EX: Make separate example for fork issue.
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Skipper Seabold
parent
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commit
15ea00540a
@ -4,86 +4,64 @@ Created on 1 Apr 2015
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@author: Jamie Hall
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'''
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if __name__ == "__main__":
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# NOTE: This *has* to be here and in the `__name__ == "__main__"` clause
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# to run XGBoost in parallel, if XGBoost was built with OpenMP support.
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# Otherwise, you can use fork, which is the default backend for joblib,
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# and omit this.
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from multiprocessing import set_start_method
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set_start_method("forkserver")
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import pickle
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import xgboost as xgb
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import pickle
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import os
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import xgboost as xgb
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import numpy as np
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from sklearn.cross_validation import KFold
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from sklearn.metrics import confusion_matrix, mean_squared_error
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from sklearn.grid_search import GridSearchCV
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from sklearn.datasets import load_iris, load_digits, load_boston
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import numpy as np
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from sklearn.cross_validation import KFold
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from sklearn.grid_search import GridSearchCV
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from sklearn.metrics import confusion_matrix, mean_squared_error
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from sklearn.datasets import load_iris, load_digits, load_boston
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rng = np.random.RandomState(31337)
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rng = np.random.RandomState(31337)
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print("Zeros and Ones from the Digits dataset: binary classification")
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(confusion_matrix(actuals, predictions))
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print("Zeros and Ones from the Digits dataset: binary classification")
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digits = load_digits(2)
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y = digits['target']
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X = digits['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(confusion_matrix(actuals, predictions))
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print("Iris: multiclass classification")
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iris = load_iris()
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y = iris['target']
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X = iris['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(confusion_matrix(actuals, predictions))
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print("Iris: multiclass classification")
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iris = load_iris()
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y = iris['target']
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X = iris['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBClassifier().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(confusion_matrix(actuals, predictions))
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print("Boston Housing: regression")
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(mean_squared_error(actuals, predictions))
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print("Boston Housing: regression")
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boston = load_boston()
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y = boston['target']
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X = boston['data']
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kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf:
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xgb_model = xgb.XGBRegressor().fit(X[train_index],y[train_index])
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predictions = xgb_model.predict(X[test_index])
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actuals = y[test_index]
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print(mean_squared_error(actuals, predictions))
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print("Parameter optimization")
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y = boston['target']
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X = boston['data']
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xgb_model = xgb.XGBRegressor()
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clf = GridSearchCV(xgb_model,
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{'max_depth': [2,4,6],
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'n_estimators': [50,100,200]}, verbose=1)
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clf.fit(X,y)
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print(clf.best_score_)
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print(clf.best_params_)
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print("Parameter optimization")
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y = boston['target']
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X = boston['data']
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xgb_model = xgb.XGBRegressor()
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clf = GridSearchCV(xgb_model,
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{'max_depth': [2,4,6],
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'n_estimators': [50,100,200]}, verbose=1)
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clf.fit(X,y)
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print(clf.best_score_)
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print(clf.best_params_)
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# The sklearn API models are picklable
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print("Pickling sklearn API models")
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# must open in binary format to pickle
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pickle.dump(clf, open("best_boston.pkl", "wb"))
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clf2 = pickle.load(open("best_boston.pkl", "rb"))
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print(np.allclose(clf.predict(X), clf2.predict(X)))
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print("Parallel Parameter optimization")
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os.environ["OMP_NUM_THREADS"] = "1"
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y = boston['target']
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X = boston['data']
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xgb_model = xgb.XGBRegressor()
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clf = GridSearchCV(xgb_model,
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{'max_depth': [2,4,6],
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'n_estimators': [50,100,200]}, verbose=1,
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n_jobs=2)
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clf.fit(X, y)
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print(clf.best_score_)
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print(clf.best_params_)
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# The sklearn API models are picklable
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print("Pickling sklearn API models")
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# must open in binary format to pickle
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pickle.dump(clf, open("best_boston.pkl", "wb"))
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clf2 = pickle.load(open("best_boston.pkl", "rb"))
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print(np.allclose(clf.predict(X), clf2.predict(X)))
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35
demo/guide-python/sklearn_parallel.py
Normal file
35
demo/guide-python/sklearn_parallel.py
Normal file
@ -0,0 +1,35 @@
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import os
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if __name__ == "__main__":
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# NOTE: on posix systems, this *has* to be here and in the
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# `__name__ == "__main__"` clause to run XGBoost in parallel processes
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# using fork, if XGBoost was built with OpenMP support. Otherwise, if you
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# build XGBoost without OpenMP support, you can use fork, which is the
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# default backend for joblib, and omit this.
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try:
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from multiprocessing import set_start_method
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except ImportError:
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raise ImportError("Unable to import multiprocessing.set_start_method."
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" This example only runs on Python 3.4")
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set_start_method("forkserver")
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import numpy as np
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from sklearn.grid_search import GridSearchCV
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from sklearn.datasets import load_boston
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import xgboost as xgb
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rng = np.random.RandomState(31337)
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print("Parallel Parameter optimization")
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boston = load_boston()
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os.environ["OMP_NUM_THREADS"] = "2" # or to whatever you want
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y = boston['target']
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X = boston['data']
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xgb_model = xgb.XGBRegressor()
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clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
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'n_estimators': [50, 100, 200]}, verbose=1,
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n_jobs=2)
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clf.fit(X, y)
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print(clf.best_score_)
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print(clf.best_params_)
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