Skip to content

GitLab

Commit e5b94fbe authored by Qiwei Ye's avatar Qiwei Ye Committed by GitHub
Browse files

Merge pull request #200 from ClimbsRocks/master 

sklearn compatibility update- renames .feature_importance_ to .featur…
parents 859f6ed0 b0f7aa50
Hide whitespace changes
Inline Side-by-side
Showing with 46 additions and 46 deletions
docs/Python-API.md
View file @ e5b94fbe
......@@ -22,7 +22,7 @@
+ [print_evaluation](Python-API.md#print_evaluationperiod1-show_stdvtrue)
+ [record_evaluation](Python-API.md#record_evaluationeval_result)
+ [early_stopping](Python-API.md#early_stoppingstopping_rounds-verbosetrue)
The methods of each Class is in alphabetical order.
----
......@@ -62,7 +62,7 @@ The methods of each Class is in alphabetical order.
Other parameters
free_raw_data : Bool
True if need to free raw data after construct inner dataset
####create_valid(data, label=None, weight=None, group=None, silent=False, params=None)
......@@ -83,7 +83,7 @@ The methods of each Class is in alphabetical order.
Whether print messages during construction
params : dict, optional
Other parameters
####get_group()
......@@ -92,7 +92,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
init_score : array
####get_init_score()
......@@ -101,7 +101,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
init_score : array
####get_label()
......@@ -110,7 +110,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
label : array
####get_weight()
......@@ -119,7 +119,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
weight : array
####num_data()
......@@ -128,7 +128,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
number of rows : int
####num_feature()
......@@ -137,7 +137,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
number of columns : int
####save_binary(filename)
......@@ -147,7 +147,7 @@ The methods of each Class is in alphabetical order.
----------
filename : str
Name of the output file.
####set_categorical_feature(categorical_feature)
......@@ -158,7 +158,7 @@ The methods of each Class is in alphabetical order.
categorical_feature : list of str or list of int
Name (str) or index (int) of categorical features
####set_feature_name(feature_name)
......@@ -168,7 +168,7 @@ The methods of each Class is in alphabetical order.
----------
feature_name : list of str
Feature names
####set_group(group)
......@@ -178,7 +178,7 @@ The methods of each Class is in alphabetical order.
----------
group : numpy array or list or None
Group size of each group
####set_init_score(init_score)
......@@ -188,7 +188,7 @@ The methods of each Class is in alphabetical order.
----------
init_score : numpy array or list or None
Init score for booster
####set_label(label)
......@@ -198,7 +198,7 @@ The methods of each Class is in alphabetical order.
----------
label : numpy array or list or None
The label information to be set into Dataset
####set_reference(reference)
......@@ -208,7 +208,7 @@ The methods of each Class is in alphabetical order.
----------
reference : Dataset
Will use reference as template to consturct current dataset
####set_weight(weight)
......@@ -218,7 +218,7 @@ The methods of each Class is in alphabetical order.
----------
weight : numpy array or list or None
Weight for each data point
####subset(used_indices, params=None)
......@@ -230,7 +230,7 @@ The methods of each Class is in alphabetical order.
Used indices of this subset
params : dict
Other parameters
###Booster
......@@ -248,7 +248,7 @@ The methods of each Class is in alphabetical order.
Path to the model file.
silent : boolean, optional
Whether print messages during construction
####add_valid(data, name)
......@@ -260,7 +260,7 @@ The methods of each Class is in alphabetical order.
Validation data
name : str
Name of validation data
####attr(key)
......@@ -275,7 +275,7 @@ The methods of each Class is in alphabetical order.
-------
value : str
The attribute value of the key, returns None if attribute do not exist.
####current_iteration()
......@@ -294,7 +294,7 @@ The methods of each Class is in alphabetical order.
-------
result : dict or list
Json format of model
####eval(data, name, feval=None)
......@@ -311,7 +311,7 @@ The methods of each Class is in alphabetical order.
-------
result : list
Evaluation result list.
####eval_train(feval=None)
......@@ -326,7 +326,7 @@ The methods of each Class is in alphabetical order.
-------
result: str
Evaluation result list.
####eval_valid(feval=None)
......@@ -341,7 +341,7 @@ The methods of each Class is in alphabetical order.
-------
result : str
Evaluation result list.
####feature_importance(importance_type="split")
......@@ -351,7 +351,7 @@ The methods of each Class is in alphabetical order.
-------
result : array
Array of feature importances
####predict(data, num_iteration=-1, raw_score=False, pred_leaf=False, data_has_header=False, is_reshape=True)
......@@ -376,7 +376,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
Prediction result
####reset_parameter(params)
......@@ -388,12 +388,12 @@ The methods of each Class is in alphabetical order.
New parameters for boosters
silent : boolean, optional
Whether print messages during construction
####rollback_one_iter()
Rollback one iteration.
####save_model(filename, num_iteration=-1)
......@@ -405,7 +405,7 @@ The methods of each Class is in alphabetical order.
Filename to save
num_iteration : int
Number of iteration that want to save. < 0 means save all
####set_attr(**kwargs)
......@@ -415,7 +415,7 @@ The methods of each Class is in alphabetical order.
----------
**kwargs
The attributes to set. Setting a value to None deletes an attribute.
####set_train_data_name(name)
......@@ -443,7 +443,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
is_finished, bool
##Training API
......@@ -515,7 +515,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
booster : a trained booster model
####cv(params, train_set, num_boost_round=10, nfold=5, stratified=False, shuffle=True, metrics=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, fpreproc=None, verbose_eval=None, show_stdv=True, seed=0, callbacks=None)
......@@ -577,7 +577,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
evaluation history : list of str
##Scikit-learn API
......@@ -664,7 +664,7 @@ The methods of each Class is in alphabetical order.
----
A custom objective function can be provided for the ``objective``
parameter. In this case, it should have the signature
``objective(y_true, y_pred) -> grad, hess``
``objective(y_true, y_pred) -> grad, hess``
or ``objective(y_true, y_pred, group) -> grad, hess``:
y_true: array_like of shape [n_samples]
......@@ -681,7 +681,7 @@ The methods of each Class is in alphabetical order.
for multi-class task, the y_pred is group by class_id first, then group by row_id
if you want to get i-th row y_pred in j-th class, the access way is y_pred[j*num_data+i]
and you should group grad and hess in this way as well
####apply(X, num_iteration=0)
......@@ -698,7 +698,7 @@ The methods of each Class is in alphabetical order.
Returns
-------
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)
......@@ -796,7 +796,7 @@ The methods of each Class is in alphabetical order.
Get the evaluation results.
####feature_importance_
####feature_importances_
Get normailized feature importances.
......@@ -826,7 +826,7 @@ The methods of each Class is in alphabetical order.
####n_classes_
Get number of classes.
###LGBMRegressor
......
examples/python-guide/sklearn_example.py
View file @ e5b94fbe
......@@ -34,7 +34,7 @@ print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)
print('Calculate feature importances...')
# feature importances
print('Feature importances:', list(gbm.feature_importance_))
print('Feature importances:', list(gbm.feature_importances_))
# other scikit-learn modules
estimator = lgb.LGBMRegressor(num_leaves=31)
......
python-package/lightgbm/sklearn.py
View file @ e5b94fbe
......@@ -463,7 +463,7 @@ class LGBMModel(LGBMModelBase):
return self.evals_result
@property
def feature_importance_(self):
def feature_importances_(self):
"""Get normailized feature importances."""
importace_array = self.booster_.feature_importance().astype(np.float32)
return importace_array / importace_array.sum()
......@@ -472,9 +472,9 @@ class LGBMModel(LGBMModelBase):
def booster(self):
return self.booster_
@LGBMDeprecated('Use attribute feature_importance_ instead.')
@LGBMDeprecated('Use attribute feature_importances_ instead.')
def feature_importance(self):
return self.feature_importance_
return self.feature_importances_
class LGBMRegressor(LGBMModel, LGBMRegressorBase):
......
tests/python_package_test/test_sklearn.py
View file @ e5b94fbe
......@@ -105,12 +105,12 @@ class TestSklearn(unittest.TestCase):
gbm = template.test_template(return_model=True)
gbm_clone = clone(gbm)
self.assertIsInstance(gbm.booster_, lgb.Booster)
self.assertIsInstance(gbm.feature_importance_, np.ndarray)
self.assertIsInstance(gbm.feature_importances_, np.ndarray)
clf = template.test_template(load_digits(2, True), model=lgb.LGBMClassifier, return_model=True)
self.assertListEqual(sorted(clf.classes_), [0, 1])
self.assertEqual(clf.n_classes_, 2)
self.assertIsInstance(clf.booster_, lgb.Booster)
self.assertIsInstance(clf.feature_importance_, np.ndarray)
self.assertIsInstance(clf.feature_importances_, np.ndarray)
def test_joblib(self):
gbm = template.test_template(num_round=10, return_model=True)
......@@ -118,7 +118,7 @@ class TestSklearn(unittest.TestCase):
gbm_pickle = joblib.load('lgb.pkl')
self.assertIsInstance(gbm_pickle.booster_, lgb.Booster)
self.assertDictEqual(gbm.get_params(), gbm_pickle.get_params())
self.assertListEqual(list(gbm.feature_importance_), list(gbm_pickle.feature_importance_))
self.assertListEqual(list(gbm.feature_importances_), list(gbm_pickle.feature_importances_))
X_train, X_test, y_train, y_test = template.test_template(return_data=True)
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
gbm_pickle.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment