notebooks, images, scripts
This commit is contained in:
Pawel Sarkowicz
976
scripts/predict.py
Normal file
976
scripts/predict.py
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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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HOLD_DIFFICULTY_PATH = ROOT / "data" / "03_hold_difficulty" / "hold_difficulty_scores.csv"
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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_hold_difficulty = pd.read_csv(HOLD_DIFFICULTY_PATH, index_col="placement_id")
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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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def lookup_hold_difficulty(placement_id, angle, role_type, is_hand, is_foot):
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"""
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Preference order:
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1. role-specific per-angle
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2. aggregate hand/foot per-angle
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3. overall_difficulty fallback
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"""
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if placement_id not in df_hold_difficulty.index:
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return np.nan
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row = df_hold_difficulty.loc[placement_id]
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diff_key = f"{role_type}_diff_{int(angle)}deg"
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hand_diff_key = f"hand_diff_{int(angle)}deg"
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foot_diff_key = f"foot_diff_{int(angle)}deg"
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difficulty = np.nan
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if diff_key in row.index:
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difficulty = row[diff_key]
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if pd.isna(difficulty):
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if is_hand and hand_diff_key in row.index:
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difficulty = row[hand_diff_key]
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elif is_foot and foot_diff_key in row.index:
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difficulty = row[foot_diff_key]
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if pd.isna(difficulty) and "overall_difficulty" in row.index:
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difficulty = row["overall_difficulty"]
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return difficulty
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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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features = {}
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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_id in HAND_ROLE_IDS
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is_foot = role_id in FOOT_ROLE_IDS
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difficulty = lookup_hold_difficulty(
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placement_id=placement_id,
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angle=angle,
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role_type=role_type,
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is_hand=is_hand,
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is_foot=is_foot,
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)
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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_id": role_id,
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"role_type": role_type,
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"is_hand": is_hand,
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"is_foot": is_foot,
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"difficulty": difficulty,
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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"].values
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ys = df_holds["y"].values
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features["angle"] = angle
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features["total_holds"] = len(df_holds)
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features["hand_holds"] = len(hand_holds)
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features["foot_holds"] = len(foot_holds)
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features["start_holds"] = len(start_holds)
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features["finish_holds"] = len(finish_holds)
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features["middle_holds"] = len(middle_holds)
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desc = str(description) if description is not None else ""
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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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features["mean_x"] = np.mean(xs)
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features["mean_y"] = np.mean(ys)
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features["std_x"] = np.std(xs) if len(xs) > 1 else 0
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features["std_y"] = np.std(ys) if len(ys) > 1 else 0
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features["range_x"] = np.max(xs) - np.min(xs)
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features["range_y"] = np.max(ys) - np.min(ys)
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features["min_y"] = np.min(ys)
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features["max_y"] = np.max(ys)
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if len(start_holds) > 0:
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features["start_height"] = start_holds["y"].mean()
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features["start_height_min"] = start_holds["y"].min()
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features["start_height_max"] = start_holds["y"].max()
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else:
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features["start_height"] = np.nan
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features["start_height_min"] = np.nan
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features["start_height_max"] = np.nan
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if len(finish_holds) > 0:
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features["finish_height"] = finish_holds["y"].mean()
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features["finish_height_min"] = finish_holds["y"].min()
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features["finish_height_max"] = finish_holds["y"].max()
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else:
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features["finish_height"] = np.nan
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features["finish_height_min"] = np.nan
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features["finish_height_max"] = np.nan
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features["height_gained"] = features["max_y"] - features["min_y"]
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if pd.notna(features["finish_height"]) and pd.notna(features["start_height"]):
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features["height_gained_start_finish"] = features["finish_height"] - features["start_height"]
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else:
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features["height_gained_start_finish"] = np.nan
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bbox_width = features["range_x"]
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bbox_height = features["range_y"]
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features["bbox_area"] = bbox_width * bbox_height
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features["bbox_aspect_ratio"] = bbox_width / bbox_height if bbox_height > 0 else 0
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features["bbox_normalized_area"] = features["bbox_area"] / (board_width * board_height)
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features["hold_density"] = features["total_holds"] / features["bbox_area"] if features["bbox_area"] > 0 else 0
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features["holds_per_vertical_foot"] = features["total_holds"] / max(features["range_y"], 1)
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center_x = (x_min + x_max) / 2
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features["left_holds"] = (df_holds["x"] < center_x).sum()
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features["right_holds"] = (df_holds["x"] >= center_x).sum()
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features["left_ratio"] = features["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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if len(hand_holds) > 0:
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hand_left = (hand_holds["x"] < center_x).sum()
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features["hand_left_ratio"] = hand_left / len(hand_holds)
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features["hand_symmetry"] = 1 - abs(features["hand_left_ratio"] - 0.5) * 2
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else:
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features["hand_left_ratio"] = np.nan
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features["hand_symmetry"] = np.nan
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y_median = np.median(ys)
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features["upper_holds"] = (df_holds["y"] > y_median).sum()
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features["lower_holds"] = (df_holds["y"] <= y_median).sum()
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features["upper_ratio"] = features["upper_holds"] / features["total_holds"]
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if len(hand_holds) >= 2:
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hand_xs = hand_holds["x"].values
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hand_ys = hand_holds["y"].values
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hand_distances = []
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for i in range(len(hand_holds)):
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for j in range(i + 1, len(hand_holds)):
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dx = hand_xs[i] - hand_xs[j]
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dy = hand_ys[i] - hand_ys[j]
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hand_distances.append(np.sqrt(dx**2 + dy**2))
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features["max_hand_reach"] = max(hand_distances)
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features["min_hand_reach"] = min(hand_distances)
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features["mean_hand_reach"] = np.mean(hand_distances)
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features["std_hand_reach"] = np.std(hand_distances)
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features["hand_spread_x"] = hand_xs.max() - hand_xs.min()
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features["hand_spread_y"] = hand_ys.max() - hand_ys.min()
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else:
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features["max_hand_reach"] = 0
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features["min_hand_reach"] = 0
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features["mean_hand_reach"] = 0
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features["std_hand_reach"] = 0
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features["hand_spread_x"] = 0
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features["hand_spread_y"] = 0
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if len(foot_holds) >= 2:
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foot_xs = foot_holds["x"].values
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foot_ys = foot_holds["y"].values
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foot_distances = []
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for i in range(len(foot_holds)):
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for j in range(i + 1, len(foot_holds)):
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dx = foot_xs[i] - foot_xs[j]
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dy = foot_ys[i] - foot_ys[j]
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foot_distances.append(np.sqrt(dx**2 + dy**2))
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features["max_foot_spread"] = max(foot_distances)
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features["mean_foot_spread"] = np.mean(foot_distances)
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features["foot_spread_x"] = foot_xs.max() - foot_xs.min()
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features["foot_spread_y"] = foot_ys.max() - foot_ys.min()
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else:
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features["max_foot_spread"] = 0
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features["mean_foot_spread"] = 0
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features["foot_spread_x"] = 0
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features["foot_spread_y"] = 0
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if len(hand_holds) > 0 and len(foot_holds) > 0:
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h2f_distances = []
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for _, h in hand_holds.iterrows():
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||||
for _, f in foot_holds.iterrows():
|
||||
dx = h["x"] - f["x"]
|
||||
dy = h["y"] - f["y"]
|
||||
h2f_distances.append(np.sqrt(dx**2 + dy**2))
|
||||
|
||||
features["max_hand_to_foot"] = max(h2f_distances)
|
||||
features["min_hand_to_foot"] = min(h2f_distances)
|
||||
features["mean_hand_to_foot"] = np.mean(h2f_distances)
|
||||
features["std_hand_to_foot"] = np.std(h2f_distances)
|
||||
else:
|
||||
features["max_hand_to_foot"] = 0
|
||||
features["min_hand_to_foot"] = 0
|
||||
features["mean_hand_to_foot"] = 0
|
||||
features["std_hand_to_foot"] = 0
|
||||
|
||||
difficulties = df_holds["difficulty"].dropna().values
|
||||
|
||||
if len(difficulties) > 0:
|
||||
features["mean_hold_difficulty"] = np.mean(difficulties)
|
||||
features["max_hold_difficulty"] = np.max(difficulties)
|
||||
features["min_hold_difficulty"] = np.min(difficulties)
|
||||
features["std_hold_difficulty"] = np.std(difficulties)
|
||||
features["median_hold_difficulty"] = np.median(difficulties)
|
||||
features["difficulty_range"] = features["max_hold_difficulty"] - features["min_hold_difficulty"]
|
||||
else:
|
||||
features["mean_hold_difficulty"] = np.nan
|
||||
features["max_hold_difficulty"] = np.nan
|
||||
features["min_hold_difficulty"] = np.nan
|
||||
features["std_hold_difficulty"] = np.nan
|
||||
features["median_hold_difficulty"] = np.nan
|
||||
features["difficulty_range"] = np.nan
|
||||
|
||||
hand_diffs = hand_holds["difficulty"].dropna().values if len(hand_holds) > 0 else np.array([])
|
||||
if len(hand_diffs) > 0:
|
||||
features["mean_hand_difficulty"] = np.mean(hand_diffs)
|
||||
features["max_hand_difficulty"] = np.max(hand_diffs)
|
||||
features["std_hand_difficulty"] = np.std(hand_diffs)
|
||||
else:
|
||||
features["mean_hand_difficulty"] = np.nan
|
||||
features["max_hand_difficulty"] = np.nan
|
||||
features["std_hand_difficulty"] = np.nan
|
||||
|
||||
foot_diffs = foot_holds["difficulty"].dropna().values if len(foot_holds) > 0 else np.array([])
|
||||
if len(foot_diffs) > 0:
|
||||
features["mean_foot_difficulty"] = np.mean(foot_diffs)
|
||||
features["max_foot_difficulty"] = np.max(foot_diffs)
|
||||
features["std_foot_difficulty"] = np.std(foot_diffs)
|
||||
else:
|
||||
features["mean_foot_difficulty"] = np.nan
|
||||
features["max_foot_difficulty"] = np.nan
|
||||
features["std_foot_difficulty"] = np.nan
|
||||
|
||||
start_diffs = start_holds["difficulty"].dropna().values if len(start_holds) > 0 else np.array([])
|
||||
finish_diffs = finish_holds["difficulty"].dropna().values if len(finish_holds) > 0 else np.array([])
|
||||
features["start_difficulty"] = np.mean(start_diffs) if len(start_diffs) > 0 else np.nan
|
||||
features["finish_difficulty"] = np.mean(finish_diffs) if len(finish_diffs) > 0 else np.nan
|
||||
|
||||
features["hand_foot_ratio"] = features["hand_holds"] / max(features["foot_holds"], 1)
|
||||
features["movement_density"] = features["total_holds"] / max(features["height_gained"], 1)
|
||||
features["hold_com_x"] = np.average(xs)
|
||||
features["hold_com_y"] = np.average(ys)
|
||||
|
||||
if len(difficulties) > 0 and len(ys) >= len(difficulties):
|
||||
weights = (ys[:len(difficulties)] - ys.min()) / max(ys.max() - ys.min(), 1) + 0.5
|
||||
features["weighted_difficulty"] = np.average(difficulties, weights=weights)
|
||||
else:
|
||||
features["weighted_difficulty"] = features["mean_hold_difficulty"]
|
||||
|
||||
if len(df_holds) >= 3:
|
||||
try:
|
||||
points = np.column_stack([xs, ys])
|
||||
hull = ConvexHull(points)
|
||||
features["convex_hull_area"] = hull.volume
|
||||
features["convex_hull_perimeter"] = hull.area
|
||||
features["hull_area_to_bbox_ratio"] = features["convex_hull_area"] / max(features["bbox_area"], 1)
|
||||
except Exception:
|
||||
features["convex_hull_area"] = np.nan
|
||||
features["convex_hull_perimeter"] = np.nan
|
||||
features["hull_area_to_bbox_ratio"] = np.nan
|
||||
else:
|
||||
features["convex_hull_area"] = 0
|
||||
features["convex_hull_perimeter"] = 0
|
||||
features["hull_area_to_bbox_ratio"] = 0
|
||||
|
||||
if len(df_holds) >= 2:
|
||||
points = np.column_stack([xs, ys])
|
||||
distances = pdist(points)
|
||||
features["min_nn_distance"] = np.min(distances)
|
||||
features["mean_nn_distance"] = np.mean(distances)
|
||||
features["max_nn_distance"] = np.max(distances)
|
||||
features["std_nn_distance"] = np.std(distances)
|
||||
else:
|
||||
features["min_nn_distance"] = 0
|
||||
features["mean_nn_distance"] = 0
|
||||
features["max_nn_distance"] = 0
|
||||
features["std_nn_distance"] = 0
|
||||
|
||||
if len(df_holds) >= 3:
|
||||
points = np.column_stack([xs, ys])
|
||||
dist_matrix = squareform(pdist(points))
|
||||
threshold = 12.0
|
||||
neighbors_count = (dist_matrix < threshold).sum(axis=1) - 1
|
||||
features["mean_neighbors_12in"] = np.mean(neighbors_count)
|
||||
features["max_neighbors_12in"] = np.max(neighbors_count)
|
||||
avg_neighbors = np.mean(neighbors_count)
|
||||
max_possible = len(df_holds) - 1
|
||||
features["clustering_ratio"] = avg_neighbors / max_possible if max_possible > 0 else 0
|
||||
else:
|
||||
features["mean_neighbors_12in"] = 0
|
||||
features["max_neighbors_12in"] = 0
|
||||
features["clustering_ratio"] = 0
|
||||
|
||||
if len(df_holds) >= 2:
|
||||
sorted_indices = np.argsort(ys)
|
||||
sorted_points = np.column_stack([xs[sorted_indices], ys[sorted_indices]])
|
||||
|
||||
path_length = 0
|
||||
for i in range(len(sorted_points) - 1):
|
||||
dx = sorted_points[i + 1, 0] - sorted_points[i, 0]
|
||||
dy = sorted_points[i + 1, 1] - sorted_points[i, 1]
|
||||
path_length += np.sqrt(dx**2 + dy**2)
|
||||
|
||||
features["path_length_vertical"] = path_length
|
||||
features["path_efficiency"] = features["height_gained"] / max(path_length, 1)
|
||||
else:
|
||||
features["path_length_vertical"] = 0
|
||||
features["path_efficiency"] = 0
|
||||
|
||||
if pd.notna(features["finish_difficulty"]) and pd.notna(features["start_difficulty"]):
|
||||
features["difficulty_gradient"] = features["finish_difficulty"] - features["start_difficulty"]
|
||||
else:
|
||||
features["difficulty_gradient"] = np.nan
|
||||
|
||||
if len(difficulties) > 0:
|
||||
y_min_val, y_max_val = ys.min(), ys.max()
|
||||
y_range = y_max_val - y_min_val
|
||||
|
||||
if y_range > 0:
|
||||
lower_mask = ys <= (y_min_val + y_range / 3)
|
||||
middle_mask = (ys > y_min_val + y_range / 3) & (ys <= y_min_val + 2 * y_range / 3)
|
||||
upper_mask = ys > (y_min_val + 2 * y_range / 3)
|
||||
|
||||
df_with_diff = df_holds.copy()
|
||||
df_with_diff["lower"] = lower_mask
|
||||
df_with_diff["middle"] = middle_mask
|
||||
df_with_diff["upper"] = upper_mask
|
||||
|
||||
lower_diffs = df_with_diff[df_with_diff["lower"] & df_with_diff["difficulty"].notna()]["difficulty"]
|
||||
middle_diffs = df_with_diff[df_with_diff["middle"] & df_with_diff["difficulty"].notna()]["difficulty"]
|
||||
upper_diffs = df_with_diff[df_with_diff["upper"] & df_with_diff["difficulty"].notna()]["difficulty"]
|
||||
|
||||
features["lower_region_difficulty"] = lower_diffs.mean() if len(lower_diffs) > 0 else np.nan
|
||||
features["middle_region_difficulty"] = middle_diffs.mean() if len(middle_diffs) > 0 else np.nan
|
||||
features["upper_region_difficulty"] = upper_diffs.mean() if len(upper_diffs) > 0 else np.nan
|
||||
|
||||
if pd.notna(features["lower_region_difficulty"]) and pd.notna(features["upper_region_difficulty"]):
|
||||
features["difficulty_progression"] = features["upper_region_difficulty"] - features["lower_region_difficulty"]
|
||||
else:
|
||||
features["difficulty_progression"] = np.nan
|
||||
else:
|
||||
features["lower_region_difficulty"] = features["mean_hold_difficulty"]
|
||||
features["middle_region_difficulty"] = features["mean_hold_difficulty"]
|
||||
features["upper_region_difficulty"] = features["mean_hold_difficulty"]
|
||||
features["difficulty_progression"] = 0
|
||||
else:
|
||||
features["lower_region_difficulty"] = np.nan
|
||||
features["middle_region_difficulty"] = np.nan
|
||||
features["upper_region_difficulty"] = np.nan
|
||||
features["difficulty_progression"] = np.nan
|
||||
|
||||
if len(hand_holds) >= 2 and len(hand_diffs) >= 2:
|
||||
hand_sorted = hand_holds.sort_values("y")
|
||||
hand_diff_sorted = hand_sorted["difficulty"].dropna().values
|
||||
|
||||
if len(hand_diff_sorted) >= 2:
|
||||
difficulty_jumps = np.abs(np.diff(hand_diff_sorted))
|
||||
features["max_difficulty_jump"] = np.max(difficulty_jumps) if len(difficulty_jumps) > 0 else 0
|
||||
features["mean_difficulty_jump"] = np.mean(difficulty_jumps) if len(difficulty_jumps) > 0 else 0
|
||||
else:
|
||||
features["max_difficulty_jump"] = 0
|
||||
features["mean_difficulty_jump"] = 0
|
||||
else:
|
||||
features["max_difficulty_jump"] = 0
|
||||
features["mean_difficulty_jump"] = 0
|
||||
|
||||
if len(hand_holds) >= 2 and len(hand_diffs) >= 2:
|
||||
hand_sorted = hand_holds.sort_values("y")
|
||||
xs_sorted = hand_sorted["x"].values
|
||||
ys_sorted = hand_sorted["y"].values
|
||||
diffs_sorted = hand_sorted["difficulty"].fillna(np.mean(hand_diffs)).values
|
||||
|
||||
weighted_reach = []
|
||||
for i in range(len(hand_sorted) - 1):
|
||||
dx = xs_sorted[i + 1] - xs_sorted[i]
|
||||
dy = ys_sorted[i + 1] - ys_sorted[i]
|
||||
dist = np.sqrt(dx**2 + dy**2)
|
||||
avg_diff = (diffs_sorted[i] + diffs_sorted[i + 1]) / 2
|
||||
weighted_reach.append(dist * avg_diff)
|
||||
|
||||
features["difficulty_weighted_reach"] = np.mean(weighted_reach) if weighted_reach else 0
|
||||
features["max_weighted_reach"] = np.max(weighted_reach) if weighted_reach else 0
|
||||
else:
|
||||
features["difficulty_weighted_reach"] = 0
|
||||
features["max_weighted_reach"] = 0
|
||||
|
||||
features["mean_x_normalized"] = (features["mean_x"] - x_min) / board_width
|
||||
features["mean_y_normalized"] = (features["mean_y"] - y_min) / board_height
|
||||
features["std_x_normalized"] = features["std_x"] / board_width
|
||||
features["std_y_normalized"] = features["std_y"] / board_height
|
||||
|
||||
if pd.notna(features["start_height"]):
|
||||
features["start_height_normalized"] = (features["start_height"] - y_min) / board_height
|
||||
else:
|
||||
features["start_height_normalized"] = np.nan
|
||||
|
||||
if pd.notna(features["finish_height"]):
|
||||
features["finish_height_normalized"] = (features["finish_height"] - y_min) / board_height
|
||||
else:
|
||||
features["finish_height_normalized"] = np.nan
|
||||
|
||||
typical_start_y = y_min + board_height * 0.15
|
||||
typical_finish_y = y_min + board_height * 0.85
|
||||
|
||||
if pd.notna(features["start_height"]):
|
||||
features["start_offset_from_typical"] = abs(features["start_height"] - typical_start_y)
|
||||
else:
|
||||
features["start_offset_from_typical"] = np.nan
|
||||
|
||||
if pd.notna(features["finish_height"]):
|
||||
features["finish_offset_from_typical"] = abs(features["finish_height"] - typical_finish_y)
|
||||
else:
|
||||
features["finish_offset_from_typical"] = np.nan
|
||||
|
||||
if len(start_holds) > 0:
|
||||
start_y = start_holds["y"].mean()
|
||||
features["mean_y_relative_to_start"] = features["mean_y"] - start_y
|
||||
features["max_y_relative_to_start"] = features["max_y"] - start_y
|
||||
else:
|
||||
features["mean_y_relative_to_start"] = np.nan
|
||||
features["max_y_relative_to_start"] = np.nan
|
||||
|
||||
features["spread_x_normalized"] = features["range_x"] / board_width
|
||||
features["spread_y_normalized"] = features["range_y"] / board_height
|
||||
features["bbox_coverage_x"] = features["range_x"] / board_width
|
||||
features["bbox_coverage_y"] = features["range_y"] / board_height
|
||||
|
||||
y_quartiles = np.percentile(ys, [25, 50, 75])
|
||||
features["y_q25"] = y_quartiles[0]
|
||||
features["y_q50"] = y_quartiles[1]
|
||||
features["y_q75"] = y_quartiles[2]
|
||||
features["y_iqr"] = y_quartiles[2] - y_quartiles[0]
|
||||
|
||||
features["holds_bottom_quartile"] = (ys < y_quartiles[0]).sum()
|
||||
features["holds_top_quartile"] = (ys >= y_quartiles[2]).sum()
|
||||
|
||||
return features
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Model input preparation
|
||||
# ============================================================
|
||||
|
||||
def prepare_feature_vector(features: dict) -> pd.DataFrame:
|
||||
row = {}
|
||||
for col in FEATURE_NAMES:
|
||||
value = features.get(col, 0.0)
|
||||
row[col] = 0.0 if pd.isna(value) else value
|
||||
return pd.DataFrame([row], columns=FEATURE_NAMES)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Prediction helpers
|
||||
# ============================================================
|
||||
|
||||
def format_prediction(pred: float):
|
||||
rounded = int(round(pred))
|
||||
rounded = max(min(rounded, MAX_GRADE), MIN_GRADE)
|
||||
|
||||
return {
|
||||
"predicted_numeric": float(pred),
|
||||
"predicted_display_difficulty": rounded,
|
||||
"predicted_boulder_grade": grade_map[rounded],
|
||||
}
|
||||
|
||||
|
||||
def predict_with_model(model, X: pd.DataFrame, model_name: str):
|
||||
model_name = normalize_model_name(model_name)
|
||||
info = MODEL_REGISTRY[model_name]
|
||||
|
||||
if info["kind"] == "sklearn":
|
||||
X_input = scaler.transform(X) if info["needs_scaling"] else X
|
||||
pred = model.predict(X_input)[0]
|
||||
return float(pred)
|
||||
|
||||
if info["kind"] == "torch_checkpoint":
|
||||
if not TORCH_AVAILABLE:
|
||||
raise ImportError("PyTorch is not installed.")
|
||||
|
||||
X_input = scaler.transform(X) if info["needs_scaling"] else X
|
||||
X_tensor = torch.tensor(np.asarray(X_input), dtype=torch.float32)
|
||||
|
||||
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)
|
||||
Reference in New Issue
Block a user