scikit-learn · ogrisel · Sep 15, 2020 · Sep 11, 2020 · NicolasHug · Sep 13, 2020
diff --git a/benchmarks/bench_hist_gradient_boosting_threading.py b/benchmarks/bench_hist_gradient_boosting_threading.py
@@ -0,0 +1,316 @@
+from time import time
+import argparse
+import os
+from pprint import pprint
+
+import numpy as np
+from threadpoolctl import threadpool_limits
+import sklearn
+from sklearn.model_selection import train_test_split
+# To use this experimental feature, we need to explicitly ask for it:
+from sklearn.experimental import enable_hist_gradient_boosting  # noqa
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.datasets import make_classification
+from sklearn.datasets import make_regression
+from sklearn.ensemble._hist_gradient_boosting.utils import (
+    get_equivalent_estimator)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--n-leaf-nodes', type=int, default=31)
+parser.add_argument('--n-trees', type=int, default=10)
+parser.add_argument('--lightgbm', action="store_true", default=False,
+                    help='also benchmark lightgbm')
+parser.add_argument('--xgboost', action="store_true", default=False,
+                    help='also benchmark xgboost')
+parser.add_argument('--catboost', action="store_true", default=False,
+                    help='also benchmark catboost')
+parser.add_argument('--learning-rate', type=float, default=.1)
+parser.add_argument('--problem', type=str, default='classification',
+                    choices=['classification', 'regression'])
+parser.add_argument('--loss', type=str, default='default')
+parser.add_argument('--missing-fraction', type=float, default=0)
+parser.add_argument('--n-classes', type=int, default=2)
+parser.add_argument('--n-samples', type=int, default=int(1e6))
+parser.add_argument('--n-features', type=int, default=100)
+parser.add_argument('--max-bins', type=int, default=255)
+
+parser.add_argument('--print-params', action="store_true", default=False)
+parser.add_argument('--random-sample-weights', action="store_true",
+                    default=False,
+                    help="generate and use random sample weights")
+parser.add_argument('--plot', action="store_true", default=False,
+                    help='show a plot results')
+parser.add_argument('--plot-filename', default=None,
+                    help='filename to save the figure to disk')
+args = parser.parse_args()
+
+n_samples = args.n_samples
+n_leaf_nodes = args.n_leaf_nodes
+n_trees = args.n_trees
+lr = args.learning_rate
+max_bins = args.max_bins
+
+
+print("Data size: %d samples train, %d samples test."
+      % (n_samples, n_samples))
+print(f"n_features: {args.n_features}")
+
+
+def get_estimator_and_data():
+    if args.problem == 'classification':
+        X, y = make_classification(args.n_samples * 2,
+                                   n_features=args.n_features,
+                                   n_classes=args.n_classes,
+                                   n_clusters_per_class=1,
+                                   n_informative=args.n_features // 2,
+                                   random_state=0)
+        return X, y, HistGradientBoostingClassifier
+    elif args.problem == 'regression':
+        X, y = make_regression(args.n_samples_max * 2,
+                               n_features=args.n_features, random_state=0)
+        return X, y, HistGradientBoostingRegressor
+
+
+X, y, Estimator = get_estimator_and_data()
+if args.missing_fraction:
+    mask = np.random.binomial(1, args.missing_fraction, size=X.shape).astype(
+        bool)
+    X[mask] = np.nan
+
+if args.random_sample_weights:
+    sample_weight = np.random.rand(len(X)) * 10
+else:
+    sample_weight = None
+
+if sample_weight is not None:
+    (X_train_, X_test_, y_train_, y_test_,
+     sample_weight_train_, _) = train_test_split(
+        X, y, sample_weight, test_size=0.5, random_state=0)
+else:
+    X_train_, X_test_, y_train_, y_test_ = train_test_split(
+        X, y, test_size=0.5, random_state=0)
+    sample_weight_train_ = None
+
+
+sklearn_est = Estimator(
+    learning_rate=lr,
+    max_iter=n_trees,
+    max_bins=max_bins,
+    max_leaf_nodes=n_leaf_nodes,
+    early_stopping=False,
+    random_state=0,
+    verbose=0,
+)
+loss = args.loss
+if args.problem == 'classification':
+    if loss == 'default':
+        # loss='auto' does not work with get_equivalent_estimator()
+        loss = 'binary_crossentropy' if args.n_classes == 2 else \
+            'categorical_crossentropy'
+else:
+    # regression
+    if loss == 'default':
+        loss = 'least_squares'
+sklearn_est.set_params(loss=loss)
+
+
+if args.print_params:
+    print("scikit-learn")
+    pprint(sklearn_est.get_params())
+
+    for libname in ["lightgbm", "xgboost", "catboost"]:
+        if getattr(args, libname):
+            print(libname)
+            est = get_equivalent_estimator(sklearn_est, lib=libname)
+            pprint(est.get_params())
+
+
+def one_run(n_threads, n_samples):
+    X_train = X_train_[:n_samples]
+    X_test = X_test_[:n_samples]
+    y_train = y_train_[:n_samples]
+    y_test = y_test_[:n_samples]
+    if sample_weight is not None:
+        sample_weight_train = sample_weight_train_[:n_samples]
+    else:
+        sample_weight_train = None
+    assert X_train.shape[0] == n_samples
+    assert X_test.shape[0] == n_samples
+    print("Fitting a sklearn model...")
+    tic = time()
+    est = sklearn.base.clone(sklearn_est)
+
+    with threadpool_limits(n_threads, user_api="openmp"):
+        est.fit(X_train, y_train, sample_weight=sample_weight_train)
+        sklearn_fit_duration = time() - tic
+        tic = time()
+        sklearn_score = est.score(X_test, y_test)
+        sklearn_score_duration = time() - tic
+    print("score: {:.4f}".format(sklearn_score))
+    print("fit duration: {:.3f}s,".format(sklearn_fit_duration))
+    print("score duration: {:.3f}s,".format(sklearn_score_duration))
+
+    lightgbm_score = None
+    lightgbm_fit_duration = None
+    lightgbm_score_duration = None
+    if args.lightgbm:
+        print("Fitting a LightGBM model...")
+        lightgbm_est = get_equivalent_estimator(est, lib='lightgbm')
+        lightgbm_est.set_params(num_threads=n_threads)
+
+        tic = time()
+        lightgbm_est.fit(X_train, y_train, sample_weight=sample_weight_train)
+        lightgbm_fit_duration = time() - tic
+        tic = time()
+        lightgbm_score = lightgbm_est.score(X_test, y_test)
+        lightgbm_score_duration = time() - tic
+        print("score: {:.4f}".format(lightgbm_score))
+        print("fit duration: {:.3f}s,".format(lightgbm_fit_duration))
+        print("score duration: {:.3f}s,".format(lightgbm_score_duration))
+
+    xgb_score = None
+    xgb_fit_duration = None
+    xgb_score_duration = None
+    if args.xgboost:
+        print("Fitting an XGBoost model...")
+        xgb_est = get_equivalent_estimator(est, lib='xgboost')
+        xgb_est.set_params(nthread=n_threads)
+
+        tic = time()
+        xgb_est.fit(X_train, y_train, sample_weight=sample_weight_train)
+        xgb_fit_duration = time() - tic
+        tic = time()
+        xgb_score = xgb_est.score(X_test, y_test)
+        xgb_score_duration = time() - tic
+        print("score: {:.4f}".format(xgb_score))
+        print("fit duration: {:.3f}s,".format(xgb_fit_duration))
+        print("score duration: {:.3f}s,".format(xgb_score_duration))
+
+    cat_score = None
+    cat_fit_duration = None
+    cat_score_duration = None
+    if args.catboost:
+        print("Fitting a CatBoost model...")
+        cat_est = get_equivalent_estimator(est, lib='catboost')
+        cat_est.set_params(thread_count=n_threads)
+
+        tic = time()
+        cat_est.fit(X_train, y_train, sample_weight=sample_weight_train)
+        cat_fit_duration = time() - tic
+        tic = time()
+        cat_score = cat_est.score(X_test, y_test)
+        cat_score_duration = time() - tic
+        print("score: {:.4f}".format(cat_score))
+        print("fit duration: {:.3f}s,".format(cat_fit_duration))
+        print("score duration: {:.3f}s,".format(cat_score_duration))
+
+    return (sklearn_score, sklearn_fit_duration, sklearn_score_duration,
+            lightgbm_score, lightgbm_fit_duration, lightgbm_score_duration,
+            xgb_score, xgb_fit_duration, xgb_score_duration,
+            cat_score, cat_fit_duration, cat_score_duration)
+
+
+max_threads = os.cpu_count()
+n_threads_list = [2 ** i for i in range(8) if (2 ** i) < max_threads]
+n_threads_list.append(max_threads)
+
+sklearn_scores = []
+sklearn_fit_durations = []
+sklearn_score_durations = []
+lightgbm_scores = []
+lightgbm_fit_durations = []
+lightgbm_score_durations = []
+xgb_scores = []
+xgb_fit_durations = []
+xgb_score_durations = []
+cat_scores = []
+cat_fit_durations = []
+cat_score_durations = []
+
+for n_threads in n_threads_list:
+    print(f"n_threads: {n_threads}")
+    (
+        sklearn_score,
+        sklearn_fit_duration,
+        sklearn_score_duration,
+        lightgbm_score,
+        lightgbm_fit_duration,
+        lightgbm_score_duration,
+        xgb_score,
+        xgb_fit_duration,
+        xgb_score_duration,
+        cat_score,
+        cat_fit_duration,
+        cat_score_duration
+    ) = one_run(n_threads, n_samples)
+
+    for scores, score in (
+            (sklearn_scores, sklearn_score),
+            (sklearn_fit_durations, sklearn_fit_duration),
+            (sklearn_score_durations, sklearn_score_duration),
+            (lightgbm_scores, lightgbm_score),
+            (lightgbm_fit_durations, lightgbm_fit_duration),
+            (lightgbm_score_durations, lightgbm_score_duration),
+            (xgb_scores, xgb_score),
+            (xgb_fit_durations, xgb_fit_duration),
+            (xgb_score_durations, xgb_score_duration),
+            (cat_scores, cat_score),
+            (cat_fit_durations, cat_fit_duration),
+            (cat_score_durations, cat_score_duration)):
+        scores.append(score)
+
+
+if args.plot or args.plot_filename:
+    import matplotlib.pyplot as plt
+    import matplotlib
+
+    fig, axs = plt.subplots(2, figsize=(12, 12))
+
+    label = f"sklearn {sklearn.__version__}"
+    axs[0].plot(n_threads_list, sklearn_fit_durations, label=label)
+    axs[1].plot(n_threads_list, sklearn_score_durations, label=label)
+
+    if args.lightgbm:
+        import lightgbm
+        label = f'LightGBM {lightgbm.__version__}'
+        axs[0].plot(n_threads_list, lightgbm_fit_durations, label=label)
+        axs[1].plot(n_threads_list, lightgbm_score_durations, label=label)
+
+    if args.xgboost:
+        import xgboost
+        label = f'XGBoost {xgboost.__version__}'
+        axs[0].plot(n_threads_list, xgb_fit_durations, label=label)
+        axs[1].plot(n_threads_list, xgb_score_durations, label=label)
+
+    if args.catboost:
+        import catboost
+        label = f'CatBoost {catboost.__version__}'
+        axs[0].plot(n_threads_list, cat_fit_durations, label=label)
+        axs[1].plot(n_threads_list, cat_score_durations, label=label)
+
+    for ax in axs:
+        ax.set_xscale('log')
+        ax.set_xlabel('n_threads')
+        ax.set_ylabel('duration (s)')
+        ax.set_ylim(0, None)
+        ax.set_xticks(n_threads_list)
+        ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
+        ax.legend(loc='best')
+
+    axs[0].set_title('fit duration (s)')
+    axs[1].set_title('score duration (s)')
+
+    title = args.problem
+    if args.problem == 'classification':
+        title += ' n_classes = {}'.format(args.n_classes)
+    fig.suptitle(title)
+
+    plt.tight_layout()
+
+    if args.plot_filename:
+        plt.savefig(args.plot_filename)
+
+    if args.plot:
+        plt.show()