Benchmark Random Forests, Tree Ensemble, Multi-Classification#

The script compares different implementations for the operator TreeEnsembleRegressor for a multi-regression. It replicates the benchmark Benchmark Random Forests, Tree Ensemble, (AoS and SoA) for multi-classification.

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 RandomForestClassifier
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())

Out:

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()
name version
0 date 2022-05-27 03:44:35.805980
1 numpy 1.22.4
2 scikit-learn 1.1.1
3 onnx 1.11.0
4 onnxruntime 1.11.1
5 skl2onnx 1.12.999
6 mlprodict 0.8.1809


Implementations to benchmark#

def fcts_model(X, y, max_depth, n_estimators, n_jobs):
    "RandomForestClassifier."
    rf = RandomForestClassifier(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,
                          options={id(rf): {'zipmap': False}})
    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[1]
    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_proba(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_f = X_train.sum(axis=1) + eps
        y_train = (y_train_f > 12).astype(numpy.int64)
        y_train[y_train_f > 15] = 2
        y_train[y_train_f < 10] = 3

        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('%1.1fx' % height,
                        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["speedup_%s" % engine] = dfr["time_skl"] / dfr["time_%s" % 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 = "RandomForestClassifier - %s" % 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["speedup_%s" % 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["speedup_%s" % 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_cls_multi"
df = run_bench(verbose=True)
df.to_csv("%s.csv" % name, index=False)
df.to_excel("%s.xlsx" % name, index=False)
fig, ax = plot_rf_models(df)
fig.savefig("%s.png" % name)
plt.show()
RandomForestClassifier - ort depth 6 - 30 features  100 estimators 8 jobs, RandomForestClassifier - ort depth 8 - 30 features  100 estimators 8 jobs, RandomForestClassifier - ort depth 10 - 30 features  100 estimators 8 jobs, RandomForestClassifier - ort depth 12 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict depth 6 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict depth 8 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict depth 10 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict depth 12 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict2 depth 6 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict2 depth 8 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict2 depth 10 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict2 depth 12 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict3 depth 6 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict3 depth 8 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict3 depth 10 - 30 features  100 estimators 8 jobs, RandomForestClassifier - mlprodict3 depth 12 - 30 features  100 estimators 8 jobs

Out:

bench 1 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06354205818752234, 'time_ort': 0.00012871431272287737, 'time_mlprodict': 0.00294123906223831, 'time_mlprodict2': 0.00010131712497241097, 'time_mlprodict3': 7.568731280116481e-05}
bench 2 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0645933948126185, 'time_ort': 0.0005140403122823045, 'time_mlprodict': 0.000538195062745217, 'time_mlprodict2': 0.00042390312501083827, 'time_mlprodict3': 0.000350292687471665}
bench 3 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.07300797035713913, 'time_ort': 0.0006493797856299872, 'time_mlprodict': 0.004251855571575496, 'time_mlprodict2': 0.002303869071121361, 'time_mlprodict3': 0.00023620978598566062}
bench 4 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.12150082655555, 'time_ort': 0.003188682778272778, 'time_mlprodict': 0.014704503000151211, 'time_mlprodict2': 0.013718607666507725, 'time_mlprodict3': 0.001574249333417457}
bench 5 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 6, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.14917438385704632, 'time_ort': 0.029229423285869416, 'time_mlprodict': 0.09533169114313621, 'time_mlprodict2': 0.08892240999973312, 'time_mlprodict3': 0.014768169428862166}
bench 6 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06329377881274922, 'time_ort': 0.00012457806269594585, 'time_mlprodict': 0.0034964814999511873, 'time_mlprodict2': 0.0001381798124384659, 'time_mlprodict3': 8.424787483818363e-05}
bench 7 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06227006700004602, 'time_ort': 0.0007976788825437645, 'time_mlprodict': 0.0008084052353618009, 'time_mlprodict2': 0.0007888353528174133, 'time_mlprodict3': 0.0007936095297576257}
bench 8 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.07467367892864527, 'time_ort': 0.001290559571706191, 'time_mlprodict': 0.0047430925712563165, 'time_mlprodict2': 0.0016066134999813844, 'time_mlprodict3': 0.0005106947142589238}
bench 9 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.1145002697780405, 'time_ort': 0.004566760777379386, 'time_mlprodict': 0.015476821777863532, 'time_mlprodict2': 0.012105793111711845, 'time_mlprodict3': 0.002302260666814012}
bench 10 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 8, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.15006543171434064, 'time_ort': 0.045116901142007136, 'time_mlprodict': 0.12711229671445576, 'time_mlprodict2': 0.11950761928580635, 'time_mlprodict3': 0.01897591757109954}
bench 11 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06181529752939241, 'time_ort': 0.00015903129422119544, 'time_mlprodict': 0.0030822223527459704, 'time_mlprodict2': 0.0001879986474009724, 'time_mlprodict3': 9.217435281778522e-05}
bench 12 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06701892919954844, 'time_ort': 0.0010383266000038324, 'time_mlprodict': 0.000994429133425001, 'time_mlprodict2': 0.0011610766664186182, 'time_mlprodict3': 0.0007278798667054313}
bench 13 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.08357718015423206, 'time_ort': 0.001646990691929554, 'time_mlprodict': 0.00477017930769272, 'time_mlprodict2': 0.002103537615487137, 'time_mlprodict3': 0.0008332406152756169}
bench 14 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.12019978111089182, 'time_ort': 0.006828377444536373, 'time_mlprodict': 0.019459850666559458, 'time_mlprodict2': 0.01877456666665643, 'time_mlprodict3': 0.003468514333589054}
bench 15 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 10, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.14767822928531263, 'time_ort': 0.04853249728642238, 'time_mlprodict': 0.1678714985713928, 'time_mlprodict2': 0.1810835467144248, 'time_mlprodict3': 0.02522408600011009}
bench 16 : {'n_obs': 1, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.06460072037498321, 'time_ort': 0.00011892062502738554, 'time_mlprodict': 0.0031357383127215144, 'time_mlprodict2': 0.0002215833746959106, 'time_mlprodict3': 9.782643746802933e-05}
bench 17 : {'n_obs': 10, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.07174957899977537, 'time_ort': 0.001167197214402092, 'time_mlprodict': 0.0011831978569846666, 'time_mlprodict2': 0.0013940542859407806, 'time_mlprodict3': 0.00033083371402296634}
bench 18 : {'n_obs': 100, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.08903703141671333, 'time_ort': 0.0019349925836043742, 'time_mlprodict': 0.005166244249873368, 'time_mlprodict2': 0.0027020139162535393, 'time_mlprodict3': 0.0011695020833334031}
bench 19 : {'n_obs': 1000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.11922896444432102, 'time_ort': 0.009758657222038083, 'time_mlprodict': 0.0216845111111373, 'time_mlprodict2': 0.02257569533362079, 'time_mlprodict3': 0.005062062332904639}
bench 20 : {'n_obs': 10000, 'nfeat': 30, 'max_depth': 12, 'n_estimators': 100, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.15181953100026085, 'time_ort': 0.0753591640002144, 'time_mlprodict': 0.18593439099952644, 'time_mlprodict2': 0.22329019057152827, 'time_mlprodict3': 0.0387260398575953}
Total time = 216.418 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              0.064601     0.07175   0.089037   0.119229   0.15182
time_ort              0.000119    0.001167   0.001935   0.009759  0.075359
time_mlprodict        0.003136    0.001183   0.005166   0.021685  0.185934
time_mlprodict2       0.000222    0.001394   0.002702   0.022576   0.22329
time_mlprodict3       0.000098    0.000331    0.00117   0.005062  0.038726
speedup_ort         543.225537   61.471685  46.014146  12.217763  2.014613
speedup_mlprodict    20.601439    60.64039  17.234383   5.498347  0.816522
speedup_mlprodict2  291.541369   51.468282  32.952099   5.281298   0.67992
speedup_mlprodict3  660.360553  216.875052  76.132427  23.553437  3.920347

Total running time of the script: ( 3 minutes 49.118 seconds)

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