add a booster to CVBooster

Make a prediction. Parameters ---------- data : string, numpy array or scipy.sparse Data source for prediction.

Check the return value of C API call Parameters ---------- ret : int return value from API calls

! \brief Get current leaf index */

! \brief Get numer of data in current leaf */

Convert a python string to cstring.

! * \brief Get the default bin when value is 0 * \return default bin */

! \brief Get number of leaves */

! \brief Get Number of used features */

! \brief Get sum of gradients of current leaf */

! \brief Get sum of hessians of current leaf */

! * \brief Get pointer of label * \return Pointer of label */

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

Build a gradient boosting model from the training set (X, y). Parameters ---------- X : array-like or sparse matrix of shape

Fall here if no OPENMP support, so just simulate a single thread running. All #pragma omp should be ignored by the compiler **/

! \brief Get number of bins */

! * \brief Generate random integer, int16 range. [0, 65536] * \param lower_bound lower bound * \param upper_bound upper bound * \return The ra

! * \brief Get number of data on one leaf * \param leaf index of leaf * \return number of data of this leaf */

Return the predicted probability for each class for each sample. Parameters ---------- X : array-like or sparse matrix of sha

! * \brief Shrinkage for the tree's output * shrinkage rate (a.k.a learning rate) is used to tune the traning process * \param rate The f

label should be in interval [0, 1]; prob should be in interval (0, 1); prob is clipped if needed

esc(c) * * Format char c suitable for printing in an error message. */

! \brief Pointer of score */

! * \brief Read all text data from file in memory * \return number of lines of text data */

Get the label of the Dataset. Returns ------- label : numpy array The label information from the Dataset.

convert to 1d numpy array

! * \brief Get Number of queries * \return Number of queries */

! * \brief Get data boundaries on queries, if not exists, will return nullptr * we assume data will order by query, * the interval

Update for one iteration. Parameters ---------- train_set : Dataset or None, optional (default=None) Training dat

! * \brief Binary size of this histogram */

get pointer of float numpy array / list

check object is not tuple or does not have 2 elements

Lazy init. Returns ------- self : Dataset Returns self.

! \brief Missing Type */

Save Booster to file. Parameters ---------- filename : string Filename to save Booster. num_iteration: in

! * \brief Push one record, will auto convert to bin and push to bin data * \param tid Thread id * \param idx Index of record * \param value f

! * \brief Sample K data from {0,1,...,N-1} * \param N * \param K * \return K Ordered sampled data from {0,1,...,N-1} */

! * \brief Get bin types */

! \brief Get Number of total features */

Reset parameters of Booster. Parameters ---------- params : dict New parameters for Booster.

! \brief Disable copy */

! * \brief Get data for the specific index * \param idx which index want to get * \param out output data will store into this * \return True i