Source code for lightgbm.basic

# coding: utf-8
"""Wrapper for C API of LightGBM."""
import abc
import ctypes
import json
import warnings
from collections import OrderedDict
from copy import deepcopy
from functools import wraps
from logging import Logger
from os import SEEK_END
from os.path import getsize
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union

import numpy as np
import scipy.sparse

from .compat import (PANDAS_INSTALLED, concat, dt_DataTable, is_dtype_sparse, pd_CategoricalDtype, pd_DataFrame,
                     pd_Series)
from .libpath import find_lib_path

ZERO_THRESHOLD = 1e-35


def _get_sample_count(total_nrow: int, params: str):
    sample_cnt = ctypes.c_int(0)
    _safe_call(_LIB.LGBM_GetSampleCount(
        ctypes.c_int32(total_nrow),
        c_str(params),
        ctypes.byref(sample_cnt),
    ))
    return sample_cnt.value


class _DummyLogger:
    def info(self, msg: str) -> None:
        print(msg)

    def warning(self, msg: str) -> None:
        warnings.warn(msg, stacklevel=3)


_LOGGER: Union[_DummyLogger, Logger] = _DummyLogger()


[docs]def register_logger(logger: Logger) -> None: """Register custom logger. Parameters ---------- logger : logging.Logger Custom logger. """ if not isinstance(logger, Logger): raise TypeError("Logger should inherit logging.Logger class") global _LOGGER _LOGGER = logger
def _normalize_native_string(func: Callable[[str], None]) -> Callable[[str], None]: """Join log messages from native library which come by chunks.""" msg_normalized: List[str] = [] @wraps(func) def wrapper(msg: str) -> None: nonlocal msg_normalized if msg.strip() == '': msg = ''.join(msg_normalized) msg_normalized = [] return func(msg) else: msg_normalized.append(msg) return wrapper def _log_info(msg: str) -> None: _LOGGER.info(msg) def _log_warning(msg: str) -> None: _LOGGER.warning(msg) @_normalize_native_string def _log_native(msg: str) -> None: _LOGGER.info(msg) def _log_callback(msg: bytes) -> None: """Redirect logs from native library into Python.""" _log_native(str(msg.decode('utf-8'))) 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 callback = ctypes.CFUNCTYPE(None, ctypes.c_char_p) lib.callback = callback(_log_callback) if lib.LGBM_RegisterLogCallback(lib.callback) != 0: raise LightGBMError(lib.LGBM_GetLastError().decode('utf-8')) return lib _LIB = _load_lib() NUMERIC_TYPES = (int, float, bool) _ArrayLike = Union[List, np.ndarray, pd_Series] def _safe_call(ret: int) -> None: """Check the return value from C API call. Parameters ---------- ret : int The return value from C API calls. """ if ret != 0: raise LightGBMError(_LIB.LGBM_GetLastError().decode('utf-8')) def is_numeric(obj): """Check whether object 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 whether data is a numpy 1-D array.""" return isinstance(data, np.ndarray) and len(data.shape) == 1 def is_numpy_column_array(data): """Check whether data is a column numpy array.""" if not isinstance(data, np.ndarray): return False shape = data.shape return len(shape) == 2 and shape[1] == 1 def cast_numpy_array_to_dtype(array, dtype): """Cast numpy array to given dtype.""" if array.dtype == dtype: return array return array.astype(dtype=dtype, copy=False) def is_1d_list(data): """Check whether data is a 1-D list.""" return isinstance(data, list) and (not data or is_numeric(data[0])) def _is_1d_collection(data: Any) -> bool: """Check whether data is a 1-D collection.""" return ( is_numpy_1d_array(data) or is_numpy_column_array(data) or is_1d_list(data) or isinstance(data, pd_Series) ) def list_to_1d_numpy(data, dtype=np.float32, name='list'): """Convert data to numpy 1-D array.""" if is_numpy_1d_array(data): return cast_numpy_array_to_dtype(data, dtype) elif is_numpy_column_array(data): _log_warning('Converting column-vector to 1d array') array = data.ravel() return cast_numpy_array_to_dtype(array, dtype) elif is_1d_list(data): return np.array(data, dtype=dtype, copy=False) elif isinstance(data, pd_Series): if _get_bad_pandas_dtypes([data.dtypes]): raise ValueError('Series.dtypes must be int, float or bool') return np.array(data, dtype=dtype, copy=False) # SparseArray should be supported as well else: raise TypeError(f"Wrong type({type(data).__name__}) for {name}.\n" "It should be list, numpy 1-D array or pandas Series") def _is_numpy_2d_array(data: Any) -> bool: """Check whether data is a numpy 2-D array.""" return isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] > 1 def _is_2d_list(data: Any) -> bool: """Check whether data is a 2-D list.""" return isinstance(data, list) and len(data) > 0 and is_1d_list(data[0]) def _is_2d_collection(data: Any) -> bool: """Check whether data is a 2-D collection.""" return ( _is_numpy_2d_array(data) or _is_2d_list(data) or isinstance(data, pd_DataFrame) ) def _data_to_2d_numpy(data: Any, dtype: type = np.float32, name: str = 'list') -> np.ndarray: """Convert data to numpy 2-D array.""" if _is_numpy_2d_array(data): return cast_numpy_array_to_dtype(data, dtype) if _is_2d_list(data): return np.array(data, dtype=dtype) if isinstance(data, pd_DataFrame): if _get_bad_pandas_dtypes(data.dtypes): raise ValueError('DataFrame.dtypes must be int, float or bool') return cast_numpy_array_to_dtype(data.values, dtype) raise TypeError(f"Wrong type({type(data).__name__}) for {name}.\n" "It should be list of lists, numpy 2-D array or pandas DataFrame") 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.ctypeslib.as_array(cptr, shape=(length,)).copy() 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.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError('Expected double pointer') def cint32_array_to_numpy(cptr, length): """Convert a ctypes int pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError('Expected int32 pointer') def cint64_array_to_numpy(cptr, length): """Convert a ctypes int pointer array to a numpy array.""" if isinstance(cptr, ctypes.POINTER(ctypes.c_int64)): return np.ctypeslib.as_array(cptr, shape=(length,)).copy() else: raise RuntimeError('Expected int64 pointer') def c_str(string): """Convert a Python string to C string.""" 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 json_default_with_numpy(obj): """Convert numpy classes to JSON serializable objects.""" if isinstance(obj, (np.integer, np.floating, np.bool_)): return obj.item() elif isinstance(obj, np.ndarray): return obj.tolist() else: return obj def param_dict_to_str(data): """Convert Python dictionary to string, which is passed to C API.""" 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): def to_string(x): if isinstance(x, list): return f"[{','.join(map(str, x))}]" else: return str(x) pairs.append(f"{key}={','.join(map(to_string, val))}") elif isinstance(val, (str, Path, NUMERIC_TYPES)) or is_numeric(val): pairs.append(f"{key}={val}") elif val is not None: raise TypeError(f'Unknown type of parameter:{key}, got:{type(val).__name__}') return ' '.join(pairs) class _TempFile: """Proxy class to workaround errors on Windows.""" def __enter__(self): with NamedTemporaryFile(prefix="lightgbm_tmp_", delete=True) as f: self.name = f.name self.path = Path(self.name) return self def __exit__(self, exc_type, exc_val, exc_tb): if self.path.is_file(): self.path.unlink() class LightGBMError(Exception): """Error thrown by LightGBM.""" pass # DeprecationWarning is not shown by default, so let's create our own with higher level class LGBMDeprecationWarning(UserWarning): """Custom deprecation warning.""" pass class _ConfigAliases: # lazy evaluation to allow import without dynamic library, e.g., for docs generation aliases = None @staticmethod def _get_all_param_aliases() -> Dict[str, Set[str]]: 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_DumpParamAliases( 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_DumpParamAliases( ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer)) aliases = json.loads( string_buffer.value.decode('utf-8'), object_hook=lambda obj: {k: set(v) | {k} for k, v in obj.items()} ) return aliases @classmethod def get(cls, *args) -> Set[str]: if cls.aliases is None: cls.aliases = cls._get_all_param_aliases() ret = set() for i in args: ret |= cls.aliases.get(i, {i}) return ret @classmethod def get_by_alias(cls, *args) -> Set[str]: if cls.aliases is None: cls.aliases = cls._get_all_param_aliases() ret = set(args) for arg in args: for aliases in cls.aliases.values(): if arg in aliases: ret |= aliases break return ret def _choose_param_value(main_param_name: str, params: Dict[str, Any], default_value: Any) -> Dict[str, Any]: """Get a single parameter value, accounting for aliases. Parameters ---------- main_param_name : str Name of the main parameter to get a value for. One of the keys of ``_ConfigAliases``. params : dict Dictionary of LightGBM parameters. default_value : Any Default value to use for the parameter, if none is found in ``params``. Returns ------- params : dict A ``params`` dict with exactly one value for ``main_param_name``, and all aliases ``main_param_name`` removed. If both ``main_param_name`` and one or more aliases for it are found, the value of ``main_param_name`` will be preferred. """ # avoid side effects on passed-in parameters params = deepcopy(params) # find a value, and remove other aliases with .pop() # prefer the value of 'main_param_name' if it exists, otherwise search the aliases found_value = None if main_param_name in params.keys(): found_value = params[main_param_name] for param in _ConfigAliases.get(main_param_name): val = params.pop(param, None) if found_value is None and val is not None: found_value = val if found_value is not None: params[main_param_name] = found_value else: params[main_param_name] = default_value return params MAX_INT32 = (1 << 31) - 1 """Macro 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 """Matrix is row major in Python""" C_API_IS_ROW_MAJOR = 1 """Macro 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 """Macro definition of sparse matrix type""" C_API_MATRIX_TYPE_CSR = 0 C_API_MATRIX_TYPE_CSC = 1 """Macro definition of feature importance type""" C_API_FEATURE_IMPORTANCE_SPLIT = 0 C_API_FEATURE_IMPORTANCE_GAIN = 1 """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} """String name to int feature importance type mapper""" FEATURE_IMPORTANCE_TYPE_MAPPER = {"split": C_API_FEATURE_IMPORTANCE_SPLIT, "gain": C_API_FEATURE_IMPORTANCE_GAIN} def convert_from_sliced_object(data): """Fix the memory of multi-dimensional sliced object.""" if isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray): if not data.flags.c_contiguous: _log_warning("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(f"Expected np.float32 or np.float64, met type({data.dtype})") else: raise TypeError(f"Unknown type({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(f"Expected np.int32 or np.int64, met type({data.dtype})") else: raise TypeError(f"Unknown type({type(data).__name__})") return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed def _get_bad_pandas_dtypes(dtypes): pandas_dtype_mapper = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int', 'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int', 'bool': 'int', 'float16': 'float', 'float32': 'float', 'float64': 'float'} bad_indices = [i for i, dtype in enumerate(dtypes) if (dtype.name not in pandas_dtype_mapper and (not is_dtype_sparse(dtype) or dtype.subtype.name not in pandas_dtype_mapper))] return bad_indices def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical): if isinstance(data, pd_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 = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)] cat_cols_not_ordered = [col for col in cat_cols if not data[col].cat.ordered] 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 list 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': # use cat cols from DataFrame categorical_feature = cat_cols_not_ordered else: # use cat cols specified by user categorical_feature = list(categorical_feature) if feature_name == 'auto': feature_name = list(data.columns) bad_indices = _get_bad_pandas_dtypes(data.dtypes) if bad_indices: bad_index_cols_str = ', '.join(data.columns[bad_indices]) raise ValueError("DataFrame.dtypes for data must be int, float or bool.\n" "Did not expect the data types in the following fields: " f"{bad_index_cols_str}") data = data.values if data.dtype != np.float32 and data.dtype != np.float64: data = data.astype(np.float32) 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, pd_DataFrame): if len(label.columns) > 1: raise ValueError('DataFrame for label cannot have multiple columns') if _get_bad_pandas_dtypes(label.dtypes): raise ValueError('DataFrame.dtypes for label must be int, float or bool') label = np.ravel(label.values.astype(np.float32, copy=False)) return label def _dump_pandas_categorical(pandas_categorical, file_name=None): categorical_json = json.dumps(pandas_categorical, default=json_default_with_numpy) pandas_str = f'\npandas_categorical:{categorical_json}\n' if file_name is not None: with open(file_name, 'a') as f: f.write(pandas_str) return pandas_str def _load_pandas_categorical(file_name=None, model_str=None): pandas_key = 'pandas_categorical:' offset = -len(pandas_key) if file_name is not None: max_offset = -getsize(file_name) with open(file_name, 'rb') as f: while True: if offset < max_offset: offset = max_offset f.seek(offset, SEEK_END) lines = f.readlines() if len(lines) >= 2: break offset *= 2 last_line = lines[-1].decode('utf-8').strip() if not last_line.startswith(pandas_key): last_line = lines[-2].decode('utf-8').strip() elif model_str is not None: idx = model_str.rfind('\n', 0, offset) last_line = model_str[idx:].strip() if last_line.startswith(pandas_key): return json.loads(last_line[len(pandas_key):]) else: return None
[docs]class Sequence(abc.ABC): """ Generic data access interface. Object should support the following operations: .. code-block:: # Get total row number. >>> len(seq) # Random access by row index. Used for data sampling. >>> seq[10] # Range data access. Used to read data in batch when constructing Dataset. >>> seq[0:100] # Optionally specify batch_size to control range data read size. >>> seq.batch_size - With random access, **data sampling does not need to go through all data**. - With range data access, there's **no need to read all data into memory thus reduce memory usage**. .. versionadded:: 3.3.0 Attributes ---------- batch_size : int Default size of a batch. """ batch_size = 4096 # Defaults to read 4K rows in each batch. @abc.abstractmethod def __getitem__(self, idx: Union[int, slice, List[int]]) -> np.ndarray: """Return data for given row index. A basic implementation should look like this: .. code-block:: python if isinstance(idx, numbers.Integral): return self._get_one_line(idx) elif isinstance(idx, slice): return np.stack([self._get_one_line(i) for i in range(idx.start, idx.stop)]) elif isinstance(idx, list): # Only required if using ``Dataset.subset()``. return np.array([self._get_one_line(i) for i in idx]) else: raise TypeError(f"Sequence index must be integer, slice or list, got {type(idx).__name__}") Parameters ---------- idx : int, slice[int], list[int] Item index. Returns ------- result : numpy 1-D array or numpy 2-D array 1-D array if idx is int, 2-D array if idx is slice or list. """ raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __getitem__()") @abc.abstractmethod def __len__(self) -> int: """Return row count of this sequence.""" raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __len__()")
class _InnerPredictor: """_InnerPredictor of LightGBM. Not exposed to user. Used only for prediction, usually used for continued training. .. note:: Can be converted from Booster, but cannot be converted to Booster. """ def __init__(self, model_file=None, booster_handle=None, pred_parameter=None): """Initialize the _InnerPredictor. Parameters ---------- model_file : str, pathlib.Path or None, optional (default=None) Path to the model file. booster_handle : object or None, optional (default=None) Handle of Booster. pred_parameter: dict or None, optional (default=None) Other parameters for the prediction. """ 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(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(file_name=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 self.num_total_iteration = self.current_iteration() 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, start_iteration=0, num_iteration=-1, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True): """Predict logic. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. num_iteration : int, optional (default=-1) Iteration used for prediction. 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. data_has_header : bool, optional (default=False) Whether data has header. Used only for txt data. is_reshape : bool, optional (default=True) Whether to reshape to (nrow, ncol). Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). """ 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 isinstance(data, (str, Path)): with _TempFile() as f: _safe_call(_LIB.LGBM_BoosterPredictForFile( self.handle, c_str(str(data)), ctypes.c_int(int_data_has_header), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), c_str(self.pred_parameter), c_str(f.name))) preds = np.loadtxt(f.name, dtype=np.float64) nrow = preds.shape[0] elif isinstance(data, scipy.sparse.csr_matrix): preds, nrow = self.__pred_for_csr(data, start_iteration, num_iteration, predict_type) elif isinstance(data, scipy.sparse.csc_matrix): preds, nrow = self.__pred_for_csc(data, start_iteration, num_iteration, predict_type) elif isinstance(data, np.ndarray): preds, nrow = self.__pred_for_np2d(data, start_iteration, 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, start_iteration, num_iteration, predict_type) elif isinstance(data, dt_DataTable): preds, nrow = self.__pred_for_np2d(data.to_numpy(), start_iteration, num_iteration, predict_type) else: try: _log_warning('Converting data to scipy sparse matrix.') csr = scipy.sparse.csr_matrix(data) except BaseException: raise TypeError(f'Cannot predict data for type {type(data).__name__}') preds, nrow = self.__pred_for_csr(csr, start_iteration, num_iteration, predict_type) if pred_leaf: preds = preds.astype(np.int32) is_sparse = scipy.sparse.issparse(preds) or isinstance(preds, list) if is_reshape and not is_sparse and preds.size != nrow: if preds.size % nrow == 0: preds = preds.reshape(nrow, -1) else: raise ValueError(f'Length of predict result ({preds.size}) cannot be divide nrow ({nrow})') return preds def __get_num_preds(self, start_iteration, num_iteration, nrow, predict_type): """Get size of prediction result.""" if nrow > MAX_INT32: raise LightGBMError('LightGBM cannot perform prediction for data ' f'with number of rows greater than MAX_INT32 ({MAX_INT32}).\n' 'You can split your data into chunks ' 'and then concatenate predictions for them') 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(start_iteration), ctypes.c_int(num_iteration), ctypes.byref(n_preds))) return n_preds.value def __pred_for_np2d(self, mat, start_iteration, 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, start_iteration, 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(start_iteration, num_iteration, mat.shape[0], predict_type) if preds is None: preds = np.empty(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_int32(mat.shape[0]), ctypes.c_int32(mat.shape[1]), ctypes.c_int(C_API_IS_ROW_MAJOR), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), 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(start_iteration, 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.empty(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, start_iteration, num_iteration, predict_type, preds[start_idx_pred:end_idx_pred]) return preds, nrow else: return inner_predict(mat, start_iteration, num_iteration, predict_type) def __create_sparse_native(self, cs, out_shape, out_ptr_indptr, out_ptr_indices, out_ptr_data, indptr_type, data_type, is_csr=True): # create numpy array from output arrays data_indices_len = out_shape[0] indptr_len = out_shape[1] if indptr_type == C_API_DTYPE_INT32: out_indptr = cint32_array_to_numpy(out_ptr_indptr, indptr_len) elif indptr_type == C_API_DTYPE_INT64: out_indptr = cint64_array_to_numpy(out_ptr_indptr, indptr_len) else: raise TypeError("Expected int32 or int64 type for indptr") if data_type == C_API_DTYPE_FLOAT32: out_data = cfloat32_array_to_numpy(out_ptr_data, data_indices_len) elif data_type == C_API_DTYPE_FLOAT64: out_data = cfloat64_array_to_numpy(out_ptr_data, data_indices_len) else: raise TypeError("Expected float32 or float64 type for data") out_indices = cint32_array_to_numpy(out_ptr_indices, data_indices_len) # break up indptr based on number of rows (note more than one matrix in multiclass case) per_class_indptr_shape = cs.indptr.shape[0] # for CSC there is extra column added if not is_csr: per_class_indptr_shape += 1 out_indptr_arrays = np.split(out_indptr, out_indptr.shape[0] / per_class_indptr_shape) # reformat output into a csr or csc matrix or list of csr or csc matrices cs_output_matrices = [] offset = 0 for cs_indptr in out_indptr_arrays: matrix_indptr_len = cs_indptr[cs_indptr.shape[0] - 1] cs_indices = out_indices[offset + cs_indptr[0]:offset + matrix_indptr_len] cs_data = out_data[offset + cs_indptr[0]:offset + matrix_indptr_len] offset += matrix_indptr_len # same shape as input csr or csc matrix except extra column for expected value cs_shape = [cs.shape[0], cs.shape[1] + 1] # note: make sure we copy data as it will be deallocated next if is_csr: cs_output_matrices.append(scipy.sparse.csr_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) else: cs_output_matrices.append(scipy.sparse.csc_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) # free the temporary native indptr, indices, and data _safe_call(_LIB.LGBM_BoosterFreePredictSparse(out_ptr_indptr, out_ptr_indices, out_ptr_data, ctypes.c_int(indptr_type), ctypes.c_int(data_type))) if len(cs_output_matrices) == 1: return cs_output_matrices[0] return cs_output_matrices def __pred_for_csr(self, csr, start_iteration, num_iteration, predict_type): """Predict for a CSR data.""" def inner_predict(csr, start_iteration, num_iteration, predict_type, preds=None): nrow = len(csr.indptr) - 1 n_preds = self.__get_num_preds(start_iteration, num_iteration, nrow, predict_type) if preds is None: preds = np.empty(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) assert csr.shape[1] <= MAX_INT32 csr_indices = csr.indices.astype(np.int32, copy=False) _safe_call(_LIB.LGBM_BoosterPredictForCSR( self.handle, 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]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), 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 def inner_predict_sparse(csr, start_iteration, num_iteration, predict_type): ptr_indptr, type_ptr_indptr, __ = c_int_array(csr.indptr) ptr_data, type_ptr_data, _ = c_float_array(csr.data) csr_indices = csr.indices.astype(np.int32, copy=False) matrix_type = C_API_MATRIX_TYPE_CSR if type_ptr_indptr == C_API_DTYPE_INT32: out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() else: out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() if type_ptr_data == C_API_DTYPE_FLOAT32: out_ptr_data = ctypes.POINTER(ctypes.c_float)() else: out_ptr_data = ctypes.POINTER(ctypes.c_double)() out_shape = np.empty(2, dtype=np.int64) _safe_call(_LIB.LGBM_BoosterPredictSparseOutput( self.handle, 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]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), c_str(self.pred_parameter), ctypes.c_int(matrix_type), out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), ctypes.byref(out_ptr_indptr), ctypes.byref(out_ptr_indices), ctypes.byref(out_ptr_data))) matrices = self.__create_sparse_native(csr, out_shape, out_ptr_indptr, out_ptr_indices, out_ptr_data, type_ptr_indptr, type_ptr_data, is_csr=True) nrow = len(csr.indptr) - 1 return matrices, nrow if predict_type == C_API_PREDICT_CONTRIB: return inner_predict_sparse(csr, start_iteration, num_iteration, predict_type) 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(start_iteration, 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.empty(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], start_iteration, num_iteration, predict_type, preds[start_idx_pred:end_idx_pred]) return preds, nrow else: return inner_predict(csr, start_iteration, num_iteration, predict_type) def __pred_for_csc(self, csc, start_iteration, num_iteration, predict_type): """Predict for a CSC data.""" def inner_predict_sparse(csc, start_iteration, num_iteration, predict_type): ptr_indptr, type_ptr_indptr, __ = c_int_array(csc.indptr) ptr_data, type_ptr_data, _ = c_float_array(csc.data) csc_indices = csc.indices.astype(np.int32, copy=False) matrix_type = C_API_MATRIX_TYPE_CSC if type_ptr_indptr == C_API_DTYPE_INT32: out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() else: out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() if type_ptr_data == C_API_DTYPE_FLOAT32: out_ptr_data = ctypes.POINTER(ctypes.c_float)() else: out_ptr_data = ctypes.POINTER(ctypes.c_double)() out_shape = np.empty(2, dtype=np.int64) _safe_call(_LIB.LGBM_BoosterPredictSparseOutput( self.handle, 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]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), c_str(self.pred_parameter), ctypes.c_int(matrix_type), out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), ctypes.byref(out_ptr_indptr), ctypes.byref(out_ptr_indices), ctypes.byref(out_ptr_data))) matrices = self.__create_sparse_native(csc, out_shape, out_ptr_indptr, out_ptr_indices, out_ptr_data, type_ptr_indptr, type_ptr_data, is_csr=False) nrow = csc.shape[0] return matrices, nrow nrow = csc.shape[0] if nrow > MAX_INT32: return self.__pred_for_csr(csc.tocsr(), start_iteration, num_iteration, predict_type) if predict_type == C_API_PREDICT_CONTRIB: return inner_predict_sparse(csc, start_iteration, num_iteration, predict_type) n_preds = self.__get_num_preds(start_iteration, num_iteration, nrow, predict_type) preds = np.empty(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) assert csc.shape[0] <= MAX_INT32 csc_indices = csc.indices.astype(np.int32, copy=False) _safe_call(_LIB.LGBM_BoosterPredictForCSC( self.handle, 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]), ctypes.c_int(predict_type), ctypes.c_int(start_iteration), 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 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]class Dataset: """Dataset in LightGBM."""
[docs] def __init__(self, data, label=None, reference=None, weight=None, group=None, init_score=None, feature_name='auto', categorical_feature='auto', params=None, free_raw_data=True): """Initialize Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame 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 data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None) Init score for Dataset. feature_name : list of str, or 'auto', optional (default="auto") Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default="auto") Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. params : dict or None, optional (default=None) Other parameters for Dataset. 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.feature_name = feature_name self.categorical_feature = categorical_feature self.params = deepcopy(params) self.free_raw_data = free_raw_data self.used_indices = None self.need_slice = True self._predictor = None self.pandas_categorical = None self.params_back_up = None self.feature_penalty = None self.monotone_constraints = None self.version = 0 self._start_row = 0 # Used when pushing rows one by one.
def __del__(self): try: self._free_handle() except AttributeError: pass def _create_sample_indices(self, total_nrow: int) -> np.ndarray: """Get an array of randomly chosen indices from this ``Dataset``. Indices are sampled without replacement. Parameters ---------- total_nrow : int Total number of rows to sample from. If this value is greater than the value of parameter ``bin_construct_sample_cnt``, only ``bin_construct_sample_cnt`` indices will be used. If Dataset has multiple input data, this should be the sum of rows of every file. Returns ------- indices : numpy array Indices for sampled data. """ param_str = param_dict_to_str(self.get_params()) sample_cnt = _get_sample_count(total_nrow, param_str) indices = np.empty(sample_cnt, dtype=np.int32) ptr_data, _, _ = c_int_array(indices) actual_sample_cnt = ctypes.c_int32(0) _safe_call(_LIB.LGBM_SampleIndices( ctypes.c_int32(total_nrow), c_str(param_str), ptr_data, ctypes.byref(actual_sample_cnt), )) assert sample_cnt == actual_sample_cnt.value return indices def _init_from_ref_dataset(self, total_nrow: int, ref_dataset: 'Dataset') -> 'Dataset': """Create dataset from a reference dataset. Parameters ---------- total_nrow : int Number of rows expected to add to dataset. ref_dataset : Dataset Reference dataset to extract meta from. Returns ------- self : Dataset Constructed Dataset object. """ self.handle = ctypes.c_void_p() _safe_call(_LIB.LGBM_DatasetCreateByReference( ref_dataset, ctypes.c_int64(total_nrow), ctypes.byref(self.handle), )) return self def _init_from_sample( self, sample_data: List[np.ndarray], sample_indices: List[np.ndarray], sample_cnt: int, total_nrow: int, ) -> "Dataset": """Create Dataset from sampled data structures. Parameters ---------- sample_data : list of numpy array Sample data for each column. sample_indices : list of numpy array Sample data row index for each column. sample_cnt : int Number of samples. total_nrow : int Total number of rows for all input files. Returns ------- self : Dataset Constructed Dataset object. """ ncol = len(sample_indices) assert len(sample_data) == ncol, "#sample data column != #column indices" for i in range(ncol): if sample_data[i].dtype != np.double: raise ValueError(f"sample_data[{i}] type {sample_data[i].dtype} is not double") if sample_indices[i].dtype != np.int32: raise ValueError(f"sample_indices[{i}] type {sample_indices[i].dtype} is not int32") # c type: double** # each double* element points to start of each column of sample data. sample_col_ptr = (ctypes.POINTER(ctypes.c_double) * ncol)() # c type int** # each int* points to start of indices for each column indices_col_ptr = (ctypes.POINTER(ctypes.c_int32) * ncol)() for i in range(ncol): sample_col_ptr[i] = c_float_array(sample_data[i])[0] indices_col_ptr[i] = c_int_array(sample_indices[i])[0] num_per_col = np.array([len(d) for d in sample_indices], dtype=np.int32) num_per_col_ptr, _, _ = c_int_array(num_per_col) self.handle = ctypes.c_void_p() params_str = param_dict_to_str(self.get_params()) _safe_call(_LIB.LGBM_DatasetCreateFromSampledColumn( ctypes.cast(sample_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), ctypes.cast(indices_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_int32))), ctypes.c_int32(ncol), num_per_col_ptr, ctypes.c_int32(sample_cnt), ctypes.c_int32(total_nrow), c_str(params_str), ctypes.byref(self.handle), )) return self def _push_rows(self, data: np.ndarray) -> 'Dataset': """Add rows to Dataset. Parameters ---------- data : numpy 1-D array New data to add to the Dataset. Returns ------- self : Dataset Dataset object. """ nrow, ncol = data.shape data = data.reshape(data.size) data_ptr, data_type, _ = c_float_array(data) _safe_call(_LIB.LGBM_DatasetPushRows( self.handle, data_ptr, data_type, ctypes.c_int32(nrow), ctypes.c_int32(ncol), ctypes.c_int32(self._start_row), )) self._start_row += nrow return self
[docs] def get_params(self): """Get the used parameters in the Dataset. Returns ------- params : dict or None The used parameters in this Dataset object. """ if self.params is not None: # no min_data, nthreads and verbose in this function dataset_params = _ConfigAliases.get("bin_construct_sample_cnt", "categorical_feature", "data_random_seed", "enable_bundle", "feature_pre_filter", "forcedbins_filename", "group_column", "header", "ignore_column", "is_enable_sparse", "label_column", "linear_tree", "max_bin", "max_bin_by_feature", "min_data_in_bin", "pre_partition", "precise_float_parser", "two_round", "use_missing", "weight_column", "zero_as_missing") return {k: v for k, v in self.params.items() if k in dataset_params}
def _free_handle(self): if self.handle is not None: _safe_call(_LIB.LGBM_DatasetFree(self.handle)) self.handle = None self.need_slice = True if self.used_indices is not None: self.data = None return self def _set_init_score_by_predictor(self, predictor, data, used_indices=None): data_has_header = False if isinstance(data, (str, Path)): # check data has header or not data_has_header = any(self.params.get(alias, False) for alias in _ConfigAliases.get("header")) num_data = self.num_data() if predictor is not None: init_score = predictor.predict(data, raw_score=True, data_has_header=data_has_header, is_reshape=False) if used_indices is not None: assert not self.need_slice if isinstance(data, (str, Path)): sub_init_score = np.empty(num_data * predictor.num_class, dtype=np.float64) assert num_data == len(used_indices) for i in range(len(used_indices)): for j in range(predictor.num_class): sub_init_score[i * predictor.num_class + j] = init_score[used_indices[i] * predictor.num_class + j] init_score = sub_init_score if predictor.num_class > 1: # need to regroup init_score new_init_score = np.empty(init_score.size, dtype=np.float64) for i in range(num_data): for j in range(predictor.num_class): new_init_score[j * num_data + i] = init_score[i * predictor.num_class + j] init_score = new_init_score elif self.init_score is not None: init_score = np.zeros(self.init_score.shape, dtype=np.float64) else: return self self.set_init_score(init_score) def _lazy_init(self, data, label=None, reference=None, weight=None, group=None, init_score=None, predictor=None, 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) # 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.keys(): if key in args_names: _log_warning(f'{key} keyword has been found in `params` and will be ignored.\n' f'Please use {key} argument of the Dataset constructor to pass this parameter.') # 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, str) and name in feature_dict: categorical_indices.add(feature_dict[name]) elif isinstance(name, int): categorical_indices.add(name) else: raise TypeError(f"Wrong type({type(name).__name__}) or unknown name({name}) in categorical_feature") if categorical_indices: for cat_alias in _ConfigAliases.get("categorical_feature"): if cat_alias in params: # If the params[cat_alias] is equal to categorical_indices, do not report the warning. if not(isinstance(params[cat_alias], list) and set(params[cat_alias]) == categorical_indices): _log_warning(f'{cat_alias} in param dict is overridden.') params.pop(cat_alias, None) params['categorical_column'] = sorted(categorical_indices) params_str = param_dict_to_str(params) self.params = 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, (str, Path)): self.handle = ctypes.c_void_p() _safe_call(_LIB.LGBM_DatasetCreateFromFile( c_str(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: if all(isinstance(x, np.ndarray) for x in data): self.__init_from_list_np2d(data, params_str, ref_dataset) elif all(isinstance(x, Sequence) for x in data): self.__init_from_seqs(data, ref_dataset) else: raise TypeError('Data list can only be of ndarray or Sequence') elif isinstance(data, Sequence): self.__init_from_seqs([data], ref_dataset) elif isinstance(data, dt_DataTable): self.__init_from_np2d(data.to_numpy(), 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(f'Cannot initialize Dataset from {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) if isinstance(predictor, _InnerPredictor): if self._predictor is None and init_score is not None: _log_warning("The init_score will be overridden by the prediction of init_model.") self._set_init_score_by_predictor(predictor, data) elif init_score is not None: self.set_init_score(init_score) elif predictor is not None: raise TypeError(f'Wrong predictor type {type(predictor).__name__}') # set feature names return self.set_feature_name(feature_name) @staticmethod def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]): offset = 0 seq_id = 0 seq = seqs[seq_id] for row_id in indices: assert row_id >= offset, "sample indices are expected to be monotonic" while row_id >= offset + len(seq): offset += len(seq) seq_id += 1 seq = seqs[seq_id] id_in_seq = row_id - offset row = seq[id_in_seq] yield row if row.flags['OWNDATA'] else row.copy() def __sample(self, seqs: List[Sequence], total_nrow: int) -> Tuple[List[np.ndarray], List[np.ndarray]]: """Sample data from seqs. Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats() Returns ------- sampled_rows, sampled_row_indices """ indices = self._create_sample_indices(total_nrow) # Select sampled rows, transpose to column order. sampled = np.array([row for row in self._yield_row_from_seqlist(seqs, indices)]) sampled = sampled.T filtered = [] filtered_idx = [] sampled_row_range = np.arange(len(indices), dtype=np.int32) for col in sampled: col_predicate = (np.abs(col) > ZERO_THRESHOLD) | np.isnan(col) filtered_col = col[col_predicate] filtered_row_idx = sampled_row_range[col_predicate] filtered.append(filtered_col) filtered_idx.append(filtered_row_idx) return filtered, filtered_idx def __init_from_seqs(self, seqs: List[Sequence], ref_dataset: Optional['Dataset'] = None): """ Initialize data from list of Sequence objects. Sequence: Generic Data Access Object Supports random access and access by batch if properly defined by user Data scheme uniformity are trusted, not checked """ total_nrow = sum(len(seq) for seq in seqs) # create validation dataset from ref_dataset if ref_dataset is not None: self._init_from_ref_dataset(total_nrow, ref_dataset) else: param_str = param_dict_to_str(self.get_params()) sample_cnt = _get_sample_count(total_nrow, param_str) sample_data, col_indices = self.__sample(seqs, total_nrow) self._init_from_sample(sample_data, col_indices, sample_cnt, total_nrow) for seq in seqs: nrow = len(seq) batch_size = getattr(seq, 'batch_size', None) or Sequence.batch_size for start in range(0, nrow, batch_size): end = min(start + batch_size, nrow) self._push_rows(seq[start:end]) return self 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_int32(mat.shape[0]), ctypes.c_int32(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 a list of 2-D numpy matrices.""" ncol = mats[0].shape[1] nrow = np.empty((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_int32(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_int32(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(f'Length mismatch: {len(csr.indices)} vs {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) assert csr.shape[1] <= MAX_INT32 csr_indices = csr.indices.astype(np.int32, copy=False) _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(f'Length mismatch: {len(csc.indices)} vs {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) assert csc.shape[0] <= MAX_INT32 csc_indices = csc.indices.astype(np.int32, copy=False) _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 @staticmethod def _compare_params_for_warning( params: Optional[Dict[str, Any]], other_params: Optional[Dict[str, Any]], ignore_keys: Set[str] ) -> bool: """Compare two dictionaries with params ignoring some keys. It is only for the warning purpose. Parameters ---------- params : dict or None One dictionary with parameters to compare. other_params : dict or None Another dictionary with parameters to compare. ignore_keys : set Keys that should be ignored during comparing two dictionaries. Returns ------- compare_result : bool Returns whether two dictionaries with params are equal. """ if params is None: params = {} if other_params is None: other_params = {} for k in other_params: if k not in ignore_keys: if k not in params or params[k] != other_params[k]: return False for k in params: if k not in ignore_keys: if k not in other_params or params[k] != other_params[k]: return False return True
[docs] def construct(self): """Lazy init. Returns ------- self : Dataset Constructed Dataset object. """ if self.handle is None: if self.reference is not None: reference_params = self.reference.get_params() params = self.get_params() if params != reference_params: if not self._compare_params_for_warning( params=params, other_params=reference_params, ignore_keys=_ConfigAliases.get("categorical_feature") ): _log_warning('Overriding the parameters from Reference Dataset.') self._update_params(reference_params) 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, 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(np.int32, copy=False) _, 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_int32(used_indices.shape[0]), c_str(params_str), ctypes.byref(self.handle))) if not self.free_raw_data: self.get_data() if self.group is not None: self.set_group(self.group) if self.get_label() is None: raise ValueError("Label should not be None.") if isinstance(self._predictor, _InnerPredictor) and self._predictor is not self.reference._predictor: self.get_data() self._set_init_score_by_predictor(self._predictor, self.data, used_indices) 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, 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, params=None): """Create validation data align with current Dataset. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array Data source of Dataset. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. label : list, numpy 1-D array, pandas Series / one-column DataFrame 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 data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None) Init score for Dataset. params : dict or None, optional (default=None) Other parameters for validation Dataset. Returns ------- valid : Dataset Validation Dataset with reference to self. """ ret = Dataset(data, label=label, reference=self, weight=weight, group=group, init_score=init_score, 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) These parameters will be passed to Dataset constructor. 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, free_raw_data=self.free_raw_data) ret._predictor = self._predictor ret.pandas_categorical = self.pandas_categorical ret.used_indices = sorted(used_indices) return ret
[docs] def save_binary(self, filename): """Save Dataset to a binary file. .. note:: Please note that `init_score` is not saved in binary file. If you need it, please set it again after loading Dataset. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call(_LIB.LGBM_DatasetSaveBinary( self.construct().handle, c_str(str(filename)))) return self
def _update_params(self, params): if not params: return self params = deepcopy(params) def update(): if not self.params: self.params = params else: self.params_back_up = deepcopy(self.params) self.params.update(params) if self.handle is None: update() elif params is not None: ret = _LIB.LGBM_DatasetUpdateParamChecking( c_str(param_dict_to_str(self.params)), c_str(param_dict_to_str(params))) if ret != 0: # could be updated if data is not freed if self.data is not None: update() self._free_handle() else: raise LightGBMError(_LIB.LGBM_GetLastError().decode('utf-8')) return self def _reverse_update_params(self): if self.handle is None: self.params = 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 : str The field name of the information. data : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None The data to be set. Returns ------- self : Dataset Dataset with set property. """ if self.handle is None: raise Exception(f"Cannot set {field_name} before construct dataset") 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 if field_name == 'init_score': dtype = np.float64 if _is_1d_collection(data): data = list_to_1d_numpy(data, dtype, name=field_name) elif _is_2d_collection(data): data = _data_to_2d_numpy(data, dtype, name=field_name) data = data.ravel(order='F') else: raise TypeError( 'init_score must be list, numpy 1-D array or pandas Series.\n' 'In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame.' ) else: dtype = np.int32 if field_name == 'group' else np.float32 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(f"Expected np.float32/64 or np.int32, met type({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))) self.version += 1 return self
[docs] def get_field(self, field_name): """Get property from the Dataset. Parameters ---------- field_name : str The field name of the information. Returns ------- info : numpy array or None A numpy array with information from the Dataset. """ if self.handle is None: raise Exception(f"Cannot get {field_name} before construct Dataset") tmp_out_len = ctypes.c_int(0) out_type = ctypes.c_int(0) 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: arr = cint32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)), tmp_out_len.value) elif out_type.value == C_API_DTYPE_FLOAT32: arr = cfloat32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)), tmp_out_len.value) elif out_type.value == C_API_DTYPE_FLOAT64: arr = cfloat64_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)), tmp_out_len.value) else: raise TypeError("Unknown type") if field_name == 'init_score': num_data = self.num_data() num_classes = arr.size // num_data if num_classes > 1: arr = arr.reshape((num_data, num_classes), order='F') return arr
[docs] def set_categorical_feature(self, categorical_feature): """Set categorical features. Parameters ---------- categorical_feature : list of int or str 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': return self else: if self.categorical_feature != 'auto': _log_warning('categorical_feature in Dataset is overridden.\n' f'New categorical_feature is {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. It is not recommended for user to call this function. Please use init_model argument in engine.train() or engine.cv() instead. """ if predictor is self._predictor and (predictor is None or predictor.current_iteration() == self._predictor.current_iteration()): return self if self.handle is None: self._predictor = predictor elif self.data is not None: self._predictor = predictor self._set_init_score_by_predictor(self._predictor, self.data) elif self.used_indices is not None and self.reference is not None and self.reference.data is not None: self._predictor = predictor self._set_init_score_by_predictor(self._predictor, self.reference.data, self.used_indices) else: raise LightGBMError("Cannot set predictor after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this.") return self
[docs] def set_reference(self, reference): """Set reference Dataset. Parameters ---------- reference : Dataset Reference that is used as a template to construct 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 upstream 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 str 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(f"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match") 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 Series / one-column DataFrame 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) self.label = self.get_field('label') # original values can be modified at cpp side 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) self.weight = self.get_field('weight') # original values can be modified at cpp side return self
[docs] def set_init_score(self, init_score): """Set init score of Booster to start from. Parameters ---------- init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), 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: self.set_field('init_score', init_score) self.init_score = self.get_field('init_score') # original values can be modified at cpp side 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/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. 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_feature_name(self): """Get the names of columns (features) in the Dataset. Returns ------- feature_names : list of str The names of columns (features) in the Dataset. """ if self.handle is None: raise LightGBMError("Cannot get feature_name before construct dataset") num_feature = self.num_feature() tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) _safe_call(_LIB.LGBM_DatasetGetFeatureNames( self.handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) if num_feature != tmp_out_len.value: raise ValueError("Length of feature names doesn't equal with num_feature") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)] ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) _safe_call(_LIB.LGBM_DatasetGetFeatureNames( self.handle, ctypes.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) return [string_buffers[i].value.decode('utf-8') for i in range(num_feature)]
[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_data(self): """Get the raw data of the Dataset. Returns ------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array or None Raw data used in the Dataset construction. """ if self.handle is None: raise Exception("Cannot get data before construct Dataset") if self.need_slice and self.used_indices is not None and self.reference is not None: self.data = self.reference.data if self.data is not None: if isinstance(self.data, np.ndarray) or scipy.sparse.issparse(self.data): self.data = self.data[self.used_indices, :] elif isinstance(self.data, pd_DataFrame): self.data = self.data.iloc[self.used_indices].copy() elif isinstance(self.data, dt_DataTable): self.data = self.data[self.used_indices, :] elif isinstance(self.data, Sequence): self.data = self.data[self.used_indices] elif isinstance(self.data, list) and len(self.data) > 0 and all(isinstance(x, Sequence) for x in self.data): self.data = np.array([row for row in self._yield_row_from_seqlist(self.data, self.used_indices)]) else: _log_warning(f"Cannot subset {type(self.data).__name__} type of raw data.\n" "Returning original raw data") self.need_slice = False if self.data is None: raise LightGBMError("Cannot call `get_data` after freed raw data, " "set free_raw_data=False when construct Dataset to avoid this.") return self.data
[docs] def get_group(self): """Get the group of the Dataset. Returns ------- group : numpy array or None Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. """ 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(0) _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(0) _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. Starts with r, then goes 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] def add_features_from(self, other): """Add features from other Dataset to the current Dataset. Both Datasets must be constructed before calling this method. Parameters ---------- other : Dataset The Dataset to take features from. Returns ------- self : Dataset Dataset with the new features added. """ if self.handle is None or other.handle is None: raise ValueError('Both source and target Datasets must be constructed before adding features') _safe_call(_LIB.LGBM_DatasetAddFeaturesFrom(self.handle, other.handle)) was_none = self.data is None old_self_data_type = type(self.data).__name__ if other.data is None: self.data = None elif self.data is not None: if isinstance(self.data, np.ndarray): if isinstance(other.data, np.ndarray): self.data = np.hstack((self.data, other.data)) elif scipy.sparse.issparse(other.data): self.data = np.hstack((self.data, other.data.toarray())) elif isinstance(other.data, pd_DataFrame): self.data = np.hstack((self.data, other.data.values)) elif isinstance(other.data, dt_DataTable): self.data = np.hstack((self.data, other.data.to_numpy())) else: self.data = None elif scipy.sparse.issparse(self.data): sparse_format = self.data.getformat() if isinstance(other.data, np.ndarray) or scipy.sparse.issparse(other.data): self.data = scipy.sparse.hstack((self.data, other.data), format=sparse_format) elif isinstance(other.data, pd_DataFrame): self.data = scipy.sparse.hstack((self.data, other.data.values), format=sparse_format) elif isinstance(other.data, dt_DataTable): self.data = scipy.sparse.hstack((self.data, other.data.to_numpy()), format=sparse_format) else: self.data = None elif isinstance(self.data, pd_DataFrame): if not PANDAS_INSTALLED: raise LightGBMError("Cannot add features to DataFrame type of raw data " "without pandas installed. " "Install pandas and restart your session.") if isinstance(other.data, np.ndarray): self.data = concat((self.data, pd_DataFrame(other.data)), axis=1, ignore_index=True) elif scipy.sparse.issparse(other.data): self.data = concat((self.data, pd_DataFrame(other.data.toarray())), axis=1, ignore_index=True) elif isinstance(other.data, pd_DataFrame): self.data = concat((self.data, other.data), axis=1, ignore_index=True) elif isinstance(other.data, dt_DataTable): self.data = concat((self.data, pd_DataFrame(other.data.to_numpy())), axis=1, ignore_index=True) else: self.data = None elif isinstance(self.data, dt_DataTable): if isinstance(other.data, np.ndarray): self.data = dt_DataTable(np.hstack((self.data.to_numpy(), other.data))) elif scipy.sparse.issparse(other.data): self.data = dt_DataTable(np.hstack((self.data.to_numpy(), other.data.toarray()))) elif isinstance(other.data, pd_DataFrame): self.data = dt_DataTable(np.hstack((self.data.to_numpy(), other.data.values))) elif isinstance(other.data, dt_DataTable): self.data = dt_DataTable(np.hstack((self.data.to_numpy(), other.data.to_numpy()))) else: self.data = None else: self.data = None if self.data is None: err_msg = (f"Cannot add features from {type(other.data).__name__} type of raw data to " f"{old_self_data_type} type of raw data.\n") err_msg += ("Set free_raw_data=False when construct Dataset to avoid this" if was_none else "Freeing raw data") _log_warning(err_msg) self.feature_name = self.get_feature_name() _log_warning("Reseting categorical features.\n" "You can set new categorical features via ``set_categorical_feature`` method") self.categorical_feature = "auto" self.pandas_categorical = None return self
def _dump_text(self, filename): """Save Dataset to a text file. This format cannot be loaded back in by LightGBM, but is useful for debugging purposes. Parameters ---------- filename : str or pathlib.Path Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call(_LIB.LGBM_DatasetDumpText( self.construct().handle, c_str(str(filename)))) return self
[docs]class Booster: """Booster in LightGBM."""
[docs] def __init__(self, params=None, train_set=None, model_file=None, model_str=None): """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 : str, pathlib.Path or None, optional (default=None) Path to the model file. model_str : str or None, optional (default=None) Model will be loaded from this string. """ 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 deepcopy(params) if train_set is not None: # Training task if not isinstance(train_set, Dataset): raise TypeError(f'Training data should be Dataset instance, met {type(train_set).__name__}') params = _choose_param_value( main_param_name="machines", params=params, default_value=None ) # if "machines" is given, assume user wants to do distributed learning, and set up network if params["machines"] is None: params.pop("machines", None) else: machines = params["machines"] if isinstance(machines, str): num_machines_from_machine_list = len(machines.split(',')) elif isinstance(machines, (list, set)): num_machines_from_machine_list = len(machines) machines = ','.join(machines) else: raise ValueError("Invalid machines in params.") params = _choose_param_value( main_param_name="num_machines", params=params, default_value=num_machines_from_machine_list ) params = _choose_param_value( main_param_name="local_listen_port", params=params, default_value=12400 ) self.set_network( machines=machines, local_listen_port=params["local_listen_port"], listen_time_out=params.get("time_out", 120), num_machines=params["num_machines"] ) # construct booster object train_set.construct() # copy the parameters from train_set params.update(train_set.get_params()) params_str = param_dict_to_str(params) self.handle = ctypes.c_void_p() _safe_call(_LIB.LGBM_BoosterCreate( train_set.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 self.train_set_version = train_set.version 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(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(file_name=model_file) elif model_str is not None: self.model_from_string(model_str, verbose="_silent_false") else: raise TypeError('Need at least one training dataset or model file or model string ' 'to create Booster instance') self.params = params
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) 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: Union[List[str], Set[str], str], local_listen_port: int = 12400, listen_time_out: int = 120, num_machines: int = 1 ) -> "Booster": """Set the network configuration. Parameters ---------- machines : list, set or str 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 distributed learning application. Returns ------- self : Booster Booster with set network. """ if isinstance(machines, (list, set)): machines = ','.join(machines) _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 trees_to_dataframe(self): """Parse the fitted model and return in an easy-to-read pandas DataFrame. The returned DataFrame has the following columns. - ``tree_index`` : int64, which tree a node belongs to. 0-based, so a value of ``6``, for example, means "this node is in the 7th tree". - ``node_depth`` : int64, how far a node is from the root of the tree. The root node has a value of ``1``, its direct children are ``2``, etc. - ``node_index`` : str, unique identifier for a node. - ``left_child`` : str, ``node_index`` of the child node to the left of a split. ``None`` for leaf nodes. - ``right_child`` : str, ``node_index`` of the child node to the right of a split. ``None`` for leaf nodes. - ``parent_index`` : str, ``node_index`` of this node's parent. ``None`` for the root node. - ``split_feature`` : str, name of the feature used for splitting. ``None`` for leaf nodes. - ``split_gain`` : float64, gain from adding this split to the tree. ``NaN`` for leaf nodes. - ``threshold`` : float64, value of the feature used to decide which side of the split a record will go down. ``NaN`` for leaf nodes. - ``decision_type`` : str, logical operator describing how to compare a value to ``threshold``. For example, ``split_feature = "Column_10", threshold = 15, decision_type = "<="`` means that records where ``Column_10 <= 15`` follow the left side of the split, otherwise follows the right side of the split. ``None`` for leaf nodes. - ``missing_direction`` : str, split direction that missing values should go to. ``None`` for leaf nodes. - ``missing_type`` : str, describes what types of values are treated as missing. - ``value`` : float64, predicted value for this leaf node, multiplied by the learning rate. - ``weight`` : float64 or int64, sum of hessian (second-order derivative of objective), summed over observations that fall in this node. - ``count`` : int64, number of records in the training data that fall into this node. Returns ------- result : pandas DataFrame Returns a pandas DataFrame of the parsed model. """ if not PANDAS_INSTALLED: raise LightGBMError('This method cannot be run without pandas installed. ' 'You must install pandas and restart your session to use this method.') if self.num_trees() == 0: raise LightGBMError('There are no trees in this Booster and thus nothing to parse') def _is_split_node(tree): return 'split_index' in tree.keys() def create_node_record(tree, node_depth=1, tree_index=None, feature_names=None, parent_node=None): def _get_node_index(tree, tree_index): tree_num = f'{tree_index}-' if tree_index is not None else '' is_split = _is_split_node(tree) node_type = 'S' if is_split else 'L' # if a single node tree it won't have `leaf_index` so return 0 node_num = tree.get('split_index' if is_split else 'leaf_index', 0) return f"{tree_num}{node_type}{node_num}" def _get_split_feature(tree, feature_names): if _is_split_node(tree): if feature_names is not None: feature_name = feature_names[tree['split_feature']] else: feature_name = tree['split_feature'] else: feature_name = None return feature_name def _is_single_node_tree(tree): return set(tree.keys()) == {'leaf_value'} # Create the node record, and populate universal data members node = OrderedDict() node['tree_index'] = tree_index node['node_depth'] = node_depth node['node_index'] = _get_node_index(tree, tree_index) node['left_child'] = None node['right_child'] = None node['parent_index'] = parent_node node['split_feature'] = _get_split_feature(tree, feature_names) node['split_gain'] = None node['threshold'] = None node['decision_type'] = None node['missing_direction'] = None node['missing_type'] = None node['value'] = None node['weight'] = None node['count'] = None # Update values to reflect node type (leaf or split) if _is_split_node(tree): node['left_child'] = _get_node_index(tree['left_child'], tree_index) node['right_child'] = _get_node_index(tree['right_child'], tree_index) node['split_gain'] = tree['split_gain'] node['threshold'] = tree['threshold'] node['decision_type'] = tree['decision_type'] node['missing_direction'] = 'left' if tree['default_left'] else 'right' node['missing_type'] = tree['missing_type'] node['value'] = tree['internal_value'] node['weight'] = tree['internal_weight'] node['count'] = tree['internal_count'] else: node['value'] = tree['leaf_value'] if not _is_single_node_tree(tree): node['weight'] = tree['leaf_weight'] node['count'] = tree['leaf_count'] return node def tree_dict_to_node_list(tree, node_depth=1, tree_index=None, feature_names=None, parent_node=None): node = create_node_record(tree, node_depth=node_depth, tree_index=tree_index, feature_names=feature_names, parent_node=parent_node) res = [node] if _is_split_node(tree): # traverse the next level of the tree children = ['left_child', 'right_child'] for child in children: subtree_list = tree_dict_to_node_list( tree[child], node_depth=node_depth + 1, tree_index=tree_index, feature_names=feature_names, parent_node=node['node_index']) # In tree format, "subtree_list" is a list of node records (dicts), # and we add node to the list. res.extend(subtree_list) return res model_dict = self.dump_model() feature_names = model_dict['feature_names'] model_list = [] for tree in model_dict['tree_info']: model_list.extend(tree_dict_to_node_list(tree['tree_structure'], tree_index=tree['tree_index'], feature_names=feature_names)) return pd_DataFrame(model_list, columns=model_list[0].keys())
[docs] def set_train_data_name(self, name): """Set the name to the training Dataset. Parameters ---------- name : str 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 : str Name of validation data. Returns ------- self : Booster Booster with set validation data. """ if not isinstance(data, Dataset): raise TypeError(f'Validation data should be Dataset instance, met {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. """ 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. Should accept two parameters: preds, train_data, and return (grad, hess). preds : numpy 1-D array The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. train_data : Dataset The training dataset. grad : list, numpy 1-D array or pandas Series The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : list, numpy 1-D array or pandas Series The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. 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 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 None and self.train_set_version != self.train_set.version: train_set = self.train_set is_the_same_train_set = False else: is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version if train_set is not None and not is_the_same_train_set: if not isinstance(train_set, Dataset): raise TypeError(f'Training data should be Dataset instance, met {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 self.train_set_version = self.train_set.version is_finished = ctypes.c_int(0) if fobj is None: if self.__set_objective_to_none: raise LightGBMError('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:: Score is returned before any transformation, e.g. it is raw margin instead of probability of positive class for binary task. 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 : list, numpy 1-D array or pandas Series The value of the first order derivative (gradient) of the loss with respect to the elements of score for each sample point. hess : list, numpy 1-D array or pandas Series The value of the second order derivative (Hessian) of the loss with respect to the elements of score for each sample point. 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(f"Lengths of gradient({len(grad)}) and hessian({len(hess)}) don't match") 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 upper_bound(self): """Get upper bound value of a model. Returns ------- upper_bound : double Upper bound value of the model. """ ret = ctypes.c_double(0) _safe_call(_LIB.LGBM_BoosterGetUpperBoundValue( self.handle, ctypes.byref(ret))) return ret.value
[docs] def lower_bound(self): """Get lower bound value of a model. Returns ------- lower_bound : double Lower bound value of the model. """ ret = ctypes.c_double(0) _safe_call(_LIB.LGBM_BoosterGetLowerBoundValue( self.handle, ctypes.byref(ret))) return ret.value
[docs] def eval(self, data, name, feval=None): """Evaluate for data. Parameters ---------- data : Dataset Data for the evaluating. name : str Name of the data. feval : callable or None, optional (default=None) Customized evaluation function. Should accept two parameters: preds, eval_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array The predicted values. If ``fobj`` is specified, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. eval_data : Dataset The evaluation dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. 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]. 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, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array The predicted values. If ``fobj`` is specified, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. train_data : Dataset The training dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. 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]. 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, valid_data, and return (eval_name, eval_result, is_higher_better) or list of such tuples. preds : numpy 1-D array The predicted values. If ``fobj`` is specified, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. valid_data : Dataset The validation dataset. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. 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]. 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, importance_type='split'): """Save Booster to file. Parameters ---------- filename : str or pathlib.Path 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. importance_type : str, optional (default="split") What type of feature importance should be saved. 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 ------- self : Booster Returns self. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] _safe_call(_LIB.LGBM_BoosterSaveModel( self.handle, ctypes.c_int(start_iteration), ctypes.c_int(num_iteration), ctypes.c_int(importance_type_int), c_str(str(filename)))) _dump_pandas_categorical(self.pandas_categorical, filename) return self
[docs] def shuffle_models(self, start_iteration=0, end_iteration=-1): """Shuffle models. Parameters ---------- start_iteration : int, optional (default=0) The first iteration that will be shuffled. end_iteration : int, optional (default=-1) The last iteration that will be shuffled. If <= 0, means the last available iteration. Returns ------- self : Booster Booster with shuffled models. """ _safe_call(_LIB.LGBM_BoosterShuffleModels( self.handle, ctypes.c_int(start_iteration), ctypes.c_int(end_iteration))) return self
[docs] def model_from_string(self, model_str, verbose='warn'): """Load Booster from a string. Parameters ---------- model_str : str 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))) self.__num_class = out_num_class.value self.pandas_categorical = _load_pandas_categorical(model_str=model_str) return self
[docs] def model_to_string(self, num_iteration=None, start_iteration=0, importance_type='split'): """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. importance_type : str, optional (default="split") What type of feature importance should be saved. 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 ------- str_repr : str String representation of Booster. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] 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_int(importance_type_int), 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_int(importance_type_int), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer)) ret = string_buffer.value.decode('utf-8') ret += _dump_pandas_categorical(self.pandas_categorical) return ret
[docs] def dump_model(self, num_iteration=None, start_iteration=0, importance_type='split', object_hook=None): """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. importance_type : str, optional (default="split") What type of feature importance should be dumped. 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. object_hook : callable or None, optional (default=None) If not None, ``object_hook`` is a function called while parsing the json string returned by the C API. It may be used to alter the json, to store specific values while building the json structure. It avoids walking through the structure again. It saves a significant amount of time if the number of trees is huge. Signature is ``def object_hook(node: dict) -> dict``. None is equivalent to ``lambda node: node``. See documentation of ``json.loads()`` for further details. Returns ------- json_repr : dict JSON format of Booster. """ if num_iteration is None: num_iteration = self.best_iteration importance_type_int = FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] 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_int(importance_type_int), 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_int(importance_type_int), ctypes.c_int64(actual_len), ctypes.byref(tmp_out_len), ptr_string_buffer)) ret = json.loads(string_buffer.value.decode('utf-8'), object_hook=object_hook) ret['pandas_categorical'] = json.loads(json.dumps(self.pandas_categorical, default=json_default_with_numpy)) return ret
[docs] def predict(self, data, start_iteration=0, num_iteration=None, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True, **kwargs): """Make a prediction. Parameters ---------- data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse Data source for prediction. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). start_iteration : int, optional (default=0) Start index of the iteration to predict. If <= 0, starts from the first iteration. num_iteration : int or None, optional (default=None) Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from ``start_iteration`` are used (no limits). If <= 0, all iterations from ``start_iteration`` are used (no limits). 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 explanations for your model's predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value. data_has_header : bool, optional (default=False) Whether the data has header. Used only if data is str. is_reshape : bool, optional (default=True) If True, result is reshaped to [nrow, ncol]. **kwargs Other parameters for the prediction. Returns ------- result : numpy array, scipy.sparse or list of scipy.sparse Prediction result. Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). """ predictor = self._to_predictor(deepcopy(kwargs)) if num_iteration is None: if start_iteration <= 0: num_iteration = self.best_iteration else: num_iteration = -1 return predictor.predict(data, start_iteration, 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 : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse Data source for refit. If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). label : list, numpy 1-D array or pandas Series / one-column DataFrame 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. """ if self.__set_objective_to_none: raise LightGBMError('Cannot refit due to null objective function.') predictor = self._to_predictor(deepcopy(kwargs)) leaf_preds = predictor.predict(data, -1, pred_leaf=True) nrow, ncol = leaf_preds.shape out_is_linear = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterGetLinear( self.handle, ctypes.byref(out_is_linear))) new_params = _choose_param_value( main_param_name="linear_tree", params=self.params, default_value=None ) new_params["linear_tree"] = bool(out_is_linear.value) train_set = Dataset(data, label, params=new_params) new_params['refit_decay_rate'] = decay_rate new_booster = Booster(new_params, train_set) # Copy models _safe_call(_LIB.LGBM_BoosterMerge( new_booster.handle, predictor.handle)) leaf_preds = leaf_preds.reshape(-1) ptr_data, _, _ = c_int_array(leaf_preds) _safe_call(_LIB.LGBM_BoosterRefit( new_booster.handle, ptr_data, ctypes.c_int32(nrow), ctypes.c_int32(ncol))) new_booster.network = self.network new_booster.__attr = self.__attr.copy() 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 of str List with names of features. """ num_feature = self.num_feature() # Get name of features tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ 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.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) if num_feature != tmp_out_len.value: raise ValueError("Length of feature names doesn't equal with num_feature") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ 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.c_int(num_feature), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) return [string_buffers[i].value.decode('utf-8') for i in range(num_feature)]
[docs] def feature_importance(self, importance_type='split', iteration=None): """Get feature importances. Parameters ---------- importance_type : str, 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. iteration : int or None, optional (default=None) Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits). Returns ------- result : numpy array Array with feature importances. """ if iteration is None: iteration = self.best_iteration importance_type_int = FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] result = np.empty(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(np.int32) else: return result
[docs] def get_split_value_histogram(self, feature, bins=None, xgboost_style=False): """Get split value histogram for the specified feature. Parameters ---------- feature : int or str The feature name or index the histogram is calculated for. If int, interpreted as index. If str, interpreted as name. .. warning:: Categorical features are not supported. bins : int, str or None, optional (default=None) The maximum number of bins. If None, or int and > number of unique split values and ``xgboost_style=True``, the number of bins equals number of unique split values. If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. xgboost_style : bool, optional (default=False) Whether the returned result should be in the same form as it is in XGBoost. If False, the returned value is tuple of 2 numpy arrays as it is in ``numpy.histogram()`` function. If True, the returned value is matrix, in which the first column is the right edges of non-empty bins and the second one is the histogram values. Returns ------- result_tuple : tuple of 2 numpy arrays If ``xgboost_style=False``, the values of the histogram of used splitting values for the specified feature and the bin edges. result_array_like : numpy array or pandas DataFrame (if pandas is installed) If ``xgboost_style=True``, the histogram of used splitting values for the specified feature. """ def add(root): """Recursively add thresholds.""" if 'split_index' in root: # non-leaf if feature_names is not None and isinstance(feature, str): split_feature = feature_names[root['split_feature']] else: split_feature = root['split_feature'] if split_feature == feature: if isinstance(root['threshold'], str): raise LightGBMError('Cannot compute split value histogram for the categorical feature') else: values.append(root['threshold']) add(root['left_child']) add(root['right_child']) model = self.dump_model() feature_names = model.get('feature_names') tree_infos = model['tree_info'] values = [] for tree_info in tree_infos: add(tree_info['tree_structure']) if bins is None or isinstance(bins, int) and xgboost_style: n_unique = len(np.unique(values)) bins = max(min(n_unique, bins) if bins is not None else n_unique, 1) hist, bin_edges = np.histogram(values, bins=bins) if xgboost_style: ret = np.column_stack((bin_edges[1:], hist)) ret = ret[ret[:, 1] > 0] if PANDAS_INSTALLED: return pd_DataFrame(ret, columns=['SplitValue', 'Count']) else: return ret else: return hist, bin_edges
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.empty(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 callable(feval): feval = [feval] if feval is not None: if data_idx == 0: cur_data = self.train_set else: cur_data = self.valid_sets[data_idx - 1] for eval_function in feval: if eval_function is None: continue feval_ret = eval_function(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.empty(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(f"Wrong length of predict results for data {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 eval metrics tmp_out_len = ctypes.c_int(0) reserved_string_buffer_size = 255 required_string_buffer_size = ctypes.c_size_t(0) string_buffers = [ ctypes.create_string_buffer(reserved_string_buffer_size) for _ 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.c_int(self.__num_inner_eval), ctypes.byref(tmp_out_len), ctypes.c_size_t(reserved_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) if self.__num_inner_eval != tmp_out_len.value: raise ValueError("Length of eval names doesn't equal with num_evals") actual_string_buffer_size = required_string_buffer_size.value # if buffer length is not long enough, reallocate buffers if reserved_string_buffer_size < actual_string_buffer_size: string_buffers = [ ctypes.create_string_buffer(actual_string_buffer_size) for _ 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.c_int(self.__num_inner_eval), ctypes.byref(tmp_out_len), ctypes.c_size_t(actual_string_buffer_size), ctypes.byref(required_string_buffer_size), ptr_string_buffers)) self.__name_inner_eval = [ string_buffers[i].value.decode('utf-8') for i in range(self.__num_inner_eval) ] self.__higher_better_inner_eval = [ name.startswith(('auc', 'ndcg@', 'map@', 'average_precision')) for name in self.__name_inner_eval ]
[docs] def attr(self, key): """Get attribute string from the Booster. Parameters ---------- key : str The name of the attribute. Returns ------- value : str 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 attributes to the Booster. Parameters ---------- **kwargs The attributes to set. Setting a value to None deletes an attribute. Returns ------- self : Booster Booster with set attributes. """ for key, value in kwargs.items(): if value is not None: if not isinstance(value, str): raise ValueError("Only string values are accepted") self.__attr[key] = value else: self.__attr.pop(key, None) return self