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