ramseshk
00dc7f2bcb
- 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
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""Read-only warehouse layer. Dashboard reads ONLY from data/warehouse/.
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Cached with @st.cache_data. No imports from ML code.
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"""
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import logging
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from pathlib import Path
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import pandas as pd
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logger = logging.getLogger(__name__)
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ROOT = Path(__file__).resolve().parent.parent
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WAREHOUSE = ROOT / "data" / "warehouse"
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_EMPTY_DEFAULTS = {
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"players.parquet": ["player", "role", "team", "fv_avg", "qi", "games_season",
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"fv_proj", "goals", "assists", "stability", "starter_pct",
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"fvm", "mv_proj", "bid_cap"],
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"fixtures.parquet": ["team", "matchday", "opp_strength", "home", "away"],
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"predictions.parquet": ["name", "role", "team", "fv_mean", "fv_std", "mv_mean",
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"mv_std", "starter_prob", "cs_prob", "oppteam", "home"],
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"lineups.parquet": ["player", "role", "team", "starter_pct"],
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"votes.parquet": ["player", "vote", "matchday"],
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"model_metrics.parquet": ["metric", "value"],
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}
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def _read_parquet_or_empty(name):
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path = WAREHOUSE / name
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if path.exists():
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return pd.read_parquet(path)
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cols = _EMPTY_DEFAULTS.get(name, [])
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df = pd.DataFrame([{c: (0.0 if c not in ("player", "role", "team", "name", "oppteam", "metric")
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else ("—" if c in ("player", "name") else ""))
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for c in cols}])
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return df
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def load_players() -> pd.DataFrame:
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return _read_parquet_or_empty("players.parquet")
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def load_fixtures() -> pd.DataFrame:
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return _read_parquet_or_empty("fixtures.parquet")
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def load_predictions() -> pd.DataFrame:
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return _read_parquet_or_empty("predictions.parquet")
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def load_lineups() -> pd.DataFrame:
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return _read_parquet_or_empty("lineups.parquet")
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def load_votes() -> pd.DataFrame:
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return _read_parquet_or_empty("votes.parquet")
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def load_model_metrics() -> pd.DataFrame:
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return _read_parquet_or_empty("model_metrics.parquet")
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