lightgbm.DaskLGBMClassifier

class lightgbm.DaskLGBMClassifier(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=-1, silent='warn', importance_type='split', client=None, **kwargs)

Bases: LGBMClassifier, _DaskLGBMModel

Distributed version of lightgbm.LGBMClassifier.

__init__(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=-1, silent='warn', importance_type='split', client=None, **kwargs)

Construct a gradient boosting model.

Parameters

• boosting_type (str, optional (default='gbdt')) – ‘gbdt’, traditional Gradient Boosting Decision Tree. ‘dart’, Dropouts meet Multiple Additive Regression Trees. ‘goss’, Gradient-based One-Side Sampling. ‘rf’, Random Forest.

• num_leaves (int, optional (default=31)) – Maximum tree leaves for base learners.

• max_depth (int, optional (default=-1)) – Maximum tree depth for base learners, <=0 means no limit.

• learning_rate (float, optional (default=0.1)) – Boosting learning rate. You can use callbacks parameter of fit method to shrink/adapt learning rate in training using reset_parameter callback. Note, that this will ignore the learning_rate argument in training.

• n_estimators (int, optional (default=100)) – Number of boosted trees to fit.

• subsample_for_bin (int, optional (default=200000)) – Number of samples for constructing bins.

• objective (str, callable or None, optional (default=None)) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: ‘regression’ for LGBMRegressor, ‘binary’ or ‘multiclass’ for LGBMClassifier, ‘lambdarank’ for LGBMRanker.

• class_weight (dict, 'balanced' or None, optional (default=None)) – Weights associated with classes in the form {class_label: weight}. Use this parameter only for multi-class classification task; for binary classification task you may use is_unbalance or scale_pos_weight parameters. Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. You may want to consider performing probability calibration (https://scikit-learn.org/stable/modules/calibration.html) of your model. The ‘balanced’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

• min_split_gain (float, optional (default=0.)) – Minimum loss reduction required to make a further partition on a leaf node of the tree.

• min_child_weight (float, optional (default=1e-3)) – Minimum sum of instance weight (hessian) needed in a child (leaf).

• min_child_samples (int, optional (default=20)) – Minimum number of data needed in a child (leaf).

• subsample (float, optional (default=1.)) – Subsample ratio of the training instance.

• subsample_freq (int, optional (default=0)) – Frequency of subsample, <=0 means no enable.

• colsample_bytree (float, optional (default=1.)) – Subsample ratio of columns when constructing each tree.

• reg_alpha (float, optional (default=0.)) – L1 regularization term on weights.

• reg_lambda (float, optional (default=0.)) – L2 regularization term on weights.

• random_state (int, RandomState object or None, optional (default=None)) – Random number seed. If int, this number is used to seed the C++ code. If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code. If None, default seeds in C++ code are used.

• n_jobs (int, optional (default=-1)) – Number of parallel threads.

• silent (bool, optional (default=True)) – Whether to print messages while running boosting.

• importance_type (str, optional (default='split')) – The type of feature importance to be filled into feature_importances_. If ‘split’, result contains numbers of times the feature is used in a model. If ‘gain’, result contains total gains of splits which use the feature.

• client (dask.distributed.Client or None, optional (default=None)) – Dask client. If None, distributed.default_client() will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.

• **kwargs –

  Other parameters for the model. Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.

  Warning

  **kwargs is not supported in sklearn, it may cause unexpected issues.

Methods

__init__([boosting_type, num_leaves, ...])	Construct a gradient boosting model.
fit(X, y[, sample_weight, init_score, ...])	Build a gradient boosting model from the training set (X, y).
get_params([deep])	Get parameters for this estimator.
predict(X, **kwargs)	Return the predicted value for each sample.
predict_proba(X, **kwargs)	Return the predicted probability for each class for each sample.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
to_local()	Create regular version of lightgbm.LGBMClassifier from the distributed version.

Attributes

best_iteration_	The best iteration of fitted model if early_stopping() callback has been specified.
best_score_	The best score of fitted model.
booster_	The underlying Booster of this model.
classes_	The class label array.
client_	Dask client.
evals_result_	The evaluation results if validation sets have been specified.
feature_importances_	The feature importances (the higher, the more important).
feature_name_	The names of features.
n_classes_	The number of classes.
n_features_	The number of features of fitted model.
n_features_in_	The number of features of fitted model.
objective_	The concrete objective used while fitting this model.

property best_iteration_

The best iteration of fitted model if early_stopping() callback has been specified.

Type

int or None

property best_score_

The best score of fitted model.

Type

dict

property booster_

The underlying Booster of this model.

Type

Booster

property classes_

The class label array.

Type

array of shape = [n_classes]

property client_

Dask client.

This property can be passed in the constructor or updated with model.set_params(client=client).

Type

dask.distributed.Client

property evals_result_

The evaluation results if validation sets have been specified.

Type

dict or None

property feature_importances_

The feature importances (the higher, the more important).

Note

importance_type attribute is passed to the function to configure the type of importance values to be extracted.

Type

array of shape = [n_features]

property feature_name_

The names of features.

Type

array of shape = [n_features]

fit(X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, **kwargs)

Build a gradient boosting model from the training set (X, y).

Parameters

• X (Dask Array or Dask DataFrame of shape = [n_samples, n_features]) – Input feature matrix.

• y (Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]) – The target values (class labels in classification, real numbers in regression).

• sample_weight (Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)) – Weights of training data.

• init_score (Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)) – Init score of training data.

• eval_set (list or None, optional (default=None)) – A list of (X, y) tuple pairs to use as validation sets.

• eval_names (list of str, or None, optional (default=None)) – Names of eval_set.

• eval_sample_weight (list of Dask Array or Dask Series, or None, optional (default=None)) – Weights of eval data.

• eval_class_weight (list or None, optional (default=None)) – Class weights of eval data.

• eval_init_score (list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)) – Init score of eval data.

• eval_metric (str, callable, list or None, optional (default=None)) – If str, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. In either case, the metric from the model parameters will be evaluated and used as well. Default: ‘l2’ for LGBMRegressor, ‘logloss’ for LGBMClassifier, ‘ndcg’ for LGBMRanker.

• verbose (bool or int, optional (default=True)) –

  Requires at least one evaluation data. If True, the eval metric on the eval set is printed at each boosting stage. If int, the eval metric on the eval set is printed at every verbose boosting stage. The last boosting stage or the boosting stage found by using early_stopping_rounds is also printed.

  Example

  With verbose = 4 and at least one item in eval_set, an evaluation metric is printed every 4 (instead of 1) boosting stages.

• feature_name (list of str, or 'auto', optional (default='auto')) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.

• categorical_feature (list of str or int, or 'auto', optional (default='auto')) – Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If ‘auto’ and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature.

• **kwargs – Other parameters passed through to LGBMClassifier.fit().

Note

Custom eval function expects a callable with following signatures: func(y_true, y_pred), func(y_true, y_pred, weight) or func(y_true, y_pred, weight, group) and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better):

y_truearray-like of shape = [n_samples]

The target values.

y_predarray-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)

The predicted values. In case of custom objective, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case.

weightarray-like of shape = [n_samples]

The weight of samples.

grouparray-like

Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with group = [10, 20, 40, 10, 10, 10], that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.

eval_namestr

The name of evaluation function (without whitespace).

eval_resultfloat

The eval result.

is_higher_betterbool

Is eval result higher better, e.g. AUC is is_higher_better.

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].

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, optional (default=True)) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

property n_classes_

The number of classes.

Type

int

property n_features_

The number of features of fitted model.

Type

int

property n_features_in_

The number of features of fitted model.

Type

int

property objective_

The concrete objective used while fitting this model.

Type

str or callable

predict(X, **kwargs)

Return the predicted value for each sample.

Parameters

• X (Dask Array or Dask DataFrame of shape = [n_samples, n_features]) – Input features matrix.

• raw_score (bool, optional (default=False)) – Whether to predict raw scores.

• start_iteration (int, optional (default=0)) – Start index of the iteration to predict. If <= 0, starts from the first iteration.

• num_iteration (int or None, optional (default=None)) – Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used (no limits). If <= 0, all iterations from start_iteration are used (no limits).

• pred_leaf (bool, optional (default=False)) – Whether to predict leaf index.

• pred_contrib (bool, optional (default=False)) –

  Whether to predict feature contributions.

  Note

  If you want to get more explanations for your model’s predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with pred_contrib we return a matrix with an extra column, where the last column is the expected value.

• **kwargs – Other parameters for the prediction.

Returns

• predicted_result (Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]) – The predicted values.

• X_leaves (Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]) – If pred_leaf=True, the predicted leaf of every tree for each sample.

• X_SHAP_values (Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of n_classes Dask Arrays of shape = [n_samples, n_features + 1]) – If pred_contrib=True, the feature contributions for each sample.

predict_proba(X, **kwargs)

Return the predicted probability for each class for each sample.

Parameters

• X (Dask Array or Dask DataFrame of shape = [n_samples, n_features]) – Input features matrix.

• raw_score (bool, optional (default=False)) – Whether to predict raw scores.

• start_iteration (int, optional (default=0)) – Start index of the iteration to predict. If <= 0, starts from the first iteration.

• num_iteration (int or None, optional (default=None)) – Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used (no limits). If <= 0, all iterations from start_iteration are used (no limits).

• pred_leaf (bool, optional (default=False)) – Whether to predict leaf index.

• pred_contrib (bool, optional (default=False)) –

  Whether to predict feature contributions.

  Note

  If you want to get more explanations for your model’s predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with pred_contrib we return a matrix with an extra column, where the last column is the expected value.

• **kwargs – Other parameters for the prediction.

Returns

• predicted_probability (Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]) – The predicted values.

• X_leaves (Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]) – If pred_leaf=True, the predicted leaf of every tree for each sample.

• X_SHAP_values (Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of n_classes Dask Arrays of shape = [n_samples, n_features + 1]) – If pred_contrib=True, the feature contributions for each sample.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

• X (array-like of shape (n_samples, n_features)) – Test samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float

set_params(**params)

Set the parameters of this estimator.

Parameters

**params – Parameter names with their new values.

Returns

self – Returns self.

Return type

object

to_local()

Create regular version of lightgbm.LGBMClassifier from the distributed version.

Returns

model – Local underlying model.

Return type

lightgbm.LGBMClassifier