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