ASV add benchmark for beam search

asv_benchmarks/benchmarks/fastcan.py

@@ -16,7 +16,7 @@ class FastCanBenchmark(Benchmark):
     """
 
     param_names = ["task", "alg"]
-    params = (["classif", "reg"], ["h", "eta"])
+    params = (["classif", "reg"], ["h", "eta", "beam"])
 
     def setup_cache(self):
         """Pickle a fitted estimator for all combinations of parameters"""
@@ -30,11 +30,17 @@ def setup_cache(self):
 
             if alg == "h":
                 eta = False
-            else:
+                beam_width = 1
+            elif alg == "eta":
                 eta = True
+                beam_width = 1
+            else:
+                eta = False
+                beam_width = 10
             estimator = FastCan(
                 n_features_to_select=20,
                 eta=eta,
+                beam_width=beam_width
             )
             estimator.fit(X, y)
 

pixi.toml

@@ -88,6 +88,7 @@ time-eta = "python -m timeit -n 5 -s 'import numpy as np; from fastcan import Fa
 profile-minibatch = { cmd = '''python -c "import cProfile; import numpy as np; from fastcan import minibatch; X = np.random.rand(100, 3000); y = np.random.rand(100, 20); cProfile.run('minibatch(X, y, 1000, 10, verbose=0)', sort='{{ SORT }}')"''', args = [{ arg = "SORT", default = "cumtime" }] }
 time-narx = '''python -m timeit -n 1 -s "import numpy as np; from fastcan.narx import make_narx; rng = np.random.default_rng(5); X = rng.random((1000, 10)); y = rng.random((1000, 2)); m = make_narx(X, y, 10, max_delay=2, poly_degree=2, verbose=0)" "m.fit(X, y, coef_init='one_step_ahead', verbose=1)"'''
 profile-narx = { cmd = '''python -c "import cProfile; import numpy as np; from fastcan.narx import make_narx; rng = np.random.default_rng(8); X = rng.random((3000, 3)); y = rng.random((3000, 3)); m = make_narx(X, y, 10, max_delay=10, poly_degree=2, verbose=0); cProfile.run('m.fit(X, y, coef_init=[0]*33)', sort='{{ SORT }}')"''', args = [{ arg = "SORT", default = "tottime" }] }
+time-beam = "python -m timeit -n 5 -s 'import numpy as np; from fastcan import FastCan; X = np.random.rand(3000, 100); y = np.random.rand(3000, 20)' 's = FastCan(20, beam_width=3, verbose=0).fit(X, y)'"
 
 [feature.asv.tasks]
 asv-build = { cmd = "python -m asv machine --machine {{ MACHINE }} --yes && python -m asv run --show-stderr -v --machine {{ MACHINE }} {{ EXTRA_ARGS }}", cwd = "asv_benchmarks", args = [{ arg = "MACHINE", default = "MacOS-M1" }, { arg = "EXTRA_ARGS", default = "" }] }