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basic.py
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# 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_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()
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)
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:
aliases = {"bin_construct_sample_cnt": {"bin_construct_sample_cnt",
"subsample_for_bin"},
"boosting": {"boosting",
"boosting_type",
"boost"},
"categorical_feature": {"categorical_feature",
"cat_feature",
"categorical_column",
"cat_column",
"categorical_features"},
"data_random_seed": {"data_random_seed",
"data_seed"},
"early_stopping_round": {"early_stopping_round",
"early_stopping_rounds",
"early_stopping",
"n_iter_no_change"},
"enable_bundle": {"enable_bundle",
"is_enable_bundle",
"bundle"},
"eval_at": {"eval_at",
"ndcg_eval_at",
"ndcg_at",
"map_eval_at",
"map_at"},
"group_column": {"group_column",
"group",
"group_id",
"query_column",
"query",
"query_id"},
"header": {"header",
"has_header"},
"ignore_column": {"ignore_column",
"ignore_feature",
"blacklist"},
"is_enable_sparse": {"is_enable_sparse",
"is_sparse",
"enable_sparse",
"sparse"},
"label_column": {"label_column",
"label"},
"linear_tree": {"linear_tree",
"linear_trees"},
"local_listen_port": {"local_listen_port",
"local_port",
"port"},
"machines": {"machines",
"workers",
"nodes"},
"max_bin": {"max_bin",
"max_bins"},
"metric": {"metric",
"metrics",
"metric_types"},
"num_class": {"num_class",
"num_classes"},
"num_iterations": {"num_iterations",
"num_iteration",
"n_iter",
"num_tree",
"num_trees",
"num_round",
"num_rounds",
"num_boost_round",
"n_estimators",
"max_iter"},
"num_machines": {"num_machines",
"num_machine"},
"num_threads": {"num_threads",
"num_thread",
"nthread",
"nthreads",
"n_jobs"},
"objective": {"objective",
"objective_type",
"app",
"application",
"loss"},
"pre_partition": {"pre_partition",
"is_pre_partition"},
"tree_learner": {"tree_learner",
"tree",
"tree_type",
"tree_learner_type"},
"two_round": {"two_round",
"two_round_loading",
"use_two_round_loading"},
"verbosity": {"verbosity",
"verbose"},
"weight_column": {"weight_column",
"weight"}}
@classmethod
def get(cls, *args):
ret = set()
for i in args:
ret |= cls.aliases.get(i, {i})
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 = list(data.select_dtypes(include=['category']).columns)
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
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, 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