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Commit de39dbcf authored by Tsukasa OMOTO's avatar Tsukasa OMOTO Committed by Guolin Ke
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python-package: support valid_names in scikit-learn API (#420)

parent 98c7c2a3
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Showing with 15 additions and 8 deletions
docs/Python-API.md
View file @ de39dbcf
......@@ -721,7 +721,7 @@ The methods of each Class is in alphabetical order.
X_leaves : array_like, shape=[n_samples, n_trees]
#### fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None)
#### fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None)
Fit the gradient boosting model.
......@@ -739,6 +739,8 @@ The methods of each Class is in alphabetical order.
group data of training data
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for early-stopping
eval_names: list of string
Names of eval_set
eval_sample_weight : list or dict of array
weight of eval data; if you use dict, the index should start from 0
eval_init_score : list or dict of array
......@@ -854,7 +856,7 @@ The methods of each Class is in alphabetical order.
### LGBMRanker
#### fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric='ndcg', eval_at=1, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None)
#### fit(X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric='ndcg', eval_at=1, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None)
Most arguments are same as Common Methods except:
......
python-package/lightgbm/sklearn.py
View file @ de39dbcf
......@@ -281,7 +281,7 @@ class LGBMModel(LGBMModelBase):
def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_sample_weight=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_group=None,
eval_metric=None,
early_stopping_rounds=None, verbose=True,
......@@ -304,6 +304,8 @@ class LGBMModel(LGBMModelBase):
group data of training data
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for early-stopping
eval_names: list of string
Names of eval_set
eval_sample_weight : List of array
weight of eval data
eval_init_score : List of array
......@@ -403,7 +405,7 @@ class LGBMModel(LGBMModelBase):
valid_sets.append(valid_set)
self._Booster = train(params, train_set,
self.n_estimators, valid_sets=valid_sets,
self.n_estimators, valid_sets=valid_sets, valid_names=eval_names,
early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result, fobj=self.fobj, feval=feval,
verbose_eval=verbose, feature_name=feature_name,
......@@ -512,7 +514,7 @@ class LGBMRegressor(LGBMModel, LGBMRegressorBase):
def fit(self, X, y,
sample_weight=None, init_score=None,
eval_set=None, eval_sample_weight=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None,
eval_metric="l2",
early_stopping_rounds=None, verbose=True,
......@@ -520,6 +522,7 @@ class LGBMRegressor(LGBMModel, LGBMRegressorBase):
super(LGBMRegressor, self).fit(X, y, sample_weight=sample_weight,
init_score=init_score, eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
eval_metric=eval_metric,
......@@ -558,7 +561,7 @@ class LGBMClassifier(LGBMModel, LGBMClassifierBase):
def fit(self, X, y,
sample_weight=None, init_score=None,
eval_set=None, eval_sample_weight=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None,
eval_metric="logloss",
early_stopping_rounds=None, verbose=True,
......@@ -587,6 +590,7 @@ class LGBMClassifier(LGBMModel, LGBMClassifierBase):
super(LGBMClassifier, self).fit(X, y, sample_weight=sample_weight,
init_score=init_score, eval_set=eval_set,
eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score,
eval_metric=eval_metric,
......@@ -666,7 +670,7 @@ class LGBMRanker(LGBMModel):
def fit(self, X, y,
sample_weight=None, init_score=None, group=None,
eval_set=None, eval_sample_weight=None,
eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_group=None,
eval_metric='ndcg', eval_at=1,
early_stopping_rounds=None, verbose=True,
......@@ -696,7 +700,8 @@ class LGBMRanker(LGBMModel):
self.eval_at = eval_at
super(LGBMRanker, self).fit(X, y, sample_weight=sample_weight,
init_score=init_score, group=group,
eval_set=eval_set, eval_sample_weight=eval_sample_weight,
eval_set=eval_set, eval_names=eval_names,
eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score, eval_group=eval_group,
eval_metric=eval_metric,
early_stopping_rounds=early_stopping_rounds,
......
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