train.py

"""
RTB Bidding Algorithm Comparison on Real Criteo Data
"""
import numpy as np
import pandas as pd
import json
from datasets import load_dataset
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split

print("="*60)
print("RTB BIDDING ON REAL CRITEO DATA")
print("="*60)

# Load Criteo_x4 dataset
ds = load_dataset("reczoo/Criteo_x4", split="train", streaming=True)
rows = []
for i, row in enumerate(ds):
    if i >= 100000:
        break
    rows.append(row)
df = pd.DataFrame(rows)
print(f"Loaded {len(df)} rows, CTR: {df['Label'].mean():.4f}")

# Feature prep
sparse_cols = [f"C{i}" for i in range(1, 27)]
dense_cols = [f"I{i}" for i in range(1, 14)]

for col in dense_cols:
    df[col] = df[col].fillna(df[col].median())
for col in sparse_cols:
    df[col] = df[col].fillna("MISSING")
    vocab = {v: i+1 for i, v in enumerate(df[col].unique())}
    df[col] = df[col].map(vocab)

for col in dense_cols:
    df[col] = (df[col] - df[col].mean()) / (df[col].std() + 1e-8)

feature_cols = dense_cols + sparse_cols
X = df[feature_cols].values
y = df['Label'].values

# Simulate market prices
np.random.seed(42)
mu_price = 1.0 + 0.02 * X[:, 0] + 0.01 * X[:, 1]
market_price = np.random.lognormal(mu_price, 0.6)
print(f"Market price mean: {market_price.mean():.2f}")

# Train/test split
train_idx, test_idx = train_test_split(range(len(df)), test_size=0.2, random_state=42)
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
price_train, price_test = market_price[train_idx], market_price[test_idx]

# CTR model
print("\nTraining CTR model...")
lr = LogisticRegression(max_iter=200, C=0.1)
lr.fit(X_train, y_train)
pctr = lr.predict_proba(X_test)[:, 1]
print(f"pCTR range: [{pctr.min():.4f}, {pctr.max():.4f}], mean: {pctr.mean():.4f}")

# Price model
print("Training price model...")
price_model = GradientBoostingRegressor(n_estimators=50, max_depth=4)
price_model.fit(X_train, price_train)
price_pred = price_model.predict(X_test)
print(f"Price prediction MAE: {np.mean(np.abs(price_pred - price_test)):.2f}")

# Bidding algorithms
VALUE_PER_CLICK = 50.0

class LinearBid:
    def __init__(self, base, avg_pctr):
        self.base = base; self.avg = avg_pctr
    def bid(self, pctr, **kw):
        return self.base * (pctr / self.avg)

class ORTB:
    def __init__(self, lam, c):
        self.lam = lam; self.c = c
    def bid(self, pctr, **kw):
        return np.sqrt((self.c / self.lam) * pctr + self.c**2) - self.c

class DualOGD:
    def __init__(self, budget, T, vpc=50, eps=None):
        self.B = budget; self.T = T; self.rho = budget / T
        self.vpc = vpc; self.eps = eps or 1.0 / np.sqrt(T)
        self.lam = 0.0; self.spent = 0.0; self.t = 0
    def bid(self, pctr, win_prob, **kw):
        self.t += 1
        rem = self.B - self.spent
        if rem <= 0: return 0.0
        v = pctr * self.vpc
        max_b = min(v * 2, rem)
        if max_b <= 0: return 0.0
        bids = np.linspace(0.5, max_b, 40)
        rewards = [(v - b) * win_prob(b) - self.lam * b * win_prob(b) for b in bids]
        return float(bids[np.argmax(rewards)])
    def update(self, cost):
        self.spent += cost
        self.lam = max(0.0, self.lam - self.eps * (self.rho - cost))

class Threshold:
    def __init__(self, th, val):
        self.th = th; self.val = val
    def bid(self, pctr, **kw):
        return self.val if pctr > self.th else 0.0

def simulate(algo, pctr, prices, clicks, budget, T):
    spent = 0.0
    clicks_got = 0
    imp = 0
    for i in range(min(T, len(pctr))):
        if spent >= budget: break
        def wp(b):
            if b <= 0: return 0.0
            return 1.0 / (1.0 + np.exp(-(b - prices[i]) / (prices[i] * 0.5)))
        if isinstance(algo, DualOGD):
            b = algo.bid(pctr[i], wp)
        else:
            b = algo.bid(pctr[i])
        if b >= prices[i] and spent + b <= budget:
            spent += b; imp += 1; clicks_got += int(clicks[i])
        if isinstance(algo, DualOGD):
            algo.update(float(b) if b >= prices[i] else 0.0)
    return {
        'clicks': int(clicks_got),
        'impressions': int(imp),
        'spent': float(spent),
        'budget': float(budget),
        'ctr': float(clicks_got / max(imp, 1)),
        'budget_used': float(spent / budget),
        'cpc': float(spent / max(clicks_got, 1))
    }

# Run comparison
budget = 5000; T = 10000
avg_pctr = float(pctr.mean())

algos = {
    'Linear': LinearBid(20, avg_pctr),
    'ORTB': ORTB(0.002, 8),
    'DualOGD': DualOGD(budget, T, VALUE_PER_CLICK),
    'Threshold': Threshold(0.3, 30)
}

print("\n" + "="*60)
print("BIDDING ALGORITHM COMPARISON ON REAL CRITEO DATA")
print("="*60)

results = {}
for name, algo in algos.items():
    results[name] = simulate(algo, pctr, price_test, y_test, budget, T)
    r = results[name]
    print(f"{name:12} Clicks:{r['clicks']:4} CTR:{r['ctr']:.4f} Budget:{r['budget_used']:.2%} CPC:{r['cpc']:.2f}")

with open('results_real.json', 'w') as f:
    json.dump(results, f, indent=2)

print("\nSaved to results_real.json")