45 lines
1.7 KiB
Cython
45 lines
1.7 KiB
Cython
import numpy as np
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# Y_DYTPE is the dtype to which the targets y are converted to. This is also
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# dtype for leaf values, gains, and sums of gradients / hessians. The gradients
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# and hessians arrays are stored as floats to avoid using too much memory.
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Y_DTYPE = np.float64
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X_DTYPE = np.float64
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X_BINNED_DTYPE = np.uint8 # hence max_bins == 256
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# dtype for gradients and hessians arrays
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G_H_DTYPE = np.float32
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X_BITSET_INNER_DTYPE = np.uint32
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# Note that we use Y_DTYPE=float64 to avoid issues with floating point precision when
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# summing gradients and hessians (both float32). Those are difficult to protect via
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# tools like (Kahan-) Neumaier summation as in CPython, see
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# https://github.com/python/cpython/issues/100425, or pairwise summation as numpy, see
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# https://github.com/numpy/numpy/pull/3685, due to the way histograms are summed
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# (number of additions per bin is not known in advance). See also comment in
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# _subtract_histograms.
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HISTOGRAM_DTYPE = np.dtype([
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('sum_gradients', Y_DTYPE), # sum of sample gradients in bin
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('sum_hessians', Y_DTYPE), # sum of sample hessians in bin
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('count', np.uint32), # number of samples in bin
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])
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PREDICTOR_RECORD_DTYPE = np.dtype([
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('value', Y_DTYPE),
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('count', np.uint32),
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('feature_idx', np.intp),
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('num_threshold', X_DTYPE),
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('missing_go_to_left', np.uint8),
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('left', np.uint32),
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('right', np.uint32),
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('gain', Y_DTYPE),
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('depth', np.uint32),
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('is_leaf', np.uint8),
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('bin_threshold', X_BINNED_DTYPE),
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('is_categorical', np.uint8),
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# The index of the corresponding bitsets in the Predictor's bitset arrays.
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# Only used if is_categorical is True
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('bitset_idx', np.uint32)
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])
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ALMOST_INF = 1e300 # see LightGBM AvoidInf()
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