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MNT Updates _isotonic.pyx to use memoryviews instead of cnp.ndarray #26068

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Merged
merged 10 commits into from
Apr 6, 2023
Merged

MNT Updates _isotonic.pyx to use memoryviews instead of cnp.ndarray #26068

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cefe99c
replacing cnp.ndarrays with memoryviews
BabaYaga1221 Apr 3, 2023
7fadfe9
Changing np.float64 to np.uint8 in file sklearn\preprocessing\_encode…
BabaYaga1221 Apr 3, 2023
30aede8
changes as per suggested
BabaYaga1221 Apr 4, 2023
b6b2d72
reverting changes for _endcoder.py file
BabaYaga1221 Apr 4, 2023
31d1a91
changing the code as per suggestion
BabaYaga1221 Apr 4, 2023
5e76ebd
changing the code as per suggestion
BabaYaga1221 Apr 5, 2023
466622c
remove line 82
BabaYaga1221 Apr 5, 2023
ba2a1f2
Merge https://github.com/scikit-learn/scikit-learn into update-scikit…
BabaYaga1221 Apr 5, 2023
f547fd9
revert the changes in _isotonic.pyx as per suggestion
BabaYaga1221 Apr 5, 2023
067e58a
Merge branch 'main' into update-scikit-learn
BabaYaga1221 Apr 5, 2023
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2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,7 +196,7 @@ def check_package_status(package, min_version):
{"sources": ["_check_build.pyx"]},
],
"": [
{"sources": ["_isotonic.pyx"], "include_np": True},
{"sources": ["_isotonic.pyx"]},
],
"_loss": [
{"sources": ["_loss.pyx.tp"]},
Expand Down
28 changes: 17 additions & 11 deletions sklearn/_isotonic.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,8 @@
# pool at each step.

import numpy as np
cimport numpy as cnp
from cython cimport floating

cnp.import_array()


def _inplace_contiguous_isotonic_regression(floating[::1] y, floating[::1] w):
Expand Down Expand Up @@ -62,9 +60,9 @@ def _inplace_contiguous_isotonic_regression(floating[::1] y, floating[::1] w):
i = k


def _make_unique(cnp.ndarray[dtype=floating] X,
cnp.ndarray[dtype=floating] y,
cnp.ndarray[dtype=floating] sample_weights):
def _make_unique(const floating[::1] X,
const floating[::1] y,
const floating[::1] sample_weights):
"""Average targets for duplicate X, drop duplicates.

Aggregates duplicate X values into a single X value where
Expand All @@ -75,10 +73,14 @@ def _make_unique(cnp.ndarray[dtype=floating] X,
"""
unique_values = len(np.unique(X))

cdef cnp.ndarray[dtype=floating] y_out = np.empty(unique_values,
dtype=X.dtype)
cdef cnp.ndarray[dtype=floating] x_out = np.empty_like(y_out)
cdef cnp.ndarray[dtype=floating] weights_out = np.empty_like(y_out)
if floating is float:
dtype = np.float32
else:
dtype = np.float64

cdef floating[::1] y_out = np.empty(unique_values, dtype=dtype)
cdef floating[::1] x_out = np.empty_like(y_out)
cdef floating[::1] weights_out = np.empty_like(y_out)

cdef floating current_x = X[0]
cdef floating current_y = 0
Expand All @@ -88,7 +90,7 @@ def _make_unique(cnp.ndarray[dtype=floating] X,
cdef int j
cdef floating x
cdef int n_samples = len(X)
cdef floating eps = np.finfo(X.dtype).resolution
cdef floating eps = np.finfo(dtype).resolution

for j in range(n_samples):
x = X[j]
Expand All @@ -108,4 +110,8 @@ def _make_unique(cnp.ndarray[dtype=floating] X,
x_out[i] = current_x
weights_out[i] = current_weight
y_out[i] = current_y / current_weight
return x_out[:i+1], y_out[:i+1], weights_out[:i+1]
return(
np.asarray(x_out[:i+1]),
np.asarray(y_out[:i+1]),
np.asarray(weights_out[:i+1]),
)