python: multiple eval_metrics changes
- allows feval to return a list of tuples (name, error/score value) - changed behavior for multiple eval_metrics in conjunction with early_stopping: Instead of raising an error, the last passed evel_metric (or last entry in return value of feval) is used for early stopping - allows list of eval_metrics in dict-typed params - unittest for new features / behavior documentation updated - example for assigning a list to 'eval_metric' - note about early stopping on last passed eval metric - info msg for used eval metric added
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FrozenFingerz
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@ -67,10 +67,17 @@ XGBoost use list of pair to save [parameters](../parameter.md). Eg
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```python
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param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
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param['nthread'] = 4
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plst = param.items()
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plst += [('eval_metric', 'auc')] # Multiple evals can be handled in this way
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plst += [('eval_metric', 'ams@0')]
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param['eval_metric'] = 'auc'
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```
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* You can also specify multiple eval metrics:
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```python
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param['eval_metric'] = ['auc', 'ams@0']
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# alternativly:
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# plst = param.items()
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# plst += [('eval_metric', 'ams@0')]
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```
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* Specify validations set to watch performance
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```python
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evallist = [(dtest,'eval'), (dtrain,'train')]
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@ -116,7 +123,7 @@ The model will train until the validation score stops improving. Validation erro
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If early stopping occurs, the model will have two additional fields: `bst.best_score` and `bst.best_iteration`. Note that `train()` will return a model from the last iteration, not the best one.
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This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC).
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This works with both metrics to minimize (RMSE, log loss, etc.) and to maximize (MAP, NDCG, AUC). Note that if you specify more than one evaluation metric the last one in `param['eval_metric']` is used for early stopping.
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Prediction
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----------
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@ -745,8 +745,13 @@ class Booster(object):
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else:
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res = '[%d]' % iteration
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for dmat, evname in evals:
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name, val = feval(self.predict(dmat), dmat)
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res += '\t%s-%s:%f' % (evname, name, val)
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feval_ret = feval(self.predict(dmat), dmat)
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if isinstance(feval_ret, list):
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for name, val in feval_ret:
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res += '\t%s-%s:%f' % (evname, name, val)
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else:
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name, val = feval_ret
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res += '\t%s-%s:%f' % (evname, name, val)
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return res
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def eval(self, data, name='eval', iteration=0):
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@ -61,6 +61,17 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
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booster : a trained booster model
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"""
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evals = list(evals)
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if isinstance(params, dict) \
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and 'eval_metric' in params \
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and isinstance(params['eval_metric'], list):
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params = dict((k, v) for k, v in params.items())
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eval_metrics = params['eval_metric']
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params.pop("eval_metric", None)
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params = list(params.items())
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for eval_metric in eval_metrics:
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params += [('eval_metric', eval_metric)]
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bst = Booster(params, [dtrain] + [d[0] for d in evals])
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ntrees = 0
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if xgb_model is not None:
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if not isinstance(xgb_model, STRING_TYPES):
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@ -70,7 +81,6 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
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else:
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bst = Booster(params, [dtrain] + [d[0] for d in evals])
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if evals_result is not None:
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if not isinstance(evals_result, dict):
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raise TypeError('evals_result has to be a dictionary')
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@ -120,9 +130,11 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
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# is params a list of tuples? are we using multiple eval metrics?
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if isinstance(params, list):
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if len(params) != len(dict(params).items()):
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raise ValueError('Check your params.'\
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'Early stopping works with single eval metric only.')
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params = dict(params)
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params = dict(params)
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sys.stderr.write("Multiple eval metrics has been passed: " \
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"'{0}' will be used for early stopping.\n\n".format(params['eval_metric']))
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else:
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params = dict(params)
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# either minimize loss or maximize AUC/MAP/NDCG
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maximize_score = False
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95
tests/python/test_eval_metrics.py
Normal file
95
tests/python/test_eval_metrics.py
Normal file
@ -0,0 +1,95 @@
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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, train_test_split
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from sklearn.metrics import 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 unittest
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rng = np.random.RandomState(1337)
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class TestEvalMetrics(unittest.TestCase):
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xgb_params_01 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': 'error'
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}
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xgb_params_02 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': ['error']
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}
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xgb_params_03 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': ['rmse', 'error']
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}
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xgb_params_04 = {
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'silent': 1,
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'nthread': 1,
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'eval_metric': ['error', 'rmse']
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}
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def evalerror_01(self, preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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def evalerror_02(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('error', float(sum(labels != (preds > 0.0))) / len(labels))]
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def evalerror_03(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('rmse', mean_squared_error(labels, preds)),
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('error', float(sum(labels != (preds > 0.0))) / len(labels))]
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def evalerror_04(self, preds, dtrain):
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labels = dtrain.get_label()
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return [('error', float(sum(labels != (preds > 0.0))) / len(labels)),
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('rmse', mean_squared_error(labels, preds))]
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def test_eval_metrics(self):
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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Xt, Xv, yt, yv = train_test_split(X, y, test_size=0.2, random_state=0)
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dtrain = xgb.DMatrix(Xt, label=yt)
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dvalid = xgb.DMatrix(Xv, label=yv)
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watchlist = [(dtrain, 'train'), (dvalid, 'val')]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=10)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, num_boost_round=10)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
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early_stopping_rounds=2)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
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gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_01)
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gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_02)
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gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_03)
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gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
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early_stopping_rounds=2, feval=self.evalerror_04)
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assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
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assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
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assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
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