basic.py

# coding: utf-8
# pylint: disable = invalid-name, C0111, C0301
# pylint: disable = R0912, R0913, R0914, W0105, W0201, W0212
"""Wrapper c_api of LightGBM"""
from __future__ import absolute_import

import ctypes
import os
from tempfile import NamedTemporaryFile

import numpy as np
import scipy.sparse

from .compat import (DataFrame, Series, integer_types, json,
                     json_default_with_numpy, numeric_types, range_,
                     string_type)
from .libpath import find_lib_path


def _load_lib():
    """Load LightGBM Library."""
    lib_path = find_lib_path()
    if len(lib_path) == 0:
        raise Exception("cannot find LightGBM library")
    lib = ctypes.cdll.LoadLibrary(lib_path[0])
    lib.LGBM_GetLastError.restype = ctypes.c_char_p
    return lib


_LIB = _load_lib()


class LightGBMError(Exception):
    """Error throwed by LightGBM"""
    pass


def _safe_call(ret):
    """Check the return value of C API call
    Parameters
    ----------
    ret : int
        return value from API calls
    """
    if ret != 0:
        raise LightGBMError(_LIB.LGBM_GetLastError())


def is_numeric(obj):
    """Check is a number or not, include numpy number etc."""
    try:
        float(obj)
        return True
    except (TypeError, ValueError):
        # TypeError: obj is not a string or a number
        # ValueError: invalid literal
        return False


def is_numpy_1d_array(data):
    """Check is 1d numpy array"""
    return isinstance(data, np.ndarray) and len(data.shape) == 1


def is_1d_list(data):
    """Check is 1d list"""
    return isinstance(data, list) and \
        (not data or isinstance(data[0], numeric_types))


def list_to_1d_numpy(data, dtype=np.float32, name='list'):
    """convert to 1d numpy array"""
    if is_numpy_1d_array(data):
        if data.dtype == dtype:
            return data
        else:
            return data.astype(dtype=dtype, copy=False)
    elif is_1d_list(data):
        return np.array(data, dtype=dtype, copy=False)
    elif isinstance(data, Series):
        return data.values.astype(dtype)
    else:
        raise TypeError("Wrong type({}) for {}, should be list or numpy array".format(type(data).__name__, name))


def cfloat32_array_to_numpy(cptr, length):
    """Convert a ctypes float pointer array to a numpy array.
    """
    if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
        return np.fromiter(cptr, dtype=np.float32, count=length)
    else:
        raise RuntimeError('Expected float pointer')


def cfloat64_array_to_numpy(cptr, length):
    """Convert a ctypes double pointer array to a numpy array.
    """
    if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
        return np.fromiter(cptr, dtype=np.float64, count=length)
    else:
        raise RuntimeError('Expected double pointer')


def cint32_array_to_numpy(cptr, length):
    """Convert a ctypes float pointer array to a numpy array.
    """
    if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
        return np.fromiter(cptr, dtype=np.int32, count=length)
    else:
        raise RuntimeError('Expected int pointer')


def c_str(string):
    """Convert a python string to cstring."""
    return ctypes.c_char_p(string.encode('utf-8'))


def c_array(ctype, values):
    """Convert a python array to c array."""
    return (ctype * len(values))(*values)


def param_dict_to_str(data):
    if data is None or not data:
        return ""
    pairs = []
    for key, val in data.items():
        if isinstance(val, (list, tuple, set)) or is_numpy_1d_array(val):
            pairs.append(str(key) + '=' + ','.join(map(str, val)))
        elif isinstance(val, string_type) or isinstance(val, numeric_types) or is_numeric(val):
            pairs.append(str(key) + '=' + str(val))
        else:
            raise TypeError('Unknown type of parameter:%s, got:%s'
                            % (key, type(val).__name__))
    return ' '.join(pairs)


class _temp_file(object):
    def __enter__(self):
        with NamedTemporaryFile(prefix="lightgbm_tmp_", delete=True) as f:
            self.name = f.name
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if os.path.isfile(self.name):
            os.remove(self.name)

    def readlines(self):
        with open(self.name, "r+") as f:
            ret = f.readlines()
        return ret

    def writelines(self, lines):
        with open(self.name, "w+") as f:
            f.writelines(lines)


"""marco definition of data type in c_api of LightGBM"""
C_API_DTYPE_FLOAT32 = 0
C_API_DTYPE_FLOAT64 = 1
C_API_DTYPE_INT32 = 2
C_API_DTYPE_INT64 = 3

"""Matric is row major in python"""
C_API_IS_ROW_MAJOR = 1

"""marco definition of prediction type in c_api of LightGBM"""
C_API_PREDICT_NORMAL = 0
C_API_PREDICT_RAW_SCORE = 1
C_API_PREDICT_LEAF_INDEX = 2

"""data type of data field"""
FIELD_TYPE_MAPPER = {"label": C_API_DTYPE_FLOAT32,
                     "weight": C_API_DTYPE_FLOAT32,
                     "init_score": C_API_DTYPE_FLOAT64,
                     "group": C_API_DTYPE_INT32}


def c_float_array(data):
    """get pointer of float numpy array / list"""
    if is_1d_list(data):
        data = np.array(data, copy=False)
    if is_numpy_1d_array(data):
        if data.dtype == np.float32:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
            type_data = C_API_DTYPE_FLOAT32
        elif data.dtype == np.float64:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
            type_data = C_API_DTYPE_FLOAT64
        else:
            raise TypeError("Expected np.float32 or np.float64, met type({})"
                            .format(data.dtype))
    else:
        raise TypeError("Unknown type({})".format(type(data).__name__))
    return (ptr_data, type_data)


def c_int_array(data):
    """get pointer of int numpy array / list"""
    if is_1d_list(data):
        data = np.array(data, copy=False)
    if is_numpy_1d_array(data):
        if data.dtype == np.int32:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32))
            type_data = C_API_DTYPE_INT32
        elif data.dtype == np.int64:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64))
            type_data = C_API_DTYPE_INT64
        else:
            raise TypeError("Expected np.int32 or np.int64, met type({})"
                            .format(data.dtype))
    else:
        raise TypeError("Unknown type({})".format(type(data).__name__))
    return (ptr_data, type_data)


PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int',
                       'int64': 'int', 'uint8': 'int', 'uint16': 'int',
                       'uint32': 'int', 'uint64': 'int', 'float16': 'float',
                       'float32': 'float', 'float64': 'float', 'bool': 'int'}


def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical):
    if isinstance(data, DataFrame):
        if feature_name == 'auto' or feature_name is None:
            data = data.rename(columns=str)
        cat_cols = data.select_dtypes(include=['category']).columns
        if pandas_categorical is None:  # train dataset
            pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]
        else:
            if len(cat_cols) != len(pandas_categorical):
                raise ValueError('train and valid dataset categorical_feature do not match.')
            for col, category in zip(cat_cols, pandas_categorical):
                if list(data[col].cat.categories) != list(category):
                    data[col] = data[col].cat.set_categories(category)
        if len(cat_cols):  # cat_cols is pandas Index object
            data = data.copy()  # not alter origin DataFrame
            data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes)
        if categorical_feature is not None:
            if feature_name is None:
                feature_name = list(data.columns)
            if categorical_feature == 'auto':
                categorical_feature = list(cat_cols)
            else:
                categorical_feature = list(categorical_feature) + list(cat_cols)
        if feature_name == 'auto':
            feature_name = list(data.columns)
        data_dtypes = data.dtypes
        if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in data_dtypes):
            bad_fields = [data.columns[i] for i, dtype in
                          enumerate(data_dtypes) if dtype.name not in PANDAS_DTYPE_MAPPER]

            msg = """DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields """
            raise ValueError(msg + ', '.join(bad_fields))
        data = data.values.astype('float')
    else:
        if feature_name == 'auto':
            feature_name = None
        if categorical_feature == 'auto':
            categorical_feature = None
    return data, feature_name, categorical_feature, pandas_categorical


def _label_from_pandas(label):
    if isinstance(label, DataFrame):
        if len(label.columns) > 1:
            raise ValueError('DataFrame for label cannot have multiple columns')
        label_dtypes = label.dtypes
        if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in label_dtypes):
            raise ValueError('DataFrame.dtypes for label must be int, float or bool')
        label = label.values.astype('float')
    return label


def _save_pandas_categorical(file_name, pandas_categorical):
    with open(file_name, 'a') as f:
        f.write('\npandas_categorical:' + json.dumps(pandas_categorical, default=json_default_with_numpy))


def _load_pandas_categorical(file_name):
    with open(file_name, 'r') as f:
        last_line = f.readlines()[-1]
        if last_line.startswith('pandas_categorical:'):
            return json.loads(last_line[len('pandas_categorical:'):])
    return None


class _InnerPredictor(object):
    """
    A _InnerPredictor of LightGBM.
    Only used for prediction, usually used for continued-train
    Note: Can convert from Booster, but cannot convert to Booster
    """
    def __init__(self, model_file=None, booster_handle=None):
        """Initialize the _InnerPredictor. Not expose to user

        Parameters
        ----------
        model_file : string
            Path to the model file.
        booster_handle : Handle of Booster
            use handle to init
        """
        self.handle = ctypes.c_void_p()
        self.__is_manage_handle = True
        if model_file is not None:
            """Prediction task"""
            out_num_iterations = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterCreateFromModelfile(
                c_str(model_file),
                ctypes.byref(out_num_iterations),
                ctypes.byref(self.handle)))
            out_num_class = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetNumClasses(
                self.handle,
                ctypes.byref(out_num_class)))
            self.num_class = out_num_class.value
            self.num_total_iteration = out_num_iterations.value
            self.pandas_categorical = _load_pandas_categorical(model_file)
        elif booster_handle is not None:
            self.__is_manage_handle = False
            self.handle = booster_handle
            out_num_class = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetNumClasses(
                self.handle,
                ctypes.byref(out_num_class)))
            self.num_class = out_num_class.value
            out_num_iterations = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
                self.handle,
                ctypes.byref(out_num_iterations)))
            self.num_total_iteration = out_num_iterations.value
            self.pandas_categorical = None
        else:
            raise TypeError('Need Model file or Booster handle to create a predictor')

    def __del__(self):
        if self.__is_manage_handle:
            _safe_call(_LIB.LGBM_BoosterFree(self.handle))

    def predict(self, data, num_iteration=-1,
                raw_score=False, pred_leaf=False, data_has_header=False,
                is_reshape=True):
        """
        Predict logic

        Parameters
        ----------
        data : string/numpy array/scipy.sparse
            Data source for prediction
            When data type is string, it represents the path of txt file
        num_iteration : int
            Used iteration for prediction
        raw_score : bool
            True for predict raw score
        pred_leaf : bool
            True for predict leaf index
        data_has_header : bool
            Used for txt data, True if txt data has header
        is_reshape : bool
            Reshape to (nrow, ncol) if true

        Returns
        -------
        Prediction result
        """
        if isinstance(data, Dataset):
            raise TypeError("Cannot use Dataset instance for prediction, please use raw data instead")
        data = _data_from_pandas(data, None, None, self.pandas_categorical)[0]
        predict_type = C_API_PREDICT_NORMAL
        if raw_score:
            predict_type = C_API_PREDICT_RAW_SCORE
        if pred_leaf:
            predict_type = C_API_PREDICT_LEAF_INDEX
        int_data_has_header = 1 if data_has_header else 0
        if num_iteration > self.num_total_iteration:
            num_iteration = self.num_total_iteration
        if isinstance(data, string_type):
            with _temp_file() as f:
                _safe_call(_LIB.LGBM_BoosterPredictForFile(
                    self.handle,
                    c_str(data),
                    ctypes.c_int(int_data_has_header),
                    ctypes.c_int(predict_type),
                    ctypes.c_int(num_iteration),
                    c_str(f.name)))
                lines = f.readlines()
                nrow = len(lines)
                preds = [float(token) for line in lines for token in line.split('\t')]
                preds = np.array(preds, dtype=np.float64, copy=False)
        elif isinstance(data, scipy.sparse.csr_matrix):
            preds, nrow = self.__pred_for_csr(data, num_iteration,
                                              predict_type)
        elif isinstance(data, scipy.sparse.csc_matrix):
            preds, nrow = self.__pred_for_csc(data, num_iteration,
                                              predict_type)
        elif isinstance(data, np.ndarray):
            preds, nrow = self.__pred_for_np2d(data, num_iteration,
                                               predict_type)
        elif isinstance(data, DataFrame):
            preds, nrow = self.__pred_for_np2d(data.values, num_iteration,
                                               predict_type)
        else:
            try:
                csr = scipy.sparse.csr_matrix(data)
                preds, nrow = self.__pred_for_csr(csr, num_iteration,
                                                  predict_type)
            except:
                raise TypeError('Cannot predict data for type {}'.format(type(data).__name__))
        if pred_leaf:
            preds = preds.astype(np.int32)
        if is_reshape and preds.size != nrow:
            if preds.size % nrow == 0:
                preds = preds.reshape(nrow, -1)
            else:
                raise ValueError('Length of predict result (%d) cannot be divide nrow (%d)'
                                 % (preds.size, nrow))
        return preds

    def __get_num_preds(self, num_iteration, nrow, predict_type):
        """
        Get size of prediction result
        """
        n_preds = ctypes.c_int64(0)
        _safe_call(_LIB.LGBM_BoosterCalcNumPredict(
            self.handle,
            ctypes.c_int(nrow),
            ctypes.c_int(predict_type),
            ctypes.c_int(num_iteration),
            ctypes.byref(n_preds)))
        return n_preds.value

    def __pred_for_np2d(self, mat, num_iteration, predict_type):
        """
        Predict for a 2-D numpy matrix.
        """
        if len(mat.shape) != 2:
            raise ValueError('Input numpy.ndarray must be 2 dimensional')

        if mat.dtype == np.float32 or mat.dtype == np.float64:
            data = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
        else:
            """change non-float data to float data, need to copy"""
            data = np.array(mat.reshape(mat.size), dtype=np.float32)
        ptr_data, type_ptr_data = c_float_array(data)
        n_preds = self.__get_num_preds(num_iteration, mat.shape[0],
                                       predict_type)
        preds = np.zeros(n_preds, dtype=np.float64)
        out_num_preds = ctypes.c_int64(0)
        _safe_call(_LIB.LGBM_BoosterPredictForMat(
            self.handle,
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int(mat.shape[0]),
            ctypes.c_int(mat.shape[1]),
            ctypes.c_int(C_API_IS_ROW_MAJOR),
            ctypes.c_int(predict_type),
            ctypes.c_int(num_iteration),
            ctypes.byref(out_num_preds),
            preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
        if n_preds != out_num_preds.value:
            raise ValueError("Wrong length for predict results")
        return preds, mat.shape[0]

    def __pred_for_csr(self, csr, num_iteration, predict_type):
        """
        Predict for a csr data
        """
        nrow = len(csr.indptr) - 1
        n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
        preds = np.zeros(n_preds, dtype=np.float64)
        out_num_preds = ctypes.c_int64(0)

        ptr_indptr, type_ptr_indptr = c_int_array(csr.indptr)
        ptr_data, type_ptr_data = c_float_array(csr.data)

        _safe_call(_LIB.LGBM_BoosterPredictForCSR(
            self.handle,
            ptr_indptr,
            ctypes.c_int32(type_ptr_indptr),
            csr.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int64(len(csr.indptr)),
            ctypes.c_int64(len(csr.data)),
            ctypes.c_int64(csr.shape[1]),
            ctypes.c_int(predict_type),
            ctypes.c_int(num_iteration),
            ctypes.byref(out_num_preds),
            preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
        if n_preds != out_num_preds.value:
            raise ValueError("Wrong length for predict results")
        return preds, nrow

    def __pred_for_csc(self, csc, num_iteration, predict_type):
        """
        Predict for a csc data
        """
        nrow = csc.shape[0]
        n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
        preds = np.zeros(n_preds, dtype=np.float64)
        out_num_preds = ctypes.c_int64(0)

        ptr_indptr, type_ptr_indptr = c_int_array(csc.indptr)
        ptr_data, type_ptr_data = c_float_array(csc.data)

        _safe_call(_LIB.LGBM_BoosterPredictForCSC(
            self.handle,
            ptr_indptr,
            ctypes.c_int32(type_ptr_indptr),
            csc.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int64(len(csc.indptr)),
            ctypes.c_int64(len(csc.data)),
            ctypes.c_int64(csc.shape[0]),
            ctypes.c_int(predict_type),
            ctypes.c_int(num_iteration),
            ctypes.byref(out_num_preds),
            preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
        if n_preds != out_num_preds.value:
            raise ValueError("Wrong length for predict results")
        return preds, nrow


class Dataset(object):
    """Dataset in LightGBM."""
    def __init__(self, data, label=None, max_bin=255, reference=None,
                 weight=None, group=None, silent=False,
                 feature_name='auto', categorical_feature='auto', params=None,
                 free_raw_data=True):
        """
        Parameters
        ----------
        data : string/numpy array/scipy.sparse
            Data source of Dataset.
            When data type is string, it represents the path of txt file
        label : list or numpy 1-D array, optional
            Label of the data
        max_bin : int, required
            Max number of discrete bin for features
        reference : Other Dataset, optional
            If this dataset validation, need to use training data as reference
        weight : list or numpy 1-D array , optional
            Weight for each instance.
        group : list or numpy 1-D array , optional
            Group/query size for dataset
        silent : boolean, optional
            Whether print messages during construction
        feature_name : list of str, or 'auto'
            Feature names
            If 'auto' and data is pandas DataFrame, use data columns name
        categorical_feature : list of str or int, or 'auto'
            Categorical features,
            type int represents index,
            type str represents feature names (need to specify feature_name as well)
            If 'auto' and data is pandas DataFrame, use pandas categorical columns
        params: dict, optional
            Other parameters
        free_raw_data: Bool
            True if need to free raw data after construct inner dataset
        """
        self.handle = None
        self.data = data
        self.label = label
        self.max_bin = max_bin
        self.reference = reference
        self.weight = weight
        self.group = group
        self.silent = silent
        self.feature_name = feature_name
        self.categorical_feature = categorical_feature
        self.params = params
        self.free_raw_data = free_raw_data
        self.used_indices = None
        self._predictor = None
        self.pandas_categorical = None

    def __del__(self):
        self._free_handle()

    def _free_handle(self):
        if self.handle is not None:
            _safe_call(_LIB.LGBM_DatasetFree(self.handle))
            self.handle = None

    def _lazy_init(self, data, label=None, max_bin=255, reference=None,
                   weight=None, group=None, predictor=None,
                   silent=False, feature_name='auto',
                   categorical_feature='auto', params=None):
        if data is None:
            self.handle = None
            return
        data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas(data, feature_name, categorical_feature, self.pandas_categorical)
        label = _label_from_pandas(label)
        self.data_has_header = False
        """process for args"""
        params = {} if params is None else params
        self.max_bin = max_bin
        self.predictor = predictor
        params["max_bin"] = max_bin
        if silent:
            params["verbose"] = 0
        elif "verbose" not in params:
            params["verbose"] = 1
        """get categorical features"""
        if categorical_feature is not None:
            categorical_indices = set()
            feature_dict = {}
            if feature_name is not None:
                feature_dict = {name: i for i, name in enumerate(feature_name)}
            for name in categorical_feature:
                if isinstance(name, string_type) and name in feature_dict:
                    categorical_indices.add(feature_dict[name])
                elif isinstance(name, integer_types):
                    categorical_indices.add(name)
                else:
                    raise TypeError("Wrong type({}) or unknown name({}) in categorical_feature"
                                    .format(type(name).__name__, name))

            params['categorical_column'] = sorted(categorical_indices)

        params_str = param_dict_to_str(params)
        """process for reference dataset"""
        ref_dataset = None
        if isinstance(reference, Dataset):
            ref_dataset = reference.construct().handle
        elif reference is not None:
            raise TypeError('Reference dataset should be None or dataset instance')
        """start construct data"""
        if isinstance(data, string_type):
            """check data has header or not"""
            if str(params.get("has_header", "")).lower() == "true" \
                    or str(params.get("header", "")).lower() == "true":
                self.data_has_header = True
            self.handle = ctypes.c_void_p()
            _safe_call(_LIB.LGBM_DatasetCreateFromFile(
                c_str(data),
                c_str(params_str),
                ref_dataset,
                ctypes.byref(self.handle)))
        elif isinstance(data, scipy.sparse.csr_matrix):
            self.__init_from_csr(data, params_str, ref_dataset)
        elif isinstance(data, scipy.sparse.csc_matrix):
            self.__init_from_csc(data, params_str, ref_dataset)
        elif isinstance(data, np.ndarray):
            self.__init_from_np2d(data, params_str, ref_dataset)
        else:
            try:
                csr = scipy.sparse.csr_matrix(data)
                self.__init_from_csr(csr, params_str, ref_dataset)
            except:
                raise TypeError('Cannot initialize Dataset from {}'.format(type(data).__name__))
        if label is not None:
            self.set_label(label)
        if self.get_label() is None:
            raise ValueError("Label should not be None")
        if weight is not None:
            self.set_weight(weight)
        if group is not None:
            self.set_group(group)
        # load init score
        if isinstance(self.predictor, _InnerPredictor):
            init_score = self.predictor.predict(data,
                                                raw_score=True,
                                                data_has_header=self.data_has_header,
                                                is_reshape=False)
            if self.predictor.num_class > 1:
                # need re group init score
                new_init_score = np.zeros(init_score.size, dtype=np.float32)
                num_data = self.num_data()
                for i in range_(num_data):
                    for j in range_(self.predictor.num_class):
                        new_init_score[j * num_data + i] = init_score[i * self.predictor.num_class + j]
                init_score = new_init_score
            self.set_init_score(init_score)
        elif self.predictor is not None:
            raise TypeError('wrong predictor type {}'.format(type(self.predictor).__name__))
        # set feature names
        self.set_feature_name(feature_name)

    def __init_from_np2d(self, mat, params_str, ref_dataset):
        """
        Initialize data from a 2-D numpy matrix.
        """
        if len(mat.shape) != 2:
            raise ValueError('Input numpy.ndarray must be 2 dimensional')

        self.handle = ctypes.c_void_p()
        if mat.dtype == np.float32 or mat.dtype == np.float64:
            data = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
        else:
            """change non-float data to float data, need to copy"""
            data = np.array(mat.reshape(mat.size), dtype=np.float32)

        ptr_data, type_ptr_data = c_float_array(data)
        _safe_call(_LIB.LGBM_DatasetCreateFromMat(
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int(mat.shape[0]),
            ctypes.c_int(mat.shape[1]),
            ctypes.c_int(C_API_IS_ROW_MAJOR),
            c_str(params_str),
            ref_dataset,
            ctypes.byref(self.handle)))

    def __init_from_csr(self, csr, params_str, ref_dataset):
        """
        Initialize data from a CSR matrix.
        """
        if len(csr.indices) != len(csr.data):
            raise ValueError('Length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
        self.handle = ctypes.c_void_p()

        ptr_indptr, type_ptr_indptr = c_int_array(csr.indptr)
        ptr_data, type_ptr_data = c_float_array(csr.data)

        _safe_call(_LIB.LGBM_DatasetCreateFromCSR(
            ptr_indptr,
            ctypes.c_int(type_ptr_indptr),
            csr.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int64(len(csr.indptr)),
            ctypes.c_int64(len(csr.data)),
            ctypes.c_int64(csr.shape[1]),
            c_str(params_str),
            ref_dataset,
            ctypes.byref(self.handle)))

    def __init_from_csc(self, csc, params_str, ref_dataset):
        """
        Initialize data from a csc matrix.
        """
        if len(csc.indices) != len(csc.data):
            raise ValueError('Length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
        self.handle = ctypes.c_void_p()

        ptr_indptr, type_ptr_indptr = c_int_array(csc.indptr)
        ptr_data, type_ptr_data = c_float_array(csc.data)

        _safe_call(_LIB.LGBM_DatasetCreateFromCSC(
            ptr_indptr,
            ctypes.c_int(type_ptr_indptr),
            csc.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
            ptr_data,
            ctypes.c_int(type_ptr_data),
            ctypes.c_int64(len(csc.indptr)),
            ctypes.c_int64(len(csc.data)),
            ctypes.c_int64(csc.shape[0]),
            c_str(params_str),
            ref_dataset,
            ctypes.byref(self.handle)))

    def construct(self):
        """Lazy init"""
        if self.handle is None:
            if self.reference is not None:
                if self.used_indices is None:
                    """create valid"""
                    self._lazy_init(self.data, label=self.label, max_bin=self.max_bin, reference=self.reference,
                                    weight=self.weight, group=self.group, predictor=self._predictor,
                                    silent=self.silent, params=self.params)
                else:
                    """construct subset"""
                    used_indices = list_to_1d_numpy(self.used_indices, np.int32, name='used_indices')
                    self.handle = ctypes.c_void_p()
                    params_str = param_dict_to_str(self.params)
                    _safe_call(_LIB.LGBM_DatasetGetSubset(
                        self.reference.construct().handle,
                        used_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
                        ctypes.c_int(used_indices.shape[0]),
                        c_str(params_str),
                        ctypes.byref(self.handle)))
                    if self.get_label() is None:
                        raise ValueError("Label should not be None.")
            else:
                """create train"""
                self._lazy_init(self.data, label=self.label, max_bin=self.max_bin,
                                weight=self.weight, group=self.group, predictor=self._predictor,
                                silent=self.silent, feature_name=self.feature_name,
                                categorical_feature=self.categorical_feature, params=self.params)
            if self.free_raw_data:
                self.data = None
        return self

    def create_valid(self, data, label=None, weight=None, group=None,
                     silent=False, params=None):
        """
        Create validation data align with current dataset

        Parameters
        ----------
        data : string/numpy array/scipy.sparse
            Data source of Dataset.
            When data type is string, it represents the path of txt file
        label : list or numpy 1-D array, optional
            Label of the training data.
        weight : list or numpy 1-D array , optional
            Weight for each instance.
        group : list or numpy 1-D array , optional
            Group/query size for dataset
        silent : boolean, optional
            Whether print messages during construction
        params: dict, optional
            Other parameters
        """
        ret = Dataset(data, label=label, max_bin=self.max_bin, reference=self,
                      weight=weight, group=group, silent=silent, params=params,
                      free_raw_data=self.free_raw_data)
        ret._predictor = self._predictor
        ret.pandas_categorical = self.pandas_categorical
        return ret

    def subset(self, used_indices, params=None):
        """
        Get subset of current dataset

        Parameters
        ----------
        used_indices : list of int
            Used indices of this subset
        params : dict
            Other parameters
        """
        ret = Dataset(None, reference=self, feature_name=self.feature_name,
                      categorical_feature=self.categorical_feature, params=params)
        ret._predictor = self._predictor
        ret.pandas_categorical = self.pandas_categorical
        ret.used_indices = used_indices
        return ret

    def save_binary(self, filename):
        """
        Save Dataset to binary file

        Parameters
        ----------
        filename : string
            Name of the output file.
        """
        _safe_call(_LIB.LGBM_DatasetSaveBinary(
            self.construct().handle,
            c_str(filename)))

    def _update_params(self, params):
        if not self.params:
            self.params = params
        else:
            self.params.update(params)

    def set_field(self, field_name, data):
        """Set property into the Dataset.

        Parameters
        ----------
        field_name: str
            The field name of the information

        data: numpy array or list or None
            The array ofdata to be set
        """
        if self.handle is None:
            raise Exception("Cannot set %s before construct dataset" % field_name)
        if data is None:
            """set to None"""
            _safe_call(_LIB.LGBM_DatasetSetField(
                self.handle,
                c_str(field_name),
                None,
                ctypes.c_int(0),
                ctypes.c_int(FIELD_TYPE_MAPPER[field_name])))
            return
        dtype = np.float32
        if field_name == 'group':
            dtype = np.int32
        elif field_name == 'init_score':
            dtype = np.float64
        data = list_to_1d_numpy(data, dtype, name=field_name)
        if data.dtype == np.float32:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
            type_data = C_API_DTYPE_FLOAT32
        elif data.dtype == np.float64:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
            type_data = C_API_DTYPE_FLOAT64
        elif data.dtype == np.int32:
            ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32))
            type_data = C_API_DTYPE_INT32
        else:
            raise TypeError("Excepted np.float32/64 or np.int32, meet type({})".format(data.dtype))
        if type_data != FIELD_TYPE_MAPPER[field_name]:
            raise TypeError("Input type error for set_field")
        _safe_call(_LIB.LGBM_DatasetSetField(
            self.handle,
            c_str(field_name),
            ptr_data,
            ctypes.c_int(len(data)),
            ctypes.c_int(type_data)))

    def get_field(self, field_name):
        """Get property from the Dataset.

        Parameters
        ----------
        field_name: str
            The field name of the information

        Returns
        -------
        info : array
            A numpy array of information of the data
        """
        if self.handle is None:
            raise Exception("Cannot get %s before construct dataset" % field_name)
        tmp_out_len = ctypes.c_int()
        out_type = ctypes.c_int()
        ret = ctypes.POINTER(ctypes.c_void_p)()
        _safe_call(_LIB.LGBM_DatasetGetField(
            self.handle,
            c_str(field_name),
            ctypes.byref(tmp_out_len),
            ctypes.byref(ret),
            ctypes.byref(out_type)))
        if out_type.value != FIELD_TYPE_MAPPER[field_name]:
            raise TypeError("Return type error for get_field")
        if tmp_out_len.value == 0:
            return None
        if out_type.value == C_API_DTYPE_INT32:
            return cint32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)), tmp_out_len.value)
        elif out_type.value == C_API_DTYPE_FLOAT32:
            return cfloat32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)), tmp_out_len.value)
        elif out_type.value == C_API_DTYPE_FLOAT64:
            return cfloat64_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)), tmp_out_len.value)
        else:
            raise TypeError("Unknown type")

    def set_categorical_feature(self, categorical_feature):
        """
        Set categorical features

        Parameters
        ----------
        categorical_feature : list of int or str
            Name/index of categorical features

        """
        if self.categorical_feature == categorical_feature:
            return
        if self.data is not None:
            self.categorical_feature = categorical_feature
            self._free_handle()
        else:
            raise LightGBMError("Cannot set categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.")

    def _set_predictor(self, predictor):
        """
        Set predictor for continued training, not recommand for user to call this function.
        Please set init_model in engine.train or engine.cv
        """
        if predictor is self._predictor:
            return
        if self.data is not None:
            self._predictor = predictor
            self._free_handle()
        else:
            raise LightGBMError("Cannot set predictor after freed raw data, set free_raw_data=False when construct Dataset to avoid this.")

    def set_reference(self, reference):
        """
        Set reference dataset

        Parameters
        ----------
        reference : Dataset
            Will use reference as template to consturct current dataset
        """
        self.set_categorical_feature(reference.categorical_feature)
        self.set_feature_name(reference.feature_name)
        self._set_predictor(reference._predictor)
        if self.reference is reference:
            return
        if self.data is not None:
            self.reference = reference
            self._free_handle()
        else:
            raise LightGBMError("Cannot set reference after freed raw data, set free_raw_data=False when construct Dataset to avoid this.")

    def set_feature_name(self, feature_name):
        """
        Set feature name

        Parameters
        ----------
        feature_name : list of str
            Feature names
        """
        self.feature_name = feature_name
        if self.handle is not None and feature_name is not None and feature_name != 'auto':
            if len(feature_name) != self.num_feature():
                raise ValueError("Length of feature_name({}) and num_feature({}) don't match".format(len(feature_name), self.num_feature()))
            c_feature_name = [c_str(name) for name in feature_name]
            _safe_call(_LIB.LGBM_DatasetSetFeatureNames(
                self.handle,
                c_array(ctypes.c_char_p, c_feature_name),
                ctypes.c_int(len(feature_name))))

    def set_label(self, label):
        """
        Set label of Dataset

        Parameters
        ----------
        label: numpy array or list or None
            The label information to be set into Dataset
        """
        self.label = label
        if self.handle is not None:
            label = list_to_1d_numpy(label, name='label')
            self.set_field('label', label)

    def set_weight(self, weight):
        """
        Set weight of each instance.

        Parameters
        ----------
        weight : numpy array or list or None
            Weight for each data point
        """
        self.weight = weight
        if self.handle is not None and weight is not None:
            weight = list_to_1d_numpy(weight, name='weight')
            self.set_field('weight', weight)

    def set_init_score(self, init_score):
        """
        Set init score of booster to start from.

        Parameters
        ----------
        init_score: numpy array or list or None
            Init score for booster
        """
        self.init_score = init_score
        if self.handle is not None and init_score is not None:
            init_score = list_to_1d_numpy(init_score, np.float64, name='init_score')
            self.set_field('init_score', init_score)

    def set_group(self, group):
        """
        Set group size of Dataset (used for ranking).

        Parameters
        ----------
        group : numpy array or list or None
            Group size of each group
        """
        self.group = group
        if self.handle is not None and group is not None:
            group = list_to_1d_numpy(group, np.int32, name='group')
            self.set_field('group', group)

    def get_label(self):
        """
        Get the label of the Dataset.

        Returns
        -------
        label : array
        """
        if self.label is None and self.handle is not None:
            self.label = self.get_field('label')
        return self.label

    def get_weight(self):
        """
        Get the weight of the Dataset.

        Returns
        -------
        weight : array
        """
        if self.weight is None and self.handle is not None:
            self.weight = self.get_field('weight')
        return self.weight

    def get_init_score(self):
        """
        Get the initial score of the Dataset.

        Returns
        -------
        init_score : array
        """
        if self.init_score is None and self.handle is not None:
            self.init_score = self.get_field('init_score')
        return self.init_score

    def get_group(self):
        """
        Get the initial score of the Dataset.

        Returns
        -------
        init_score : array
        """
        if self.group is None and self.handle is not None:
            self.group = self.get_field('group')
            if self.group is not None:
                # group data from LightGBM is boundaries data, need to convert to group size
                new_group = []
                for i in range_(len(self.group) - 1):
                    new_group.append(self.group[i + 1] - self.group[i])
                self.group = new_group
        return self.group

    def num_data(self):
        """
        Get the number of rows in the Dataset.

        Returns
        -------
        number of rows : int
        """
        if self.handle is not None:
            ret = ctypes.c_int()
            _safe_call(_LIB.LGBM_DatasetGetNumData(self.handle,
                                                   ctypes.byref(ret)))
            return ret.value
        else:
            raise LightGBMError("Cannot get num_data before construct dataset")

    def num_feature(self):
        """
        Get the number of columns (features) in the Dataset.

        Returns
        -------
        number of columns : int
        """
        if self.handle is not None:
            ret = ctypes.c_int()
            _safe_call(_LIB.LGBM_DatasetGetNumFeature(self.handle,
                                                      ctypes.byref(ret)))
            return ret.value
        else:
            raise LightGBMError("Cannot get num_feature before construct dataset")


class Booster(object):
    """"Booster in LightGBM."""
    def __init__(self, params=None, train_set=None, model_file=None, silent=False):
        """
        Initialize the Booster.

        Parameters
        ----------
        params : dict
            Parameters for boosters.
        train_set : Dataset
            Training dataset
        model_file : string
            Path to the model file.
        silent : boolean, optional
            Whether print messages during construction
        """
        self.handle = ctypes.c_void_p()
        self.__need_reload_eval_info = True
        self.__train_data_name = "training"
        self.__attr = {}
        self.best_iteration = -1
        params = {} if params is None else params
        if silent:
            params["verbose"] = 0
        elif "verbose" not in params:
            params["verbose"] = 1
        if train_set is not None:
            """Training task"""
            if not isinstance(train_set, Dataset):
                raise TypeError('Training data should be Dataset instance, met {}'.format(type(train_set).__name__))
            params_str = param_dict_to_str(params)
            """construct booster object"""
            _safe_call(_LIB.LGBM_BoosterCreate(
                train_set.construct().handle,
                c_str(params_str),
                ctypes.byref(self.handle)))
            """save reference to data"""
            self.train_set = train_set
            self.valid_sets = []
            self.name_valid_sets = []
            self.__num_dataset = 1
            self.__init_predictor = train_set._predictor
            if self.__init_predictor is not None:
                _safe_call(_LIB.LGBM_BoosterMerge(
                    self.handle,
                    self.__init_predictor.handle))
            out_num_class = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetNumClasses(
                self.handle,
                ctypes.byref(out_num_class)))
            self.__num_class = out_num_class.value
            """buffer for inner predict"""
            self.__inner_predict_buffer = [None]
            self.__is_predicted_cur_iter = [False]
            self.__get_eval_info()
            self.pandas_categorical = train_set.pandas_categorical
        elif model_file is not None:
            """Prediction task"""
            out_num_iterations = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterCreateFromModelfile(
                c_str(model_file),
                ctypes.byref(out_num_iterations),
                ctypes.byref(self.handle)))
            out_num_class = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetNumClasses(
                self.handle,
                ctypes.byref(out_num_class)))
            self.__num_class = out_num_class.value
            self.pandas_categorical = _load_pandas_categorical(model_file)
        elif 'model_str' in params:
            self.__load_model_from_string(params['model_str'])
        else:
            raise TypeError('Need at least one training dataset or model file to create booster instance')

    def __del__(self):
        if self.handle is not None:
            _safe_call(_LIB.LGBM_BoosterFree(self.handle))

    def __copy__(self):
        return self.__deepcopy__(None)

    def __deepcopy__(self, _):
        model_str = self.__save_model_to_string()
        booster = Booster({'model_str': model_str})
        booster.pandas_categorical = self.pandas_categorical
        return booster

    def __getstate__(self):
        this = self.__dict__.copy()
        handle = this['handle']
        this.pop('train_set', None)
        this.pop('valid_sets', None)
        if handle is not None:
            this["handle"] = self.__save_model_to_string()
        return this

    def __setstate__(self, state):
        model_str = state.get('handle', None)
        if model_str is not None:
            handle = ctypes.c_void_p()
            out_num_iterations = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterLoadModelFromString(
                c_str(model_str),
                ctypes.byref(out_num_iterations),
                ctypes.byref(handle)))
            state['handle'] = handle
        self.__dict__.update(state)

    def set_train_data_name(self, name):
        self.__train_data_name = name

    def add_valid(self, data, name):
        """
        Add an validation data

        Parameters
        ----------
        data : Dataset
            Validation data
        name : String
            Name of validation data
        """
        if not isinstance(data, Dataset):
            raise TypeError('valid data should be Dataset instance, met {}'.format(type(data).__name__))
        if data._predictor is not self.__init_predictor:
            raise LightGBMError("Add validation data failed, you should use same predictor for these data")
        _safe_call(_LIB.LGBM_BoosterAddValidData(
            self.handle,
            data.construct().handle))
        self.valid_sets.append(data)
        self.name_valid_sets.append(name)
        self.__num_dataset += 1
        self.__inner_predict_buffer.append(None)
        self.__is_predicted_cur_iter.append(False)

    def reset_parameter(self, params):
        """
        Reset parameters for booster

        Parameters
        ----------
        params : dict
            New parameters for boosters
        silent : boolean, optional
            Whether print messages during construction
        """
        if 'metric' in params:
            self.__need_reload_eval_info = True
        params_str = param_dict_to_str(params)
        if params_str:
            _safe_call(_LIB.LGBM_BoosterResetParameter(
                self.handle,
                c_str(params_str)))

    def update(self, train_set=None, fobj=None):
        """
        Update for one iteration
        Note: for multi-class task, the score is group by class_id first, then group by row_id
              if you want to get i-th row score in j-th class, the access way is score[j*num_data+i]
              and you should group grad and hess in this way as well

        Parameters
        ----------
        train_set :
            Training data, None means use last training data
        fobj : function
            Customized objective function.

        Returns
        -------
        is_finished, bool
        """

        """need reset training data"""
        if train_set is not None and train_set is not self.train_set:
            if not isinstance(train_set, Dataset):
                raise TypeError('Training data should be Dataset instance, met {}'.format(type(train_set).__name__))
            if train_set._predictor is not self.__init_predictor:
                raise LightGBMError("Replace training data failed, you should use same predictor for these data")
            self.train_set = train_set
            _safe_call(_LIB.LGBM_BoosterResetTrainingData(
                self.handle,
                self.train_set.construct().handle))
            self.__inner_predict_buffer[0] = None
        is_finished = ctypes.c_int(0)
        if fobj is None:
            _safe_call(_LIB.LGBM_BoosterUpdateOneIter(
                self.handle,
                ctypes.byref(is_finished)))
            self.__is_predicted_cur_iter = [False for _ in range_(self.__num_dataset)]
            return is_finished.value == 1
        else:
            grad, hess = fobj(self.__inner_predict(0), self.train_set)
            return self.__boost(grad, hess)

    def __boost(self, grad, hess):
        """
        Boost the booster for one iteration, with customized gradient statistics.
        Note: for multi-class task, the score is group by class_id first, then group by row_id
              if you want to get i-th row score in j-th class, the access way is score[j*num_data+i]
              and you should group grad and hess in this way as well

        Parameters
        ----------
        grad : 1d numpy or 1d list
            The first order of gradient.
        hess : 1d numpy or 1d list
            The second order of gradient.

        Returns
        -------
        is_finished, bool
        """
        grad = list_to_1d_numpy(grad, name='gradient')
        hess = list_to_1d_numpy(hess, name='hessian')
        if len(grad) != len(hess):
            raise ValueError("Lengths of gradient({}) and hessian({}) don't match".format(len(grad), len(hess)))
        is_finished = ctypes.c_int(0)
        _safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
            self.handle,
            grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
            hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
            ctypes.byref(is_finished)))
        self.__is_predicted_cur_iter = [False for _ in range_(self.__num_dataset)]
        return is_finished.value == 1

    def rollback_one_iter(self):
        """
        Rollback one iteration
        """
        _safe_call(_LIB.LGBM_BoosterRollbackOneIter(
            self.handle))
        self.__is_predicted_cur_iter = [False for _ in range_(self.__num_dataset)]

    def current_iteration(self):
        out_cur_iter = ctypes.c_int(0)
        _safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
            self.handle,
            ctypes.byref(out_cur_iter)))
        return out_cur_iter.value

    def eval(self, data, name, feval=None):
        """
        Evaluate for data

        Parameters
        ----------
        data : Dataset object
        name :
            Name of data
        feval : function
            Custom evaluation function.
        Returns
        -------
        result: list
            Evaluation result list.
        """
        if not isinstance(data, Dataset):
            raise TypeError("Can only eval for Dataset instance")
        data_idx = -1
        if data is self.train_set:
            data_idx = 0
        else:
            for i in range_(len(self.valid_sets)):
                if data is self.valid_sets[i]:
                    data_idx = i + 1
                    break
        """need to push new valid data"""
        if data_idx == -1:
            self.add_valid(data, name)
            data_idx = self.__num_dataset - 1

        return self.__inner_eval(name, data_idx, feval)

    def eval_train(self, feval=None):
        """
        Evaluate for training data

        Parameters
        ----------
        feval : function
            Custom evaluation function.

        Returns
        -------
        result: str
            Evaluation result list.
        """
        return self.__inner_eval(self.__train_data_name, 0, feval)

    def eval_valid(self, feval=None):
        """
        Evaluate for validation data

        Parameters
        ----------
        feval : function
            Custom evaluation function.

        Returns
        -------
        result: str
            Evaluation result list.
        """
        return [item for i in range_(1, self.__num_dataset)
                for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)]

    def save_model(self, filename, num_iteration=-1):
        """
        Save model of booster to file

        Parameters
        ----------
        filename : str
            Filename to save
        num_iteration: int
            Number of iteration that want to save. < 0 means save the best iteration(if have)
        """
        if num_iteration <= 0:
            num_iteration = self.best_iteration
        _safe_call(_LIB.LGBM_BoosterSaveModel(
            self.handle,
            ctypes.c_int(num_iteration),
            c_str(filename)))
        _save_pandas_categorical(filename, self.pandas_categorical)

    def __load_model_from_string(self, model_str):
        """[Private] Load model from string"""
        out_num_iterations = ctypes.c_int(0)
        _safe_call(_LIB.LGBM_BoosterLoadModelFromString(
            c_str(model_str),
            ctypes.byref(out_num_iterations),
            ctypes.byref(self.handle)))
        out_num_class = ctypes.c_int(0)
        _safe_call(_LIB.LGBM_BoosterGetNumClasses(
            self.handle,
            ctypes.byref(out_num_class)))
        self.__num_class = out_num_class.value

    def __save_model_to_string(self, num_iteration=-1):
        """[Private] Save model to string"""
        if num_iteration <= 0:
            num_iteration = self.best_iteration
        buffer_len = 1 << 20
        tmp_out_len = ctypes.c_int(0)
        string_buffer = ctypes.create_string_buffer(buffer_len)
        ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
        _safe_call(_LIB.LGBM_BoosterSaveModelToString(
            self.handle,
            ctypes.c_int(num_iteration),
            ctypes.c_int(buffer_len),
            ctypes.byref(tmp_out_len),
            ptr_string_buffer))
        actual_len = tmp_out_len.value
        '''if buffer length is not long enough, re-allocate a buffer'''
        if actual_len > buffer_len:
            string_buffer = ctypes.create_string_buffer(actual_len)
            ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
            _safe_call(_LIB.LGBM_BoosterSaveModelToString(
                self.handle,
                ctypes.c_int(num_iteration),
                ctypes.c_int(actual_len),
                ctypes.byref(tmp_out_len),
                ptr_string_buffer))
        return string_buffer.value.decode()

    def dump_model(self, num_iteration=-1):
        """
        Dump model to json format

        Parameters
        ----------
        num_iteration: int
            Number of iteration that want to dump. < 0 means dump to best iteration(if have)

        Returns
        -------
        Json format of model
        """
        if num_iteration <= 0:
            num_iteration = self.best_iteration
        buffer_len = 1 << 20
        tmp_out_len = ctypes.c_int(0)
        string_buffer = ctypes.create_string_buffer(buffer_len)
        ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
        _safe_call(_LIB.LGBM_BoosterDumpModel(
            self.handle,
            ctypes.c_int(num_iteration),
            ctypes.c_int(buffer_len),
            ctypes.byref(tmp_out_len),
            ptr_string_buffer))
        actual_len = tmp_out_len.value
        '''if buffer length is not long enough, reallocate a buffer'''
        if actual_len > buffer_len:
            string_buffer = ctypes.create_string_buffer(actual_len)
            ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
            _safe_call(_LIB.LGBM_BoosterDumpModel(
                self.handle,
                ctypes.c_int(num_iteration),
                ctypes.c_int(actual_len),
                ctypes.byref(tmp_out_len),
                ptr_string_buffer))
        return json.loads(string_buffer.value.decode())

    def predict(self, data, num_iteration=-1, raw_score=False, pred_leaf=False, data_has_header=False, is_reshape=True):
        """
        Predict logic

        Parameters
        ----------
        data : string/numpy array/scipy.sparse
            Data source for prediction
            When data type is string, it represents the path of txt file
        num_iteration : int
            Used iteration for prediction, < 0 means predict for best iteration(if have)
        raw_score : bool
            True for predict raw score
        pred_leaf : bool
            True for predict leaf index
        data_has_header : bool
            Used for txt data
        is_reshape : bool
            Reshape to (nrow, ncol) if true

        Returns
        -------
        Prediction result
        """
        predictor = self._to_predictor()
        if num_iteration <= 0:
            num_iteration = self.best_iteration
        return predictor.predict(data, num_iteration, raw_score, pred_leaf, data_has_header, is_reshape)

    def _to_predictor(self):
        """Convert to predictor"""
        predictor = _InnerPredictor(booster_handle=self.handle)
        predictor.pandas_categorical = self.pandas_categorical
        return predictor

    def feature_name(self):
        """
        Get feature names.

        Returns
        -------
        result : array
            Array of feature names.
        """
        out_num_feature = ctypes.c_int(0)
        """Get num of features"""
        _safe_call(_LIB.LGBM_BoosterGetNumFeature(
            self.handle,
            ctypes.byref(out_num_feature)))
        num_feature = out_num_feature.value
        """Get name of features"""
        tmp_out_len = ctypes.c_int(0)
        string_buffers = [ctypes.create_string_buffer(255) for i in range_(num_feature)]
        ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers))
        _safe_call(_LIB.LGBM_BoosterGetFeatureNames(
            self.handle,
            ctypes.byref(tmp_out_len),
            ptr_string_buffers))
        if num_feature != tmp_out_len.value:
            raise ValueError("Length of feature names doesn't equal with num_feature")
        return [string_buffers[i].value.decode() for i in range_(num_feature)]

    def feature_importance(self, importance_type='split'):
        """
        Get feature importances

        Parameters
        ----------
        importance_type : str, default "split"
        How the importance is calculated: "split" or "gain"
        "split" is the number of times a feature is used in a model
        "gain" is the total gain of splits which use the feature

        Returns
        -------
        result : array
            Array of feature importances.
        """
        if importance_type not in ["split", "gain"]:
            raise KeyError("importance_type must be split or gain")
        dump_model = self.dump_model()
        ret = [0] * (dump_model["max_feature_idx"] + 1)

        def dfs(root):
            if "split_feature" in root:
                if importance_type == 'split':
                    ret[root["split_feature"]] += 1
                elif importance_type == 'gain':
                    ret[root["split_feature"]] += root["split_gain"]
                dfs(root["left_child"])
                dfs(root["right_child"])
        for tree in dump_model["tree_info"]:
            dfs(tree["tree_structure"])
        return np.array(ret)

    def __inner_eval(self, data_name, data_idx, feval=None):
        """
        Evaulate training or validation data
        """
        if data_idx >= self.__num_dataset:
            raise ValueError("Data_idx should be smaller than number of dataset")
        self.__get_eval_info()
        ret = []
        if self.__num_inner_eval > 0:
            result = np.array([0.0 for _ in range_(self.__num_inner_eval)], dtype=np.float64)
            tmp_out_len = ctypes.c_int(0)
            _safe_call(_LIB.LGBM_BoosterGetEval(
                self.handle,
                ctypes.c_int(data_idx),
                ctypes.byref(tmp_out_len),
                result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
            if tmp_out_len.value != self.__num_inner_eval:
                raise ValueError("Wrong length of eval results")
            for i in range_(self.__num_inner_eval):
                ret.append((data_name, self.__name_inner_eval[i], result[i], self.__higher_better_inner_eval[i]))
        if feval is not None:
            if data_idx == 0:
                cur_data = self.train_set
            else:
                cur_data = self.valid_sets[data_idx - 1]
            feval_ret = feval(self.__inner_predict(data_idx), cur_data)
            if isinstance(feval_ret, list):
                for eval_name, val, is_higher_better in feval_ret:
                    ret.append((data_name, eval_name, val, is_higher_better))
            else:
                eval_name, val, is_higher_better = feval_ret
                ret.append((data_name, eval_name, val, is_higher_better))
        return ret

    def __inner_predict(self, data_idx):
        """
        Predict for training and validation dataset
        """
        if data_idx >= self.__num_dataset:
            raise ValueError("Data_idx should be smaller than number of dataset")
        if self.__inner_predict_buffer[data_idx] is None:
            if data_idx == 0:
                n_preds = self.train_set.num_data() * self.__num_class
            else:
                n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class
            self.__inner_predict_buffer[data_idx] = \
                np.array([0.0 for _ in range_(n_preds)], dtype=np.float64, copy=False)
        """avoid to predict many time in one iteration"""
        if not self.__is_predicted_cur_iter[data_idx]:
            tmp_out_len = ctypes.c_int64(0)
            data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double))
            _safe_call(_LIB.LGBM_BoosterGetPredict(
                self.handle,
                ctypes.c_int(data_idx),
                ctypes.byref(tmp_out_len),
                data_ptr))
            if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]):
                raise ValueError("Wrong length of predict results for data %d" % (data_idx))
            self.__is_predicted_cur_iter[data_idx] = True
        return self.__inner_predict_buffer[data_idx]

    def __get_eval_info(self):
        """
        Get inner evaluation count and names
        """
        if self.__need_reload_eval_info:
            self.__need_reload_eval_info = False
            out_num_eval = ctypes.c_int(0)
            """Get num of inner evals"""
            _safe_call(_LIB.LGBM_BoosterGetEvalCounts(
                self.handle,
                ctypes.byref(out_num_eval)))
            self.__num_inner_eval = out_num_eval.value
            if self.__num_inner_eval > 0:
                """Get name of evals"""
                tmp_out_len = ctypes.c_int(0)
                string_buffers = [ctypes.create_string_buffer(255) for i in range_(self.__num_inner_eval)]
                ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers))
                _safe_call(_LIB.LGBM_BoosterGetEvalNames(
                    self.handle,
                    ctypes.byref(tmp_out_len),
                    ptr_string_buffers))
                if self.__num_inner_eval != tmp_out_len.value:
                    raise ValueError("Length of eval names doesn't equal with num_evals")
                self.__name_inner_eval = \
                    [string_buffers[i].value.decode() for i in range_(self.__num_inner_eval)]
                self.__higher_better_inner_eval = \
                    [name.startswith(('auc', 'ndcg')) for name in self.__name_inner_eval]

    def attr(self, key):
        """
        Get attribute string from the Booster.

        Parameters
        ----------
        key : str
            The key to get attribute from.

        Returns
        -------
        value : str
            The attribute value of the key, returns None if attribute do not exist.
        """
        return self.__attr.get(key, None)

    def set_attr(self, **kwargs):
        """
        Set the attribute of the Booster.

        Parameters
        ----------
        **kwargs
            The attributes to set. Setting a value to None deletes an attribute.
        """
        for key, value in kwargs.items():
            if value is not None:
                if not isinstance(value, string_type):
                    raise ValueError("Set attr only accepts strings")
                self.__attr[key] = value
            else:
                self.__attr.pop(key, None)