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ml/feature_selection.py — SHAP-based feature selection & importance analysis.
Capabilities:
1. SHAP value computation for tree-based models (XGB/LGB)
2. Drop redundant and noisy features
3. Regime-specific feature importance analysis
4. Stable feature set extraction across walk-forward windows
"""
from __future__ import annotations
import logging
from dataclasses import dataclass, field
import numpy as np
import pandas as pd
logger = logging.getLogger("trading_system.ml.feature_selection")
@dataclass
class FeatureImportance:
"""Results of feature importance analysis."""
feature_names: list[str]
mean_abs_shap: np.ndarray # mean |SHAP| per feature
std_shap: np.ndarray # std of SHAP per feature
regime_importance: dict[str, np.ndarray] = field(default_factory=dict)
selected_features: list[str] = field(default_factory=list)
dropped_features: list[str] = field(default_factory=list)
class SHAPFeatureSelector:
"""SHAP-based feature selection with stability analysis.
Strategy:
- Compute SHAP values for fitted tree model
- Rank features by mean |SHAP|
- Drop features with negligible contribution
- Remove highly correlated redundant features
- Optionally analyze per-regime importance
"""
def __init__(
self,
min_importance_pct: float = 0.5, # drop features < 0.5% of max importance
max_correlation: float = 0.90, # drop correlated pairs
min_features: int = 10, # keep at least this many
max_features: int = 50, # cap features
stability_threshold: float = 0.3, # feature must appear in 30% of WF windows
):
self.min_importance_pct = min_importance_pct
self.max_correlation = max_correlation
self.min_features = min_features
self.max_features = max_features
self.stability_threshold = stability_threshold
self._window_selections: list[list[str]] = []
def compute_importance(
self,
model,
X: pd.DataFrame,
n_samples: int = 500,
) -> FeatureImportance:
"""Compute SHAP-based feature importance.
Args:
model: Fitted tree model (XGB/LGB/sklearn).
X: Feature DataFrame.
n_samples: Max samples for SHAP computation (speed).
Returns:
FeatureImportance with rankings.
"""
import shap
feature_names = list(X.columns)
# Subsample for speed
if len(X) > n_samples:
X_sample = X.sample(n=n_samples, random_state=42)
else:
X_sample = X
try:
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_sample)
except Exception:
# Fallback to permutation explainer
try:
explainer = shap.Explainer(model, X_sample)
shap_obj = explainer(X_sample)
shap_values = shap_obj.values
except Exception as e:
logger.warning("SHAP computation failed: %s. Using model importances.", e)
return self._fallback_importance(model, feature_names)
# Handle multi-class SHAP (list of arrays)
if isinstance(shap_values, list):
# Average across classes
stacked = np.stack([np.abs(sv) for sv in shap_values], axis=0)
abs_shap = stacked.mean(axis=0) # (n_samples, n_features)
elif shap_values.ndim == 3:
abs_shap = np.abs(shap_values).mean(axis=2)
else:
abs_shap = np.abs(shap_values)
mean_abs = abs_shap.mean(axis=0)
std_vals = abs_shap.std(axis=0)
return FeatureImportance(
feature_names=feature_names,
mean_abs_shap=mean_abs,
std_shap=std_vals,
)
def _fallback_importance(self, model, feature_names) -> FeatureImportance:
"""Use model's built-in feature importance if SHAP fails."""
try:
imp = model.feature_importances_
except AttributeError:
imp = np.ones(len(feature_names))
return FeatureImportance(
feature_names=feature_names,
mean_abs_shap=imp / imp.sum() if imp.sum() > 0 else imp,
std_shap=np.zeros(len(feature_names)),
)
def select_features(
self,
importance: FeatureImportance,
X: pd.DataFrame,
) -> FeatureImportance:
"""Select features based on importance and correlation.
Returns updated FeatureImportance with selected/dropped lists.
"""
feat_names = importance.feature_names
scores = importance.mean_abs_shap
# Rank by importance
sorted_idx = np.argsort(-scores)
threshold = scores.max() * (self.min_importance_pct / 100.0) if scores.max() > 0 else 0
# Keep features above threshold
kept_idx = [i for i in sorted_idx if scores[i] >= threshold]
# Enforce min/max
if len(kept_idx) < self.min_features:
kept_idx = list(sorted_idx[:self.min_features])
if len(kept_idx) > self.max_features:
kept_idx = kept_idx[:self.max_features]
kept_names = [feat_names[i] for i in kept_idx]
# Remove correlated features (keep the more important one)
kept_names = self._remove_correlated(X, kept_names, scores, feat_names)
if len(kept_names) < self.min_features:
# Re-add top features if too many removed
for i in sorted_idx:
if feat_names[i] not in kept_names:
kept_names.append(feat_names[i])
if len(kept_names) >= self.min_features:
break
dropped = [f for f in feat_names if f not in kept_names]
importance.selected_features = kept_names
importance.dropped_features = dropped
# Record for stability tracking
self._window_selections.append(kept_names)
logger.info(
"Selected %d/%d features (dropped %d)",
len(kept_names), len(feat_names), len(dropped),
)
return importance
def _remove_correlated(
self,
X: pd.DataFrame,
kept_names: list[str],
scores: np.ndarray,
all_names: list[str],
) -> list[str]:
"""Remove highly correlated features, keeping the more important one."""
if len(kept_names) < 2:
return kept_names
subset = X[kept_names].copy()
corr = subset.corr().abs()
to_drop = set()
for i in range(len(kept_names)):
if kept_names[i] in to_drop:
continue
for j in range(i + 1, len(kept_names)):
if kept_names[j] in to_drop:
continue
if corr.iloc[i, j] > self.max_correlation:
# Drop the less important one
idx_i = all_names.index(kept_names[i])
idx_j = all_names.index(kept_names[j])
if scores[idx_i] >= scores[idx_j]:
to_drop.add(kept_names[j])
else:
to_drop.add(kept_names[i])
return [f for f in kept_names if f not in to_drop]
def get_stable_features(self) -> list[str]:
"""Get features that appear consistently across walk-forward windows."""
if not self._window_selections:
return []
n_windows = len(self._window_selections)
feature_counts: dict[str, int] = {}
for selection in self._window_selections:
for feat in selection:
feature_counts[feat] = feature_counts.get(feat, 0) + 1
threshold = max(1, int(n_windows * self.stability_threshold))
stable = [f for f, c in feature_counts.items() if c >= threshold]
# Sort by frequency
stable.sort(key=lambda f: -feature_counts.get(f, 0))
return stable
def regime_importance(
self,
model,
X: pd.DataFrame,
regime_labels: np.ndarray,
regime_names: dict[int, str] | None = None,
) -> dict[str, np.ndarray]:
"""Compute SHAP importance per regime.
Args:
model: Fitted tree model.
X: Feature DataFrame.
regime_labels: Array of regime integers per row.
regime_names: Optional mapping int→name.
Returns:
Dict of regime_name → mean |SHAP| array (per feature).
"""
import shap
if regime_names is None:
regime_names = {0: "SIDEWAYS", 1: "BULL", 2: "BEAR"}
result = {}
for r_int, r_name in regime_names.items():
mask = regime_labels == r_int
if mask.sum() < 50:
continue
X_regime = X[mask]
if len(X_regime) > 300:
X_regime = X_regime.sample(n=300, random_state=42)
try:
explainer = shap.TreeExplainer(model)
sv = explainer.shap_values(X_regime)
if isinstance(sv, list):
abs_sv = np.mean([np.abs(s) for s in sv], axis=0)
else:
abs_sv = np.abs(sv)
result[r_name] = abs_sv.mean(axis=0)
except Exception as e:
logger.warning("Regime SHAP for %s failed: %s", r_name, e)
return result
def print_feature_report(importance: FeatureImportance, top_n: int = 20):
"""Print a feature importance summary."""
names = importance.feature_names
scores = importance.mean_abs_shap
sorted_idx = np.argsort(-scores)
print("\n" + "=" * 60)
print(" SHAP FEATURE IMPORTANCE REPORT")
print("=" * 60)
for rank, i in enumerate(sorted_idx[:top_n], 1):
bar = "█" * int(scores[i] / (scores.max() + 1e-9) * 30)
selected = "✓" if names[i] in importance.selected_features else "✗"
print(f" {rank:2d}. [{selected}] {names[i]:<30s} {scores[i]:.4f} {bar}")
if importance.selected_features:
print(f"\n Selected: {len(importance.selected_features)} features")
print(f" Dropped: {len(importance.dropped_features)} features")
if importance.regime_importance:
print("\n --- Per-Regime Top-5 ---")
for regime, imp in importance.regime_importance.items():
top5 = np.argsort(-imp)[:5]
feats = ", ".join(names[i] for i in top5)
print(f" {regime}: {feats}")
print("=" * 60)
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