import numpy as np

# Y_DYTPE is the dtype to which the targets y are converted to. This is also
# dtype for leaf values, gains, and sums of gradients / hessians. The gradients
# and hessians arrays are stored as floats to avoid using too much memory.
Y_DTYPE = np.float64
X_DTYPE = np.float64
X_BINNED_DTYPE = np.uint8  # hence max_bins == 256
# dtype for gradients and hessians arrays
G_H_DTYPE = np.float32
X_BITSET_INNER_DTYPE = np.uint32

# Note that we use Y_DTYPE=float64 to avoid issues with floating point precision when
# summing gradients and hessians (both float32). Those are difficult to protect via
# tools like (Kahan-) Neumaier summation as in CPython, see
# https://github.com/python/cpython/issues/100425, or pairwise summation as numpy, see
# https://github.com/numpy/numpy/pull/3685, due to the way histograms are summed
# (number of additions per bin is not known in advance). See also comment in
# _subtract_histograms.
HISTOGRAM_DTYPE = np.dtype([
    ('sum_gradients', Y_DTYPE),  # sum of sample gradients in bin
    ('sum_hessians', Y_DTYPE),  # sum of sample hessians in bin
    ('count', np.uint32),  # number of samples in bin
])

PREDICTOR_RECORD_DTYPE = np.dtype([
    ('value', Y_DTYPE),
    ('count', np.uint32),
    ('feature_idx', np.intp),
    ('num_threshold', X_DTYPE),
    ('missing_go_to_left', np.uint8),
    ('left', np.uint32),
    ('right', np.uint32),
    ('gain', Y_DTYPE),
    ('depth', np.uint32),
    ('is_leaf', np.uint8),
    ('bin_threshold', X_BINNED_DTYPE),
    ('is_categorical', np.uint8),
    # The index of the corresponding bitsets in the Predictor's bitset arrays.
    # Only used if is_categorical is True
    ('bitset_idx', np.uint32)
])

ALMOST_INF = 1e300  # see LightGBM AvoidInf()