65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
'''
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Created on 1 Apr 2015
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@author: Jamie Hall
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'''
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import sys
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sys.path.append('../../wrapper')
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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.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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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("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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