Note
Click here to download the full example code
Benchmark Random Forests, Tree Ensemble, (AoS and SoA)#
The script compares different implementations for the operator TreeEnsembleRegressor.
baseline: RandomForestRegressor from scikit-learn
ort: onnxruntime,
mlprodict: an implementation based on an array of structures, every structure describes a node,
mlprodict2 similar implementation but instead of having an array of structures, it relies on a structure of arrays, it parallelizes by blocks of 128 observations and inside every block, goes through trees then through observations (double loop),
mlprodict3: parallelizes by trees, this implementation is faster when the depth is higher than 10.
A structure of arrays has better performance: Case study: Comparing Arrays of Structures and Structures of Arrays Data Layouts for a Compute-Intensive Loop. See also AoS and SoA.
Profile the execution
py-spy can be used to profile the execution of a program. The profile is more informative if the code is compiled with debug information.
py-spy record --native -r 10 -o plot_random_forest_reg.svg -- python plot_random_forest_reg.py
Import#
import warnings
from time import perf_counter as time
from multiprocessing import cpu_count
import numpy
from numpy.random import rand
from numpy.testing import assert_almost_equal
import pandas
import matplotlib.pyplot as plt
from sklearn import config_context
from sklearn.ensemble import RandomForestRegressor
from sklearn.utils._testing import ignore_warnings
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from onnxruntime import InferenceSession
from mlprodict.onnxrt import OnnxInference
Available optimisation on this machine.
from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
print(code_optimisation())
AVX-omp=8
Versions#
def version():
from datetime import datetime
import sklearn
import numpy
import onnx
import onnxruntime
import skl2onnx
import mlprodict
df = pandas.DataFrame([
{"name": "date", "version": str(datetime.now())},
{"name": "numpy", "version": numpy.__version__},
{"name": "scikit-learn", "version": sklearn.__version__},
{"name": "onnx", "version": onnx.__version__},
{"name": "onnxruntime", "version": onnxruntime.__version__},
{"name": "skl2onnx", "version": skl2onnx.__version__},
{"name": "mlprodict", "version": mlprodict.__version__},
])
return df
version()
Implementations to benchmark#
def fcts_model(X, y, max_depth, n_estimators, n_jobs):
"RandomForestClassifier."
rf = RandomForestRegressor(max_depth=max_depth, n_estimators=n_estimators,
n_jobs=n_jobs)
rf.fit(X, y)
initial_types = [('X', FloatTensorType([None, X.shape[1]]))]
onx = convert_sklearn(rf, initial_types=initial_types)
sess = InferenceSession(onx.SerializeToString())
outputs = [o.name for o in sess.get_outputs()]
oinf = OnnxInference(onx, runtime="python")
oinf.sequence_[0].ops_._init(numpy.float32, 1)
name = outputs[0]
oinf2 = OnnxInference(onx, runtime="python")
oinf2.sequence_[0].ops_._init(numpy.float32, 2)
oinf3 = OnnxInference(onx, runtime="python")
oinf3.sequence_[0].ops_._init(numpy.float32, 3)
def predict_skl_predict(X, model=rf):
return rf.predict(X)
def predict_onnxrt_predict(X, sess=sess):
return sess.run(outputs[:1], {'X': X})[0]
def predict_onnx_inference(X, oinf=oinf):
return oinf.run({'X': X})[name]
def predict_onnx_inference2(X, oinf2=oinf2):
return oinf2.run({'X': X})[name]
def predict_onnx_inference3(X, oinf3=oinf3):
return oinf3.run({'X': X})[name]
return {'predict': (
predict_skl_predict, predict_onnxrt_predict,
predict_onnx_inference, predict_onnx_inference2,
predict_onnx_inference3)}
Benchmarks#
def allow_configuration(**kwargs):
return True
def bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
methods, repeat=10, verbose=False):
res = []
for nfeat in n_features:
ntrain = 50000
X_train = numpy.empty((ntrain, nfeat)).astype(numpy.float32)
X_train[:, :] = rand(ntrain, nfeat)[:, :]
eps = rand(ntrain) - 0.5
y_train = X_train.sum(axis=1) + eps
for n_jobs in n_jobss:
for max_depth in max_depths:
for n_estimators in n_estimatorss:
fcts = fcts_model(X_train, y_train,
max_depth, n_estimators, n_jobs)
for n in n_obs:
for method in methods:
fct1, fct2, fct3, fct4, fct5 = fcts[method]
if not allow_configuration(
n=n, nfeat=nfeat, max_depth=max_depth,
n_estimator=n_estimators, n_jobs=n_jobs,
method=method):
continue
obs = dict(n_obs=n, nfeat=nfeat,
max_depth=max_depth,
n_estimators=n_estimators,
method=method,
n_jobs=n_jobs)
# creates different inputs to avoid caching
Xs = []
for r in range(repeat):
x = numpy.empty((n, nfeat))
x[:, :] = rand(n, nfeat)[:, :]
Xs.append(x.astype(numpy.float32))
# measures the baseline
with config_context(assume_finite=True):
st = time()
repeated = 0
for X in Xs:
p1 = fct1(X)
repeated += 1
if time() - st >= 1:
break # stops if longer than a second
end = time()
obs["time_skl"] = (end - st) / repeated
# measures the new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct2(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_ort"] = (end - st) / r2
# measures the other new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct3(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict"] = (end - st) / r2
# measures the other new implementation 2
st = time()
r2 = 0
for X in Xs:
p2 = fct4(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict2"] = (end - st) / r2
# measures the other new implementation 3
st = time()
r2 = 0
for X in Xs:
p2 = fct5(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict3"] = (end - st) / r2
# final
res.append(obs)
if verbose and (len(res) % 1 == 0 or n >= 10000):
print("bench", len(res), ":", obs)
# checks that both produce the same outputs
if n <= 10000:
if len(p1.shape) == 1 and len(p2.shape) == 2:
p2 = p2.ravel()
try:
assert_almost_equal(
p1.ravel(), p2.ravel(), decimal=5)
except AssertionError as e:
warnings.warn(str(e))
return res
Graphs#
def plot_rf_models(dfr):
def autolabel(ax, rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height:1.1f}x',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom',
fontsize=8)
engines = [_.split('_')[-1] for _ in dfr.columns if _.startswith("time_")]
engines = [_ for _ in engines if _ != 'skl']
for engine in engines:
dfr[f"speedup_{engine}"] = dfr["time_skl"] / dfr[f"time_{engine}"]
print(dfr.tail().T)
ncols = 4
fig, axs = plt.subplots(len(engines), ncols, figsize=(
14, 4 * len(engines)), sharey=True)
row = 0
for row, engine in enumerate(engines):
pos = 0
name = f"RandomForestRegressor - {engine}"
for max_depth in sorted(set(dfr.max_depth)):
for nf in sorted(set(dfr.nfeat)):
for est in sorted(set(dfr.n_estimators)):
for n_jobs in sorted(set(dfr.n_jobs)):
sub = dfr[(dfr.max_depth == max_depth) &
(dfr.nfeat == nf) &
(dfr.n_estimators == est) &
(dfr.n_jobs == n_jobs)]
ax = axs[row, pos]
labels = sub.n_obs
means = sub[f"speedup_{engine}"]
x = numpy.arange(len(labels))
width = 0.90
rects1 = ax.bar(x, means, width, label='Speedup')
if pos == 0:
ax.set_yscale('log')
ax.set_ylim([0.1, max(dfr[f"speedup_{engine}"])])
if pos == 0:
ax.set_ylabel('Speedup')
ax.set_title(
'%s\ndepth %d - %d features\n %d estimators %d '
'jobs' % (name, max_depth, nf, est, n_jobs))
if row == len(engines) - 1:
ax.set_xlabel('batch size')
ax.set_xticks(x)
ax.set_xticklabels(labels)
autolabel(ax, rects1)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
pos += 1
fig.tight_layout()
return fig, ax
Run benchs#
@ignore_warnings(category=FutureWarning)
def run_bench(repeat=100, verbose=False):
n_obs = [1, 10, 100, 1000, 10000]
methods = ['predict']
n_features = [30]
max_depths = [6, 8, 10, 12]
n_estimatorss = [100]
n_jobss = [cpu_count()]
start = time()
results = bench(n_obs, n_features, max_depths, n_estimatorss, n_jobss,
methods, repeat=repeat, verbose=verbose)
end = time()
results_df = pandas.DataFrame(results)
print("Total time = %0.3f sec cpu=%d\n" % (end - start, cpu_count()))
# plot the results
return results_df
name = "plot_random_forest_reg"
df = run_bench(verbose=True)
df.to_csv(f"{name}.csv", index=False)
df.to_excel(f"{name}.xlsx", index=False)
fig, ax = plot_rf_models(df)
fig.savefig(f"{name}.png")
plt.show()
bench 1 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043522358821381044, 'time_ort': 0.000966933039624406, 'time_mlprodict': 0.0023239386146483216, 'time_mlprodict2': 6.138283313940401e-05, 'time_mlprodict3': 5.920417606830597e-05}
bench 2 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04179508175002411, 'time_ort': 0.0004311122077827652, 'time_mlprodict': 9.501399472355843e-05, 'time_mlprodict2': 0.0002291614170341442, 'time_mlprodict3': 8.111457767275472e-05}
bench 3 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.046121531864628196, 'time_ort': 0.0009078537748957223, 'time_mlprodict': 0.0026506475829096003, 'time_mlprodict2': 0.0006340589077973908, 'time_mlprodict3': 0.00017222867939959872}
bench 4 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06601385137764737, 'time_ort': 0.0018302304379176348, 'time_mlprodict': 0.0050989934970857576, 'time_mlprodict2': 0.005368530823034234, 'time_mlprodict3': 0.0012545301287900656}
bench 5 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0890412821706074, 'time_ort': 0.016116651745202642, 'time_mlprodict': 0.01661338492219026, 'time_mlprodict2': 0.010571761580649763, 'time_mlprodict3': 0.011603824173410734}
bench 6 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04327450553925397, 'time_ort': 7.10517536693563e-05, 'time_mlprodict': 0.0022573787427973002, 'time_mlprodict2': 6.53064150052766e-05, 'time_mlprodict3': 6.276016938500106e-05}
bench 7 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0425184375878113, 'time_ort': 0.0006993165394912163, 'time_mlprodict': 0.00033775775227695704, 'time_mlprodict2': 0.0006189310806803405, 'time_mlprodict3': 9.746858268044889e-05}
bench 8 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04521715212815806, 'time_ort': 0.000907271918233322, 'time_mlprodict': 0.0030274070070489593, 'time_mlprodict2': 0.0010632537811508646, 'time_mlprodict3': 0.00023952448416663253}
bench 9 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06683690845966339, 'time_ort': 0.0029535561334341764, 'time_mlprodict': 0.006857608476032813, 'time_mlprodict2': 0.008710083939755956, 'time_mlprodict3': 0.0017239815400292475}
bench 10 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.088407274413233, 'time_ort': 0.023452326752400648, 'time_mlprodict': 0.03608330533218881, 'time_mlprodict2': 0.018711901580293972, 'time_mlprodict3': 0.016169406997505575}
bench 11 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.04291946887193868, 'time_ort': 0.00010073812639651199, 'time_mlprodict': 0.0023651322117075324, 'time_mlprodict2': 7.061512830356757e-05, 'time_mlprodict3': 6.846054263102512e-05}
bench 12 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.043205117127702884, 'time_ort': 0.0009078622873251637, 'time_mlprodict': 0.0004598619125317782, 'time_mlprodict2': 0.0009409177582710981, 'time_mlprodict3': 0.00019884129869751632}
bench 13 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.054252566524634234, 'time_ort': 0.001753107693634535, 'time_mlprodict': 0.0042793904676249155, 'time_mlprodict2': 0.0016905888424892175, 'time_mlprodict3': 0.0007021306327691203}
bench 14 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06563319530687295, 'time_ort': 0.005373259496991523, 'time_mlprodict': 0.009688018573797308, 'time_mlprodict2': 0.014862701369565912, 'time_mlprodict3': 0.004831281316000968}
bench 15 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09056370883869629, 'time_ort': 0.035183390408443906, 'time_mlprodict': 0.06634832916703697, 'time_mlprodict2': 0.036599469177114465, 'time_mlprodict3': 0.046151463757269084}
bench 16 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0429855689969069, 'time_ort': 9.491279100378354e-05, 'time_mlprodict': 0.0022313590064489595, 'time_mlprodict2': 7.53804633859545e-05, 'time_mlprodict3': 7.772545601862173e-05}
bench 17 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.044195184474001115, 'time_ort': 0.001168547481622385, 'time_mlprodict': 0.0002531804367090049, 'time_mlprodict2': 0.0013627554796150198, 'time_mlprodict3': 0.0002931539151493622}
bench 18 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.056304315713027284, 'time_ort': 0.001885780508423017, 'time_mlprodict': 0.003985001058835123, 'time_mlprodict2': 0.002384304780409568, 'time_mlprodict3': 0.0010702139439268245}
bench 19 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0698889575432986, 'time_ort': 0.009377015056088567, 'time_mlprodict': 0.012755915460487207, 'time_mlprodict2': 0.021200994200383624, 'time_mlprodict3': 0.00855348485832413}
bench 20 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.09663259108889509, 'time_ort': 0.07558634409426966, 'time_mlprodict': 0.09629837364297021, 'time_mlprodict2': 0.06625216780230403, 'time_mlprodict3': 0.08335775382478129}
Total time = 291.746 sec cpu=8
15 16 17 18 19
n_obs 1 10 100 1000 10000
nfeat 30 30 30 30 30
max_depth 12 12 12 12 12
n_estimators 100 100 100 100 100
method predict predict predict predict predict
n_jobs 8 8 8 8 8
time_skl 0.042986 0.044195 0.056304 0.069889 0.096633
time_ort 0.000095 0.001169 0.001886 0.009377 0.075586
time_mlprodict 0.002231 0.000253 0.003985 0.012756 0.096298
time_mlprodict2 0.000075 0.001363 0.002384 0.021201 0.066252
time_mlprodict3 0.000078 0.000293 0.00107 0.008553 0.083358
speedup_ort 452.895427 37.820615 29.857301 7.45322 1.27844
speedup_mlprodict 19.2643 174.560029 14.129059 5.478945 1.003471
speedup_mlprodict2 570.248139 32.430752 23.614563 3.296494 1.458557
speedup_mlprodict3 553.043638 150.757613 52.610336 8.170817 1.159251
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:323: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_random_forest_reg.py:325: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
Total running time of the script: ( 5 minutes 6.064 seconds)