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Removed trailing whitespaces and Change Error to XGBoostError

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This commit is contained in:
Johan Manders 2015-10-14 14:17:57 +02:00
parent edf4595bc1
commit 82c2ba4c44
1 changed files with 21 additions and 19 deletions
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python-package/xgboost/sklearn.py
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@ -203,11 +203,11 @@ class XGBModel(XGBModelBase):
# pylint: disable=missing-docstring,invalid-name # pylint: disable=missing-docstring,invalid-name
test_dmatrix = DMatrix(data, missing=self.missing) test_dmatrix = DMatrix(data, missing=self.missing)
return self.booster().predict(test_dmatrix) return self.booster().predict(test_dmatrix)
def evals_result(self): def evals_result(self):
"""Return the evaluation results. """Return the evaluation results.
If eval_set is passed to the `fit` function, you can call evals_result() to If eval_set is passed to the `fit` function, you can call evals_result() to
get evaluation results for all passed eval_sets. When eval_metric is also get evaluation results for all passed eval_sets. When eval_metric is also
passed to the `fit` function, the evals_result will contain the eval_metrics passed to the `fit` function, the evals_result will contain the eval_metrics
passed to the `fit` function passed to the `fit` function
@ -215,27 +215,28 @@ class XGBModel(XGBModelBase):
Returns Returns
------- -------
evals_result : dictionary evals_result : dictionary
Example Example
------- -------
param_dist = {'objective':'binary:logistic', 'n_estimators':2} param_dist = {'objective':'binary:logistic', 'n_estimators':2}
clf = xgb.XGBModel(**param_dist) clf = xgb.XGBModel(**param_dist)
clf.fit(X_train, y_train, clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)], eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss', eval_metric='logloss',
verbose=True) verbose=True)
evals_result = clf.evals_result() evals_result = clf.evals_result()
The variable evals_result will contain: The variable evals_result will contain:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}} {'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}
""" """
if self.evals_result_: if self.evals_result_:
evals_result = self.evals_result_ evals_result = self.evals_result_
else: else:
raise Error('No results.') raise XGBoostError('No results.')
return evals_result return evals_result
@ -373,7 +374,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
def evals_result(self): def evals_result(self):
"""Return the evaluation results. """Return the evaluation results.
If eval_set is passed to the `fit` function, you can call evals_result() to If eval_set is passed to the `fit` function, you can call evals_result() to
get evaluation results for all passed eval_sets. When eval_metric is also get evaluation results for all passed eval_sets. When eval_metric is also
passed to the `fit` function, the evals_result will contain the eval_metrics passed to the `fit` function, the evals_result will contain the eval_metrics
passed to the `fit` function passed to the `fit` function
@ -381,27 +382,28 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
Returns Returns
------- -------
evals_result : dictionary evals_result : dictionary
Example Example
------- -------
param_dist = {'objective':'binary:logistic', 'n_estimators':2} param_dist = {'objective':'binary:logistic', 'n_estimators':2}
clf = xgb.XGBClassifier(**param_dist) clf = xgb.XGBClassifier(**param_dist)
clf.fit(X_train, y_train, clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)], eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss', eval_metric='logloss',
verbose=True) verbose=True)
evals_result = clf.evals_result() evals_result = clf.evals_result()
The variable evals_result will contain: The variable evals_result will contain:
{'validation_0': {'logloss': ['0.604835', '0.531479']}, 'validation_1': {'logloss': ['0.41965', '0.17686']}} {'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}
""" """
if self.evals_result_: if self.evals_result_:
evals_result = self.evals_result_ evals_result = self.evals_result_
else: else:
raise Error('No results.') raise XGBoostError('No results.')
return evals_result return evals_result