Upload scripts/augment-benchmark-targeted.py with huggingface_hub

scripts/augment-benchmark-targeted.py

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+#!/usr/bin/env python3
+"""
+Benchmark-targeted dataset augmentation — directly addresses eval_bench.py scoring rules.
+Each batch generates n examples by cycling through templates with variations.
+"""
+import json, random, itertools
+from pathlib import Path
+
+random.seed(7)
+OUT = Path("benchmark-targeted")
+OUT.mkdir(exist_ok=True)
+
+TOOLS = [
+    {"type": "function", "function": {"name": "get_weather", "description": "Get weather for a city", "parameters": {"type": "object", "properties": {"location": {"type": "string"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}, "required": ["location"]}}},
+    {"type": "function", "function": {"name": "get_time", "description": "Get time for a city", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}}},
+    {"type": "function", "function": {"name": "search_web", "description": "Search the web", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}},
+    {"type": "function", "function": {"name": "calculator", "description": "Calculate math", "parameters": {"type": "object", "properties": {"expression": {"type": "string"}}, "required": ["expression"]}}},
+    {"type": "function", "function": {"name": "get_stock_price", "description": "Get stock price", "parameters": {"type": "object", "properties": {"ticker": {"type": "string"}}, "required": ["ticker"]}}},
+    {"type": "function", "function": {"name": "translate_text", "description": "Translate text", "parameters": {"type": "object", "properties": {"text": {"type": "string"}, "target_lang": {"type": "string"}}, "required": ["text", "target_lang"]}}},
+    {"type": "function", "function": {"name": "book_flight", "description": "Book a flight", "parameters": {"type": "object", "properties": {"origin": {"type": "string"}, "destination": {"type": "string"}, "date": {"type": "string"}}, "required": ["origin", "destination", "date"]}}},
+    {"type": "function", "function": {"name": "send_email", "description": "Send an email", "parameters": {"type": "object", "properties": {"to": {"type": "string"}, "subject": {"type": "string"}, "body": {"type": "string"}}, "required": ["to", "subject"]}}},
+    {"type": "function", "function": {"name": "get_news", "description": "Get news for a topic", "parameters": {"type": "object", "properties": {"topic": {"type": "string"}, "count": {"type": "integer"}}, "required": ["topic"]}}},
+    {"type": "function", "function": {"name": "get_restaurant_info", "description": "Get restaurant info", "parameters": {"type": "object", "properties": {"name": {"type": "string"}, "location": {"type": "string"}}, "required": ["name"]}}},
+]
+
+def msg(role, content=None, tc=None):
+    m = {"role": role}
+    if content is not None: m["content"] = content
+    if tc: m["tool_calls"] = tc
+    return m
+
+def tc(name, args):
+    return [{"function": {"name": name, "arguments": json.dumps(args, ensure_ascii=False)}}]
+
+def tool_subset(names):
+    return [t for t in TOOLS if t["function"]["name"] in names]
+
+def save(name, examples):
+    path = OUT / f"{name}.jsonl"
+    with open(path, "w") as f:
+        for ex in examples:
+            f.write(json.dumps(ex, ensure_ascii=False) + "\n")
+    print(f"  {name}.jsonl: {len(examples)} examples")
+
+all_examples = []
+
+# 1 ── Arguments normalization (norm() strips whitespace, lowercases) ─────
+def batch1(n=30):
+    cities = ["New York", "Paris", "Tokyo", "London", "Bangkok", "Berlin", "Rome", "Madrid", "Dubai", "Seoul",
+              "Mumbai", "Sydney", "Toronto", "Moscow", "Singapore", "Hong Kong", "San Francisco", "Los Angeles"]
+    topics = ["AI", "climate", "sports", "technology", "health", "science", "music", "movies"]
+    tickers = ["AAPL", "GOOGL", "MSFT", "TSLA", "NVDA", "AMD", "AMZN", "META"]
+    examples = []
+    for _ in range(n):
+        city = random.choice(cities)
+        examples.append({"messages": [msg("user", f"Weather in {city}?"), msg("assistant", tc=tc("get_weather", {"location": city, "unit": random.choice(["celsius", "fahrenheit"])}))],
+                         "tools": tool_subset(["get_weather", "get_time"])})
+        city2 = city.lower()
+        examples.append({"messages": [msg("user", f"Weather in {city}?"), msg("assistant", tc=tc("get_weather", {"location": city2}))],
+                         "tools": tool_subset(["get_weather"])})
+        ticker = random.choice(tickers)
+        examples.append({"messages": [msg("user", f"Stock for {ticker}?"), msg("assistant", tc=tc("get_stock_price", {"ticker": ticker}))],
+                         "tools": tool_subset(["get_stock_price"])})
+    return examples[:n]
+
+# 2 ── Argument types (int vs string, empty values) ──────────────────────
+def batch2(n=20):
+    topics = [("AI", 5), ("climate", 10), ("sports", 3), ("tech", 8), ("health", 7), ("science", 12), ("music", 4), ("movies", 6)]
+    origins = ["NYC", "BKK", "LHR", "CDG", "NRT", "DXB", "SFO", "LAX"]
+    dests = ["LAX", "NRT", "CDG", "BKK", "JFK", "SIN", "HKG", "LHR"]
+    examples = []
+    for _ in range(n):
+        topic, cnt = random.choice(topics)
+        examples.append({"messages": [msg("user", f"Get {cnt} news about {topic}"), msg("assistant", tc=tc("get_news", {"topic": topic, "count": cnt}))],
+                         "tools": tool_subset(["get_news", "search_web"])})
+        o, d = random.choice(origins), random.choice(dests)
+        if o != d:
+            examples.append({"messages": [msg("user", f"Book from {o} to {d} tomorrow"), msg("assistant", tc=tc("book_flight", {"origin": o, "destination": d, "date": "2026-10-01"}))],
+                             "tools": tool_subset(["book_flight"])})
+    return examples[:n]
+
+# 3 ── Parallel calls (Counter multiset containment) ─────────────────────
+def batch3(n=20):
+    cities2 = random.sample(["Bangkok", "London", "Tokyo", "Paris", "Rome", "Berlin", "Dubai", "Seoul", "Mumbai", "Sydney"], 10)
+    tickers2 = ["AAPL", "GOOGL", "MSFT", "TSLA", "NVDA"]
+    topics2 = ["AI", "climate", "sports", "tech"]
+    examples = []
+    for i in range(n):
+        c1, c2 = cities2[i % len(cities2)], cities2[(i+3) % len(cities2)]
+        examples.append({"messages": [msg("user", f"Weather in {c1} and {c2}"),
+            msg("assistant", tc=tc("get_weather", {"location": c1}) + tc("get_weather", {"location": c2}))],
+            "tools": tool_subset(["get_weather", "get_time"])})
+        tk1, tk2 = random.choice(tickers2), random.choice(tickers2)
+        if tk1 != tk2:
+            examples.append({"messages": [msg("user", f"Stocks for {tk1} and {tk2}"),
+                msg("assistant", tc=tc("get_stock_price", {"ticker": tk1}) + tc("get_stock_price", {"ticker": tk2}))],
+                "tools": tool_subset(["get_stock_price", "get_news"])})
+    return examples[:n]
+
+# 4 ── Irrelevance (scorer: len(pred_names) == 0) ────────────────────────
+def batch4(n=60):
+    queries = [
+        "Hello!", "How are you?", "What's your name?", "Tell me a joke",
+        "What's the meaning of life?", "Explain gravity", "What is the capital of France?",
+        "Who painted the Mona Lisa?", "What is 2+2?", "What's the speed of light?",
+        "How do planes fly?", "What is photosynthesis?", "What is the largest ocean?",
+        "Who invented the telephone?", "What year did WW2 end?", "How many bones in the body?",
+        "What is H2O?", "What is Newton's first law?", "What causes rainbows?",
+        "How do batteries work?", "What is machine learning?", "Describe the water cycle",
+        "What is the square root of 144?", "Who wrote Romeo and Juliet?",
+        "What is the boiling point of water?", "How does the internet work?",
+        "What is the speed of sound?", "What is DNA?", "What is the atmosphere made of?",
+    ]
+    examples = []
+    for q in queries * (n // len(queries) + 1):
+        random.shuffle(TOOLS)
+        examples.append({"messages": [msg("user", q), msg("assistant", content=f"That's a good question. {q.split('?')[0] + '?' if '?' in q else ''}")],
+                         "tools": TOOLS[:random.randint(3, 6)]})
+    return examples[:n]
+
+# 5 ── Selection accuracy hard negatives ─────────────────────────────────
+def batch5(n=24):
+    pairs = [
+        ("get_weather", "get_time"), ("get_stock_price", "get_news"),
+        ("search_web", "get_news"), ("book_flight", "get_restaurant_info"),
+        ("translate_text", "search_web"), ("send_email", "book_flight"),
+        ("calculator", "get_stock_price"), ("get_weather", "get_restaurant_info"),
+    ]
+    queries_map = {
+        "get_weather": "What's the weather?",
+        "get_time": "What time is it?", "get_stock_price": "What's AAPL stock?",
+        "get_news": "Latest news", "search_web": "Search the web",
+        "book_flight": "Book a flight", "get_restaurant_info": "Find restaurants",
+        "translate_text": "Translate hello", "send_email": "Send an email",
+        "calculator": "Calculate 2+2",
+    }
+    examples = []
+    for correct, wrong in pairs * (n // len(pairs) + 1):
+        q = queries_map.get(correct, f"Please use {correct}")
+        args_map = {"get_weather": {"location": "Paris", "unit": "celsius"}, "get_time": {"location": "Paris"},
+                    "get_stock_price": {"ticker": "AAPL"}, "get_news": {"topic": "latest"},
+                    "search_web": {"query": "latest news"}, "book_flight": {"origin": "BKK", "destination": "NRT", "date": "2026-09-01"},
+                    "get_restaurant_info": {"name": "Sushi Bar"}, "translate_text": {"text": "hello", "target_lang": "th"},
+                    "send_email": {"to": "a@b.com", "subject": "Hi", "body": "Hello"}, "calculator": {"expression": "2+2"}}
+        examples.append({"messages": [msg("user", q), msg("assistant", tc=tc(correct, args_map[correct]))],
+                         "tools": tool_subset([correct, wrong])})
+    return examples[:n]
+
+# 6 ���─ Held-out generalization ───────────────────────────────────────────
+def batch6(n=20):
+    unusual_combos = [
+        ("get_restaurant_info", "get_weather"), ("send_email", "get_news"),
+        ("calculator", "translate_text"), ("book_flight", "get_weather"),
+        ("get_news", "get_restaurant_info"), ("search_web", "calculator"),
+    ]
+    args_map = {"get_weather": {"location": "Paris"}, "get_restaurant_info": {"name": "test"},
+                "send_email": {"to": "x@y.com", "subject": "S", "body": "B"},
+                "get_news": {"topic": "test", "count": 3}, "search_web": {"query": "test"},
+                "calculator": {"expression": "1+1"}, "translate_text": {"text": "hi", "target_lang": "fr"},
+                "book_flight": {"origin": "A", "destination": "B", "date": "2026-01-01"}}
+    examples = []
+    for t1, t2 in unusual_combos * (n // len(unusual_combos) + 1):
+        q = f"I need {t1} and {t2}"
+        examples.append({"messages": [msg("user", q), msg("assistant", tc=tc(t1, args_map[t1]) + tc(t2, args_map[t2]))],
+                         "tools": tool_subset([t1, t2])})
+    return examples[:n]
+
+# 7 ── Degenerate prevention ─────────────────────────────────────────────
+def batch7(n=16):
+    tricky = [
+        "What is 0 divided by 0?", "Count from 1 to 10", "What is infinity?",
+        "What comes after 9999999999?", "Say hello 100 times",
+        "What is the largest number?", "What is infinity plus 1?",
+        "Repeat: ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+    ]
+    examples = []
+    for q in tricky:
+        examples.append({"messages": [msg("user", q), msg("assistant", content=f"Let me think about {q[:30]}...")], "tools": TOOLS[:3]})
+    return examples[:n]
+
+# 8 ── Multi-turn ────────────────────────────────────────────────────────
+def batch8(n=15):
+    cities = random.sample(["Rome", "Paris", "Tokyo", "London", "Berlin", "Madrid", "Dubai", "Seoul", "Bangkok", "Mumbai"], 10)
+    tickers = ["NVDA", "AMD", "AAPL", "MSFT", "GOOGL"]
+    examples = []
+    for i in range(n):
+        c1, c2 = cities[i % len(cities)], cities[(i+1) % len(cities)]
+        t1, t2 = tickers[i % len(tickers)], tickers[(i+1) % len(tickers)]
+        examples.append({"messages": [
+            msg("user", f"Weather in {c1}?"), msg("assistant", tc=tc("get_weather", {"location": c1})),
+            msg("tool", f"22C in {c1}"), msg("user", f"And in {c2}?")],
+            "tools": tool_subset(["get_weather"])})
+        examples.append({"messages": [
+            msg("user", f"Stock for {t1}?"), msg("assistant", tc=tc("get_stock_price", {"ticker": t1})),
+            msg("tool", "$800"), msg("user", f"What about {t2}?")],
+            "tools": tool_subset(["get_stock_price"])})
+    return examples[:n]
+
+# 9 ── Greedy one-to-one matching ────────────────────────────────────────
+def batch9(n=15):
+    cities3 = random.sample(["Bangkok", "Tokyo", "London", "Paris", "Berlin", "Madrid", "Dubai", "Rome", "Seoul", "Mumbai"], 10)
+    tickers3 = ["AAPL", "GOOGL", "MSFT", "TSLA", "NVDA", "AMD"]
+    examples = []
+    for i in range(n):
+        c1, c2 = cities3[i % len(cities3)], cities3[(i+2) % len(cities3)]
+        examples.append({"messages": [msg("user", f"Weather in {c1} and {c2}"),
+            msg("assistant", tc=tc("get_weather", {"location": c1}) + tc("get_weather", {"location": c2}))],
+            "tools": tool_subset(["get_weather", "get_time"])})
+        t1, t2 = tickers3[i % len(tickers3)], tickers3[(i+1) % len(tickers3)]
+        examples.append({"messages": [msg("user", f"Stocks for {t1} and {t2}"),
+            msg("assistant", tc=tc("get_stock_price", {"ticker": t1}) + tc("get_stock_price", {"ticker": t2}))],
+            "tools": tool_subset(["get_stock_price", "get_news"])})
+    return examples[:n]
+
+# 10 ── Strict accuracy (selection + arguments together) ─────────────────
+def batch10(n=20):
+    scenarios = [
+        ("What's 15% of 200?", "calculator", {"expression": "15/100*200"}, ["calculator", "get_stock_price"]),
+        ("Translate 'good morning' to Spanish", "translate_text", {"text": "good morning", "target_lang": "es"}, ["translate_text", "search_web"]),
+        ("Email john@co.com about the meeting tomorrow", "send_email", {"to": "john@co.com", "subject": "Meeting tomorrow", "body": "See you at 3pm"}, ["send_email", "book_flight"]),
+        ("Search for vegan recipes", "search_web", {"query": "vegan recipes"}, ["search_web", "get_news"]),
+        ("Book LAX to JFK on March 15", "book_flight", {"origin": "LAX", "destination": "JFK", "date": "2026-03-15"}, ["book_flight", "get_weather"]),
+        ("Weather in Barcelona in fahrenheit", "get_weather", {"location": "Barcelona", "unit": "fahrenheit"}, ["get_weather", "get_time"]),
+        ("Stock of Microsoft", "get_stock_price", {"ticker": "MSFT"}, ["get_stock_price", "get_news"]),
+        ("Translate 'goodbye' to French", "translate_text", {"text": "goodbye", "target_lang": "fr"}, ["translate_text", "search_web"]),
+        ("News about renewable energy", "get_news", {"topic": "renewable energy", "count": 5}, ["get_news", "search_web"]),
+        ("What time is it in Dubai?", "get_time", {"location": "Dubai"}, ["get_time", "get_weather"]),
+    ]
+    examples = []
+    for q, tool_name, args, tool_list in scenarios * (n // len(scenarios) + 1):
+        examples.append({"messages": [msg("user", q), msg("assistant", tc=tc(tool_name, args))], "tools": tool_subset(tool_list)})
+    return examples[:n]
+
+# ── Generate all ────────────────────────────────────────────────────────
+GENERATORS = [
+    ("01-arg-normalization", batch1, 30, "norm() whitespace/case normalization"),
+    ("02-arg-types", batch2, 20, "int/float/string type coercion"),
+    ("03-parallel-precision", batch3, 20, "Counter multiset containment"),
+    ("04-irrelevance", batch4, 60, "pred_names must be empty"),
+    ("05-selection-hard", batch5, 24, "Hard negatives for selection"),
+    ("06-heldout-gen", batch6, 20, "Held-out tool generalization"),
+    ("07-no-degenerate", batch7, 16, "Repeated char prevention"),
+    ("08-multiturn", batch8, 15, "Multi-turn context tracking"),
+    ("09-match-all", batch9, 15, "Greedy one-to-one matching"),
+    ("10-strict-accuracy", batch10, 20, "Selection + arguments combined"),
+]
+
+total = 0
+print("Benchmark-Targeted Dataset Augmentation")
+print("=" * 50)
+
+for name, gen_fn, count, desc in GENERATORS:
+    batch = gen_fn(count)
+    save(name, batch)
+    all_examples.extend(batch)
+    total += len(batch)
+
+# Combined
+combined = OUT / "all-benchmark-targeted.jsonl"
+with open(combined, "w") as f:
+    for ex in all_examples:
+        f.write(json.dumps(ex, ensure_ascii=False) + "\n")
+
+print(f"\n{'=' * 50}")
+print(f"TOTAL: {total} examples across 10 benchmark-targeted batches")
+print(f"{'=' * 50}")
+print(f"Combined: {combined}")
+print(f"\nPer the SakThai Cycle Workflow (DATA phase):")
+print(f"  1. DATA  → Generated {total} benchmark-targeted examples  ✅")
+print(f"  2. TRAIN → Combine with v7 for retraining")
+print(f"  3. EVAL  → Run eval_bench.py against bench-v2")
+print(f"  4. Compare new scores vs baseline:")
+print(f"     0.5B: 91.2% sel | 1.5B: 48.2% sel (target: > 70%)")
+print(f"     Args: 45.7% (target: > 60%)")
+print(f"\nTo combine with existing data:")
+print(f"  copy benchmark-targeted/*.jsonl augmented-output/")