c621775f34
* Replace all uses of deprecated function sklearn.datasets.load_boston * More renaming * Fix bad name * Update assertion * Fix n boosted rounds. * Avoid over regularization. * Rebase. * Avoid over regularization. * Whac-a-mole Co-authored-by: fis <jm.yuan@outlook.com>
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
'''
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Collection of examples for using sklearn interface
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==================================================
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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 pickle
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import xgboost as xgb
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import numpy as np
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from sklearn.model_selection import KFold, train_test_split, 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, fetch_california_housing
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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(n_class=2)
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y = digits['target']
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X = digits['data']
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X):
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xgb_model = xgb.XGBClassifier(n_jobs=1).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(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X):
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xgb_model = xgb.XGBClassifier(n_jobs=1).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("California Housing: regression")
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X, y = fetch_california_housing(return_X_y=True)
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kf = KFold(n_splits=2, shuffle=True, random_state=rng)
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for train_index, test_index in kf.split(X):
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xgb_model = xgb.XGBRegressor(n_jobs=1).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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xgb_model = xgb.XGBRegressor(n_jobs=1)
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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, n_jobs=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_calif.pkl", "wb"))
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clf2 = pickle.load(open("best_calif.pkl", "rb"))
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print(np.allclose(clf.predict(X), clf2.predict(X)))
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# Early-stopping
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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clf = xgb.XGBClassifier(n_jobs=1)
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clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
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eval_set=[(X_test, y_test)])
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