mirror of
https://github.com/eigent-ai/eigent.git
synced 2026-05-23 12:44:45 +00:00
f7bf29a40a
Co-authored-by: bytecii <bytecii@users.noreply.github.com> Co-authored-by: Wendong-Fan <w3ndong.fan@gmail.com> Co-authored-by: Wendong-Fan <133094783+Wendong-Fan@users.noreply.github.com>
261 lines
8.1 KiB
Python
261 lines
8.1 KiB
Python
# ========= Copyright 2025-2026 @ Eigent.ai All Rights Reserved. =========
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ========= Copyright 2025-2026 @ Eigent.ai All Rights Reserved. =========
|
|
"""Grader for benchmark 2: evaluate yc_w25_b2b_ai.csv milestones."""
|
|
|
|
import csv
|
|
import json
|
|
import sys
|
|
from collections import Counter
|
|
from pathlib import Path
|
|
from urllib.parse import urlparse
|
|
|
|
BROWSER_LOG_DIR = Path(__file__).resolve().parents[2] / "browser_log"
|
|
ANSWER_CSV = (
|
|
Path(__file__).resolve().parents[1] / "answer" / "2" / "yc_w25_b2b_ai.csv"
|
|
)
|
|
|
|
VALID_CATEGORIES = {
|
|
"ai-agents",
|
|
"ai-infrastructure",
|
|
"ai-developer-tools",
|
|
"ai-analytics",
|
|
"ai-security",
|
|
"ai-healthcare",
|
|
"ai-sales",
|
|
"ai-productivity",
|
|
"ai-customer-support",
|
|
"ai-coding",
|
|
"ai-data",
|
|
"ai-fintech",
|
|
"ai-legal",
|
|
"ai-hr",
|
|
"ai-marketing",
|
|
"ai-other",
|
|
}
|
|
|
|
REQUIRED_COLUMNS = {"company_name", "product_description", "ai_category"}
|
|
|
|
|
|
def _visited_urls() -> set[str]:
|
|
"""Extract all URLs seen in browser logs."""
|
|
urls: set[str] = set()
|
|
if not BROWSER_LOG_DIR.exists():
|
|
return urls
|
|
for log_file in BROWSER_LOG_DIR.glob("hybrid_browser_toolkit_ws_*.log"):
|
|
decoder = json.JSONDecoder()
|
|
raw = log_file.read_text()
|
|
pos = 0
|
|
while pos < len(raw):
|
|
stripped = raw[pos:].lstrip()
|
|
if not stripped:
|
|
break
|
|
pos = len(raw) - len(stripped)
|
|
try:
|
|
obj, end = decoder.raw_decode(raw, pos)
|
|
pos = end
|
|
if not isinstance(obj, dict):
|
|
continue
|
|
action = obj.get("action", "")
|
|
if action == "visit_page":
|
|
args = obj.get("inputs", {}).get("args", [])
|
|
if args:
|
|
urls.add(args[0])
|
|
except (json.JSONDecodeError, ValueError):
|
|
pos += 1
|
|
return urls
|
|
|
|
|
|
def _load_answer() -> tuple[int, Counter]:
|
|
"""Load expected company count and category distribution from answer CSV."""
|
|
cat_counts: Counter = Counter()
|
|
count = 0
|
|
if not ANSWER_CSV.exists():
|
|
return 0, cat_counts
|
|
with open(ANSWER_CSV, newline="", encoding="utf-8") as f:
|
|
reader = csv.DictReader(f)
|
|
for row in reader:
|
|
count += 1
|
|
cat = row.get("ai_category", "")
|
|
if cat:
|
|
cat_counts[cat] += 1
|
|
return count, cat_counts
|
|
|
|
|
|
def _category_overlap(expected: Counter, actual: Counter) -> float:
|
|
"""Compute distribution overlap between expected and actual categories.
|
|
|
|
Normalizes both to proportions, then sums min(expected_pct, actual_pct)
|
|
for each category. Returns a value between 0.0 and 1.0.
|
|
"""
|
|
exp_total = sum(expected.values())
|
|
act_total = sum(actual.values())
|
|
if exp_total == 0 or act_total == 0:
|
|
return 0.0
|
|
all_cats = set(expected.keys()) | set(actual.keys())
|
|
overlap = 0.0
|
|
for cat in all_cats:
|
|
exp_pct = expected.get(cat, 0) / exp_total
|
|
act_pct = actual.get(cat, 0) / act_total
|
|
overlap += min(exp_pct, act_pct)
|
|
return overlap
|
|
|
|
|
|
def grade(working_directory: str) -> tuple[int, int]:
|
|
total = 10
|
|
completed = 0
|
|
|
|
csv_file = Path(working_directory) / "yc_w25_b2b_ai.csv"
|
|
|
|
# 1. Visited YC W25 companies page
|
|
visited = _visited_urls()
|
|
if any(
|
|
(p := urlparse(u)).hostname is not None
|
|
and (
|
|
p.hostname == "ycombinator.com"
|
|
or p.hostname.endswith(".ycombinator.com")
|
|
)
|
|
and "W25" in u
|
|
for u in visited
|
|
):
|
|
completed += 1
|
|
else:
|
|
print("MISS [1]: did not visit ycombinator.com W25 companies page")
|
|
|
|
# 2. CSV file exists
|
|
if not csv_file.exists():
|
|
print(f"MISS [2-10]: {csv_file.name} does not exist")
|
|
return completed, total
|
|
completed += 1
|
|
|
|
try:
|
|
with open(csv_file, newline="", encoding="utf-8") as f:
|
|
reader = csv.DictReader(f)
|
|
headers = set(reader.fieldnames or [])
|
|
rows = list(reader)
|
|
except Exception as e:
|
|
print(f"MISS [3-10]: failed to parse CSV: {e}")
|
|
return completed, total
|
|
|
|
# 3. Has correct columns
|
|
if REQUIRED_COLUMNS.issubset(headers):
|
|
completed += 1
|
|
else:
|
|
missing = REQUIRED_COLUMNS - headers
|
|
print(f"MISS [3]: missing columns: {missing}")
|
|
|
|
# 4. All company_name values are lowercase
|
|
non_lower = [
|
|
row.get("company_name", "")
|
|
for row in rows
|
|
if row.get("company_name", "") != row.get("company_name", "").lower()
|
|
]
|
|
if rows and not non_lower:
|
|
completed += 1
|
|
else:
|
|
print(
|
|
f"MISS [4]: {len(non_lower)} company_name(s) not lowercase, "
|
|
f"e.g. {non_lower[:3]}"
|
|
)
|
|
|
|
# 5. All product_description values are <= 100 chars
|
|
too_long = [
|
|
(i, len(row.get("product_description", "")))
|
|
for i, row in enumerate(rows)
|
|
if len(row.get("product_description", "")) > 100
|
|
]
|
|
if rows and not too_long:
|
|
completed += 1
|
|
else:
|
|
print(
|
|
f"MISS [5]: {len(too_long)} description(s) exceed 100 chars, "
|
|
f"e.g. row {too_long[0][0]} has {too_long[0][1]} chars"
|
|
if too_long
|
|
else "MISS [5]: no rows found"
|
|
)
|
|
|
|
# 6. All ai_category values are valid enums
|
|
invalid_cats = [
|
|
(i, row.get("ai_category", ""))
|
|
for i, row in enumerate(rows)
|
|
if row.get("ai_category", "") not in VALID_CATEGORIES
|
|
]
|
|
if rows and not invalid_cats:
|
|
completed += 1
|
|
else:
|
|
print(
|
|
f"MISS [6]: {len(invalid_cats)} invalid category value(s), "
|
|
f"e.g. row {invalid_cats[0][0]}: '{invalid_cats[0][1]}'"
|
|
if invalid_cats
|
|
else "MISS [6]: no rows found"
|
|
)
|
|
|
|
# Load answer for approximate matching
|
|
expected_count, expected_cats = _load_answer()
|
|
actual_count = len(rows)
|
|
|
|
# 7-8. Company count within 50% → +1, within 25% → +1 more
|
|
if expected_count > 0 and actual_count > 0:
|
|
ratio = actual_count / expected_count
|
|
if 0.5 <= ratio <= 1.5:
|
|
completed += 1
|
|
if 0.75 <= ratio <= 1.25:
|
|
completed += 1
|
|
else:
|
|
print(
|
|
f"MISS [8]: count {actual_count} is within 50% but not "
|
|
f"25% of expected {expected_count} (ratio={ratio:.2f})"
|
|
)
|
|
else:
|
|
print(
|
|
f"MISS [7-8]: count {actual_count} is not within 50% of "
|
|
f"expected {expected_count} (ratio={ratio:.2f})"
|
|
)
|
|
else:
|
|
print(
|
|
f"MISS [7-8]: expected_count={expected_count}, "
|
|
f"actual_count={actual_count}"
|
|
)
|
|
|
|
# 9-10. Category distribution overlap >= 50% → +1, >= 75% → +1 more
|
|
actual_cats: Counter = Counter()
|
|
for row in rows:
|
|
cat = row.get("ai_category", "")
|
|
if cat:
|
|
actual_cats[cat] += 1
|
|
overlap = _category_overlap(expected_cats, actual_cats)
|
|
if overlap >= 0.50:
|
|
completed += 1
|
|
if overlap >= 0.75:
|
|
completed += 1
|
|
else:
|
|
print(
|
|
f"MISS [10]: category overlap {overlap:.2%} >= 50% but < 75%"
|
|
)
|
|
else:
|
|
print(
|
|
f"MISS [9-10]: category overlap {overlap:.2%} < 50%. "
|
|
f"Expected dist: {dict(expected_cats)}, "
|
|
f"actual dist: {dict(actual_cats)}"
|
|
)
|
|
|
|
return completed, total
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if len(sys.argv) != 2:
|
|
print(f"Usage: {sys.argv[0]} <working_directory>")
|
|
sys.exit(1)
|
|
completed, total = grade(sys.argv[1])
|
|
print(f"{completed}/{total}")
|
|
sys.exit(0 if completed == total else 1)
|