Examples

Plotting tree

Suppose the model is dumped into gbdtmo.txt, plot 5th tree by:

>>> from gbdtmo import create_graph
>>> from graphviz import Digraph
>>> graph = create_graph("gbdtmo.txt", 5, [0, 3])
>>> graph.render("tree_5", format='pdf')

Then tree_5.pdf will be generated.

Using GBDTMO

First import gbdtmo

>>> from gbdtmo import GBDTMulti, load_lib

Load from gbdtmo.so

>>> LIB = load_lib("path to gbdtmo.so")

Build an instance of GBDTMO. Here the out_dim is set to 10 and MSE loss is used.

>>> inp_dim, out_dim = 10, 5
>>> params = {"max_depth": 5, "lr": 0.1, 'loss': b"mse"}
>>> booster = GBDTMulti(LIB, out_dim=out_dim, params=params)

Set the training and eval datasets.

>>> x_train, y_train = np.random.rand(10000, inp_dim), np.random.rand(10000, out_dim)
>>> x_valid, y_valid = np.random.rand(10000, inp_dim), np.random.rand(10000, out_dim)
>>> booster.set_data((x_train, y_train), (x_valid, y_valid))

Training with 30 rounds and dump it into text file.

>>> booster.train(30)
>>> booster.dump(b"tree.txt")

Custom loss

We show how to train GBDTMO via custom loss. Here is an example of MSE.

def MSE(x, y):
  g = x - y
  h = np.ones_like(x)
  return g, h

>>> g, h = MSE(booster.preds_train.copy(), booster.label.copy())
>>> booster._set_gh(g, h)
>>> booster.boost()

In this way, a new tree is constructed and the predictions are updated.