varvar 1.0.0

Model variance with multiplicative variance trees

varvar

Python package to model variance in different ways

Multiplicative variance trees and the varvar algorithm

varvar is a greedy algorithm for multiplicative variance trees.

varvar is to variance as lightgbm/xgboost/... are to expectation.

There are currently two implementations of varvar algorithms:

1. using quantile search at every split (in varvar.qtrees)
2. using histograms, with binning before starting (in varvar.htrees)

Quantile search is much slower, but can be more accurate.

This is similar to the "exact" and "hist" modes in xgboost, except our "exact" algorithm goes over a small (exact) subset of each feature.

Both implementation modules have a multiplicative_variance_trees function.

Use treespredict.predict for prediction.

The trees are returned as plain python types and can be serialized with pickle or even as json.

Here is an example:

from varvar.htrees import multiplicative_variance_trees  # takes time because numba compiles functions
from varvar.treespredict import predict
import numpy as np

random = np.random.RandomState(1729)
n = 200000
x = random.uniform(-1000, 1000, n)
correct_threshold = 300
sigma = 1 * (x <= correct_threshold) + 30 * (x > correct_threshold)
e = sigma * random.randn(n)

trees = multiplicative_variance_trees(
    [x], e**2,
    num_trees=1, max_depth=1, mingain=1, learning_rate=1,
    q=np.linspace(0, 1, 100)[1:-1]
)
preds = predict(trees, [x])

found_threshold = trees[1][0][1]
print(correct_threshold, found_threshold)  # 300, 295
print(np.sqrt(min(preds)), np.sqrt(max(preds)))  # 1, 30