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print('=' * 70)
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def print_table(headers, rows, col_widths=None):
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if not col_widths:
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col_widths = [max(len(str(h)), max(len(str(r[i])) for r in rows) if rows else 0)
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for i, h in enumerate(headers)]
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header_line = " | ".join(str(h).ljust(w) for h, w in zip(headers, col_widths))
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print(header_line)
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print("-" * len(header_line))
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for row in rows:
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print(" | ".join(str(v).ljust(w) for v, w in zip(row, col_widths)))
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def run_analysis(cursor):
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results = {}
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# 1. Most Common Tags
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print_section("1. MOST COMMON TAGS")
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cursor.execute("""
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SELECT
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t.name AS tag_name,
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t.type AS tag_type,
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COUNT(*) AS usage_count
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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GROUP BY t.id, t.name, t.type
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ORDER BY usage_count DESC
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LIMIT 20
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""")
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rows = cursor.fetchall()
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print_table(["Tag Name", "Type", "Usage"], rows, [40, 25, 10])
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results['most_common'] = rows
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# 2. Tag Correlations
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print_section("2. TAG CORRELATIONS (tags that frequently appear together)")
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cursor.execute("""
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SELECT
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t1.name AS tag1,
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t2.name AS tag2,
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COUNT(*) AS co_occurrence
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FROM arc_puzzles_tags apt1
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JOIN arc_puzzles_tags apt2 ON apt1.arc_puzzles_id = apt2.arc_puzzles_id AND apt1.tags_id < apt2.tags_id
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JOIN tags t1 ON apt1.tags_id = t1.id
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JOIN tags t2 ON apt2.tags_id = t2.id
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GROUP BY t1.id, t2.id, t1.name, t2.name
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HAVING co_occurrence >= 3
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ORDER BY co_occurrence DESC
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LIMIT 25
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""")
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rows = cursor.fetchall()
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print_table(["Tag 1", "Tag 2", "Co-occurrences"], rows, [35, 35, 15])
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results['correlations'] = rows
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# 3. Possible Duplicate Tags
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print_section("3. POSSIBLE DUPLICATE TAGS (similar names)")
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cursor.execute("""
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SELECT
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t1.id AS id1,
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t1.name AS name1,
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t1.type AS type1,
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t2.id AS id2,
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t2.name AS name2,
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t2.type AS type2
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FROM tags t1
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JOIN tags t2 ON t1.id < t2.id
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WHERE
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LOWER(REPLACE(t1.name, ' ', '')) = LOWER(REPLACE(t2.name, ' ', ''))
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OR LOWER(t1.name) LIKE CONCAT('%', LOWER(t2.name), '%')
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OR LOWER(t2.name) LIKE CONCAT('%', LOWER(t1.name), '%')
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OR SOUNDEX(t1.name) = SOUNDEX(t2.name)
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ORDER BY t1.name
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""")
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rows = cursor.fetchall()
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print_table(["ID1", "Name 1", "Type 1", "ID2", "Name 2", "Type 2"], rows, [5, 30, 15, 5, 30, 15])
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results['duplicates'] = rows
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# 4. Tag Types Distribution
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print_section("4. TAG TYPES DISTRIBUTION")
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cursor.execute("""
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SELECT
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t.type AS tag_type,
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COUNT(DISTINCT t.id) AS unique_tags,
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COUNT(*) AS total_usage
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FROM tags t
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LEFT JOIN arc_puzzles_tags apt ON t.id = apt.tags_id
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GROUP BY t.type
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ORDER BY total_usage DESC
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""")
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rows = cursor.fetchall()
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print_table(["Tag Type", "Unique Tags", "Total Usage"], rows, [25, 15, 15])
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results['types'] = rows
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# 5. Puzzles with Most Tags
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print_section("5. PUZZLES WITH MOST TAGS")
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cursor.execute("""
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SELECT
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apt.arc_puzzles_id,
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COUNT(*) AS tag_count,
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GROUP_CONCAT(t.name SEPARATOR ', ') AS tags
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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GROUP BY apt.arc_puzzles_id
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ORDER BY tag_count DESC
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LIMIT 10
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""")
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rows = cursor.fetchall()
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for row in rows:
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print(f"\n{row[0]} ({row[1]} tags):")
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print(f" {row[2][:100]}..." if len(row[2]) > 100 else f" {row[2]}")
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results['most_tagged'] = rows
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# 6. Puzzles with Fewest Tags
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print_section("6. PUZZLES WITH FEWEST TAGS")
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cursor.execute("""
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SELECT
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apt.arc_puzzles_id,
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COUNT(*) AS tag_count,
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GROUP_CONCAT(t.name SEPARATOR ', ') AS tags
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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GROUP BY apt.arc_puzzles_id
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ORDER BY tag_count ASC
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LIMIT 10
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""")
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rows = cursor.fetchall()
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for row in rows:
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print(f"{row[0]} ({row[1]} tags): {row[2]}")
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results['least_tagged'] = rows
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# 7. Duplicate Tag Assignments (same tag assigned multiple times to same puzzle)
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print_section("7. DUPLICATE TAG ASSIGNMENTS (same tag on same puzzle by different users)")
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cursor.execute("""
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SELECT
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apt.arc_puzzles_id,
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t.name AS tag_name,
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COUNT(*) AS times_assigned,
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GROUP_CONCAT(DISTINCT u.name SEPARATOR ', ') AS assigned_by_users
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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LEFT JOIN users u ON apt.user_id = u.id
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GROUP BY apt.arc_puzzles_id, apt.tags_id, t.name
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HAVING times_assigned > 1
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ORDER BY times_assigned DESC
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LIMIT 15
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""")
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rows = cursor.fetchall()
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print_table(["Puzzle ID", "Tag Name", "Times", "Assigned By"], rows, [15, 35, 8, 25])
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results['duplicate_assignments'] = rows
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# 8. User Tagging Activity
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print_section("8. USER TAGGING ACTIVITY")
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cursor.execute("""
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SELECT
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u.name AS user_name,
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COUNT(*) AS tags_assigned,
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COUNT(DISTINCT apt.arc_puzzles_id) AS puzzles_tagged,
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COUNT(DISTINCT apt.tags_id) AS unique_tags_used
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FROM arc_puzzles_tags apt
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JOIN users u ON apt.user_id = u.id
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GROUP BY u.id, u.name
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ORDER BY tags_assigned DESC
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""")
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rows = cursor.fetchall()
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print_table(["User", "Tags Assigned", "Puzzles Tagged", "Unique Tags Used"], rows, [20, 15, 15, 17])
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results['user_activity'] = rows
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# 9. Unused Tags
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print_section("9. UNUSED TAGS (defined but never assigned)")
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cursor.execute("""
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SELECT
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t.id,
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t.name,
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t.type,
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COALESCE(t.description, '') as description
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FROM tags t
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LEFT JOIN arc_puzzles_tags apt ON t.id = apt.tags_id
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WHERE apt.id IS NULL
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ORDER BY t.type, t.name
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""")
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rows = cursor.fetchall()
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print_table(["ID", "Name", "Type", "Description"], rows, [5, 25, 15, 40])
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results['unused'] = rows
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# 10. Tag Agreement Rate
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print_section("10. TAG AGREEMENT RATE (when both users tagged same puzzle)")
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cursor.execute("""
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SELECT
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t.name AS tag_name,
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SUM(CASE WHEN user_count = 2 THEN 1 ELSE 0 END) AS both_users_agreed,
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SUM(CASE WHEN user_count = 1 THEN 1 ELSE 0 END) AS only_one_user,
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ROUND(SUM(CASE WHEN user_count = 2 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 1) AS agreement_pct
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FROM (
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SELECT apt.arc_puzzles_id, apt.tags_id, COUNT(DISTINCT apt.user_id) AS user_count
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FROM arc_puzzles_tags apt
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GROUP BY apt.arc_puzzles_id, apt.tags_id
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) sub
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JOIN tags t ON sub.tags_id = t.id
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GROUP BY t.id, t.name
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HAVING COUNT(*) >= 5
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ORDER BY agreement_pct DESC
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LIMIT 20
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""")
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rows = cursor.fetchall()
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print_table(["Tag Name", "Both Agreed", "Only One", "Agreement %"], rows, [40, 12, 10, 12])
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results['agreement'] = rows
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# 11. High Agreement Puzzles (puzzles where users agreed on tags)
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print_section("11. PUZZLES WITH HIGH TAG AGREEMENT")
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cursor.execute("""
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SELECT
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sub.arc_puzzles_id,
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SUM(CASE WHEN sub.user_count = 2 THEN 1 ELSE 0 END) AS agreed_tags,
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SUM(CASE WHEN sub.user_count = 1 THEN 1 ELSE 0 END) AS single_user_tags,
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COUNT(*) AS total_unique_tags,
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ROUND(SUM(CASE WHEN sub.user_count = 2 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 1) AS agreement_pct
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FROM (
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SELECT arc_puzzles_id, tags_id, COUNT(DISTINCT user_id) AS user_count
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id, tags_id
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) sub
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WHERE sub.arc_puzzles_id IN (
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SELECT arc_puzzles_id
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id
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HAVING COUNT(DISTINCT user_id) > 1
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)
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GROUP BY sub.arc_puzzles_id
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ORDER BY agreement_pct DESC, agreed_tags DESC
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""")
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rows = cursor.fetchall()
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print_table(["Puzzle ID", "Agreed", "Single User", "Total Tags", "Agreement %"], rows, [15, 8, 12, 12, 12])
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results['high_agreement_puzzles'] = rows
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# 12. User Overlap Analysis
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print_section("12. USER OVERLAP ANALYSIS")
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cursor.execute("""
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SELECT
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(SELECT COUNT(DISTINCT arc_puzzles_id) FROM arc_puzzles_tags apt
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JOIN users u ON apt.user_id = u.id WHERE u.name = 'Bernardo') AS bernardo_total,
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(SELECT COUNT(DISTINCT arc_puzzles_id) FROM arc_puzzles_tags apt
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JOIN users u ON apt.user_id = u.id WHERE u.name = 'Eric') AS eric_total,
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(SELECT COUNT(DISTINCT arc_puzzles_id) FROM arc_puzzles_tags
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WHERE arc_puzzles_id IN (
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SELECT arc_puzzles_id FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id HAVING COUNT(DISTINCT user_id) > 1
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)) AS both_tagged
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""")
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row = cursor.fetchone()
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print(f" Bernardo tagged: {row[0]} puzzles")
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print(f" Eric tagged: {row[1]} puzzles")
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print(f" Both tagged (overlap): {row[2]} puzzles")
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print(f" Overlap rate: {round(row[2] * 100.0 / min(row[0], row[1]), 1)}%")
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results['user_overlap'] = row
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# 13. Detailed Agreement for Overlapping Puzzles
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print_section("13. DETAILED TAG AGREEMENT FOR OVERLAPPING PUZZLES")
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cursor.execute("""
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SELECT
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apt.arc_puzzles_id,
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t.name AS tag_name,
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t.type AS tag_type,
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GROUP_CONCAT(DISTINCT u.name ORDER BY u.name SEPARATOR ', ') AS tagged_by,
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COUNT(DISTINCT apt.user_id) AS user_count,
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CASE WHEN COUNT(DISTINCT apt.user_id) = 2 THEN 'AGREED' ELSE 'SINGLE' END AS status
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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JOIN users u ON apt.user_id = u.id
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WHERE apt.arc_puzzles_id IN (
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SELECT arc_puzzles_id
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id
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HAVING COUNT(DISTINCT user_id) > 1
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)
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GROUP BY apt.arc_puzzles_id, apt.tags_id, t.name, t.type
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ORDER BY apt.arc_puzzles_id, user_count DESC, t.name
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""")
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rows = cursor.fetchall()
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current_puzzle = None
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for row in rows:
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if row[0] != current_puzzle:
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current_puzzle = row[0]
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print(f"\n Puzzle: {current_puzzle}")
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print(f" {'-' * 60}")
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status_marker = "+" if row[5] == 'AGREED' else " "
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print(f" {status_marker} {row[1]:<35} ({row[2]:<20}) - {row[3]}")
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results['detailed_agreement'] = rows
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# 14. Tags Users Tend to Disagree On
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print_section("14. TAGS WITH MOST DISAGREEMENT (one user tags, other doesn't)")
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cursor.execute("""
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SELECT
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t.name AS tag_name,
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t.type AS tag_type,
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u.name AS only_tagged_by,
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COUNT(*) AS times_only_one_tagged
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FROM arc_puzzles_tags apt
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JOIN tags t ON apt.tags_id = t.id
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JOIN users u ON apt.user_id = u.id
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WHERE (apt.arc_puzzles_id, apt.tags_id) IN (
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SELECT arc_puzzles_id, tags_id
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id, tags_id
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HAVING COUNT(DISTINCT user_id) = 1
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)
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AND apt.arc_puzzles_id IN (
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SELECT arc_puzzles_id
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id
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HAVING COUNT(DISTINCT user_id) > 1
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)
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GROUP BY t.id, t.name, t.type, u.name
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ORDER BY times_only_one_tagged DESC
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LIMIT 20
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""")
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rows = cursor.fetchall()
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print_table(["Tag Name", "Type", "Only Tagged By", "Times"], rows, [35, 20, 15, 8])
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results['disagreement_tags'] = rows
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# 15. Tags Users Consistently Agree On
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print_section("15. TAGS USERS CONSISTENTLY AGREE ON")
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cursor.execute("""
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SELECT
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t.name AS tag_name,
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t.type AS tag_type,
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COUNT(*) AS times_both_agreed
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FROM (
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SELECT arc_puzzles_id, tags_id
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FROM arc_puzzles_tags
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GROUP BY arc_puzzles_id, tags_id
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HAVING COUNT(DISTINCT user_id) = 2
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) agreed
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JOIN tags t ON agreed.tags_id = t.id
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GROUP BY t.id, t.name, t.type
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ORDER BY times_both_agreed DESC
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""")
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rows = cursor.fetchall()
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print_table(["Tag Name", "Type", "Times Agreed"], rows, [40, 20, 12])
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results['agreement_tags'] = rows
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return results
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def main():
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print("ARC Puzzles Tags Analysis")
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print("=" * 70)
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try:
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config = load_env_config()
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print(f"Connecting to {config['host']}:{config['port']}/{config['database']}...")
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connection = pymysql.connect(**config)
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print("Connected successfully!\n")
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except Exception as e:
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print(f"Error connecting to database: {e}")
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return 1
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try:
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cursor = connection.cursor()
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results = run_analysis(cursor)
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# Summary
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print_section("SUMMARY")
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print(f"Total tag assignments analyzed")
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cursor.execute("SELECT COUNT(*) FROM arc_puzzles_tags")
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print(f" - Total assignments: {cursor.fetchone()[0]}")
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cursor.execute("SELECT COUNT(DISTINCT arc_puzzles_id) FROM arc_puzzles_tags")
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print(f" - Puzzles with tags: {cursor.fetchone()[0]}")
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cursor.execute("SELECT COUNT(*) FROM tags")
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print(f" - Total tags defined: {cursor.fetchone()[0]}")
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print(f" - Unused tags: {len(results['unused'])}")
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print(f" - Possible duplicate tags: {len(results['duplicates'])}")
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except Exception as e:
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print(f"\nError during analysis: {e}")
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import traceback
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traceback.print_exc()
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return 1
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finally:
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connection.close()
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print(f"\nDatabase connection closed.")
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return 0
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if __name__ == '__main__':
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sys.exit(main())
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5
todo-2.md
Normal file
5
todo-2.md
Normal file
@ -0,0 +1,5 @@
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- [x] Editing of Existing Answers
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- [x] Editing of Existing Skills
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- [x] Add the problem viewer to the User Eval Viewer.
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- Refresh the list in the user eval viewer when selected
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- In the "Explorer" tab I'd like to see a way to link to the annotations for each retrieved example.
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Reference in New Issue
Block a user