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790a885 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | """Minimal inference snippet for jc-builds/polymarket-edge-bot.
Install: pip install lightgbm scikit-learn sentence-transformers numpy
"""
import json
import pickle
import numpy as np
import lightgbm as lgb
from sklearn.isotonic import IsotonicRegression
from sentence_transformers import SentenceTransformer
from huggingface_hub import hf_hub_download
REPO = "jc-builds/polymarket-edge-bot"
# --- load artifacts ---
spec = json.loads(open(hf_hub_download(REPO, "feature_spec.json")).read())
gbm = lgb.Booster(model_file=hf_hub_download(REPO, "lightgbm_model.txt"))
iso = pickle.load(open(hf_hub_download(REPO, "isotonic_calibrator.pkl"), "rb"))
encoder = SentenceTransformer(spec["embedding_model"])
# --- build a tabular row matching feature_spec.json ---
# In production, derive these from the live Polymarket Gamma API payload.
tabular_row = {col: 0.0 for col in spec["tabular_columns_in_order"]}
tabular_row["first_yes_price"] = 0.30 # current YES price
tabular_row["first_yes_price_log_odds"] = float(np.log(0.30 / 0.70))
tabular_row["first_yes_price_distance_from_half"] = 0.20
tabular_row["log_total_usd"] = float(np.log1p(50_000))
tabular_row["duration_days"] = 7
# Question embedding
question = "Will Bitcoin close above $100,000 on Friday?"
emb = encoder.encode([question], normalize_embeddings=True)[0]
# Concatenate
x = np.concatenate([
np.array([tabular_row[c] for c in spec["tabular_columns_in_order"]],
dtype=np.float32),
emb,
])[None, :]
# --- score ---
p_raw = gbm.predict(x)[0]
p_yes = float(iso.predict([p_raw])[0])
edge = p_yes - tabular_row["first_yes_price"]
print(f"P(YES) = {p_yes:.3f} market = {tabular_row['first_yes_price']:.3f} "
f"edge = {edge:+.3f} {'BUY YES' if edge > 0.05 else 'BUY NO' if edge < -0.05 else 'skip'}")
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