def test_machine_information(self):
res = machine_information()
self.assertIsInstance(res, list)
self.assertGreater(len(res), 3)
for r in res:
self.assertIn('name', r)
self.assertEqual(res[0]['name'], 'date')
def test_machine_information_pack(self):
res = machine_information({'numpy', 'onnx'})
self.assertIsInstance(res, list)
self.assertGreater(len(res), 3)
nb = 0
for r in res:
self.assertIn('name', r)
if r['name'] == 'numpy':
nb += 1
self.assertEqual(nb, 1)
def test_perf_onnxruntime_logreg(self):
res = onnxruntime_perf_binary_classifiers()[0]
bp = BenchPerf(res['pbefore'], res['pafter'], res['fct'])
results = list(bp.enumerate_run_benchs(repeat=10, verbose=True))
results_df = pandas.DataFrame(results)
temp = get_temp_folder(__file__, "temp_perf_onnxruntime_logreg")
out = os.path.join(temp, "onnxruntime_logreg.perf.csv")
results_df.to_csv(out, index=False)
self.assertExists(out)
subset = {
'sklearn', 'numpy', 'pandas', 'onnxruntime', 'skl2onnx',
'mlprodict'
}
df = pandas.DataFrame(machine_information(subset))
out = os.path.join(temp, "onnxruntime_logreg.time.csv")
df.to_csv(out, index=False)
self.assertExists(out)
# Runs the benchmark
# ++++++++++++++++++
df = run_bench(verbose=True)
df.to_csv("%s.perf.csv" % filename, index=False)
print(df.head())
#########################
# Extract information about the machine used
# ++++++++++++++++++++++++++++++++++++++++++
pkgs = [
'numpy', 'pandas', 'sklearn', 'skl2onnx', 'onnxruntime', 'onnx',
'mlprodict'
]
dfi = pandas.DataFrame(machine_information(pkgs))
dfi.to_csv("%s.time.csv" % filename, index=False)
print(dfi)
else:
df = pandas.read_csv("%s.perf.csv" % filename)
print(df.head())
#############################
# Plot the results
# ++++++++++++++++
def label_fct(la):
la = la.replace("-lib=", "")
la = la.replace("rt=", "-")
return la
