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Updated sklearn_examples.py for soon-to-be-deprecated modules (#2117)

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Yang Zhang 2017-03-21 23:07:27 -04:00 committed by Tianqi Chen
parent e65564ba59
commit f6f5003f79
1 changed files with 7 additions and 11 deletions
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18
demo/guide-python/sklearn_examples.py
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@ -8,12 +8,8 @@ import pickle
import xgboost as xgb import xgboost as xgb
import numpy as np import numpy as np
try: from sklearn.model_selection import KFold, train_test_split, GridSearchCV
from sklearn.model_selection import KFold, train_test_split
except:
from sklearn.cross_validation import KFold, train_test_split
from sklearn.metrics import confusion_matrix, mean_squared_error from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.grid_search import GridSearchCV
from sklearn.datasets import load_iris, load_digits, load_boston from sklearn.datasets import load_iris, load_digits, load_boston
rng = np.random.RandomState(31337) rng = np.random.RandomState(31337)
@ -22,8 +18,8 @@ print("Zeros and Ones from the Digits dataset: binary classification")
digits = load_digits(2) digits = load_digits(2)
y = digits['target'] y = digits['target']
X = digits['data'] X = digits['data']
kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf: for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index]) xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index]) predictions = xgb_model.predict(X[test_index])
actuals = y[test_index] actuals = y[test_index]
@ -33,8 +29,8 @@ print("Iris: multiclass classification")
iris = load_iris() iris = load_iris()
y = iris['target'] y = iris['target']
X = iris['data'] X = iris['data']
kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf: for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index]) xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index]) predictions = xgb_model.predict(X[test_index])
actuals = y[test_index] actuals = y[test_index]
@ -44,8 +40,8 @@ print("Boston Housing: regression")
boston = load_boston() boston = load_boston()
y = boston['target'] y = boston['target']
X = boston['data'] X = boston['data']
kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf: for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index]) xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
predictions = xgb_model.predict(X[test_index]) predictions = xgb_model.predict(X[test_index])
actuals = y[test_index] actuals = y[test_index]