def test_plot_logreg_xtime_none(self):
from matplotlib import pyplot as plt
temp = get_temp_folder(__file__, "temp_plot_logreg_xtime")
img = os.path.join(temp, "plot_logreg.png")
data = os.path.join(temp, "..", "data",
"onnxruntime_LogisticRegression.perf.csv")
df = pandas.read_csv(data)
ax = plot_bench_xtime(df, row_cols='N', col_cols='method',
hue_cols=None,
title="unittest")
fig = ax[0, 0].get_figure()
fig.savefig(img)
if __name__ == "__main__":
plt.show()
plt.close('all')
self.assertExists(img)
def test_plot_logreg_xtime_bug(self):
from matplotlib import pyplot as plt
temp = get_temp_folder(__file__, "temp_plot_logreg_xtime_bug")
img = os.path.join(temp, "plot_cache.png")
data = os.path.join(temp, "..", "data",
"bench_plot_gridsearch_cache.csv")
df = pandas.read_csv(data)
ax = plot_bench_xtime(df, row_cols=['n_jobs'],
x_value='mean',
hue_cols=['N'],
cmp_col_values='test',
title="unittest")
fig = ax[0].get_figure()
fig.savefig(img)
if __name__ == "__main__":
plt.show()
plt.close('all')
self.assertExists(img)
def label_fct(la):
la = la.replace("onxpython_compiled", "opyc")
la = la.replace("onxpython", "opy")
la = la.replace("onxonnxruntime1", "ort")
la = la.replace("fit_intercept", "fi")
la = la.replace("True", "1")
la = la.replace("False", "0")
la = la.replace("max_depth", "mxd")
return la
plot_bench_xtime(
df[df.norm],
col_cols='dataset',
hue_cols='model',
title=
"Numerical datasets - norm=False\nBenchmark scikit-learn / onnxruntime")
plt.savefig("%s.normT.time.png" % filename)
# plt.show()
plot_bench_xtime(
df[~df.norm],
col_cols='dataset',
hue_cols='model',
title=
"Numerical datasets - norm=False\nBenchmark scikit-learn / onnxruntime")
plt.savefig("%s.normF.time.png" % filename)
# plt.show()
plot_bench_results(