c024c42dce
* Use sphinx gallery to integrate examples. * Remove mock objects. * Add dask doc inventory.
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
"""
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Demo for accessing the xgboost eval metrics by using sklearn interface
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======================================================================
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"""
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import xgboost as xgb
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import numpy as np
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from sklearn.datasets import make_hastie_10_2
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X, y = make_hastie_10_2(n_samples=2000, random_state=42)
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# Map labels from {-1, 1} to {0, 1}
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labels, y = np.unique(y, return_inverse=True)
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X_train, X_test = X[:1600], X[1600:]
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y_train, y_test = y[:1600], y[1600:]
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param_dist = {'objective':'binary:logistic', 'n_estimators':2}
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clf = xgb.XGBModel(**param_dist)
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# Or you can use: clf = xgb.XGBClassifier(**param_dist)
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clf.fit(X_train, y_train,
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eval_set=[(X_train, y_train), (X_test, y_test)],
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eval_metric='logloss',
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verbose=True)
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# Load evals result by calling the evals_result() function
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evals_result = clf.evals_result()
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print('Access logloss metric directly from validation_0:')
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print(evals_result['validation_0']['logloss'])
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print('')
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print('Access metrics through a loop:')
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for e_name, e_mtrs in evals_result.items():
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print('- {}'.format(e_name))
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for e_mtr_name, e_mtr_vals in e_mtrs.items():
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print(' - {}'.format(e_mtr_name))
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print(' - {}'.format(e_mtr_vals))
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print('')
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print('Access complete dict:')
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print(evals_result)
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