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# cython: cdivision=True
# cython: boundscheck=False
# cython: wraparound=False
# cython: language_level=3
# Author: Nicolas Hug
cimport cython
from cython.parallel import prange
import numpy as np
cimport numpy as np
from .common import Y_DTYPE
from .common cimport Y_DTYPE_C
np.import_array()
def _update_raw_predictions(
Y_DTYPE_C [::1] raw_predictions, # OUT
grower):
"""Update raw_predictions with the predictions of the newest tree.
This is equivalent to (and much faster than):
raw_predictions += last_estimator.predict(X_train)
It's only possible for data X_train that is used to train the trees (it
isn't usable for e.g. X_val).
"""
cdef:
unsigned int [::1] starts # start of each leaf in partition
unsigned int [::1] stops # end of each leaf in partition
Y_DTYPE_C [::1] values # value of each leaf
const unsigned int [::1] partition = grower.splitter.partition
list leaves
leaves = grower.finalized_leaves
starts = np.array([leaf.partition_start for leaf in leaves],
dtype=np.uint32)
stops = np.array([leaf.partition_stop for leaf in leaves],
dtype=np.uint32)
values = np.array([leaf.value for leaf in leaves], dtype=Y_DTYPE)
_update_raw_predictions_helper(raw_predictions, starts, stops, partition,
values)
cdef inline void _update_raw_predictions_helper(
Y_DTYPE_C [::1] raw_predictions, # OUT
const unsigned int [::1] starts,
const unsigned int [::1] stops,
const unsigned int [::1] partition,
const Y_DTYPE_C [::1] values):
cdef:
unsigned int position
int leaf_idx
int n_leaves = starts.shape[0]
for leaf_idx in prange(n_leaves, schedule='static', nogil=True):
for position in range(starts[leaf_idx], stops[leaf_idx]):
raw_predictions[partition[position]] += values[leaf_idx]
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