Skip to content

Latest commit

 

History

History
87 lines (70 loc) · 3.02 KB

README.md

File metadata and controls

87 lines (70 loc) · 3.02 KB
Raw

We provide a scikit-learn wrapper interface. Before you use the Python interface, you must build ThunderGBM. Note that both dense and sparse matrices are supported in ThunderGBM. The dense matrices are converted into csr format which the the parallel training algorithm on GPUs is based on.

Instructions for building ThunderGBM

  • Please refer to Installation for building ThunderGBM.

  • Then, if you want to install the Python package from source, go to the project root directory and run:

cd python && python setup.py install

Or you can install via pip

pip3 install thundergbm
  • After you have successfully installed ThunderGBM, you can import TGBMModel:
from thundergbm import TGBMClassifier
from sklearn import datasets
clf = TGBMClassifier()
X, y = datasets.load_digits(return_X_y=True)
clf.fit(X, y)
clf.save_model(model_path)
  • Load model
from thundergbm import TGBMClassifier
clf = TGBMClassifier(objective="your-objective") 
# You should specific objective here as in training stage
clf.load_model(model_path)
y_pred = clf.predict(X)

Prerequisites

  • numpy | scipy | sklearn

Example

  • Step 1: Create a file called tgbm_test.py which has the following content.
from thundergbm import *
from sklearn.datasets import *
from sklearn.metrics import mean_squared_error
from math import sqrt

x,y = load_svmlight_file("../dataset/test_dataset.txt")
clf = TGBMRegressor()
clf.fit(x,y)

x2,y2=load_svmlight_file("../dataset/test_dataset.txt")
y_predict=clf.predict(x2)

rms = sqrt(mean_squared_error(y2, y_predict))
print(rms)
  • Step 2: Run the python script.
python tgbm_test.py

ThunderGBM class

class TGBMModel(depth = 6, num_round = 40, n_device = 1, min_child_weight = 1.0, lambda_tgbm = 1.0, gamma = 1.0, max_num_bin = 255, verbose = 0, column_sampling_rate = 1.0, bagging = 0, n_parallel_trees = 1, learning_rate = 1.0, objective = "reg:linear", num_class = 1, path = "../dataset/test_dataset.txt"))

Please note that TGBMClassifier and TGBMRegressor are wrappers of TGBMModel, and their constructors have the same parameters to TGBMModel.

Parametes

Please refer to Parameters in ThunderGBM documentations.

Methods

fit(X, y):
Fit the GBM model according to the given training data.

predict(X):
Perform prediction on samples in X.

save_model(path):
Save the model to the file path.

load_model(path):
Load the model from the file path.

cv(X, y, folds=None, nfold=5, shuffle=True,seed=0):\

  • folds: A length n list of tuples. Each tuple is (in,out) where in is a list of indices to be used as the training samples for the n th fold and out is a list of indices to be used as the testing samples for the n th fold.
  • shuffle (bool): Whether to shuffle data before creating folds.
  • seed (int): Seed used to generate the folds.

get_shap_trees():
Return the model that used for the SHAP explainer.