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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,208 @@ | ||
| import json | ||
| import os | ||
| import timeit | ||
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| import bioframe as bf | ||
| import numpy as np | ||
| import pandas as pd | ||
| import pyranges as pr | ||
| import pyranges1 as pr1 | ||
| from rich import print | ||
| from rich.box import MARKDOWN | ||
| from rich.table import Table | ||
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| import polars_bio as pb | ||
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| BENCH_DATA_ROOT = os.getenv("BENCH_DATA_ROOT") | ||
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| if BENCH_DATA_ROOT is None: | ||
| raise ValueError("BENCH_DATA_ROOT is not set") | ||
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| pb.ctx.set_option("datafusion.optimizer.repartition_joins", "true") | ||
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| columns = ("contig", "pos_start", "pos_end") | ||
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| test_threads = [1, 2, 4, 8, 16] | ||
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| test_cases = [ | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/fBrain-DS14718/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/exons/*.parquet", | ||
| # "name": "1-2", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/exons/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/ex-anno/*.parquet", | ||
| # "name": "2-7", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/fBrain-DS14718/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| # "name": "1-0", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/ex-anno/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| # "name": "7-0", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/ex-anno/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainOrnAna1/*.parquet", | ||
| # "name": "7-3", | ||
| # }, | ||
| { | ||
| "df_path_1": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| "df_path_2": f"{BENCH_DATA_ROOT}/ex-rna/*.parquet", | ||
| "name": "0-8", | ||
| }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/chainVicPac2/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/ex-rna/*.parquet", | ||
| # "name": "4-8", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/ex-anno/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/ex-rna/*.parquet", | ||
| # "name": "7-8", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/chainOrnAna1/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| # "name": "3-0", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainVicPac2/*.parquet", | ||
| # "name": "0-4", | ||
| # }, | ||
| # { | ||
| # "df_path_1": f"{BENCH_DATA_ROOT}/chainRn4/*.parquet", | ||
| # "df_path_2": f"{BENCH_DATA_ROOT}/chainXenTro3Link/*.parquet", | ||
| # "name": "0-5", | ||
| # }, | ||
| ] | ||
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|
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| # pyranges0 | ||
| def df2pr0(df): | ||
| return pr.PyRanges( | ||
| chromosomes=df.contig, | ||
| starts=df.pos_start, | ||
| ends=df.pos_end, | ||
| ) | ||
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| # df_1_pr0 = df2pr0(df_1) | ||
| # df_2_pr0 = df2pr0(df_2) | ||
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|
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| ### pyranges1 | ||
| def df2pr1(df): | ||
| return pr1.PyRanges( | ||
| { | ||
| "Chromosome": df.contig, | ||
| "Start": df.pos_start, | ||
| "End": df.pos_end, | ||
| } | ||
| ) | ||
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| def polars_bio(df_path_1, df_path_2): | ||
| pb.overlap(df_path_1, df_path_2, col1=columns, col2=columns).collect().count() | ||
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| def pyranges0(df_1_pr0, df_2_pr0): | ||
| len(df_1_pr0.join(df_2_pr0)) | ||
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|
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| functions = [ | ||
| pyranges0, | ||
| polars_bio, | ||
| ] | ||
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| num_repeats = 3 | ||
| num_executions = 3 | ||
|
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| # mkdir | ||
| # mkdir results directory if it does not exist | ||
|
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| os.makedirs("results", exist_ok=True) | ||
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| for t in test_cases: | ||
| results = [] | ||
| df_1 = pd.read_parquet(t["df_path_1"].replace("*.parquet", ""), engine="pyarrow") | ||
| df_2 = pd.read_parquet(t["df_path_2"].replace("*.parquet", ""), engine="pyarrow") | ||
| df_1_pr0 = df2pr0(df_1) | ||
| df_2_pr0 = df2pr0(df_2) | ||
| df_1_pr1 = df2pr1(df_1) | ||
| df_2_pr1 = df2pr1(df_2) | ||
| for p in test_threads: | ||
| pb.ctx.set_option("datafusion.execution.target_partitions", str(p)) | ||
| for func in functions: | ||
| times = None | ||
| print(f"Running {func.__name__}...") | ||
| if func == polars_bio: | ||
| times = timeit.repeat( | ||
| lambda: func(t["df_path_1"], t["df_path_2"]), | ||
| repeat=num_repeats, | ||
| number=num_executions, | ||
| ) | ||
| elif func == pyranges0 and p == 1: | ||
| times = timeit.repeat( | ||
| lambda: func(df_1_pr0, df_2_pr0), | ||
| repeat=num_repeats, | ||
| number=num_executions, | ||
| ) | ||
| else: | ||
| continue | ||
| per_run_times = [ | ||
| time / num_executions for time in times | ||
| ] # Convert to per-run times | ||
| results.append( | ||
| { | ||
| "name": f"{func.__name__}-{p}", | ||
| "min": min(per_run_times), | ||
| "max": max(per_run_times), | ||
| "mean": np.mean(per_run_times), | ||
| } | ||
| ) | ||
|
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| # fastest_mean = min(result["mean"] for result in results) | ||
| fastest_mean = results[1]["mean"] | ||
| for result in results: | ||
| result["speedup"] = fastest_mean / result["mean"] | ||
|
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| # Create Rich table | ||
| table = Table(title="Benchmark Results", box=MARKDOWN) | ||
| table.add_column("Library", justify="left", style="cyan", no_wrap=True) | ||
| table.add_column("Min (s)", justify="right", style="green") | ||
| table.add_column("Max (s)", justify="right", style="green") | ||
| table.add_column("Mean (s)", justify="right", style="green") | ||
| table.add_column("Speedup", justify="right", style="magenta") | ||
|
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| # Add rows to the table | ||
| for result in results: | ||
| table.add_row( | ||
| result["name"], | ||
| f"{result['min']:.6f}", | ||
| f"{result['max']:.6f}", | ||
| f"{result['mean']:.6f}", | ||
| f"{result['speedup']:.2f}x", | ||
| ) | ||
|
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| # Display the table | ||
| benchmark_results = { | ||
| "inputs": { | ||
| "df_1_num": len(df_1), | ||
| "df_2_num": len(df_2), | ||
| }, | ||
| "output_num": len( | ||
| bf.overlap(df_1, df_2, cols1=columns, cols2=columns, how="inner") | ||
| ), | ||
| "results": results, | ||
| } | ||
| print(t["name"]) | ||
| print(json.dumps(benchmark_results, indent=4)) | ||
| json.dump(benchmark_results, open(f"results/{t['name']}.json", "w")) | ||
| print(table) |
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