Merge pull request #2 from edahelsinki/threading

Threading

.github/workflows/python-pytest.yml

@@ -4,6 +4,8 @@
 name: Test Python Package
 
 on:
+  push:
+    branches: [ master ]
   pull_request:
     branches: [ master ]
 

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = slise
-version = 2.0.0
+version = 2.1.0
 author = Anton Björklund
 author_email = anton.bjorklund@helsinki.fi
 description = The SLISE algorithm for robust regression and explanations of black box models
@@ -35,3 +35,6 @@ install_requires =
 exclude =
     examples
     tests
+
+[options.extras_require]
+tbb = tbb

slise/__init__.py

@@ -21,16 +21,27 @@
     constraints used to generate the data, e.g., the laws of physics).
 
 
-    More in-depth details about the algorithm can be found in the paper:
+    More in-depth details about the algorithm can be found in the papers:
 
     Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.
     Sparse Robust Regression for Explaining Classifiers.
     Discovery Science (DS 2019).
     Lecture Notes in Computer Science, vol 11828, Springer.
     https://doi.org/10.1007/978-3-030-33778-0_27
 
+    Björklund A., Henelius A., Oikarinen E., Kallonen K., Puolamäki K.
+    Robust regression via error tolerance.
+    Data Mining and Knowledge Discovery (2022).
+    https://doi.org/10.1007/s10618-022-00819-2
+
 """
 
-from slise.slise import SliseRegression, regression, SliseExplainer, explain
+from slise.slise import (
+    SliseRegression,
+    regression,
+    SliseExplainer,
+    explain,
+    SliseWarning,
+)
 from slise.utils import limited_logit as logit
 from slise.data import normalise_robust

slise/optimisation.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 from lbfgs import LBFGSError, fmin_lbfgs
-from numba import jit
+from numba import jit, get_num_threads, set_num_threads, threading_layer
 from scipy.optimize import brentq
 
 from slise.utils import (
@@ -375,6 +375,33 @@ def debug_log(
     )
 
 
+def set_threads(num: int = -1) -> int:
+    """Set the number of numba threads
+
+    Args:
+        num (int, optional): The number of threads. Defaults to -1.
+
+    Returns:
+        int: The old number of theads (or -1 if unchanged).
+    """
+    if num > 0:
+        old = get_num_threads()
+        set_num_threads(num)
+        return old
+    return -1
+
+
+def check_threading_layer():
+    """Check which numba threading_layer is active, and warn if it is "workqueue".
+    """
+    loss_residuals(np.ones(1), np.ones(1), 1)
+    if threading_layer() == "workqueue":
+        warn(
+            'Using `numba.threading_layer()=="workqueue"` can be devastatingly slow! See https://numba.pydata.org/numba-doc/latest/user/threading-layer.html for alternatives.',
+            SliseWarning,
+        )
+
+
 def graduated_optimisation(
     alpha: np.ndarray,
     X: np.ndarray,