surrogate-forest 0.1.1

Random Forest with surrogate splits for native missing data handling

surrogate-forest

Random Forest with surrogate splits for native missing data handling.

Python implementation combining the best ideas from CART (surrogate splits), XGBoost (learned NaN directions), LightGBM (histogram binning), and CatBoost (symmetric trees) into a single sklearn-compatible library.

Installation

pip install -e .

Quick Start

from surrogate_forest import SurrogateRandomForestClassifier
import numpy as np

X = np.random.randn(500, 10)
X[np.random.rand(*X.shape) < 0.2] = np.nan  # 20% missing
y = (X[:, 0] > 0).astype(int)  # NaN-safe comparison not needed — handled internally

clf = SurrogateRandomForestClassifier(n_estimators=100, max_surrogates=5, random_state=42)
clf.fit(X, y)
predictions = clf.predict(X)
probabilities = clf.predict_proba(X)

How It Works

Missing Data Handling (3-layer fallback)

At each internal node during prediction:

1. Primary split — if the split feature is available, use the threshold
2. Surrogate splits — if primary is missing, try backup splits on correlated features (first non-missing wins)
3. Learned direction — if all surrogates are also missing, go left or right based on the direction that was optimal during training (XGBoost-style)

Surrogate Split Discovery

After choosing the primary split at each node:

1. Determine primary's left/right assignment for all samples
2. For every other feature, find the threshold maximizing concordance with the primary
3. Compute predictive measure of association: λ = (p_naive_error - disagreement) / p_naive_error
4. Support mirrored surrogates (negatively correlated features)
5. Rank by λ, keep top max_surrogates

Estimators

Estimator	Task	Key Default
SurrogateDecisionTreeClassifier	Classification	max_features=None
SurrogateDecisionTreeRegressor	Regression	criterion='squared_error'
SurrogateRandomForestClassifier	Classification	max_features='sqrt'
SurrogateRandomForestRegressor	Regression	max_features='sqrt'

Key Parameters

Parameter	Default	Description
max_surrogates	5	Surrogate splits per node
use_histogram	True	Histogram-based splitting (LightGBM-style)
max_bins	255	Histogram resolution
symmetric_tree	False	CatBoost-style oblivious trees
max_leaf_nodes	None	If set, enables best-first (leaf-wise) growth

All standard sklearn tree/forest parameters are also supported: max_depth, min_samples_split, min_samples_leaf, max_features, n_estimators, bootstrap, oob_score, n_jobs, etc.

Feature Importance

Three types of feature importance:

from surrogate_forest import impurity_importance, surrogate_importance, permutation_importance

# Standard impurity-based (MDI)
imp = impurity_importance(fitted_model)

# Surrogate-based (unique to this library)
# Captures feature redundancy/substitutability
surr_imp = surrogate_importance(fitted_model)

# Permutation importance (correctly shuffles NaN patterns)
perm_imp = permutation_importance(fitted_model, X, y, n_repeats=10)

Growth Modes

from surrogate_forest import SurrogateDecisionTreeClassifier

# Depth-first (default CART)
tree = SurrogateDecisionTreeClassifier(max_depth=5)

# Best-first / leaf-wise (LightGBM-style)
tree = SurrogateDecisionTreeClassifier(max_leaf_nodes=31)

# Symmetric / oblivious (CatBoost-style)
tree = SurrogateDecisionTreeClassifier(max_depth=6, symmetric_tree=True)

sklearn Compatibility

Full compatibility with sklearn's API:

from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score, GridSearchCV

# Works in pipelines
pipe = Pipeline([("clf", SurrogateRandomForestClassifier())])

# Works with cross-validation
scores = cross_val_score(clf, X, y, cv=5)

# Works with grid search
param_grid = {"max_depth": [3, 5, 10], "max_surrogates": [0, 3, 5]}
gs = GridSearchCV(SurrogateDecisionTreeClassifier(), param_grid, cv=3)

Testing

pytest tests/ -v