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- cimport numpy as cnp
- from sklearn.utils._typedefs cimport intp_t
- cnp.import_array()
- ctypedef cnp.npy_float64 X_DTYPE_C
- ctypedef cnp.npy_uint8 X_BINNED_DTYPE_C
- ctypedef cnp.npy_float64 Y_DTYPE_C
- ctypedef cnp.npy_float32 G_H_DTYPE_C
- ctypedef cnp.npy_uint32 BITSET_INNER_DTYPE_C
- ctypedef BITSET_INNER_DTYPE_C[8] BITSET_DTYPE_C
- cdef packed struct hist_struct:
- # Same as histogram dtype but we need a struct to declare views. It needs
- # to be packed since by default numpy dtypes aren't aligned
- Y_DTYPE_C sum_gradients
- Y_DTYPE_C sum_hessians
- unsigned int count
- cdef packed struct node_struct:
- # Equivalent struct to PREDICTOR_RECORD_DTYPE to use in memory views. It
- # needs to be packed since by default numpy dtypes aren't aligned
- Y_DTYPE_C value
- unsigned int count
- intp_t feature_idx
- X_DTYPE_C num_threshold
- unsigned char missing_go_to_left
- unsigned int left
- unsigned int right
- Y_DTYPE_C gain
- unsigned int depth
- unsigned char is_leaf
- X_BINNED_DTYPE_C bin_threshold
- unsigned char is_categorical
- # The index of the corresponding bitsets in the Predictor's bitset arrays.
- # Only used if is_categorical is True
- unsigned int bitset_idx
- cpdef enum MonotonicConstraint:
- NO_CST = 0
- POS = 1
- NEG = -1
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