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