Skip to content

Instantly share code, notes, and snippets.

@nagadomi

nagadomi/iris_xgboost.py

Forked from yagays/iris_xgboost.py
Last active September 22, 2016 01:29
Show Gist options
  • Save nagadomi/8aa20a6820bd80116ab9 to your computer and use it in GitHub Desktop.
Save nagadomi/8aa20a6820bd80116ab9 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
iris_xgboost.py
import numpy as np
import scipy as sp
import xgboost as xgb
from hyperopt import hp, fmin, tpe
from sklearn import datasets
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
np.random.seed(71)
iris = datasets.load_iris()
trainX = iris.data[0::2,:]
trainY = iris.target[0::2]
testX = iris.data[1::2,:]
testY = iris.target[1::2]
K = 10
kfold = cross_validation.KFold(n=len(trainX), n_folds=K,
shuffle=True, random_state=0)
params = []
def f(param):
score = 0
iteration = 0
for train_index, test_index in kfold:
xgb_model = xgb.XGBClassifier(**param)
xgb_model.fit(trainX[train_index], trainY[train_index],
eval_set=[(trainX[test_index], trainY[test_index])],
early_stopping_rounds=30,
eval_metric=('mlogloss'),
verbose=False)
score += xgb_model.best_score
iteration += xgb_model.best_iteration
param["score"] = score / K
param["n_estimators"] = int(iteration / K)
params.append(param)
return param["score"]
param_space = {
'nthread': 4,
'silent': 1,
'n_estimators': 1000,
'objective': 'multi:softprob',
'max_depth': hp.quniform('max_depth', 1, 10, 1),
'learning_rate': hp.quniform("eta", 0.001, 0.1, 0.001),
'subsample': hp.quniform('subsample', 0.1, 1.0, 0.01),
#'colsample_bytree': hp.quniform('colsample_bytree', 0.1, 1.0, 0.01),
'colsample_bytree': hp.quniform('colsample_bytree', 0.25, 1.0, 0.25), # iris has only 4 feat. 1/4 = 0.25
'gamma': hp.quniform('gamma', 0.1, 20.0, 0.1),
'min_child_weight': hp.quniform('min_child_weight', 1.0, 20.0, 0.1)
}
fmin(f, param_space, algo=tpe.suggest, max_evals=200)
params = sorted(params, key=lambda param : param['score'])
best = params[0]
print("**best param")
print(best)
del best["score"]
xgb_model = xgb.XGBClassifier(**best)
xgb_model.fit(trainX, trainY)
predict = xgb_model.predict(testX)
print confusion_matrix(testY, predict)
# **best param
# {'colsample_bytree': 0.5, 'silent': 1, 'learning_rate': 0.063, 'nthread': 4, 'min_child_weight': 1.0, 'n_estimators': 168, 'subsample': 0.78, 'score': 0.1174425, 'objective': 'multi:softprob', 'max_depth': 3.0, 'gamma': 0.1}
# [[25 0 0]
# [ 0 23 2]
# [ 0 0 25]]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment