feat: add dev preview dashboard showcasing all 12 new ML models
- New page 06_dev_preview.py: interactive ML model showcase - All 12 models initialized from synthetic data with live charts - Live Auction Simulator: bandit + opponent model + budget optimizer - Tabbed UI: 6 tabs, one per phase - Resilient warehouse: returns typed empty DataFrames when no data - Dev Preview set as default landing page for demo mode - Live at http://localhost:8507
This commit is contained in:
ramseshk
@@ -19,6 +19,7 @@ from dashboard.viz.components import inject_css
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from dashboard.viz.template import stub_render # triggers template registration
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PAGES = {
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"🔬 Dev Preview": "dashboard.pages.06_dev_preview",
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"⚽ Matchday": "dashboard.pages.01_matchday",
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"👤 Players": "dashboard.pages.02_players",
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"💰 Auction": "dashboard.pages.03_auction",
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@@ -69,6 +69,9 @@ def _build_fixture_heatmap(players, fixtures):
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def run():
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inject_css()
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preds, fixtures, players, lineups, votes = _get_data()
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if preds.empty or len(preds) <= 1:
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st.warning("No warehouse data. Run pipeline or use Dev Preview tab.")
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return
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projected, league_avg, risk_count, trend = _compute_kpis(preds, players, votes)
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@@ -0,0 +1,875 @@
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"""Page 6 — Dev Preview: New ML Models for Auction Optimization.
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Showcases all 10 new ML modules from Phases 1-6 with interactive visualizations.
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Runs on synthetic data so it works without the full data pipeline.
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"""
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import sys
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from pathlib import Path
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_p = Path(__file__).resolve().parent.parent.parent
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_str_p_ = str(_p)
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if _str_p_ not in sys.path:
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sys.path.insert(0, _str_p_)
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import numpy as np
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import pandas as pd
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import streamlit as st
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from dashboard.viz.components import inject_css, section, insight, role_chip, kpi_card
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from dashboard.viz.template import (
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PITCH_GREEN, GOLD, RED, SKY, VIOLET, BG, CARD_BG, BORDER,
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TEXT, TEXT_SECONDARY, WHITE, ROLE_COLORS, ROLE_ICONS,
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FANTABETO_TEMPLATE, HEATMAP_COLORS, GRIDLINE,
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)
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# ─── Synthetic data generation ──────────────────────────────────────
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def _generate_player_pool(n_players=120, seed=42):
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rng = np.random.RandomState(seed)
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roles_dist = ["P"] * 10 + ["D"] * 40 + ["C"] * 40 + ["A"] * 30
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teams = [f"Team_{i}" for i in range(20)]
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data = []
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for i in range(n_players):
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role = roles_dist[i % len(roles_dist)]
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base_fv = {"P": 6.2, "D": 6.3, "C": 6.5, "A": 7.0}[role]
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fv = base_fv + rng.normal(0, 0.8)
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fv = max(4.5, min(9.5, fv))
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qi = np.exp(fv - 3.5) * rng.uniform(0.8, 1.5)
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games = int(rng.choice([38, 35, 30, 25, 20, 15, 10, 5], p=[0.15, 0.15, 0.2, 0.15, 0.12, 0.1, 0.08, 0.05]))
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minutes_last_3 = rng.uniform(0, 90) if rng.random() < 0.7 else rng.uniform(50, 90)
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data.append({
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"name": f"Player_{i}",
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"role": role,
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"team": rng.choice(teams),
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"projected_points": round(fv, 2),
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"fv_std": round(rng.uniform(0.3, 1.5), 2),
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"market_value": round(qi, 1),
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"starter_pct": round(rng.uniform(0.3, 0.98), 2),
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"minutes_last_3": round(minutes_last_3, 1),
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"games_played": games,
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"xg_p90": round(rng.uniform(0.01, 0.8), 3),
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"xa_p90": round(rng.uniform(0.01, 0.5), 3),
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"goals_season": round(rng.poisson(max(fv - 5.5, 0.01)) if fv > 5.5 else 0),
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"assists_season": round(rng.poisson(max((fv - 5.5) * 0.5, 0.01)) if fv > 5.5 else 0),
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"yellow_per_game": round(rng.beta(2, 20), 3),
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"red_per_game": round(rng.beta(1, 50), 4),
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"rest_days": int(rng.uniform(2, 10)),
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"fatigue_rolling_3": round(rng.uniform(0, 90), 1),
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"feature1": round(rng.normal(0, 1), 2),
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"feature2": round(rng.normal(0, 1), 2),
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})
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return pd.DataFrame(data)
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def _generate_team_rosters(player_pool, n_teams=10, seed=42):
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rng = np.random.RandomState(seed)
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rosters = []
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for t in range(n_teams):
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idx = rng.choice(len(player_pool), 25, replace=False)
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roster = player_pool.iloc[idx].copy()
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roster["team"] = f"Team_{t}"
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rosters.append(roster)
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return rosters
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def _generate_interaction_data(player_pool, seed=42):
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rng = np.random.RandomState(seed)
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rows = []
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players = player_pool["name"].tolist()
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for _ in range(300):
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a = rng.choice(players)
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b = rng.choice(players)
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if a == b:
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continue
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rows.append({
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"player": a,
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"teammate": b,
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"passes_to": rng.randint(0, 15),
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"assists_to": rng.randint(0, 2),
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"crosses_to": rng.randint(0, 5),
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"matchday": rng.randint(1, 39),
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})
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return pd.DataFrame(rows)
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def _generate_auction_logs(player_pool, seed=42):
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rng = np.random.RandomState(seed)
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rows = []
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for _ in range(500):
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player = player_pool.iloc[rng.randint(0, len(player_pool))]
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rows.append({
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"player_name": player["name"],
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"player_role": player["role"],
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"player_projected_points": player["projected_points"],
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"opponent_budget_remaining": rng.uniform(50, 500),
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"opponent_slots_remaining": rng.randint(1, 8),
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"role_needed_count": rng.randint(1, 5),
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"round_number": rng.randint(1, 15),
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"winning_bid": max(1, int(player["market_value"] * rng.uniform(0.5, 2.0))),
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})
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return pd.DataFrame(rows)
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# ─── Model initialization cache ─────────────────────────────────────
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@st.cache_resource
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def _init_models(player_pool, interaction_data, auction_logs):
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results = {}
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# Phase 1: Quantile Ensemble
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try:
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from src.models.quantile_model import QuantileEnsemble
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X = player_pool[["projected_points", "fv_std", "minutes_last_3", "games_played",
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"xg_p90", "xa_p90", "rest_days", "fatigue_rolling_3",
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"feature1", "feature2"]].fillna(0)
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y = player_pool["projected_points"]
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qe = QuantileEnsemble(quantiles=(0.10, 0.50, 0.90), n_estimators=50)
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qe.fit(X, y)
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preds = qe.predict(X.head(60))
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risk = qe.predict_downside_risk(X.head(60), threshold=5.5)
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results["quantile"] = {"model": qe, "preds": preds, "risk": risk, "X": X.head(60)}
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except Exception as e:
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results["quantile"] = {"error": str(e)}
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# Phase 1: Survival Model
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try:
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from src.models.survival_model import MinutesSurvivalModel
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surv_df = player_pool[
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["minutes_last_3", "games_played", "rest_days", "fatigue_rolling_3",
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"feature1", "feature2"]
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].fillna(0)
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durations = np.clip(player_pool["minutes_last_3"].values + np.random.normal(0, 10, len(player_pool)), 1, 90)
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events = (player_pool["starter_pct"].values > 0.5).astype(int)
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ms = MinutesSurvivalModel(force_scipy=True)
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ms.fit(surv_df, durations, events)
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expected, lower, upper = ms.predict_distribution(surv_df.head(30))
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starter_probs = ms.predict_starter_probability(surv_df.head(30))
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results["survival"] = {"model": ms, "expected": expected.tolist(),
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"lower": lower.tolist(), "upper": upper.tolist(),
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"starter_probs": starter_probs.tolist()}
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except Exception as e:
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results["survival"] = {"error": str(e)}
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# Phase 4: Bayesian Pooling
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try:
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from src.models.bayesian_pooling import BayesianPlayerModel
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Xb = player_pool[["role", "projected_points", "minutes_last_3", "games_played"]].copy()
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yb = player_pool["projected_points"]
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bp = BayesianPlayerModel()
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bp.fit(Xb, yb)
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mean, std = bp.predict_with_uncertainty(Xb.head(30))
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reliability = bp.get_player_reliability(Xb.head(30))
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results["bayesian"] = {"model": bp, "mean": mean.tolist(),
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"std": std.tolist(), "reliability": reliability.tolist()}
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except Exception as e:
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results["bayesian"] = {"error": str(e)}
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# Phase 2: Bandit Auction
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try:
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from src.optimization.bandit_auction import BanditAuctionSolver
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from src.optimization.auction_solver import AuctionConfig, PlayerValuation
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config = AuctionConfig(total_budget=500, n_gk=3, n_def=8, n_mid=8, n_fwd=6)
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bandit = BanditAuctionSolver(config=config)
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trial_bids = []
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for _ in range(20):
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player = player_pool.iloc[np.random.randint(0, 60)]
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pv = PlayerValuation(
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name=player["name"], team=player["team"], role=player["role"],
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projected_points=player["projected_points"],
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market_value=player["market_value"],
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ceiling_price=player["projected_points"] * 5,
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)
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state = {
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"budget_remaining": 500 - _ * 20,
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"total_budget": 500,
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"slots_remaining": {"P": 1, "D": 3, "C": 4, "A": 2},
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"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"slot_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"round_number": _ + 1,
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"total_rounds": 20,
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"opponent_budgets": [400, 350, 420],
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"players_remaining_in_role": {"P": 5, "D": 10, "C": 10, "A": 8},
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"player_pool": [pv],
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}
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arm, bid = bandit.select_bid(pv, state)
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bandit.update(arm, 0.6, pv.role)
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trial_bids.append({"player": pv.name, "role": pv.role, "bid": bid, "arm": arm})
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results["bandit"] = {"trial_bids": trial_bids, "arm_stats": bandit.get_arm_stats()}
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except Exception as e:
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results["bandit"] = {"error": str(e)}
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# Phase 5: GAT Chemistry
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try:
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from src.models.gat_model import PlayerChemistryGAT
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gat = PlayerChemistryGAT()
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gat.build_graph(interaction_data)
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chem_features = gat.extract_interaction_features("Player_0", player_pool.head(5)["name"].tolist())
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bonus_matrix = []
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for p in ["Player_0", "Player_1", "Player_2", "Player_3", "Player_4"]:
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row = []
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for q in ["Player_0", "Player_1", "Player_2", "Player_3", "Player_4"]:
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row.append(gat.compute_interaction_bonus(p, q))
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bonus_matrix.append(row)
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results["chemistry"] = {"features": chem_features, "bonus_matrix": bonus_matrix}
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except Exception as e:
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results["chemistry"] = {"error": str(e)}
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# Phase 5: Hawkes Form
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try:
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from src.models.hawkes_form import PlayerFormModel
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dates = pd.date_range("2023-08-20", periods=38, freq="7D")
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form_data = pd.DataFrame({
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"player": np.repeat(["Player_0", "Player_1", "Player_5", "Player_10", "Player_20"], 8)[:38][:30],
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"match_date": dates[:30],
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"minutes": np.random.uniform(30, 90, 30),
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})
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form_y = pd.Series(np.random.randn(30) * 1.5 + 6.5)
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hf = PlayerFormModel()
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hf.fit(form_data, form_y)
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statuses = hf.get_form_status(form_data)
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results["hawkes"] = {"statuses": dict(zip(form_data["player"].tolist()[:10], statuses[:10]))}
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except Exception as e:
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results["hawkes"] = {"error": str(e)}
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# Phase 3: RL Auction
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try:
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from src.optimization.rl_auction_agent import AuctionEnv, RLAuctionPolicy, RLAuctionTrainer
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from src.optimization.auction_solver import AuctionConfig
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config_small = AuctionConfig(total_budget=500, n_gk=1, n_def=3, n_mid=3, n_fwd=2)
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env = AuctionEnv(player_pool.head(30), n_opponents=2, config=config_small)
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agent = RLAuctionPolicy(state_dim=14, action_dim=11, hidden_dim=32)
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trainer = RLAuctionTrainer(
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player_pool.head(30), n_opponents=2, config=config_small,
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)
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agent = trainer.train(n_episodes=20, verbose=False)
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rl_state = env.reset()
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action = agent.act(rl_state, epsilon=0.0)
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results["rl"] = {"observation_dim": len(rl_state), "action_taken": int(action),
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"action_dim": 11, "buffer_size": len(agent.replay_buffer)}
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except Exception as e:
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results["rl"] = {"error": str(e)}
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# Phase 3: Set Transformer
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try:
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from src.models.set_transformer import SetTransformer
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rosters = _generate_team_rosters(player_pool, n_teams=6)
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team_vals = [
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sum(r["projected_points"]) + np.random.normal(0, 8)
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for r in rosters
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]
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stf = SetTransformer(use_torch=False)
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stf.fit(rosters, team_vals)
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base_val = stf.predict(rosters[0])
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new_p = pd.DataFrame([{
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"name": "NewPlayer", "role": "A", "feature1": 1.5,
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"projected_points": 8.5, "team": "Team_0",
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}])
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added = stf.value_added(rosters[0], new_p)
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redundancy = stf.get_redundancy_score(rosters[0])
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results["set"] = {"base_value": float(base_val), "marginal_value": float(added),
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"redundancy": float(redundancy)}
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except Exception as e:
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results["set"] = {"error": str(e)}
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# Phase 6: Causal Forest
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try:
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from src.models.causal_forest import TransferCausalModel
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Xc = player_pool.head(200)[["projected_points", "minutes_last_3", "games_played",
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"feature1", "feature2"]].copy()
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Xc["role"] = player_pool.head(200)["role"]
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Xc["team_strength"] = np.random.uniform(0.5, 1.5, 200)
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Tc = pd.DataFrame({
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"role": player_pool.head(200)["role"],
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"projected_points": player_pool.head(200)["projected_points"],
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"days_since_last_transfer": np.random.randint(1, 30, 200),
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})
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Yc = pd.Series(np.random.randn(200) + 6.5)
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cf = TransferCausalModel()
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cf.fit(Xc, Tc, Yc)
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result_causal = cf.predict_effect(Xc.head(5), Tc.head(5))
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results["causal"] = {"ate": float(result_causal.get("ate", 0)),
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"ate_lower": float(result_causal.get("ate_lower", -1)),
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"ate_upper": float(result_causal.get("ate_upper", 1))}
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except Exception as e:
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results["causal"] = {"error": str(e)}
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# Phase 4: Conformal Predictor
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try:
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from src.models.conformal_predictor import ConformalPredictor
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from sklearn.linear_model import Ridge
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Xcp = player_pool[["minutes_last_3", "games_played", "xg_p90", "xa_p90",
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"fatigue_rolling_3", "feature1", "feature2"]].fillna(0)
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ycp = player_pool["projected_points"]
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base = Ridge(alpha=1.0)
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base.fit(Xcp.head(100), ycp.head(100))
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cp = ConformalPredictor(base, alpha=0.10)
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cp.calibrate(Xcp.iloc[100:150], ycp.iloc[100:150])
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yp, yl, yu = cp.predict_with_band(Xcp.iloc[150:160])
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coverage = cp.coverage(Xcp.iloc[150:160], ycp.iloc[150:160])
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results["conformal"] = {"coverage": float(coverage), "n_test": 10,
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"predictions": yp.tolist()[:10],
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"lowers": yl.tolist()[:10],
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"uppers": yu.tolist()[:10]}
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except Exception as e:
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results["conformal"] = {"error": str(e)}
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# Phase 2: Opponent Bidding
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try:
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from src.optimization.opponent_bidding_model import OpponentBidModel
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obm = OpponentBidModel()
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sample_players = player_pool.head(30).rename(columns={
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"name": "player_name", "role": "player_role",
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"projected_points": "player_projected_points",
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})
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opp_state = {
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"budget_remaining": 400, "total_budget": 500, "initial_budget": 500,
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"slots_remaining": {"P": 2, "D": 6, "C": 6, "A": 4},
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"slots_total": {"P": 3, "D": 8, "C": 8, "A": 6},
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"slots_filled": {"P": 1, "D": 2, "C": 2, "A": 2},
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"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"aggression_factor": 1.0,
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}
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bids = obm.predict_opponent_bids(sample_players, opp_state)
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my_bids_sample = np.array([10, 15, 20, 5, 8, 12, 3, 25, 18, 7])
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if len(my_bids_sample) == min(10, len(bids)):
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probs = obm.predict_p_acquire(sample_players.head(min(10, len(bids))), my_bids_sample[:min(10, len(bids))], opp_state)
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results["opponent_bidding"] = {"sample_bids": bids.head(10).tolist() if len(bids) >= 10 else bids.tolist(),
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"acq_probs": probs.tolist()[:10] if len(bids) >= 10 else []}
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else:
|
||||
results["opponent_bidding"] = {"sample_bids": bids.head(10).tolist()}
|
||||
except Exception as e:
|
||||
results["opponent_bidding"] = {"error": str(e)}
|
||||
|
||||
# Phase 2: Budget Optimizer
|
||||
try:
|
||||
from src.optimization.budget_optimizer import BudgetOptimizer
|
||||
bo = BudgetOptimizer(total_budget=500)
|
||||
allocation = bo.optimize(player_pool, n_calls=10)
|
||||
curves = bo.get_role_value_curves(player_pool)
|
||||
results["budget_opt"] = {"allocation": allocation, "curves": curves}
|
||||
except Exception as e:
|
||||
results["budget_opt"] = {"error": str(e)}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# ─── Chart helpers ──────────────────────────────────────────────────
|
||||
|
||||
def _phase_chart_quantile(preds, risk):
|
||||
fig = make_subplots(rows=1, cols=2, subplot_titles=("Quantile Predictions", "Downside Risk Distribution"))
|
||||
n = 30
|
||||
x = list(range(n))
|
||||
fig.add_trace(go.Scatter(x=x, y=preds["P90"].tolist()[:n], name="P90 (upside)",
|
||||
line=dict(color=PITCH_GREEN, width=2, dash="dot")), row=1, col=1)
|
||||
fig.add_trace(go.Scatter(x=x, y=preds["P50"].tolist()[:n], name="P50 (median)",
|
||||
line=dict(color=SKY, width=2.5)), row=1, col=1)
|
||||
fig.add_trace(go.Scatter(x=x, y=preds["P10"].tolist()[:n], name="P10 (floor)",
|
||||
line=dict(color=RED, width=2, dash="dot"),
|
||||
fill="tonexty", fillcolor="rgba(255,77,94,0.08)"), row=1, col=1)
|
||||
fig.add_trace(go.Histogram(x=risk.tolist(), nbinsx=20, name="P(FV < 5.5)",
|
||||
marker_color=RED, opacity=0.7), row=1, col=2)
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=350, showlegend=True,
|
||||
legend=dict(orientation="h", yanchor="bottom", y=1.02))
|
||||
fig.update_xaxes(title_text="Player", row=1, col=1)
|
||||
fig.update_yaxes(title_text="FV", row=1, col=1)
|
||||
fig.update_xaxes(title_text="P(downside)", row=1, col=2)
|
||||
fig.update_yaxes(title_text="Count", row=1, col=2)
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_survival(expected, lower, upper, probs):
|
||||
fig = make_subplots(rows=1, cols=2, subplot_titles=("Minutes Distribution (P95)", "Starter Probability"))
|
||||
n = min(30, len(expected))
|
||||
x = list(range(n))
|
||||
fig.add_trace(go.Scatter(x=x, y=upper[:n], mode="lines", line=dict(width=0),
|
||||
showlegend=False), row=1, col=1)
|
||||
fig.add_trace(go.Scatter(x=x, y=lower[:n], mode="lines", fill="tonexty",
|
||||
fillcolor="rgba(56,189,248,0.15)", line=dict(width=0),
|
||||
name="95% CI"), row=1, col=1)
|
||||
fig.add_trace(go.Scatter(x=x, y=expected[:n], mode="lines+markers",
|
||||
line=dict(color=SKY, width=2.5),
|
||||
marker=dict(size=5, color=SKY),
|
||||
name="Expected min"), row=1, col=1)
|
||||
max_line = [90] * n
|
||||
fig.add_trace(go.Scatter(x=x, y=max_line, mode="lines",
|
||||
line=dict(color=TEXT_SECONDARY, width=0.5, dash="dash"),
|
||||
name="Full match"), row=1, col=1)
|
||||
fig.add_trace(go.Bar(x=x[:len(probs)], y=probs[:n], name="P(starter)",
|
||||
marker_color=PITCH_GREEN, opacity=0.8), row=1, col=2)
|
||||
fig.add_hline(y=0.7, line=dict(color=GOLD, width=1, dash="dot"), row=1, col=2)
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=350)
|
||||
fig.update_xaxes(title_text="Player", row=1, col=1)
|
||||
fig.update_yaxes(title_text="Minutes", range=[0, 95], row=1, col=1)
|
||||
fig.update_xaxes(title_text="Player", row=1, col=2)
|
||||
fig.update_yaxes(title_text="P(≥60 min)", range=[0, 1], row=1, col=2)
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_bayesian(mean, std, reliability):
|
||||
fig = make_subplots(rows=1, cols=2, subplot_titles=("Predictions ± Uncertainty", "Reliability Score"))
|
||||
n = min(30, len(mean))
|
||||
x = list(range(n))
|
||||
fig.add_trace(go.Scatter(
|
||||
x=x, y=mean[:n], mode="markers",
|
||||
error_y=dict(type="data", array=std[:n], visible=True, color=VIOLET),
|
||||
marker=dict(size=7, color=VIOLET),
|
||||
name="Bayesian estimate",
|
||||
), row=1, col=1)
|
||||
fig.add_trace(go.Bar(x=x, y=reliability[:n], marker_color=GOLD, opacity=0.8,
|
||||
name="Reliability"), row=1, col=2)
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=350)
|
||||
fig.update_xaxes(title_text="Player", row=1, col=1)
|
||||
fig.update_yaxes(title_text="FV", row=1, col=1)
|
||||
fig.update_xaxes(title_text="Player", row=1, col=2)
|
||||
fig.update_yaxes(title_text="Score (0-1)", range=[0, 1], row=1, col=2)
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_conformal(preds, lowers, uppers, coverage):
|
||||
n = min(10, len(preds))
|
||||
x = list(range(n))
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(x=x, y=uppers[:n], mode="lines", line=dict(width=0),
|
||||
showlegend=False))
|
||||
fig.add_trace(go.Scatter(x=x, y=lowers[:n], mode="lines", fill="tonexty",
|
||||
fillcolor="rgba(167,139,250,0.15)", line=dict(width=0),
|
||||
name=f"{coverage*100:.0f}% band"))
|
||||
fig.add_trace(go.Scatter(x=x, y=preds[:n], mode="lines+markers",
|
||||
line=dict(color=VIOLET, width=2.5),
|
||||
marker=dict(size=6, color=VIOLET),
|
||||
name="Prediction"))
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=300)
|
||||
fig.update_xaxes(title_text="Player")
|
||||
fig.update_yaxes(title_text="FV")
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_bandit(trial_bids):
|
||||
fig = make_subplots(rows=1, cols=2, subplot_titles=("Bid History", "Bid by Role"))
|
||||
roles = ["P", "D", "C", "A"]
|
||||
bids_by_role = {r: [] for r in roles}
|
||||
for b in trial_bids:
|
||||
bids_by_role.get(b["role"], []).append(b["bid"])
|
||||
for role in roles:
|
||||
if bids_by_role[role]:
|
||||
y = bids_by_role[role]
|
||||
x = list(range(len(y)))
|
||||
fig.add_trace(go.Scatter(
|
||||
x=x, y=y, mode="lines+markers", name=f"{ROLE_ICONS[role]} {role}",
|
||||
line=dict(color=ROLE_COLORS.get(role, SKY), width=2),
|
||||
marker=dict(size=6, color=ROLE_COLORS.get(role, SKY)),
|
||||
), row=1, col=1)
|
||||
for role in roles:
|
||||
if bids_by_role[role]:
|
||||
fig.add_trace(go.Box(y=bids_by_role[role], name=f"{role}",
|
||||
marker_color=ROLE_COLORS.get(role, SKY)),
|
||||
row=1, col=2)
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=350, showlegend=True,
|
||||
legend=dict(orientation="h", yanchor="bottom", y=1.02))
|
||||
fig.update_xaxes(title_text="Decision #", row=1, col=1)
|
||||
fig.update_yaxes(title_text="Bid (cr)", row=1, col=1)
|
||||
fig.update_yaxes(title_text="Bid (cr)", row=1, col=2)
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_chemistry(bonus_matrix):
|
||||
labels = ["P0", "P1", "P2", "P3", "P4"]
|
||||
fig = go.Figure(data=go.Heatmap(
|
||||
z=bonus_matrix, x=labels, y=labels,
|
||||
colorscale=HEATMAP_COLORS,
|
||||
text=np.round(bonus_matrix, 3),
|
||||
texttemplate="%{text}",
|
||||
textfont=dict(size=10),
|
||||
zmin=0, zmax=1,
|
||||
))
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=300,
|
||||
xaxis=dict(side="top"), yaxis=dict(autorange="reversed"))
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_hawkes(statuses):
|
||||
labels = list(statuses.keys())
|
||||
vals = list(statuses.values())
|
||||
color_map = {"HOT": RED, "COLD": SKY, "NEUTRAL": TEXT_SECONDARY}
|
||||
colors = [color_map.get(v, TEXT_SECONDARY) for v in vals]
|
||||
fig = go.Figure(data=[go.Bar(x=labels, y=[1] * len(labels),
|
||||
marker_color=colors,
|
||||
text=vals, textposition="auto",
|
||||
textfont=dict(color=WHITE, size=11))])
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=200,
|
||||
showlegend=False, yaxis=dict(showticklabels=False))
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_opponent_bids(bids):
|
||||
if not bids:
|
||||
return go.Figure()
|
||||
fig = go.Figure(data=[go.Bar(
|
||||
x=list(range(len(bids))), y=bids,
|
||||
marker_color=SKY, opacity=0.8,
|
||||
text=[f"{b:.0f}" for b in bids], textposition="outside",
|
||||
)])
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=250)
|
||||
fig.update_xaxes(title_text="Player")
|
||||
fig.update_yaxes(title_text="Predicted Opponent Bid (cr)")
|
||||
return fig
|
||||
|
||||
|
||||
def _phase_chart_budget_opt(allocation):
|
||||
labels = list(allocation.keys())
|
||||
values = list(allocation.values())
|
||||
colors = [ROLE_COLORS.get(r, SKY) for r in labels]
|
||||
fig = go.Figure(data=[go.Pie(
|
||||
labels=labels, values=values, hole=0.5,
|
||||
marker_colors=colors, textinfo="label+value",
|
||||
texttemplate="%{label}: %{value:.0f} cr",
|
||||
)])
|
||||
fig.update_layout(template=FANTABETO_TEMPLATE, height=280)
|
||||
return fig
|
||||
|
||||
|
||||
# ─── Main page ──────────────────────────────────────────────────────
|
||||
|
||||
def run():
|
||||
inject_css()
|
||||
st.markdown("## 🔬 Dev Preview — New ML Models for Auction Optimization")
|
||||
st.caption("Phase 1–6 models running on synthetic data. Interact with the auction components below.")
|
||||
|
||||
# Generate data
|
||||
with st.spinner("Generating synthetic data & training models..."):
|
||||
player_pool = _generate_player_pool(n_players=120)
|
||||
interaction_data = _generate_interaction_data(player_pool)
|
||||
auction_logs = _generate_auction_logs(player_pool)
|
||||
results = _init_models(player_pool, interaction_data, auction_logs)
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Header KPIs ──
|
||||
k1, k2, k3, k4, k5, k6 = st.columns(6)
|
||||
phases_working = sum(1 for v in results.values() if isinstance(v, dict) and "error" not in v)
|
||||
with k1:
|
||||
st.markdown(kpi_card("MODELS ACTIVE", f"{phases_working}/12", "", PITCH_GREEN), unsafe_allow_html=True)
|
||||
with k2:
|
||||
st.markdown(kpi_card("PLAYERS", str(len(player_pool)), "synthetic", SKY), unsafe_allow_html=True)
|
||||
with k3:
|
||||
st.markdown(kpi_card("AUCTION BUDGET", "500 cr", "total", GOLD), unsafe_allow_html=True)
|
||||
with k4:
|
||||
rl_size = results.get("rl", {}).get("buffer_size", 0)
|
||||
st.markdown(kpi_card("RL BUFFER", str(rl_size), "experiences", VIOLET), unsafe_allow_html=True)
|
||||
with k5:
|
||||
st.markdown(kpi_card("INTERACTIONS", str(len(interaction_data)), "edges", PITCH_GREEN), unsafe_allow_html=True)
|
||||
with k6:
|
||||
cf_ate = results.get("causal", {}).get("ate", 0)
|
||||
st.markdown(kpi_card("AVG CAUSAL EFFECT", f"{cf_ate:+.2f}", "ATE", GOLD), unsafe_allow_html=True)
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Live Auction Simulator ──
|
||||
st.markdown("### 🎮 Live Auction Simulator")
|
||||
st.caption("Run a mock auction round to see the bandit + opponent model + budget optimizer in action.")
|
||||
|
||||
c_sim1, c_sim2 = st.columns(2)
|
||||
with c_sim1:
|
||||
sim_budget = st.slider("Your Budget", 100, 700, 450, step=10)
|
||||
sim_player = st.selectbox("Available Player for Bidding", player_pool.head(30)["name"].tolist())
|
||||
with c_sim2:
|
||||
sim_round = st.slider("Round", 1, 20, 5)
|
||||
st.markdown(f"<br><span style='color:{TEXT_SECONDARY};font-size:12px;'>Opponents: 3 remaining, avg budget ~{sim_budget}cr</span>",
|
||||
unsafe_allow_html=True)
|
||||
|
||||
if st.button("🎯 Run Live Auction Decision", type="primary"):
|
||||
try:
|
||||
from src.optimization.bandit_auction import BanditAuctionSolver
|
||||
from src.optimization.auction_solver import AuctionConfig, PlayerValuation
|
||||
from src.optimization.opponent_bidding_model import OpponentBidModel
|
||||
from src.optimization.budget_optimizer import BudgetOptimizer
|
||||
|
||||
config = AuctionConfig(total_budget=500)
|
||||
bandit = BanditAuctionSolver(config=config)
|
||||
|
||||
target = player_pool[player_pool["name"] == sim_player].iloc[0]
|
||||
qe_result = results.get("quantile", {})
|
||||
if "preds" in qe_result:
|
||||
idx = player_pool.head(60)[player_pool.head(60)["name"] == sim_player].index
|
||||
if len(idx) > 0:
|
||||
i = list(player_pool.head(60).index).index(idx[0])
|
||||
p10 = qe_result["preds"]["P10"][i]
|
||||
p50 = qe_result["preds"]["P50"][i]
|
||||
p90 = qe_result["preds"]["P90"][i]
|
||||
else:
|
||||
p10, p50, p90 = target["projected_points"] * 0.8, target["projected_points"], target["projected_points"] * 1.2
|
||||
else:
|
||||
p10, p50, p90 = target["projected_points"] * 0.8, target["projected_points"], target["projected_points"] * 1.2
|
||||
|
||||
pv = PlayerValuation(
|
||||
name=target["name"], team=target["team"], role=target["role"],
|
||||
projected_points=target["projected_points"],
|
||||
market_value=target["market_value"],
|
||||
ceiling_price=target["projected_points"] * 5,
|
||||
)
|
||||
|
||||
state = {
|
||||
"budget_remaining": sim_budget,
|
||||
"total_budget": 500,
|
||||
"slots_remaining": {"P": 1, "D": 3, "C": 4, "A": 2},
|
||||
"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
|
||||
"slot_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
|
||||
"round_number": sim_round,
|
||||
"total_rounds": 20,
|
||||
"opponent_budgets": [sim_budget, sim_budget - 50, sim_budget + 30],
|
||||
"players_remaining_in_role": {"P": 5, "D": 10, "C": 10, "A": 8},
|
||||
"player_pool": [pv],
|
||||
}
|
||||
|
||||
arm_idx, bandit_bid = bandit.select_bid(pv, state)
|
||||
|
||||
obm = OpponentBidModel()
|
||||
bid_row = pd.DataFrame([{
|
||||
"player_name": target["name"], "player_role": target["role"],
|
||||
"player_projected_points": target["projected_points"],
|
||||
}])
|
||||
opp_state = {
|
||||
"budget_remaining": sim_budget, "total_budget": 500, "initial_budget": 500,
|
||||
"slots_total": {"P": 3, "D": 8, "C": 8, "A": 6},
|
||||
"slots_filled": {"P": 1, "D": 2, "C": 2, "A": 2},
|
||||
"slots_remaining": {"P": 2, "D": 6, "C": 6, "A": 4},
|
||||
"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
|
||||
"aggression_factor": 1.0,
|
||||
}
|
||||
opp_bid = float(obm.predict_opponent_bids(bid_row, opp_state).values[0])
|
||||
win_pct = bandit_bid / max(bandit_bid + opp_bid, 1) * 100
|
||||
|
||||
bo = BudgetOptimizer(total_budget=500)
|
||||
role_budget = bo.optimize(player_pool.head(30), n_calls=5)
|
||||
role_rec = role_budget.get(target["role"], 50)
|
||||
|
||||
with st.container():
|
||||
st.markdown("### 📊 Decision Analysis")
|
||||
|
||||
kd1, kd2, kd3, kd4, kd5 = st.columns(5)
|
||||
with kd1:
|
||||
bid_color = PITCH_GREEN if bandit_bid > opp_bid else RED
|
||||
st.markdown(kpi_card("RECOMMENDED BID", f"{bandit_bid} cr",
|
||||
f"vs opponent ~{opp_bid:.0f} cr", bid_color),
|
||||
unsafe_allow_html=True)
|
||||
with kd2:
|
||||
st.markdown(kpi_card("WIN PROBABILITY", f"{min(win_pct, 95):.0f}%",
|
||||
"", GOLD), unsafe_allow_html=True)
|
||||
with kd3:
|
||||
st.markdown(kpi_card("P50 PROJECTION", f"{p50:.2f}",
|
||||
f"P10:{p10:.1f} P90:{p90:.1f}", SKY),
|
||||
unsafe_allow_html=True)
|
||||
with kd4:
|
||||
st.markdown(kpi_card("ROLE BUDGET", f"{role_rec:.0f} cr",
|
||||
f"for {target['role']} players", VIOLET),
|
||||
unsafe_allow_html=True)
|
||||
with kd5:
|
||||
risk_label = "LOW" if p10 > 5.5 else ("MED" if p10 > 4.5 else "HIGH")
|
||||
risk_color = PITCH_GREEN if risk_label == "LOW" else (GOLD if risk_label == "MED" else RED)
|
||||
st.markdown(kpi_card("DOWNSIDE RISK", risk_label,
|
||||
f"VaR floor: {p10:.1f}", risk_color),
|
||||
unsafe_allow_html=True)
|
||||
|
||||
info_msg = (
|
||||
f"**{target['name']}** ({ROLE_ICONS.get(target['role'], '')} {target['role']}) — "
|
||||
f"Bandit recommends **{bandit_bid} cr** bid. Opponents likely to bid ~{opp_bid:.0f} cr. "
|
||||
f"Budget optimizer allocates ~{role_rec:.0f} cr for {target['role']} role."
|
||||
)
|
||||
if bandit_bid > opp_bid:
|
||||
info_msg += f"\n\n🟢 Expected to win this player. Value ratio: {target['projected_points'] / max(bandit_bid, 1):.3f} FV/cr."
|
||||
else:
|
||||
info_msg += f"\n\n🔴 Opponent likely to outbid. Consider increasing bid or skipping for better value."
|
||||
insight(info_msg)
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Simulation error: {e}")
|
||||
|
||||
st.divider()
|
||||
|
||||
# ── Phase-by-phase showcase ──
|
||||
tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
|
||||
"⚡ Phase 1: Quantile & Survival",
|
||||
"🎰 Phase 2: Adaptive Auction",
|
||||
"🧠 Phase 3: RL & Set Transformer",
|
||||
"📊 Phase 4: Bayesian & Conformal",
|
||||
"🔗 Phase 5: Chemistry & Form",
|
||||
"🔬 Phase 6: Causal Inference",
|
||||
])
|
||||
|
||||
with tab1:
|
||||
section("⚡ Phase 1 — Prediction Quality: Quantile Ensemble")
|
||||
if "error" in results.get("quantile", {}):
|
||||
st.warning(f"Quantile model error: {results['quantile']['error']}")
|
||||
else:
|
||||
q = results["quantile"]
|
||||
fig = _phase_chart_quantile(q["preds"], q["risk"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("P10/P50/P90 predictions enable VaR-constrained bidding. "
|
||||
"The risk histogram shows P(FV < 5.5) per player — yellow cards kill your matchday score.")
|
||||
|
||||
section("⏱ Phase 1 — Prediction Quality: Minutes Survival Model")
|
||||
if "error" in results.get("survival", {}):
|
||||
st.warning(f"Survival model error: {results['survival']['error']}")
|
||||
else:
|
||||
s = results["survival"]
|
||||
fig = _phase_chart_survival(s["expected"], s["lower"], s["upper"], s["starter_probs"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Weibull AFT predicts full minutes distribution — not just binary starter flag. "
|
||||
"A player projected at 7.5 who only plays 60% of matches is auction poison.")
|
||||
|
||||
with tab2:
|
||||
section("🎰 Phase 2 — Thompson Sampling Bandit for Live Bidding")
|
||||
if "error" in results.get("bandit", {}):
|
||||
st.warning(f"Bandit model error: {results['bandit']['error']}")
|
||||
else:
|
||||
fig = _phase_chart_bandit(results["bandit"]["trial_bids"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Thompson Sampling learns optimal bid levels per role over time. "
|
||||
"Explores cheap sleepers when uncertainty is high, exploits known stars when confident.")
|
||||
|
||||
section("💰 Phase 2 — Opponent Bidding Model")
|
||||
if "error" in results.get("opponent_bidding", {}):
|
||||
st.warning(f"Opponent bidding error: {results['opponent_bidding']['error']}")
|
||||
else:
|
||||
ob = results["opponent_bidding"]
|
||||
fig = _phase_chart_opponent_bids(ob["sample_bids"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("LightGBM predicts opponent max bids per player. Outbid intelligently — "
|
||||
"don't overpay when no competitor is interested.")
|
||||
|
||||
section("📐 Phase 2 — Bayesian Budget Optimization")
|
||||
if "error" in results.get("budget_opt", {}):
|
||||
st.warning(f"Budget optimizer error: {results['budget_opt']['error']}")
|
||||
else:
|
||||
fig = _phase_chart_budget_opt(results["budget_opt"]["allocation"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Gaussian Process optimization finds the optimal budget split across roles. "
|
||||
"GK gets ~8%, DEF ~35%, MID ~32%, FWD ~25% — adapts to pool quality.")
|
||||
|
||||
with tab3:
|
||||
section("🧠 Phase 3 — Double DQN Auction Agent")
|
||||
if "error" in results.get("rl", {}):
|
||||
st.warning(f"RL agent error: {results['rl']['error']}")
|
||||
else:
|
||||
rl = results["rl"]
|
||||
k_rl1, k_rl2, k_rl3 = st.columns(3)
|
||||
with k_rl1:
|
||||
st.markdown(kpi_card("STATE DIM", str(rl["observation_dim"]), "features", SKY), unsafe_allow_html=True)
|
||||
with k_rl2:
|
||||
st.markdown(kpi_card("ACTION SPACE", str(rl["action_dim"]), "bid levels", GOLD), unsafe_allow_html=True)
|
||||
with k_rl3:
|
||||
st.markdown(kpi_card("EXPERIENCES", str(rl["buffer_size"]), "stored", VIOLET), unsafe_allow_html=True)
|
||||
insight("Double DQN agent trained on 20 episodes (fast demo). In production, train 5000+ episodes "
|
||||
"against diverse simulated opponents. Learns to delay big bids until rivals are exhausted.")
|
||||
|
||||
section("🧩 Phase 3 — Set Transformer: Team Value ≠ Sum of Parts")
|
||||
if "error" in results.get("set", {}):
|
||||
st.warning(f"Set Transformer error: {results['set']['error']}")
|
||||
else:
|
||||
sf = results["set"]
|
||||
s_k1, s_k2, s_k3 = st.columns(3)
|
||||
with s_k1:
|
||||
st.markdown(kpi_card("BASE TEAM VALUE", f"{sf['base_value']:.0f} PTS",
|
||||
"sum of projections", SKY), unsafe_allow_html=True)
|
||||
with s_k2:
|
||||
delta_color = PITCH_GREEN if sf["marginal_value"] > 0 else RED
|
||||
st.markdown(kpi_card("MARGINAL PLAYER", f"{sf['marginal_value']:+.1f} PTS",
|
||||
"value added by 1 player", delta_color), unsafe_allow_html=True)
|
||||
with s_k3:
|
||||
red_color = PITCH_GREEN if sf["redundancy"] < 0.5 else GOLD
|
||||
st.markdown(kpi_card("REDUNDANCY", f"{sf['redundancy']:.2f}",
|
||||
"0=diverse 1=overlapping", red_color), unsafe_allow_html=True)
|
||||
insight("Set Transformer captures non-linear synergies. Two playmakers overlapping = "
|
||||
"worth less than sum of parts. Redundancy score warns you before overpaying.")
|
||||
|
||||
with tab4:
|
||||
section("📊 Phase 4 — Hierarchical Bayesian Pooling")
|
||||
if "error" in results.get("bayesian", {}):
|
||||
st.warning(f"Bayesian model error: {results['bayesian']['error']}")
|
||||
else:
|
||||
b = results["bayesian"]
|
||||
fig = _phase_chart_bayesian(b["mean"], b["std"], b["reliability"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Hierarchical model shrinks rookies toward role mean. Low reliability = high uncertainty. "
|
||||
"Don't pay premium prices for players with <10 career games.")
|
||||
|
||||
section("🎯 Phase 4 — Conformal Prediction Bands")
|
||||
if "error" in results.get("conformal", {}):
|
||||
st.warning(f"Conformal predictor error: {results['conformal']['error']}")
|
||||
else:
|
||||
cp_r = results["conformal"]
|
||||
fig = _phase_chart_conformal(cp_r["predictions"], cp_r["lowers"], cp_r["uppers"], cp_r["coverage"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight(f"Conformal bands: {cp_r['coverage']*100:.0f}% coverage on test set. "
|
||||
"Model-agnostic calibrated intervals — no distributional assumptions needed.")
|
||||
|
||||
with tab5:
|
||||
section("🔗 Phase 5 — Graph Attention Network: Player Chemistry")
|
||||
if "error" in results.get("chemistry", {}):
|
||||
st.warning(f"Chemistry model error: {results['chemistry']['error']}")
|
||||
else:
|
||||
fig = _phase_chart_chemistry(results["chemistry"]["bonus_matrix"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Pairwise chemistry bonuses from pass networks, assists, and crosses. "
|
||||
"A strong winger→striker edge boosts both players. Target linked pairs in the auction.")
|
||||
|
||||
section("🔥 Phase 5 — Hawkes Process: Form Momentum")
|
||||
if "error" in results.get("hawkes", {}):
|
||||
st.warning(f"Form model error: {results['hawkes']['error']}")
|
||||
else:
|
||||
fig = _phase_chart_hawkes(results["hawkes"]["statuses"])
|
||||
st.plotly_chart(fig, width="stretch")
|
||||
insight("Self-exciting process detects HOT/COLD streaks. A HOT player on a 5-game scoring run "
|
||||
"has temporarily elevated projection. Exploit recency bias in your opponents.")
|
||||
|
||||
with tab6:
|
||||
section("🔬 Phase 6 — Causal Forest: Transfer Effect Analysis")
|
||||
if "error" in results.get("causal", {}):
|
||||
st.warning(f"Causal forest error: {results['causal']['error']}")
|
||||
else:
|
||||
cf = results["causal"]
|
||||
k_c1, k_c2, k_c3 = st.columns(3)
|
||||
with k_c1:
|
||||
color = PITCH_GREEN if cf["ate"] > 0 else RED
|
||||
st.markdown(kpi_card("AVG TREATMENT EFFECT", f"{cf['ate']:+.3f}",
|
||||
"adding a player", color), unsafe_allow_html=True)
|
||||
with k_c2:
|
||||
st.markdown(kpi_card("CI LOWER", f"{cf['ate_lower']:+.3f}", "95% confidence", TEXT_SECONDARY),
|
||||
unsafe_allow_html=True)
|
||||
with k_c3:
|
||||
st.markdown(kpi_card("CI UPPER", f"{cf['ate_upper']:+.3f}", "95% confidence", TEXT_SECONDARY),
|
||||
unsafe_allow_html=True)
|
||||
insight("Causal forest estimates the TRUE effect of a roster change, controlling for confounders. "
|
||||
"Adding a top midfielder doesn't help if you already have 5 strong mids — "
|
||||
"the diminishing returns are captured in the CATE.")
|
||||
|
||||
# ── Methodology ──
|
||||
st.divider()
|
||||
with st.expander("⚙️ Methodology — 10 ML Models Explained"):
|
||||
st.markdown("""
|
||||
| # | Model | Type | What It Does |
|
||||
|---|-------|------|-------------|
|
||||
| 1 | QuantileEnsemble | LightGBM quantile | P10/P50/P90 predictions → risk-aware bidding |
|
||||
| 2 | MinutesSurvivalModel | Weibull AFT | Full minutes distribution, starter probability |
|
||||
| 3 | BanditAuctionSolver | Thompson Sampling | Optimal bid per round balancing explore/exploit |
|
||||
| 4 | OpponentBidModel | LightGBM regressor | Predicts competitor max bid per player |
|
||||
| 5 | BudgetOptimizer | Bayesian Optimization | Optimal budget split across GK/DEF/MID/FWD |
|
||||
| 6 | PlayerChemistryGAT | Graph Attention Network | Player synergy bonuses from pass networks |
|
||||
| 7 | PlayerFormModel | Hawkes Process | Momentum/decorrelation hot streak detection |
|
||||
| 8 | BayesianPlayerModel | Hierarchical Bayes | Rookie uncertainty via role-level shrinkage |
|
||||
| 9 | ConformalPredictor | Conformal inference | Calibrated prediction bands, model-agnostic |
|
||||
| 10 | SetTransformer | Transformer on sets | Team composition value beyond sum-of-parts |
|
||||
| 11 | RLAuctionPolicy | Double DQN | RL agent for sequential auction strategy |
|
||||
| 12 | TransferCausalModel | Causal Forest | Causal effect of transfer on team performance |
|
||||
""", unsafe_allow_html=False)
|
||||
|
||||
st.divider()
|
||||
st.caption("Dev Preview v1.0 — all models running on synthetic data. Connect real pipeline for production use.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run()
|
||||
+30
-11
@@ -2,40 +2,59 @@
|
||||
Cached with @st.cache_data. No imports from ML code.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
WAREHOUSE = ROOT / "data" / "warehouse"
|
||||
|
||||
_EMPTY_DEFAULTS = {
|
||||
"players.parquet": ["player", "role", "team", "fv_avg", "qi", "games_season",
|
||||
"fv_proj", "goals", "assists", "stability", "starter_pct",
|
||||
"fvm", "mv_proj", "bid_cap"],
|
||||
"fixtures.parquet": ["team", "matchday", "opp_strength", "home", "away"],
|
||||
"predictions.parquet": ["name", "role", "team", "fv_mean", "fv_std", "mv_mean",
|
||||
"mv_std", "starter_prob", "cs_prob", "oppteam", "home"],
|
||||
"lineups.parquet": ["player", "role", "team", "starter_pct"],
|
||||
"votes.parquet": ["player", "vote", "matchday"],
|
||||
"model_metrics.parquet": ["metric", "value"],
|
||||
}
|
||||
|
||||
def _cache_key():
|
||||
"""Bust cache when parquet files change."""
|
||||
files = sorted(WAREHOUSE.glob("*.parquet"))
|
||||
mtimes = tuple(f.stat().st_mtime for f in files)
|
||||
return (len(files), mtimes)
|
||||
|
||||
def _read_parquet_or_empty(name):
|
||||
path = WAREHOUSE / name
|
||||
if path.exists():
|
||||
return pd.read_parquet(path)
|
||||
cols = _EMPTY_DEFAULTS.get(name, [])
|
||||
df = pd.DataFrame([{c: (0.0 if c not in ("player", "role", "team", "name", "oppteam", "metric")
|
||||
else ("—" if c in ("player", "name") else ""))
|
||||
for c in cols}])
|
||||
return df
|
||||
|
||||
|
||||
def load_players() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "players.parquet")
|
||||
return _read_parquet_or_empty("players.parquet")
|
||||
|
||||
|
||||
def load_fixtures() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "fixtures.parquet")
|
||||
return _read_parquet_or_empty("fixtures.parquet")
|
||||
|
||||
|
||||
def load_predictions() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "predictions.parquet")
|
||||
return _read_parquet_or_empty("predictions.parquet")
|
||||
|
||||
|
||||
def load_lineups() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "lineups.parquet")
|
||||
return _read_parquet_or_empty("lineups.parquet")
|
||||
|
||||
|
||||
def load_votes() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "votes.parquet")
|
||||
return _read_parquet_or_empty("votes.parquet")
|
||||
|
||||
|
||||
def load_model_metrics() -> pd.DataFrame:
|
||||
return pd.read_parquet(WAREHOUSE / "model_metrics.parquet")
|
||||
return _read_parquet_or_empty("model_metrics.parquet")
|
||||
|
||||
Reference in New Issue
Block a user