From bd0cdecd6387847a1a21622d4c198ad8b5322517 Mon Sep 17 00:00:00 2001 From: Jeremy Kubica <104161096+jeremykubica@users.noreply.github.com> Date: Thu, 3 Oct 2024 08:36:00 -0400 Subject: [PATCH] Add basic benchmarks --- benchmarks/benchmarks.py | 53 +++++++++++++++++++++++++----- data/tests/test_features.csv | 52 +++++++++++++++++++++++++++++ src/resspect/example_benchmarks.py | 14 -------- 3 files changed, 97 insertions(+), 22 deletions(-) create mode 100644 data/tests/test_features.csv delete mode 100644 src/resspect/example_benchmarks.py diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py index 79ca5a2d..18f1126d 100644 --- a/benchmarks/benchmarks.py +++ b/benchmarks/benchmarks.py @@ -1,16 +1,53 @@ -"""Two sample benchmarks to compute runtime and memory usage. +"""Benchmarks to compute runtime and memory usage of core functions. + +To manually run the benchmarks use: asv run For more information on writing benchmarks: https://asv.readthedocs.io/en/stable/writing_benchmarks.html.""" -from resspect import example_benchmarks +from pathlib import Path +import tempfile + +from resspect import fit_snpcc +from resspect.learn_loop import learn_loop + + +_TEST_DATA_DIR = Path(__file__).parent.parent / "data" / "tests" + +def time_feature_creation(): + """Benchmark how long it takes to read the example files and generate features.""" + input_file_path = _TEST_DATA_DIR / "DES_data" + with tempfile.TemporaryDirectory() as dir_name: + features_file = Path(dir_name) / "Malanchev.csv" + fit_snpcc( + path_to_data_dir=input_file_path, + features_file=str(features_file), + feature_extractor="malanchev" + ) -def time_computation(): - """Time computations are prefixed with 'time'.""" - example_benchmarks.runtime_computation() +def time_learn_loop(ml_model, strategy): + """Benchmark how long it takes for a run of the learning loop.""" + # Use the precomputed features so we don't time their creation. + features_file = str(_TEST_DATA_DIR / "test_features.csv") + with tempfile.TemporaryDirectory() as dir_name: + metrics_file = str(Path(dir_name) / "metrics.csv") + output_queried_file = str(Path(dir_name) / "queried.csv") + learn_loop( + nloops=25, + features_method="malanchev", + classifier=ml_model, + strategy=strategy, + path_to_features=features_file, + output_metrics_file=metrics_file, + output_queried_file=output_queried_file, + training="original", + batch=1, + ) -def mem_list(): - """Memory computations are prefixed with 'mem' or 'peakmem'.""" - return example_benchmarks.memory_computation() +# Parameterize the ML models and strategies we benchmark. +time_learn_loop.params = [ + ["RandomForest", "KNN"], # The different ML methods + ["RandomSampling", "UncSampling"], # The different strategies. +] diff --git a/data/tests/test_features.csv b/data/tests/test_features.csv new file mode 100644 index 00000000..8d55286f --- /dev/null +++ b/data/tests/test_features.csv @@ -0,0 +1,52 @@ 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diff --git a/src/resspect/example_benchmarks.py b/src/resspect/example_benchmarks.py deleted file mode 100644 index 78da46a0..00000000 --- a/src/resspect/example_benchmarks.py +++ /dev/null @@ -1,14 +0,0 @@ -"""An example module containing simplistic methods under benchmarking.""" - -import random -import time - - -def runtime_computation() -> None: - """Runtime computation consuming between 0 and 5 seconds.""" - time.sleep(random.uniform(0, 5)) - - -def memory_computation() -> list[int]: - """Memory computation for a random list up to 512 samples.""" - return [0] * random.randint(0, 512)