Source code for lightgbm.basic

# 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 copy
import ctypes
import os
import warnings
from tempfile import NamedTemporaryFile

import numpy as np
import scipy.sparse

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


def _load_lib():
    """Load LightGBM Library."""
    lib_path = find_lib_path()
    if len(lib_path) == 0:
        return None
    lib = ctypes.cdll.LoadLibrary(lib_path[0])
    lib.LGBM_GetLastError.restype = ctypes.c_char_p
    return lib


_LIB = _load_lib()


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(decode_string(_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 is_numeric(data[0]))


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({0}) for {1}.\n"
                        "It should be list, numpy 1-D array or pandas Series".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))
        elif val is not None:
            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)


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


MAX_INT32 = (1 << 31) - 1

"""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
C_API_PREDICT_CONTRIB = 3

"""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}

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 convert_from_sliced_object(data):
    """fix the memory of multi-dimensional sliced object"""
    if data.base is not None and isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray):
        if not data.flags.c_contiguous:
            warnings.warn("Usage of np.ndarray subset (sliced data) is not recommended due to it will double the peak memory cost in LightGBM.")
            return np.copy(data)
    return data


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):
        data = convert_from_sliced_object(data)
        assert data.flags.c_contiguous
        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, data)  # return `data` to avoid the temporary copy is freed


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):
        data = convert_from_sliced_object(data)
        assert data.flags.c_contiguous
        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, data)  # return `data` to avoid the temporary copy is freed


def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical):
    if isinstance(data, DataFrame):
        if len(data.shape) != 2 or data.shape[0] < 1:
            raise ValueError('Input data must be 2 dimensional and non empty.')
        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).replace({-1: np.nan})
        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').flatten()
    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) + '\n')


def _load_pandas_categorical(file_name):
    with open(file_name, 'r') as f:
        lines = f.readlines()
        last_line = lines[-1]
        if last_line.strip() == "":
            last_line = lines[-2]
        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, pred_parameter=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
        pred_parameter: dict
            Other parameters for the prediciton
        """
        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')

        pred_parameter = {} if pred_parameter is None else pred_parameter
        self.pred_parameter = param_dict_to_str(pred_parameter)

    def __del__(self):
        try:
            if self.__is_manage_handle:
                _safe_call(_LIB.LGBM_BoosterFree(self.handle))
        except AttributeError:
            pass

    def __getstate__(self):
        this = self.__dict__.copy()
        this.pop('handle', None)
        return this

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

        Parameters
        ----------
        data : string, numpy array, pandas DataFrame or 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
        pred_contrib : bool
            True for predict feature contributions
        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
        if pred_contrib:
            predict_type = C_API_PREDICT_CONTRIB
        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(self.pred_parameter),
                    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, list):
            try:
                data = np.array(data)
            except BaseException:
                raise ValueError('Cannot convert data list to numpy array.')
            preds, nrow = self.__pred_for_np2d(data, num_iteration, predict_type)
        else:
            try:
                warnings.warn('Converting data to scipy sparse matrix.')
                csr = scipy.sparse.csr_matrix(data)
            except BaseException:
                raise TypeError('Cannot predict data for type {}'.format(type(data).__name__))
            preds, nrow = self.__pred_for_csr(csr, num_iteration, predict_type)
        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
        """
        if nrow > MAX_INT32:
            raise LightGBMError('LightGBM cannot perform prediction for data'
                                'with number of rows greater than MAX_INT32 (%d).\n'
                                'You can split your data into chunks'
                                'and then concatenate predictions for them' % MAX_INT32)
        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 or list must be 2 dimensional')

        def inner_predict(mat, num_iteration, predict_type, preds=None):
            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)
            if preds is None:
                preds = np.zeros(n_preds, dtype=np.float64)
            elif len(preds.shape) != 1 or len(preds) != n_preds:
                raise ValueError("Wrong length of pre-allocated predict array")
            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),
                c_str(self.pred_parameter),
                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]

        nrow = mat.shape[0]
        if nrow > MAX_INT32:
            sections = np.arange(start=MAX_INT32, stop=nrow, step=MAX_INT32)
            # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal
            n_preds = [self.__get_num_preds(num_iteration, i, predict_type) for i in np.diff([0] + list(sections) + [nrow])]
            n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum()
            preds = np.zeros(sum(n_preds), dtype=np.float64)
            for chunk, (start_idx_pred, end_idx_pred) in zip_(np.array_split(mat, sections), zip_(n_preds_sections, n_preds_sections[1:])):
                # avoid memory consumption by arrays concatenation operations
                inner_predict(chunk, num_iteration, predict_type, preds[start_idx_pred:end_idx_pred])
            return preds, nrow
        else:
            return inner_predict(mat, num_iteration, predict_type)

    def __pred_for_csr(self, csr, num_iteration, predict_type):
        """
        Predict for a csr data
        """
        def inner_predict(csr, num_iteration, predict_type, preds=None):
            nrow = len(csr.indptr) - 1
            n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
            if preds is None:
                preds = np.zeros(n_preds, dtype=np.float64)
            elif len(preds.shape) != 1 or len(preds) != n_preds:
                raise ValueError("Wrong length of pre-allocated predict array")
            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),
                c_str(self.pred_parameter),
                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

        nrow = len(csr.indptr) - 1
        if nrow > MAX_INT32:
            sections = [0] + list(np.arange(start=MAX_INT32, stop=nrow, step=MAX_INT32)) + [nrow]
            # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal
            n_preds = [self.__get_num_preds(num_iteration, i, predict_type) for i in np.diff(sections)]
            n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum()
            preds = np.zeros(sum(n_preds), dtype=np.float64)
            for (start_idx, end_idx), (start_idx_pred, end_idx_pred) in zip_(zip_(sections, sections[1:]),
                                                                             zip_(n_preds_sections, n_preds_sections[1:])):
                # avoid memory consumption by arrays concatenation operations
                inner_predict(csr[start_idx:end_idx], num_iteration, predict_type, preds[start_idx_pred:end_idx_pred])
            return preds, nrow
        else:
            return inner_predict(csr, num_iteration, predict_type)

    def __pred_for_csc(self, csc, num_iteration, predict_type):
        """
        Predict for a csc data
        """
        nrow = csc.shape[0]
        if nrow > MAX_INT32:
            return self.__pred_for_csr(csc.tocsr(), num_iteration, predict_type)
        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),
            c_str(self.pred_parameter),
            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


[docs]class Dataset(object): """Dataset in LightGBM.""" def __init__(self, data, label=None, reference=None, weight=None, group=None, init_score=None, silent=False, feature_name='auto', categorical_feature='auto', params=None, free_raw_data=True): """Constract Dataset. Parameters ---------- data : string, numpy array, pandas DataFrame, scipy.sparse or list of numpy arrays Data source of Dataset. If string, it represents the path to txt file. label : list, numpy 1-D array, pandas one-column DataFrame/Series or None, optional (default=None) Label of the data. reference : Dataset or None, optional (default=None) If this is Dataset for validation, training data should be used as reference. weight : list, numpy 1-D array, pandas Series or None, optional (default=None) Weight for each instance. group : list, numpy 1-D array, pandas Series or None, optional (default=None) Group/query size for Dataset. init_score : list, numpy 1-D array, pandas Series or None, optional (default=None) Init score for Dataset. silent : bool, optional (default=False) Whether to print messages during construction. feature_name : list of strings or 'auto', optional (default="auto") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of strings or int, or 'auto', optional (default="auto") Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). Large values could be memory consuming. Consider to use consecutive integers started from zero. All negative values in categorical features will be treated as missing values. params : dict or None, optional (default=None) Other parameters. free_raw_data : bool, optional (default=True) If True, raw data is freed after constructing inner Dataset. """ self.handle = None self.data = data self.label = label self.reference = reference self.weight = weight self.group = group self.init_score = init_score self.silent = silent self.feature_name = feature_name self.categorical_feature = categorical_feature self.params = copy.deepcopy(params) self.free_raw_data = free_raw_data self.used_indices = None self._predictor = None self.pandas_categorical = None self.params_back_up = None def __del__(self): try: self._free_handle() except AttributeError: pass def _free_handle(self): if self.handle is not None: _safe_call(_LIB.LGBM_DatasetFree(self.handle)) self.handle = None return self def _lazy_init(self, data, label=None, reference=None, weight=None, group=None, init_score=None, predictor=None, silent=False, feature_name='auto', categorical_feature='auto', params=None): if data is None: self.handle = None return self if reference is not None: self.pandas_categorical = reference.pandas_categorical categorical_feature = reference.categorical_feature 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 args_names = getattr(self.__class__, '_lazy_init').__code__.co_varnames[:getattr(self.__class__, '_lazy_init').__code__.co_argcount] for key, _ in params.items(): if key in args_names: warnings.warn('{0} keyword has been found in `params` and will be ignored. ' 'Please use {0} argument of the Dataset constructor to pass this parameter.'.format(key)) self.predictor = predictor # user can set verbose with params, it has higher priority if not any(verbose_alias in params for verbose_alias in ('verbose', 'verbosity')) and silent: 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)) if categorical_indices: if "categorical_feature" in params or "categorical_column" in params: warnings.warn('categorical_feature in param dict is overridden.') params.pop("categorical_feature", None) params.pop("categorical_column", None) 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) elif isinstance(data, list) and len(data) > 0 and all(isinstance(x, np.ndarray) for x in data): self.__init_from_list_np2d(data, params_str, ref_dataset) else: try: csr = scipy.sparse.csr_matrix(data) self.__init_from_csr(csr, params_str, ref_dataset) except BaseException: 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 init_score is not None: self.set_init_score(init_score) if self.predictor is not None: warnings.warn("The prediction of init_model will be overridden by init_score.") elif 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 return 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))) return self def __init_from_list_np2d(self, mats, params_str, ref_dataset): """ Initialize data from list of 2-D numpy matrices. """ ncol = mats[0].shape[1] nrow = np.zeros((len(mats),), np.int32) if mats[0].dtype == np.float64: ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))() else: ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))() holders = [] type_ptr_data = None for i, mat in enumerate(mats): if len(mat.shape) != 2: raise ValueError('Input numpy.ndarray must be 2 dimensional') if mat.shape[1] != ncol: raise ValueError('Input arrays must have same number of columns') nrow[i] = mat.shape[0] if mat.dtype == np.float32 or mat.dtype == np.float64: mats[i] = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False) else: # change non-float data to float data, need to copy mats[i] = np.array(mat.reshape(mat.size), dtype=np.float32) chunk_ptr_data, chunk_type_ptr_data, holder = c_float_array(mats[i]) if type_ptr_data is not None and chunk_type_ptr_data != type_ptr_data: raise ValueError('Input chunks must have same type') ptr_data[i] = chunk_ptr_data type_ptr_data = chunk_type_ptr_data holders.append(holder) self.handle = ctypes.c_void_p() _safe_call(_LIB.LGBM_DatasetCreateFromMats( ctypes.c_int(len(mats)), ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), ctypes.c_int(type_ptr_data), nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), ctypes.c_int(ncol), ctypes.c_int(C_API_IS_ROW_MAJOR), c_str(params_str), ref_dataset, ctypes.byref(self.handle))) return self 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))) return self 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))) return self
[docs] def construct(self): """Lazy init. Returns ------- self : Dataset Constructed Dataset object. """ 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, reference=self.reference, weight=self.weight, group=self.group, init_score=self.init_score, predictor=self._predictor, silent=self.silent, feature_name=self.feature_name, params=self.params) else: # construct subset used_indices = list_to_1d_numpy(self.used_indices, np.int32, name='used_indices') assert used_indices.flags.c_contiguous if self.reference.group is not None: group_info = np.array(self.reference.group).astype(int) _, self.group = np.unique(np.repeat(range_(len(group_info)), repeats=group_info)[self.used_indices], return_counts=True) 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.group is not None: self.set_group(self.group) if self.get_label() is None: raise ValueError("Label should not be None.") else: # create train self._lazy_init(self.data, label=self.label, weight=self.weight, group=self.group, init_score=self.init_score, 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
[docs] def create_valid(self, data, label=None, weight=None, group=None, init_score=None, silent=False, params=None): """Create validation data align with current Dataset. Parameters ---------- data : string, numpy array, pandas DataFrame, scipy.sparse or list of numpy arrays Data source of Dataset. If string, it represents the path to txt file. label : list, numpy 1-D array, pandas one-column DataFrame/Series or None, optional (default=None) Label of the data. weight : list, numpy 1-D array, pandas Series or None, optional (default=None) Weight for each instance. group : list, numpy 1-D array, pandas Series or None, optional (default=None) Group/query size for Dataset. init_score : list, numpy 1-D array, pandas Series or None, optional (default=None) Init score for Dataset. silent : bool, optional (default=False) Whether to print messages during construction. params : dict or None, optional (default=None) Other parameters. Returns ------- valid : Dataset Validation Dataset with reference to self. """ ret = Dataset(data, label=label, reference=self, weight=weight, group=group, init_score=init_score, silent=silent, params=params, free_raw_data=self.free_raw_data) ret._predictor = self._predictor ret.pandas_categorical = self.pandas_categorical return ret
[docs] def subset(self, used_indices, params=None): """Get subset of current Dataset. Parameters ---------- used_indices : list of int Indices used to create the subset. params : dict or None, optional (default=None) Other parameters. Returns ------- subset : Dataset Subset of the current Dataset. """ if params is None: params = self.params 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
[docs] def save_binary(self, filename): """Save Dataset to binary file. Parameters ---------- filename : string Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call(_LIB.LGBM_DatasetSaveBinary( self.construct().handle, c_str(filename))) return self
def _update_params(self, params): if not self.params: self.params = params else: self.params_back_up = copy.deepcopy(self.params) self.params.update(params) return self def _reverse_update_params(self): self.params = copy.deepcopy(self.params_back_up) self.params_back_up = None return self
[docs] def set_field(self, field_name, data): """Set property into the Dataset. Parameters ---------- field_name : string The field name of the information. data : list, numpy 1-D array, pandas Series or None The array of data to be set. Returns ------- self : Dataset Dataset with set property. """ 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 self 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 or data.dtype == np.float64: ptr_data, type_data, _ = c_float_array(data) elif data.dtype == np.int32: ptr_data, type_data, _ = c_int_array(data) 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))) return self
[docs] def get_field(self, field_name): """Get property from the Dataset. Parameters ---------- field_name : string The field name of the information. Returns ------- info : numpy array A numpy array with information from the Dataset. """ 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")
[docs] def set_categorical_feature(self, categorical_feature): """Set categorical features. Parameters ---------- categorical_feature : list of int or strings Names or indices of categorical features. Returns ------- self : Dataset Dataset with set categorical features. """ if self.categorical_feature == categorical_feature: return self if self.data is not None: if self.categorical_feature is None: self.categorical_feature = categorical_feature return self._free_handle() elif categorical_feature == 'auto': warnings.warn('Using categorical_feature in Dataset.') return self else: warnings.warn('categorical_feature in Dataset is overridden. New categorical_feature is {}'.format(sorted(list(categorical_feature)))) self.categorical_feature = categorical_feature return 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 recommended for user to call this function. Please set init_model in engine.train or engine.cv """ if predictor is self._predictor: return self if self.data is not None: self._predictor = predictor return self._free_handle() else: raise LightGBMError("Cannot set predictor after freed raw data, set free_raw_data=False when construct Dataset to avoid this.")
[docs] def set_reference(self, reference): """Set reference Dataset. Parameters ---------- reference : Dataset Reference that is used as a template to consturct the current Dataset. Returns ------- self : Dataset Dataset with set reference. """ self.set_categorical_feature(reference.categorical_feature).set_feature_name(reference.feature_name)._set_predictor(reference._predictor) # we're done if self and reference share a common upstrem reference if self.get_ref_chain().intersection(reference.get_ref_chain()): return self if self.data is not None: self.reference = reference return self._free_handle() else: raise LightGBMError("Cannot set reference after freed raw data, set free_raw_data=False when construct Dataset to avoid this.")
[docs] def set_feature_name(self, feature_name): """Set feature name. Parameters ---------- feature_name : list of strings Feature names. Returns ------- self : Dataset Dataset with set feature name. """ if feature_name != 'auto': 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)))) return self
[docs] def set_label(self, label): """Set label of Dataset Parameters ---------- label : list, numpy 1-D array, pandas one-column DataFrame/Series or None The label information to be set into Dataset. Returns ------- self : Dataset Dataset with set label. """ self.label = label if self.handle is not None: label = list_to_1d_numpy(_label_from_pandas(label), name='label') self.set_field('label', label) return self
[docs] def set_weight(self, weight): """Set weight of each instance. Parameters ---------- weight : list, numpy 1-D array, pandas Series or None Weight to be set for each data point. Returns ------- self : Dataset Dataset with set weight. """ if weight is not None and np.all(weight == 1): weight = None 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) return self
[docs] def set_init_score(self, init_score): """Set init score of Booster to start from. Parameters ---------- init_score : list, numpy 1-D array, pandas Series or None Init score for Booster. Returns ------- self : Dataset Dataset with set init score. """ 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) return self
[docs] def set_group(self, group): """Set group size of Dataset (used for ranking). Parameters ---------- group : list, numpy 1-D array, pandas Series or None Group size of each group. Returns ------- self : Dataset Dataset with set 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) return self
[docs] def get_label(self): """Get the label of the Dataset. Returns ------- label : numpy array or None The label information from the Dataset. """ if self.label is None: self.label = self.get_field('label') return self.label
[docs] def get_weight(self): """Get the weight of the Dataset. Returns ------- weight : numpy array or None Weight for each data point from the Dataset. """ if self.weight is None: self.weight = self.get_field('weight') return self.weight
[docs] def get_init_score(self): """Get the initial score of the Dataset. Returns ------- init_score : numpy array or None Init score of Booster. """ if self.init_score is None: self.init_score = self.get_field('init_score') return self.init_score
[docs] def get_group(self): """Get the group of the Dataset. Returns ------- group : numpy array or None Group size of each group. """ if self.group is 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 self.group = np.diff(self.group) return self.group
[docs] def num_data(self): """Get the number of rows in the Dataset. Returns ------- number_of_rows : int The number of rows in the Dataset. """ 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")
[docs] def num_feature(self): """Get the number of columns (features) in the Dataset. Returns ------- number_of_columns : int The number of columns (features) in the Dataset. """ 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")
[docs] def get_ref_chain(self, ref_limit=100): """Get a chain of Dataset objects, starting with r, then going to r.reference if exists, then to r.reference.reference, etc. until we hit ``ref_limit`` or a reference loop. Parameters ---------- ref_limit : int, optional (default=100) The limit number of references. Returns ------- ref_chain : set of Dataset Chain of references of the Datasets. """ head = self ref_chain = set() while len(ref_chain) < ref_limit: if isinstance(head, Dataset): ref_chain.add(head) if (head.reference is not None) and (head.reference not in ref_chain): head = head.reference else: break else: break return ref_chain
[docs]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 or None, optional (default=None) Parameters for Booster. train_set : Dataset or None, optional (default=None) Training dataset. model_file : string or None, optional (default=None) Path to the model file. silent : bool, optional (default=False) Whether to print messages during construction. """ self.handle = None self.network = False self.__need_reload_eval_info = True self.__train_data_name = "training" self.__attr = {} self.__set_objective_to_none = False self.best_iteration = -1 self.best_score = {} params = {} if params is None else params # user can set verbose with params, it has higher priority if not any(verbose_alias in params for verbose_alias in ('verbose', 'verbosity')) and silent: 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 self.handle = ctypes.c_void_p() _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 # set network if necessary for alias in ["machines", "workers", "nodes"]: if alias in params: machines = params[alias] if isinstance(machines, string_type): num_machines = len(machines.split(',')) elif isinstance(machines, (list, set)): num_machines = len(machines) machines = ','.join(machines) else: raise ValueError("Invalid machines in params.") self.set_network(machines, local_listen_port=params.get("local_listen_port", 12400), listen_time_out=params.get("listen_time_out", 120), num_machines=params.get("num_machines", num_machines)) break elif model_file is not None: # Prediction task out_num_iterations = ctypes.c_int(0) self.handle = ctypes.c_void_p() _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.model_from_string(params['model_str']) else: raise TypeError('Need at least one training dataset or model file to create booster instance') self.params = params.copy() def __del__(self): try: if self.network: self.free_network() except AttributeError: pass try: if self.handle is not None: _safe_call(_LIB.LGBM_BoosterFree(self.handle)) except AttributeError: pass def __copy__(self): return self.__deepcopy__(None) def __deepcopy__(self, _): model_str = self.model_to_string(num_iteration=-1) 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.model_to_string(num_iteration=-1) 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)
[docs] def free_dataset(self): """Free Booster's Datasets. Returns ------- self : Booster Booster without Datasets. """ self.__dict__.pop('train_set', None) self.__dict__.pop('valid_sets', None) self.__num_dataset = 0 return self
def _free_buffer(self): self.__inner_predict_buffer = [] self.__is_predicted_cur_iter = [] return self
[docs] def set_network(self, machines, local_listen_port=12400, listen_time_out=120, num_machines=1): """Set the network configuration. Parameters ---------- machines : list, set or string Names of machines. local_listen_port : int, optional (default=12400) TCP listen port for local machines. listen_time_out : int, optional (default=120) Socket time-out in minutes. num_machines : int, optional (default=1) The number of machines for parallel learning application. Returns ------- self : Booster Booster with set network. """ _safe_call(_LIB.LGBM_NetworkInit(c_str(machines), ctypes.c_int(local_listen_port), ctypes.c_int(listen_time_out), ctypes.c_int(num_machines))) self.network = True return self
[docs] def free_network(self): """Free Booster's network. Returns ------- self : Booster Booster with freed network. """ _safe_call(_LIB.LGBM_NetworkFree()) self.network = False return self
[docs] def set_train_data_name(self, name): """Set the name to the training Dataset. Parameters ---------- name : string Name for the training Dataset. Returns ------- self : Booster Booster with set training Dataset name. """ self.__train_data_name = name return self
[docs] def add_valid(self, data, name): """Add validation data. Parameters ---------- data : Dataset Validation data. name : string Name of validation data. Returns ------- self : Booster Booster with set validation data. """ if not isinstance(data, Dataset): raise TypeError('Validation 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) return self
[docs] def reset_parameter(self, params): """Reset parameters of Booster. Parameters ---------- params : dict New parameters for Booster. Returns ------- self : Booster Booster with new parameters. """ if any(metric_alias in params for metric_alias in ('metric', 'metrics', 'metric_types')): 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))) self.params.update(params) return self
[docs] def update(self, train_set=None, fobj=None): """Update Booster for one iteration. Parameters ---------- train_set : Dataset or None, optional (default=None) Training data. If None, last training data is used. fobj : callable or None, optional (default=None) Customized objective function. 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. Returns ------- is_finished : bool Whether the update was successfully finished. """ # 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: if self.__set_objective_to_none: raise ValueError('Cannot update due to null objective function.') _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: if not self.__set_objective_to_none: self.reset_parameter({"objective": "none"}).__set_objective_to_none = True grad, hess = fobj(self.__inner_predict(0), self.train_set) return self.__boost(grad, hess)
def __boost(self, grad, hess): """ Boost 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 array or list The first order derivative (gradient). hess : 1d numpy or 1d list The second order derivative (Hessian). Returns ------- is_finished : bool Whether the boost was successfully finished. """ grad = list_to_1d_numpy(grad, name='gradient') hess = list_to_1d_numpy(hess, name='hessian') assert grad.flags.c_contiguous assert hess.flags.c_contiguous 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
[docs] def rollback_one_iter(self): """Rollback one iteration. Returns ------- self : Booster Booster with rolled back one iteration. """ _safe_call(_LIB.LGBM_BoosterRollbackOneIter( self.handle)) self.__is_predicted_cur_iter = [False for _ in range_(self.__num_dataset)] return self
[docs] def current_iteration(self): """Get the index of the current iteration. Returns ------- cur_iter : int The index of the current iteration. """ out_cur_iter = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterGetCurrentIteration( self.handle, ctypes.byref(out_cur_iter))) return out_cur_iter.value
[docs] def num_model_per_iteration(self): """Get number of models per iteration. Returns ------- model_per_iter : int The number of models per iteration. """ model_per_iter = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterNumModelPerIteration( self.handle, ctypes.byref(model_per_iter))) return model_per_iter.value
[docs] def num_trees(self): """Get number of weak sub-models. Returns ------- num_trees : int The number of weak sub-models. """ num_trees = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterNumberOfTotalModel( self.handle, ctypes.byref(num_trees))) return num_trees.value
[docs] def eval(self, data, name, feval=None): """Evaluate for data. Parameters ---------- data : Dataset Data for the evaluating. name : string Name of the data. feval : callable or None, optional (default=None) Customized evaluation function. Should accept two parameters: preds, train_data. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples. Returns ------- result : list List with evaluation results. """ 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)
[docs] def eval_train(self, feval=None): """Evaluate for training data. Parameters ---------- feval : callable or None, optional (default=None) Customized evaluation function. Should accept two parameters: preds, train_data. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples. Returns ------- result : list List with evaluation results. """ return self.__inner_eval(self.__train_data_name, 0, feval)
[docs] def eval_valid(self, feval=None): """Evaluate for validation data. Parameters ---------- feval : callable or None, optional (default=None) Customized evaluation function. Should accept two parameters: preds, train_data. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples. Returns ------- result : list List with evaluation results. """ return [item for i in range_(1, self.__num_dataset) for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)]
[docs] def save_model(self, filename, num_iteration=None, start_iteration=0): """Save Booster to file. Parameters ---------- filename : string Filename to save Booster. num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. Returns ------- self : Booster Returns self. """ if num_iteration is None: num_iteration = self.best_iteration _safe_call(_LIB.LGBM_BoosterSaveModel( self.handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), c_str(filename))) _save_pandas_categorical(filename, self.pandas_categorical) return self
[docs] def shuffle_models(self): """Shuffle models. Returns ------- self : Booster Booster with shuffled models. """ _safe_call(_LIB.LGBM_BoosterShuffleModels(self.handle)) return self
[docs] def model_from_string(self, model_str, verbose=True): """Load Booster from a string. Parameters ---------- model_str : string Model will be loaded from this string. verbose : bool, optional (default=True) Whether to print messages while loading model. Returns ------- self : Booster Loaded Booster object. """ if self.handle is not None: _safe_call(_LIB.LGBM_BoosterFree(self.handle)) self._free_buffer() self.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(self.handle))) out_num_class = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterGetNumClasses( self.handle, ctypes.byref(out_num_class))) if verbose: print('Finished loading model, total used %d iterations' % int(out_num_iterations.value)) self.__num_class = out_num_class.value return self
[docs] def model_to_string(self, num_iteration=None, start_iteration=0): """Save Booster to string. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be saved. If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. If <= 0, all iterations are saved. start_iteration : int, optional (default=0) Start index of the iteration that should be saved. Returns ------- str_repr : string String representation of Booster. """ if num_iteration is None: num_iteration = self.best_iteration buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(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(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int64(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(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer)) return string_buffer.value.decode()
[docs] def dump_model(self, num_iteration=None, start_iteration=0): """Dump Booster to JSON format. Parameters ---------- num_iteration : int or None, optional (default=None) Index of the iteration that should be dumped. If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped. If <= 0, all iterations are dumped. start_iteration : int, optional (default=0) Start index of the iteration that should be dumped. Returns ------- json_repr : dict JSON format of Booster. """ if num_iteration is None: num_iteration = self.best_iteration buffer_len = 1 << 20 tmp_out_len = ctypes.c_int64(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(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int64(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(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer)) return json.loads(string_buffer.value.decode())
[docs] def predict(self, data, num_iteration=None, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True, pred_parameter=None, **kwargs): """Make a prediction. Parameters ---------- data : string, numpy array, pandas DataFrame or scipy.sparse Data source for prediction. If string, it represents the path to txt file. num_iteration : int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists, it is used; otherwise, all iterations are used. If <= 0, all iterations are used (no limits). raw_score : bool, optional (default=False) Whether to predict raw scores. 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 explanation for your model's predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap). data_has_header : bool, optional (default=False) Whether the data has header. Used only if data is string. is_reshape : bool, optional (default=True) If True, result is reshaped to [nrow, ncol]. **kwargs : other parameters for the prediction Returns ------- result : numpy array Prediction result. """ predictor = self._to_predictor(kwargs) if num_iteration is None: num_iteration = self.best_iteration return predictor.predict(data, num_iteration, raw_score, pred_leaf, pred_contrib, data_has_header, is_reshape)
[docs] def refit(self, data, label, decay_rate=0.9, **kwargs): """Refit the existing Booster by new data. Parameters ---------- data : string, numpy array, pandas DataFrame or scipy.sparse Data source for refit. If string, it represents the path to txt file. label : list, numpy 1-D array or pandas one-column DataFrame/Series Label for refit. decay_rate : float, optional (default=0.9) Decay rate of refit, will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees. **kwargs : other parameters for refit These parameters will be passed to ``predict`` method. Returns ------- result : Booster Refitted Booster. """ predictor = self._to_predictor(kwargs) leaf_preds = predictor.predict(data, -1, pred_leaf=True) nrow, ncol = leaf_preds.shape train_set = Dataset(data, label, silent=True) new_booster = Booster(self.params, train_set, silent=True) # Copy models _safe_call(_LIB.LGBM_BoosterMerge( new_booster.handle, predictor.handle)) leaf_preds = leaf_preds.reshape(-1) ptr_data, type_ptr_data, _ = c_int_array(leaf_preds) _safe_call(_LIB.LGBM_BoosterRefit( new_booster.handle, ptr_data, ctypes.c_int(nrow), ctypes.c_int(ncol))) return new_booster
[docs] def get_leaf_output(self, tree_id, leaf_id): """Get the output of a leaf. Parameters ---------- tree_id : int The index of the tree. leaf_id : int The index of the leaf in the tree. Returns ------- result : float The output of the leaf. """ ret = ctypes.c_double(0) _safe_call(_LIB.LGBM_BoosterGetLeafValue( self.handle, ctypes.c_int(tree_id), ctypes.c_int(leaf_id), ctypes.byref(ret))) return ret.value
def _to_predictor(self, pred_parameter=None): """Convert to predictor""" predictor = _InnerPredictor(booster_handle=self.handle, pred_parameter=pred_parameter) predictor.pandas_categorical = self.pandas_categorical return predictor
[docs] def num_feature(self): """Get number of features. Returns ------- num_feature : int The number of features. """ out_num_feature = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterGetNumFeature( self.handle, ctypes.byref(out_num_feature))) return out_num_feature.value
[docs] def feature_name(self): """Get names of features. Returns ------- result : list List with names of features. """ num_feature = self.num_feature() # 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)]
[docs] def feature_importance(self, importance_type='split', iteration=-1): """Get feature importances. Parameters ---------- importance_type : string, optional (default="split") How the importance is calculated. 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. Returns ------- result : numpy array Array with feature importances. """ if importance_type == "split": importance_type_int = 0 elif importance_type == "gain": importance_type_int = 1 else: importance_type_int = -1 result = np.zeros(self.num_feature(), dtype=np.float64) _safe_call(_LIB.LGBM_BoosterFeatureImportance( self.handle, ctypes.c_int(iteration), ctypes.c_int(importance_type_int), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))) if importance_type_int == 0: return result.astype(int) else: return result
def __inner_eval(self, data_name, data_idx, feval=None): """ Evaluate 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.zeros(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.zeros(n_preds, dtype=np.float64) # 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@', 'map@')) for name in self.__name_inner_eval]
[docs] def attr(self, key): """Get attribute string from the Booster. Parameters ---------- key : string The name of the attribute. Returns ------- value : string or None The attribute value. Returns None if attribute does not exist. """ return self.__attr.get(key, None)
[docs] 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. Returns ------- self : Booster Booster with set attribute. """ for key, value in kwargs.items(): if value is not None: if not isinstance(value, string_type): raise ValueError("Only string values are accepted") self.__attr[key] = value else: self.__attr.pop(key, None) return self