predict script update
This commit is contained in:
Pawel Sarkowicz
10
README.md
10
README.md
@@ -13,10 +13,10 @@ I recently got into *board climbing*, and have been enjoying using the <a href="
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This project analyzes ~130,000 climbs from the Tension Boards in order to do the following.
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This project analyzes ~130,000 climbs from the Tension Boards in order to do the following.
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> 1. **Understand** hold usage patterns and difficulty distributions
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> 1. **Understand** hold usage patterns and difficulty distributions
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> 2. **Quantify** empircal hold difficulty scores (for analysis)
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> 2. **Quantify** empircal hold difficulty scores
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> 3. **Predict** climb grades from spatial and structural features of climbs
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> 3. **Predict** climb grades from spatial and structural features of climbs
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Climbing grades are inherently subjective. Different climbers use different beta, setters have different grading standards, and difficulty depends on factors not always captured in data. What makes it harder in the case of the board climbing is that the grade is displayed almost democratically -- it is determined by user input.
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Climbing grades are inherently subjective. Different climbers use different beta, setters have different grading standards, and difficulty depends on factors not always captured in data. Moreover, on the boards, the displayed grade for any specific climb is based on user input.
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Using a Tension Board (2) dataset, this project combines:
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Using a Tension Board (2) dataset, this project combines:
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@@ -153,7 +153,7 @@ Beyond structural analysis, we can also study how board-climbers behave over tim
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* Hold difficulty is estimated from climb data
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* Hold difficulty is estimated from climb data
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* We averaged (pre-role/per-angle) difficulty for each hold (with Bayesian smoothing)
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* We averaged (per-role/per-angle) difficulty for each hold (with Bayesian smoothing)
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* Took advantage of the mirrored layout to increase the amount of data per hold
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* Took advantage of the mirrored layout to increase the amount of data per hold
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### Key technique: Bayesian smoothing
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### Key technique: Bayesian smoothing
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@@ -273,7 +273,7 @@ Models tested:
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### Feature importance
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### Feature importance
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Key drivers:
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Key drivers:
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@@ -289,7 +289,7 @@ This confirms that **difficulty is strongly tied to spatial arrangement and move
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## 10. Model Performance
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## 10. Model Performance
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### Results (in terms of V-grade)
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### Results (in terms of V-grade)
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Before Width: | Height: | Size: 84 KiB After Width: | Height: | Size: 84 KiB |
@@ -866,8 +866,7 @@
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"print(\"\"\"\\nInterpretation:\n",
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"print(\"\"\"\\nInterpretation:\n",
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"- Each row is a climb-angle observation.\n",
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"- Each row is a climb-angle observation.\n",
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"- The target is `display_difficulty`.\n",
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"- The target is `display_difficulty`.\n",
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"- The predictors combine geometry, hold statistics, and aggregate difficulty information.\n",
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"- The predictors combine geometry and structure\n",
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"- Hold-difficulty-based features use Bayesian-smoothed hold scores from Notebook 03.\n",
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"- The next notebook tests how much predictive signal these engineered features actually contain.\n",
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"- The next notebook tests how much predictive signal these engineered features actually contain.\n",
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"\"\"\")\n"
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"\"\"\")\n"
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]
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]
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@@ -706,7 +706,7 @@
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"ax.invert_yaxis()\n",
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"ax.invert_yaxis()\n",
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"\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.tight_layout()\n",
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"plt.savefig('../images/05_predictive_modelling/random_forest_importance.png', dpi=150, bbox_inches='tight')\n",
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"plt.savefig('../images/05_predictive_modelling/random_forest_feature_importance.png', dpi=150, bbox_inches='tight')\n",
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"plt.show()"
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"plt.show()"
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]
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]
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},
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},
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@@ -23,7 +23,6 @@ ROOT = Path(__file__).resolve().parents[1]
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SCALER_PATH = ROOT / "models" / "feature_scaler.pkl"
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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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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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PLACEMENTS_PATH = ROOT / "data" / "placements.csv" # adjust if needed
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@@ -164,7 +163,6 @@ scaler = joblib.load(SCALER_PATH)
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with open(FEATURE_NAMES_PATH, "r") as f:
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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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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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df_placements = pd.read_csv(PLACEMENTS_PATH)
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placement_coords = {
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placement_coords = {
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@@ -260,46 +258,14 @@ def parse_frames(frames: str):
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return [(int(p), int(r)) for p, r in matches]
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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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# ============================================================
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# Feature extraction
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# Feature extraction
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# ============================================================
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# ============================================================
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def extract_features_from_raw(angle, frames, is_nomatch=0, description=""):
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def extract_features_from_raw(angle, frames, is_nomatch=0, description=""):
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features = {}
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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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holds = parse_frames(frames)
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if not holds:
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if not holds:
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raise ValueError("Could not parse any holds from frames.")
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raise ValueError("Could not parse any holds from frames.")
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@@ -311,26 +277,16 @@ def extract_features_from_raw(angle, frames, is_nomatch=0, description=""):
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continue
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continue
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role_type = get_role_type(role_id)
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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_hand_role = role_id in HAND_ROLE_IDS
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is_foot = role_id in FOOT_ROLE_IDS
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is_foot_role = 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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hold_data.append({
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"placement_id": placement_id,
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"placement_id": placement_id,
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"x": coords[0],
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"x": coords[0],
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"y": coords[1],
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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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"role_type": role_type,
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"is_hand": is_hand,
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"is_hand": is_hand_role,
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"is_foot": is_foot,
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"is_foot": is_foot_role,
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"difficulty": difficulty,
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})
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})
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if not hold_data:
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if not hold_data:
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@@ -344,389 +300,157 @@ def extract_features_from_raw(angle, frames, is_nomatch=0, description=""):
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finish_holds = df_holds[df_holds["role_type"] == "finish"]
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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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middle_holds = df_holds[df_holds["role_type"] == "middle"]
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xs = df_holds["x"].values
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xs = df_holds["x"].to_numpy()
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ys = df_holds["y"].values
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ys = df_holds["y"].to_numpy()
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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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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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features["is_nomatch"] = int(
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(is_nomatch == 1) or
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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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bool(re.search(r"\bno\s*match(ing)?\b", desc, flags=re.IGNORECASE))
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)
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)
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features["mean_x"] = np.mean(xs)
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# Spatial
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features["mean_y"] = np.mean(ys)
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features["mean_y"] = float(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_x"] = float(np.std(xs)) if len(xs) > 1 else 0.0
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features["std_y"] = np.std(ys) if len(ys) > 1 else 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"] = np.max(xs) - np.min(xs)
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features["range_x"] = float(np.max(xs) - np.min(xs))
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features["range_y"] = np.max(ys) - np.min(ys)
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features["range_y"] = float(np.max(ys) - np.min(ys))
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features["min_y"] = np.min(ys)
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features["min_y"] = float(np.min(ys))
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features["max_y"] = np.max(ys)
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features["max_y"] = float(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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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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start_height = float(start_holds["y"].mean()) if len(start_holds) > 0 else np.nan
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features["height_gained_start_finish"] = features["finish_height"] - features["start_height"]
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finish_height = float(finish_holds["y"].mean()) if len(finish_holds) > 0 else np.nan
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else:
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features["height_gained_start_finish"] = (
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features["height_gained_start_finish"] = np.nan
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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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bbox_width = features["range_x"]
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# Density / symmetry
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bbox_height = features["range_y"]
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bbox_area = features["range_x"] * features["range_y"]
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features["bbox_area"] = bbox_width * bbox_height
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features["bbox_area"] = float(bbox_area)
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features["bbox_aspect_ratio"] = bbox_width / bbox_height if bbox_height > 0 else 0
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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["bbox_normalized_area"] = features["bbox_area"] / (board_width * board_height)
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features["holds_per_vertical_foot"] = float(features["total_holds"] / max(features["range_y"], 1))
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features["hold_density"] = features["total_holds"] / features["bbox_area"] if features["bbox_area"] > 0 else 0
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left_holds = int((df_holds["x"] < center_x).sum())
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features["holds_per_vertical_foot"] = features["total_holds"] / max(features["range_y"], 1)
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features["left_ratio"] = left_holds / features["total_holds"] if features["total_holds"] > 0 else 0.5
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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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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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y_median = np.median(ys)
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features["upper_holds"] = (df_holds["y"] > y_median).sum()
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upper_holds = int((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"] = upper_holds / features["total_holds"]
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features["upper_ratio"] = features["upper_holds"] / features["total_holds"]
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# Hand reach
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if len(hand_holds) >= 2:
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if len(hand_holds) >= 2:
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hand_xs = hand_holds["x"].values
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hand_points = hand_holds[["x", "y"]].to_numpy()
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hand_ys = hand_holds["y"].values
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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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hand_distances = []
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features["mean_hand_reach"] = float(np.mean(hand_distances))
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for i in range(len(hand_holds)):
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features["max_hand_reach"] = float(np.max(hand_distances))
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for j in range(i + 1, len(hand_holds)):
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features["std_hand_reach"] = float(np.std(hand_distances))
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dx = hand_xs[i] - hand_xs[j]
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features["hand_spread_x"] = float(hand_xs.max() - hand_xs.min())
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dy = hand_ys[i] - hand_ys[j]
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features["hand_spread_y"] = float(hand_ys.max() - hand_ys.min())
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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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else:
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features["max_hand_reach"] = 0
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features["mean_hand_reach"] = 0.0
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features["min_hand_reach"] = 0
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features["max_hand_reach"] = 0.0
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features["mean_hand_reach"] = 0
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features["std_hand_reach"] = 0.0
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features["std_hand_reach"] = 0
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features["hand_spread_x"] = 0.0
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features["hand_spread_x"] = 0
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features["hand_spread_y"] = 0.0
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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 = []
|
|
||||||
for i in range(len(foot_holds)):
|
|
||||||
for j in range(i + 1, len(foot_holds)):
|
|
||||||
dx = foot_xs[i] - foot_xs[j]
|
|
||||||
dy = foot_ys[i] - foot_ys[j]
|
|
||||||
foot_distances.append(np.sqrt(dx**2 + dy**2))
|
|
||||||
|
|
||||||
features["max_foot_spread"] = max(foot_distances)
|
|
||||||
features["mean_foot_spread"] = np.mean(foot_distances)
|
|
||||||
features["foot_spread_x"] = foot_xs.max() - foot_xs.min()
|
|
||||||
features["foot_spread_y"] = foot_ys.max() - foot_ys.min()
|
|
||||||
else:
|
|
||||||
features["max_foot_spread"] = 0
|
|
||||||
features["mean_foot_spread"] = 0
|
|
||||||
features["foot_spread_x"] = 0
|
|
||||||
features["foot_spread_y"] = 0
|
|
||||||
|
|
||||||
|
# Hand-foot distances
|
||||||
if len(hand_holds) > 0 and len(foot_holds) > 0:
|
if len(hand_holds) > 0 and len(foot_holds) > 0:
|
||||||
h2f_distances = []
|
hand_points = hand_holds[["x", "y"]].to_numpy()
|
||||||
for _, h in hand_holds.iterrows():
|
foot_points = foot_holds[["x", "y"]].to_numpy()
|
||||||
for _, f in foot_holds.iterrows():
|
dists = []
|
||||||
dx = h["x"] - f["x"]
|
for hx, hy in hand_points:
|
||||||
dy = h["y"] - f["y"]
|
for fx, fy in foot_points:
|
||||||
h2f_distances.append(np.sqrt(dx**2 + dy**2))
|
dists.append(np.sqrt((hx - fx) ** 2 + (hy - fy) ** 2))
|
||||||
|
dists = np.asarray(dists, dtype=float)
|
||||||
|
|
||||||
features["max_hand_to_foot"] = max(h2f_distances)
|
features["min_hand_to_foot"] = float(np.min(dists))
|
||||||
features["min_hand_to_foot"] = min(h2f_distances)
|
features["mean_hand_to_foot"] = float(np.mean(dists))
|
||||||
features["mean_hand_to_foot"] = np.mean(h2f_distances)
|
features["std_hand_to_foot"] = float(np.std(dists))
|
||||||
features["std_hand_to_foot"] = np.std(h2f_distances)
|
|
||||||
else:
|
else:
|
||||||
features["max_hand_to_foot"] = 0
|
features["min_hand_to_foot"] = 0.0
|
||||||
features["min_hand_to_foot"] = 0
|
features["mean_hand_to_foot"] = 0.0
|
||||||
features["mean_hand_to_foot"] = 0
|
features["std_hand_to_foot"] = 0.0
|
||||||
features["std_hand_to_foot"] = 0
|
|
||||||
|
|
||||||
difficulties = df_holds["difficulty"].dropna().values
|
# Global geometry
|
||||||
|
points = np.column_stack([xs, ys])
|
||||||
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:
|
if len(df_holds) >= 3:
|
||||||
try:
|
try:
|
||||||
points = np.column_stack([xs, ys])
|
|
||||||
hull = ConvexHull(points)
|
hull = ConvexHull(points)
|
||||||
features["convex_hull_area"] = hull.volume
|
features["convex_hull_area"] = float(hull.volume)
|
||||||
features["convex_hull_perimeter"] = hull.area
|
features["hull_area_to_bbox_ratio"] = float(features["convex_hull_area"] / max(bbox_area, 1))
|
||||||
features["hull_area_to_bbox_ratio"] = features["convex_hull_area"] / max(features["bbox_area"], 1)
|
|
||||||
except Exception:
|
except Exception:
|
||||||
features["convex_hull_area"] = np.nan
|
features["convex_hull_area"] = np.nan
|
||||||
features["convex_hull_perimeter"] = np.nan
|
|
||||||
features["hull_area_to_bbox_ratio"] = np.nan
|
features["hull_area_to_bbox_ratio"] = np.nan
|
||||||
else:
|
else:
|
||||||
features["convex_hull_area"] = 0
|
features["convex_hull_area"] = 0.0
|
||||||
features["convex_hull_perimeter"] = 0
|
features["hull_area_to_bbox_ratio"] = 0.0
|
||||||
features["hull_area_to_bbox_ratio"] = 0
|
|
||||||
|
|
||||||
if len(df_holds) >= 2:
|
if len(df_holds) >= 2:
|
||||||
points = np.column_stack([xs, ys])
|
pairwise = pdist(points)
|
||||||
distances = pdist(points)
|
features["mean_pairwise_distance"] = float(np.mean(pairwise))
|
||||||
features["min_nn_distance"] = np.min(distances)
|
features["std_pairwise_distance"] = float(np.std(pairwise))
|
||||||
features["mean_nn_distance"] = np.mean(distances)
|
|
||||||
features["max_nn_distance"] = np.max(distances)
|
|
||||||
features["std_nn_distance"] = np.std(distances)
|
|
||||||
else:
|
else:
|
||||||
features["min_nn_distance"] = 0
|
features["mean_pairwise_distance"] = 0.0
|
||||||
features["mean_nn_distance"] = 0
|
features["std_pairwise_distance"] = 0.0
|
||||||
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:
|
if len(df_holds) >= 2:
|
||||||
sorted_indices = np.argsort(ys)
|
sorted_idx = np.argsort(ys)
|
||||||
sorted_points = np.column_stack([xs[sorted_indices], ys[sorted_indices]])
|
sorted_points = points[sorted_idx]
|
||||||
|
path_length = 0.0
|
||||||
path_length = 0
|
|
||||||
for i in range(len(sorted_points) - 1):
|
for i in range(len(sorted_points) - 1):
|
||||||
dx = sorted_points[i + 1, 0] - sorted_points[i, 0]
|
dx = sorted_points[i + 1, 0] - sorted_points[i, 0]
|
||||||
dy = sorted_points[i + 1, 1] - sorted_points[i, 1]
|
dy = sorted_points[i + 1, 1] - sorted_points[i, 1]
|
||||||
path_length += np.sqrt(dx**2 + dy**2)
|
path_length += np.sqrt(dx ** 2 + dy ** 2)
|
||||||
|
|
||||||
features["path_length_vertical"] = path_length
|
features["path_length_vertical"] = float(path_length)
|
||||||
features["path_efficiency"] = features["height_gained"] / max(path_length, 1)
|
features["path_efficiency"] = float(features["height_gained"] / max(path_length, 1))
|
||||||
else:
|
else:
|
||||||
features["path_length_vertical"] = 0
|
features["path_length_vertical"] = 0.0
|
||||||
features["path_efficiency"] = 0
|
features["path_efficiency"] = 0.0
|
||||||
|
|
||||||
if pd.notna(features["finish_difficulty"]) and pd.notna(features["start_difficulty"]):
|
# Normalized / relative
|
||||||
features["difficulty_gradient"] = features["finish_difficulty"] - features["start_difficulty"]
|
features["mean_y_normalized"] = float((features["mean_y"] - y_min) / board_height)
|
||||||
else:
|
features["start_height_normalized"] = float((start_height - y_min) / board_height) if pd.notna(start_height) else np.nan
|
||||||
features["difficulty_gradient"] = np.nan
|
features["finish_height_normalized"] = float((finish_height - y_min) / board_height) if pd.notna(finish_height) else np.nan
|
||||||
|
features["mean_y_relative_to_start"] = float(features["mean_y"] - start_height) if pd.notna(start_height) else np.nan
|
||||||
|
features["spread_x_normalized"] = float(features["range_x"] / board_width)
|
||||||
|
features["spread_y_normalized"] = float(features["range_y"] / board_height)
|
||||||
|
|
||||||
if len(difficulties) > 0:
|
y_q75 = np.percentile(ys, 75)
|
||||||
y_min_val, y_max_val = ys.min(), ys.max()
|
y_q25 = np.percentile(ys, 25)
|
||||||
y_range = y_max_val - y_min_val
|
features["y_q75"] = float(y_q75)
|
||||||
|
features["y_iqr"] = float(y_q75 - y_q25)
|
||||||
|
|
||||||
if y_range > 0:
|
# Engineered clean features
|
||||||
lower_mask = ys <= (y_min_val + y_range / 3)
|
features["complexity_score"] = float(
|
||||||
middle_mask = (ys > y_min_val + y_range / 3) & (ys <= y_min_val + 2 * y_range / 3)
|
features["mean_hand_reach"]
|
||||||
upper_mask = ys > (y_min_val + 2 * y_range / 3)
|
* np.log1p(features["total_holds"])
|
||||||
|
* (1 + features["hold_density"])
|
||||||
df_with_diff = df_holds.copy()
|
)
|
||||||
df_with_diff["lower"] = lower_mask
|
features["angle_x_holds"] = float(features["angle"] * features["total_holds"])
|
||||||
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
|
return features
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user