! * \brief Get the data indices of one leaf * \param leaf index of leaf * \param indices output data indices * \return number of data on this

! \brief Get the output of one leaf */

! * \brief Generate random float data * \return The random float between [0.0, 1.0) */

! * \brief Read data from a file, use pipeline methods * \param filename Filename of data * \process_fun Process function */

! \brief Set the output of one leaf */

Add validation data. Parameters ---------- data : Dataset Validation data. name : string Name

get pointer of int numpy array / list

! \brief Get Number of feature groups */

Get subset of current Dataset. Parameters ---------- used_indices : list of int Indices used to create the subset

! * \brief Get current iteration */

! * \brief Get number of prediction for one data * \param num_iteration number of used iterations * \param is_pred_leaf True if predicting leaf

hhat >(=) 0 assumed; and weight > 0 required; but not checked here

! \brief Get indices of data of current leaf */

Get names of features. Returns ------- result : list List with names of features.

Get property from the Dataset. Parameters ---------- field_name: string The field name of the information.

Check is 1d numpy array

! * \brief Get leaf begin * \param leaf index of leaf * \return begin index of this leaf */

Get the number of rows in the Dataset. Returns ------- number_of_rows : int The number of rows in the Dataset.

Set feature name. Parameters ---------- feature_name : list of strings Feature names.

Set property into the Dataset. Parameters ---------- field_name: string The field name of the information.

! * \brief Get feature names of this model * \return Feature names of this model */

! * \brief Restore histogram from memory */

! * \brief Get max feature index of this model * \return Max feature index of this model */

! * \brief Get number of tree per iteration * \return number of tree per iteration */

! * \brief Set label for one record * \param idx Index of this record * \param value Label value of this record */

! * \brief Set Query Id for one record * \param idx Index of this record * \param value Query Id value of this record */

! * \brief Set Weight for one record * \param idx Index of this record * \param value Weight value of this record */

! * \brief Do inplace softmax transformaton on p_rec * \param p_rec The input/output vector of the values. */

! * \brief Subtract current histograms with other * \param other The histogram that want to subtract */

To-do: reduce the communication cost by using bitset to communicate.

Get size of prediction result

Evaulate training or validation data

format metric string

[Private] Save model to string

Set predictor for continued training, not recommand for user to call this function. Please set init_model in engine.train or engine.c

Convert to predictor

Evaluate for validation data. Parameters ---------- feval : callable or None, optional (default=None) Custom eval

Get feature importances. Parameters ---------- importance_type : string, optional (default="split") How the impor

! * \brief return first line of data */

Get the examination probability for the click model. Parameters ---------- rank : [type] [description] R

Get the group of the Dataset. Returns ------- group : numpy array Group size of each group.

Get the weight of the Dataset. Returns ------- weight : numpy array Weight for each data point from the Dataset.

! * \brief Get initial scores, if not exists, will return nullptr * \return Pointer of initial scores */

Check is 1d list

! * \brief True if this histogram can be splitted */

! \brief Get depth of specific leaf*/

! * \brief Get size of initial scores */

Sample clicks for a list. Parameters ---------- label_list : [type] [description] Returns ------

Set categorical features. Parameters ---------- categorical_feature : list of int or strings Names or indices of