700 lines
21 KiB
Python
700 lines
21 KiB
Python
import re
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from pathlib import Path
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import joblib
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import numpy as np
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import pandas as pd
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from scipy.spatial import ConvexHull
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from scipy.spatial.distance import pdist, squareform
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try:
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import torch
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import torch.nn as nn
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TORCH_AVAILABLE = True
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except ImportError:
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TORCH_AVAILABLE = False
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# ============================================================
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# Paths
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# ============================================================
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ROOT = Path(__file__).resolve().parents[1]
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SCALER_PATH = ROOT / "models" / "feature_scaler.pkl"
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FEATURE_NAMES_PATH = ROOT / "models" / "feature_names.txt"
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PLACEMENTS_PATH = ROOT / "data" / "placements.csv" # adjust if needed
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# ============================================================
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# Model registry
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# ============================================================
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MODEL_REGISTRY = {
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"linear": {
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"path": ROOT / "models" / "linear_regression.pkl",
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"kind": "sklearn",
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"needs_scaling": True,
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},
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"ridge": {
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"path": ROOT / "models" / "ridge_regression.pkl",
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"kind": "sklearn",
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"needs_scaling": True,
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},
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"lasso": {
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"path": ROOT / "models" / "lasso_regression.pkl",
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"kind": "sklearn",
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"needs_scaling": True,
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},
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"random_forest": {
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"path": ROOT / "models" / "random_forest_tuned.pkl",
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"kind": "sklearn",
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"needs_scaling": False,
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},
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"nn_best": {
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"path": ROOT / "models" / "neural_network_best.pth",
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"kind": "torch_checkpoint",
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"needs_scaling": True,
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},
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}
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DEFAULT_MODEL = "random_forest"
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# ============================================================
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# Board constants
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# Adjust if your board coordinate system differs
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# ============================================================
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x_min, x_max = 0.0, 144.0
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y_min, y_max = 0.0, 144.0
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board_width = x_max - x_min
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board_height = y_max - y_min
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# ============================================================
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# Role mappings
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# ============================================================
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HAND_ROLE_IDS = {5, 6, 7}
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FOOT_ROLE_IDS = {8}
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def get_role_type(role_id: int) -> str:
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mapping = {
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5: "start",
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6: "middle",
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7: "finish",
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8: "foot",
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}
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return mapping.get(role_id, "middle")
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# ============================================================
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# Grade map
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# ============================================================
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grade_map = {
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10: '4a/V0',
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11: '4b/V0',
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12: '4c/V0',
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13: '5a/V1',
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14: '5b/V1',
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15: '5c/V2',
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16: '6a/V3',
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17: '6a+/V3',
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18: '6b/V4',
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19: '6b+/V4',
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20: '6c/V5',
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21: '6c+/V5',
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22: '7a/V6',
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23: '7a+/V7',
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24: '7b/V8',
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25: '7b+/V8',
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26: '7c/V9',
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27: '7c+/V10',
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28: '8a/V11',
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29: '8a+/V12',
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30: '8b/V13',
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31: '8b+/V14',
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32: '8c/V15',
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33: '8c+/V16'
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}
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MIN_GRADE = min(grade_map)
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MAX_GRADE = max(grade_map)
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# ============================================================
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# Neural network architecture from Notebook 06
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# ============================================================
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if TORCH_AVAILABLE:
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class ClimbGradePredictor(nn.Module):
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def __init__(self, input_dim, hidden_layers=None, dropout_rate=0.2):
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super().__init__()
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if hidden_layers is None:
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hidden_layers = [256, 128, 64]
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layers = []
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prev_dim = input_dim
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for hidden_dim in hidden_layers:
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layers.append(nn.Linear(prev_dim, hidden_dim))
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layers.append(nn.BatchNorm1d(hidden_dim))
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layers.append(nn.ReLU())
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layers.append(nn.Dropout(dropout_rate))
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prev_dim = hidden_dim
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layers.append(nn.Linear(prev_dim, 1))
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self.network = nn.Sequential(*layers)
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def forward(self, x):
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return self.network(x)
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# ============================================================
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# Load shared artifacts
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# ============================================================
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scaler = joblib.load(SCALER_PATH)
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with open(FEATURE_NAMES_PATH, "r") as f:
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FEATURE_NAMES = [line.strip() for line in f if line.strip()]
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df_placements = pd.read_csv(PLACEMENTS_PATH)
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placement_coords = {
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int(row["placement_id"]): (row["x"], row["y"])
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for _, row in df_placements.iterrows()
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}
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# ============================================================
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# Model loading
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# ============================================================
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_MODEL_CACHE = {}
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def normalize_model_name(model_name: str) -> str:
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if model_name == "nn":
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return "nn_best"
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return model_name
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def load_model(model_name=DEFAULT_MODEL):
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model_name = normalize_model_name(model_name)
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if model_name not in MODEL_REGISTRY:
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raise ValueError(
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f"Unknown model '{model_name}'. Choose from: {list(MODEL_REGISTRY.keys()) + ['nn']}"
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)
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if model_name in _MODEL_CACHE:
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return _MODEL_CACHE[model_name]
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info = MODEL_REGISTRY[model_name]
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path = info["path"]
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if info["kind"] == "sklearn":
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model = joblib.load(path)
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elif info["kind"] == "torch_checkpoint":
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if not TORCH_AVAILABLE:
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raise ImportError("PyTorch is not installed, so the neural network model cannot be used.")
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checkpoint = torch.load(path, map_location="cpu")
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if hasattr(checkpoint, "eval"):
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model = checkpoint
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model.eval()
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elif isinstance(checkpoint, dict):
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input_dim = checkpoint.get("input_dim", len(FEATURE_NAMES))
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hidden_layers = checkpoint.get("hidden_layers", [256, 128, 64])
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dropout_rate = checkpoint.get("dropout_rate", 0.2)
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model = ClimbGradePredictor(
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input_dim=input_dim,
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hidden_layers=hidden_layers,
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dropout_rate=dropout_rate,
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)
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if "model_state_dict" in checkpoint:
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model.load_state_dict(checkpoint["model_state_dict"])
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else:
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model.load_state_dict(checkpoint)
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model.eval()
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else:
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raise RuntimeError(
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f"Unsupported checkpoint type for {model_name}: {type(checkpoint)}"
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)
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else:
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raise ValueError(f"Unsupported model kind: {info['kind']}")
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_MODEL_CACHE[model_name] = model
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return model
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# ============================================================
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# Helpers
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# ============================================================
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def parse_frames(frames: str):
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"""
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Parse strings like:
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p304r8p378r6p552r6
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into:
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[(304, 8), (378, 6), (552, 6)]
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"""
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if not isinstance(frames, str) or not frames.strip():
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return []
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matches = re.findall(r"p(\d+)r(\d+)", frames)
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return [(int(p), int(r)) for p, r in matches]
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# ============================================================
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# Feature extraction
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# ============================================================
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def extract_features_from_raw(angle, frames, is_nomatch=0, description=""):
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"""
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Extract the clean, leakage-free feature set used by the updated models.
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"""
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holds = parse_frames(frames)
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if not holds:
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raise ValueError("Could not parse any holds from frames.")
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hold_data = []
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for placement_id, role_id in holds:
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coords = placement_coords.get(placement_id, (None, None))
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if coords[0] is None:
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continue
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role_type = get_role_type(role_id)
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is_hand_role = role_id in HAND_ROLE_IDS
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is_foot_role = role_id in FOOT_ROLE_IDS
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hold_data.append({
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"placement_id": placement_id,
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"x": coords[0],
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"y": coords[1],
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"role_type": role_type,
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"is_hand": is_hand_role,
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"is_foot": is_foot_role,
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})
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if not hold_data:
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raise ValueError("No valid holds found after parsing frames.")
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df_holds = pd.DataFrame(hold_data)
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hand_holds = df_holds[df_holds["is_hand"]]
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foot_holds = df_holds[df_holds["is_foot"]]
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start_holds = df_holds[df_holds["role_type"] == "start"]
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finish_holds = df_holds[df_holds["role_type"] == "finish"]
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middle_holds = df_holds[df_holds["role_type"] == "middle"]
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xs = df_holds["x"].to_numpy()
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ys = df_holds["y"].to_numpy()
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desc = str(description) if description is not None else ""
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if pd.isna(desc):
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desc = ""
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center_x = (x_min + x_max) / 2
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features = {}
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# Core / counts
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features["angle"] = float(angle)
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features["angle_squared"] = float(angle) ** 2
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features["total_holds"] = int(len(df_holds))
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features["hand_holds"] = int(len(hand_holds))
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features["foot_holds"] = int(len(foot_holds))
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features["start_holds"] = int(len(start_holds))
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features["finish_holds"] = int(len(finish_holds))
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features["middle_holds"] = int(len(middle_holds))
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features["is_nomatch"] = int(
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(is_nomatch == 1) or
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bool(re.search(r"\bno\s*match(ing)?\b", desc, flags=re.IGNORECASE))
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)
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# Spatial
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features["mean_y"] = float(np.mean(ys))
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features["std_x"] = float(np.std(xs)) if len(xs) > 1 else 0.0
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features["std_y"] = float(np.std(ys)) if len(ys) > 1 else 0.0
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features["range_x"] = float(np.max(xs) - np.min(xs))
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features["range_y"] = float(np.max(ys) - np.min(ys))
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features["min_y"] = float(np.min(ys))
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features["max_y"] = float(np.max(ys))
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features["height_gained"] = features["max_y"] - features["min_y"]
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start_height = float(start_holds["y"].mean()) if len(start_holds) > 0 else np.nan
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finish_height = float(finish_holds["y"].mean()) if len(finish_holds) > 0 else np.nan
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features["height_gained_start_finish"] = (
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finish_height - start_height
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if pd.notna(start_height) and pd.notna(finish_height)
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else np.nan
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)
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# Density / symmetry
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bbox_area = features["range_x"] * features["range_y"]
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features["bbox_area"] = float(bbox_area)
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features["hold_density"] = float(features["total_holds"] / bbox_area) if bbox_area > 0 else 0.0
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features["holds_per_vertical_foot"] = float(features["total_holds"] / max(features["range_y"], 1))
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left_holds = int((df_holds["x"] < center_x).sum())
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features["left_ratio"] = left_holds / features["total_holds"] if features["total_holds"] > 0 else 0.5
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features["symmetry_score"] = 1 - abs(features["left_ratio"] - 0.5) * 2
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y_median = np.median(ys)
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upper_holds = int((df_holds["y"] > y_median).sum())
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features["upper_ratio"] = upper_holds / features["total_holds"]
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# Hand reach
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if len(hand_holds) >= 2:
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hand_points = hand_holds[["x", "y"]].to_numpy()
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hand_distances = pdist(hand_points)
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hand_xs = hand_holds["x"].to_numpy()
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hand_ys = hand_holds["y"].to_numpy()
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features["mean_hand_reach"] = float(np.mean(hand_distances))
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features["max_hand_reach"] = float(np.max(hand_distances))
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features["std_hand_reach"] = float(np.std(hand_distances))
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features["hand_spread_x"] = float(hand_xs.max() - hand_xs.min())
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features["hand_spread_y"] = float(hand_ys.max() - hand_ys.min())
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else:
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features["mean_hand_reach"] = 0.0
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features["max_hand_reach"] = 0.0
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features["std_hand_reach"] = 0.0
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features["hand_spread_x"] = 0.0
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features["hand_spread_y"] = 0.0
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# Hand-foot distances
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if len(hand_holds) > 0 and len(foot_holds) > 0:
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hand_points = hand_holds[["x", "y"]].to_numpy()
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foot_points = foot_holds[["x", "y"]].to_numpy()
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dists = []
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for hx, hy in hand_points:
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for fx, fy in foot_points:
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dists.append(np.sqrt((hx - fx) ** 2 + (hy - fy) ** 2))
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dists = np.asarray(dists, dtype=float)
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features["min_hand_to_foot"] = float(np.min(dists))
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features["mean_hand_to_foot"] = float(np.mean(dists))
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features["std_hand_to_foot"] = float(np.std(dists))
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else:
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features["min_hand_to_foot"] = 0.0
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features["mean_hand_to_foot"] = 0.0
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features["std_hand_to_foot"] = 0.0
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# Global geometry
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points = np.column_stack([xs, ys])
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if len(df_holds) >= 3:
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try:
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hull = ConvexHull(points)
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features["convex_hull_area"] = float(hull.volume)
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features["hull_area_to_bbox_ratio"] = float(features["convex_hull_area"] / max(bbox_area, 1))
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except Exception:
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features["convex_hull_area"] = np.nan
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features["hull_area_to_bbox_ratio"] = np.nan
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else:
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features["convex_hull_area"] = 0.0
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features["hull_area_to_bbox_ratio"] = 0.0
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if len(df_holds) >= 2:
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pairwise = pdist(points)
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features["mean_pairwise_distance"] = float(np.mean(pairwise))
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features["std_pairwise_distance"] = float(np.std(pairwise))
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else:
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features["mean_pairwise_distance"] = 0.0
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features["std_pairwise_distance"] = 0.0
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if len(df_holds) >= 2:
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sorted_idx = np.argsort(ys)
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sorted_points = points[sorted_idx]
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path_length = 0.0
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for i in range(len(sorted_points) - 1):
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dx = sorted_points[i + 1, 0] - sorted_points[i, 0]
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dy = sorted_points[i + 1, 1] - sorted_points[i, 1]
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path_length += np.sqrt(dx ** 2 + dy ** 2)
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features["path_length_vertical"] = float(path_length)
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features["path_efficiency"] = float(features["height_gained"] / max(path_length, 1))
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else:
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features["path_length_vertical"] = 0.0
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features["path_efficiency"] = 0.0
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# Normalized / relative
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features["mean_y_normalized"] = float((features["mean_y"] - y_min) / board_height)
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features["start_height_normalized"] = float((start_height - y_min) / board_height) if pd.notna(start_height) else np.nan
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features["finish_height_normalized"] = float((finish_height - y_min) / board_height) if pd.notna(finish_height) else np.nan
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features["mean_y_relative_to_start"] = float(features["mean_y"] - start_height) if pd.notna(start_height) else np.nan
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features["spread_x_normalized"] = float(features["range_x"] / board_width)
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features["spread_y_normalized"] = float(features["range_y"] / board_height)
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y_q75 = np.percentile(ys, 75)
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y_q25 = np.percentile(ys, 25)
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features["y_q75"] = float(y_q75)
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features["y_iqr"] = float(y_q75 - y_q25)
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# Engineered clean features
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features["complexity_score"] = float(
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features["mean_hand_reach"]
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* np.log1p(features["total_holds"])
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* (1 + features["hold_density"])
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)
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features["angle_x_holds"] = float(features["angle"] * features["total_holds"])
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return features
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# ============================================================
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# Model input preparation
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# ============================================================
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def prepare_feature_vector(features: dict) -> pd.DataFrame:
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row = {}
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for col in FEATURE_NAMES:
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value = features.get(col, 0.0)
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row[col] = 0.0 if pd.isna(value) else value
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return pd.DataFrame([row], columns=FEATURE_NAMES)
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# ============================================================
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# Prediction helpers
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# ============================================================
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def format_prediction(pred: float):
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rounded = int(round(pred))
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rounded = max(min(rounded, MAX_GRADE), MIN_GRADE)
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return {
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"predicted_numeric": float(pred),
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"predicted_display_difficulty": rounded,
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"predicted_boulder_grade": grade_map[rounded],
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}
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def predict_with_model(model, X: pd.DataFrame, model_name: str):
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model_name = normalize_model_name(model_name)
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info = MODEL_REGISTRY[model_name]
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if info["kind"] == "sklearn":
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X_input = scaler.transform(X) if info["needs_scaling"] else X
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pred = model.predict(X_input)[0]
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return float(pred)
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if info["kind"] == "torch_checkpoint":
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if not TORCH_AVAILABLE:
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raise ImportError("PyTorch is not installed.")
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X_input = scaler.transform(X) if info["needs_scaling"] else X
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X_tensor = torch.tensor(np.asarray(X_input), dtype=torch.float32)
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with torch.no_grad():
|
|
out = model(X_tensor)
|
|
|
|
if isinstance(out, tuple):
|
|
out = out[0]
|
|
|
|
pred = np.asarray(out).reshape(-1)[0]
|
|
return float(pred)
|
|
|
|
raise ValueError(f"Unsupported model kind: {info['kind']}")
|
|
|
|
|
|
# ============================================================
|
|
# Public API
|
|
# ============================================================
|
|
|
|
def predict(
|
|
angle,
|
|
frames,
|
|
is_nomatch=0,
|
|
description="",
|
|
model_name=DEFAULT_MODEL,
|
|
return_numeric=False,
|
|
debug=False,
|
|
):
|
|
model_name = normalize_model_name(model_name)
|
|
model = load_model(model_name)
|
|
|
|
features = extract_features_from_raw(
|
|
angle=angle,
|
|
frames=frames,
|
|
is_nomatch=is_nomatch,
|
|
description=description,
|
|
)
|
|
|
|
X = prepare_feature_vector(features)
|
|
|
|
if debug:
|
|
print("\nNonzero / non-null feature values:")
|
|
for col, val in X.iloc[0].items():
|
|
if pd.notna(val) and val != 0:
|
|
print(f"{col}: {val}")
|
|
|
|
pred = predict_with_model(model, X, model_name=model_name)
|
|
|
|
if return_numeric:
|
|
return float(pred)
|
|
|
|
result = format_prediction(pred)
|
|
result["model"] = model_name
|
|
return result
|
|
|
|
|
|
def predict_csv(
|
|
input_csv,
|
|
output_csv=None,
|
|
model_name=DEFAULT_MODEL,
|
|
angle_col="angle",
|
|
frames_col="frames",
|
|
is_nomatch_col="is_nomatch",
|
|
description_col="description",
|
|
):
|
|
"""
|
|
Batch prediction over a CSV file.
|
|
|
|
Required columns:
|
|
- angle
|
|
- frames
|
|
|
|
Optional columns:
|
|
- is_nomatch
|
|
- description
|
|
"""
|
|
model_name = normalize_model_name(model_name)
|
|
|
|
df = pd.read_csv(input_csv)
|
|
|
|
if angle_col not in df.columns:
|
|
raise ValueError(f"Missing required column: '{angle_col}'")
|
|
if frames_col not in df.columns:
|
|
raise ValueError(f"Missing required column: '{frames_col}'")
|
|
|
|
results = []
|
|
|
|
for _, row in df.iterrows():
|
|
angle = row[angle_col]
|
|
frames = row[frames_col]
|
|
is_nomatch = row[is_nomatch_col] if is_nomatch_col in df.columns and pd.notna(row[is_nomatch_col]) else 0
|
|
description = row[description_col] if description_col in df.columns and pd.notna(row[description_col]) else ""
|
|
|
|
pred = predict(
|
|
angle=angle,
|
|
frames=frames,
|
|
is_nomatch=is_nomatch,
|
|
description=description,
|
|
model_name=model_name,
|
|
return_numeric=False,
|
|
debug=False,
|
|
)
|
|
|
|
results.append(pred)
|
|
|
|
pred_df = pd.DataFrame(results)
|
|
out = pd.concat([df.reset_index(drop=True), pred_df.reset_index(drop=True)], axis=1)
|
|
|
|
if output_csv is not None:
|
|
out.to_csv(output_csv, index=False)
|
|
|
|
return out
|
|
|
|
|
|
def evaluate_predictions(df, true_col="display_difficulty", pred_col="predicted_numeric"):
|
|
"""
|
|
Simple evaluation summary for labeled batch predictions.
|
|
"""
|
|
if true_col not in df.columns:
|
|
raise ValueError(f"Missing true target column: '{true_col}'")
|
|
if pred_col not in df.columns:
|
|
raise ValueError(f"Missing prediction column: '{pred_col}'")
|
|
|
|
y_true = df[true_col].astype(float)
|
|
y_pred = df[pred_col].astype(float)
|
|
|
|
mae = np.mean(np.abs(y_true - y_pred))
|
|
rmse = np.sqrt(np.mean((y_true - y_pred) ** 2))
|
|
within_1 = np.mean(np.abs(y_true - y_pred) <= 1)
|
|
within_2 = np.mean(np.abs(y_true - y_pred) <= 2)
|
|
|
|
return {
|
|
"mae": float(mae),
|
|
"rmse": float(rmse),
|
|
"within_1": float(within_1),
|
|
"within_2": float(within_2),
|
|
}
|
|
|
|
|
|
# ============================================================
|
|
# CLI
|
|
# ============================================================
|
|
|
|
if __name__ == "__main__":
|
|
import argparse
|
|
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# Single prediction mode
|
|
parser.add_argument("--angle", type=int)
|
|
parser.add_argument("--frames", type=str)
|
|
parser.add_argument("--is_nomatch", type=int, default=0)
|
|
parser.add_argument("--description", type=str, default="")
|
|
|
|
# Batch mode
|
|
parser.add_argument("--input_csv", type=str)
|
|
parser.add_argument("--output_csv", type=str)
|
|
|
|
parser.add_argument(
|
|
"--model",
|
|
type=str,
|
|
default=DEFAULT_MODEL,
|
|
choices=list(MODEL_REGISTRY.keys()) + ["nn"],
|
|
help="Which trained model to use",
|
|
)
|
|
parser.add_argument("--numeric", action="store_true")
|
|
parser.add_argument("--debug", action="store_true")
|
|
parser.add_argument("--evaluate", action="store_true")
|
|
|
|
args = parser.parse_args()
|
|
|
|
if args.input_csv:
|
|
df_out = predict_csv(
|
|
input_csv=args.input_csv,
|
|
output_csv=args.output_csv,
|
|
model_name=args.model,
|
|
)
|
|
|
|
print(df_out.head())
|
|
|
|
if args.evaluate:
|
|
try:
|
|
metrics = evaluate_predictions(df_out)
|
|
print("\nEvaluation:")
|
|
for k, v in metrics.items():
|
|
print(f"{k}: {v:.4f}")
|
|
except Exception as e:
|
|
print(f"\nCould not evaluate predictions: {e}")
|
|
|
|
else:
|
|
if args.angle is None or args.frames is None:
|
|
raise ValueError("For single prediction, you must provide --angle and --frames")
|
|
|
|
pred = predict(
|
|
angle=args.angle,
|
|
frames=args.frames,
|
|
is_nomatch=args.is_nomatch,
|
|
description=args.description,
|
|
model_name=args.model,
|
|
return_numeric=args.numeric,
|
|
debug=args.debug,
|
|
)
|
|
print(pred) |