.. _l-bench-plot-onnxruntime-gpr-reg: Benchmark (ONNX) for GaussianProcessRegressor ============================================= .. contents:: :local: .. index:: onnxruntime, GaussianProcessRegressor Overview ++++++++ .. plot:: def label_fct(la): la = la.replace("onxpython_compiled", "opy") la = la.replace("onxpython", "opy") la = la.replace("onxonnxruntime1", "ort") la = la.replace("True", "1") la = la.replace("False", "0") la = la.replace("max_depth", "mxd") la = la.replace("method=predict", "cl") la = la.replace("method=proba", "prob") la = la.replace("onnx_options={: {'optim': 'cdist'}}", 'cdist') la = la.replace("onnx_options=nan", '-') return la import matplotlib.pyplot as plt import pandas from pymlbenchmark.plotting import plot_bench_xtime name = "../../onnx/results/bench_plot_onnxruntime_gpr.perf.csv" df = pandas.read_csv(name) plot_bench_xtime(df, row_cols='N', col_cols='onnx_options', cmp_col_values=('lib', 'skl'), x_value='mean', y_value='xtime', parallel=(1., 0.5), title=None, fontsize=12, ax=None, box_side=4, label_fct=label_fct) plt.suptitle("Acceleration onnxruntime / scikit-learn for GaussianProcessRegressor") plt.show() Detailed graphs +++++++++++++++ .. plot:: def label_fct(la): la = la.replace("onxpython_compiled", "opy") la = la.replace("onxpython", "opy") la = la.replace("onxonnxruntime1", "ort") la = la.replace("True", "1") la = la.replace("False", "0") la = la.replace("max_depth", "mxd") la = la.replace("method=predict", "cl") la = la.replace("method=proba", "prob") la = la.replace("onnx_options={: {'optim': 'cdist'}}", 'cdist') la = la.replace("onnx_options=nan", '-') return la import matplotlib.pyplot as plt import pandas from pymlbenchmark.plotting import plot_bench_results name = "../../onnx/results/bench_plot_onnxruntime_gpr.perf.csv" df = pandas.read_csv(name) plot_bench_results(df, row_cols=('alpha', 'N'), col_cols='onnx_options', cmp_col_values=('lib', 'skl'), x_value='dim', y_value='mean', title=None, label_fct=label_fct, fontsize=12, ax=None, box_side=4) plt.suptitle("Acceleration onnxruntime / scikit-learn for GaussianProcessRegressor") plt.show() Configuration +++++++++++++ .. runpython:: :rst: :warningout: RuntimeWarning :showcode: from pyquickhelper.pandashelper import df2rst import pandas name = os.path.join(__WD__, "../../onnx/results/bench_plot_onnxruntime_gpr.time.csv") df = pandas.read_csv(name) print(df2rst(df, number_format=4)) Raw results +++++++++++ :download:`bench_plot_onnxruntime_gpr.csv <../../onnx/results/bench_plot_onnxruntime_gpr.perf.csv>` .. runpython:: :rst: :warningout: RuntimeWarning :showcode: :toggle: out from pyquickhelper.pandashelper import df2rst from pymlbenchmark.benchmark.bench_helper import bench_pivot import pandas name = os.path.join(__WD__, "../../onnx/results/bench_plot_onnxruntime_gpr.perf.csv") df = pandas.read_csv(name) piv = bench_pivot(df).reset_index(drop=False) piv['speedup_py'] = piv['skl'] / piv['onxpython_compiled'] piv['speedup_ort'] = piv['skl'] / piv['onxonnxruntime1'] print(df2rst(piv, number_format=4)) .. runpython:: :rst: :warningout: RuntimeWarning :showcode: :toggle: out from pyquickhelper.pandashelper import df2rst import pandas name = os.path.join(__WD__, "../../onnx/results/bench_plot_onnxruntime_gpr.perf.csv") df = pandas.read_csv(name) df = df[df['lib'] == 'skl'] print(df2rst(df, number_format=4)) Benchmark code ++++++++++++++ `bench_plot_onnxruntime_gpr.py `_ .. literalinclude:: ../../onnx/bench_plot_onnxruntime_gpr.py :language: python