""" ================== Prediction Latency ================== This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either in bulk or atomic (i.e. one by one) mode. The plots represent the distribution of the prediction latency as a boxplot. """ # Authors: Eustache Diemert # License: BSD 3 clause from __future__ import print_function from collections import defaultdict import time import gc import numpy as np import matplotlib.pyplot as plt from scipy.stats import scoreatpercentile from sklearn.datasets.samples_generator import make_regression from sklearn.ensemble.forest import RandomForestRegressor from sklearn.linear_model.ridge import Ridge from sklearn.linear_model.stochastic_gradient import SGDRegressor from sklearn.svm.classes import SVR def _not_in_sphinx(): # Hack to detect whether we are running by the sphinx builder return '__file__' in globals() def atomic_benchmark_estimator(estimator, X_test, verbose=False): """Measure runtime prediction of each instance.""" n_instances = X_test.shape[0] runtimes = np.zeros(n_instances, dtype=np.float) for i in range(n_instances): instance = X_test[i, :] start = time.time() estimator.predict(instance) runtimes[i] = time.time() - start if verbose: print("atomic_benchmark runtimes:", min(runtimes), scoreatpercentile( runtimes, 50), max(runtimes)) return runtimes def bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose): """Measure runtime prediction of the whole input.""" n_instances = X_test.shape[0] runtimes = np.zeros(n_bulk_repeats, dtype=np.float) for i in range(n_bulk_repeats): start = time.time() estimator.predict(X_test) runtimes[i] = time.time() - start runtimes = np.array(list(map(lambda x: x / float(n_instances), runtimes))) if verbose: print("bulk_benchmark runtimes:", min(runtimes), scoreatpercentile( runtimes, 50), max(runtimes)) return runtimes def benchmark_estimator(estimator, X_test, n_bulk_repeats=30, verbose=False): """ Measure runtimes of prediction in both atomic and bulk mode. Parameters ---------- estimator : already trained estimator supporting `predict()` X_test : test input n_bulk_repeats : how many times to repeat when evaluating bulk mode Returns ------- atomic_runtimes, bulk_runtimes : a pair of `np.array` which contain the runtimes in seconds. """ atomic_runtimes = atomic_benchmark_estimator(estimator, X_test, verbose) bulk_runtimes = bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose) return atomic_runtimes, bulk_runtimes def generate_dataset(n_train, n_test, n_features, noise=0.1, verbose=False): """Generate a regression dataset with the given parameters.""" if verbose: print("generating dataset...") X, y, coef = make_regression(n_samples=n_train + n_test, n_features=n_features, noise=noise, coef=True) X_train = X[:n_train] y_train = y[:n_train] X_test = X[n_train:] y_test = y[n_train:] idx = np.arange(n_train) np.random.seed(13) np.random.shuffle(idx) X_train = X_train[idx] y_train = y_train[idx] std = X_train.std(axis=0) mean = X_train.mean(axis=0) X_train = (X_train - mean) / std X_test = (X_test - mean) / std std = y_train.std(axis=0) mean = y_train.mean(axis=0) y_train = (y_train - mean) / std y_test = (y_test - mean) / std gc.collect() if verbose: print("ok") return X_train, y_train, X_test, y_test def boxplot_runtimes(runtimes, pred_type, configuration): """ Plot a new `Figure` with boxplots of prediction runtimes. Parameters ---------- runtimes : list of `np.array` of latencies in micro-seconds cls_names : list of estimator class names that generated the runtimes pred_type : 'bulk' or 'atomic' """ fig, ax1 = plt.subplots(figsize=(10, 6)) bp = plt.boxplot(runtimes, ) cls_infos = ['%s\n(%d %s)' % (estimator_conf['name'], estimator_conf['complexity_computer']( estimator_conf['instance']), estimator_conf['complexity_label']) for estimator_conf in configuration['estimators']] xtick_names = plt.setp(ax1, xticklabels=cls_infos) plt.setp(xtick_names) plt.setp(bp['boxes'], color='black') plt.setp(bp['whiskers'], color='black') plt.setp(bp['fliers'], color='red', marker='+') ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax1.set_axisbelow(True) ax1.set_title('Prediction Time per Instance - %s, %d feats.' % ( pred_type.capitalize(), configuration['n_features'])) ax1.set_ylabel('Prediction Time (us)') plt.show() def benchmark(configuration): """Run the whole benchmark.""" X_train, y_train, X_test, y_test = generate_dataset( configuration['n_train'], configuration['n_test'], configuration['n_features']) stats = {} for estimator_conf in configuration['estimators']: print("Benchmarking", estimator_conf['instance']) estimator_conf['instance'].fit(X_train, y_train) gc.collect() a, b = benchmark_estimator(estimator_conf['instance'], X_test) stats[estimator_conf['name']] = {'atomic': a, 'bulk': b} cls_names = [estimator_conf['name'] for estimator_conf in configuration[ 'estimators']] runtimes = [1e6 * stats[clf_name]['atomic'] for clf_name in cls_names] boxplot_runtimes(runtimes, 'atomic', configuration) runtimes = [1e6 * stats[clf_name]['bulk'] for clf_name in cls_names] boxplot_runtimes(runtimes, 'bulk (%d)' % configuration['n_test'], configuration) def n_feature_influence(estimators, n_train, n_test, n_features, percentile): """ Estimate influence of the number of features on prediction time. Parameters ---------- estimators : dict of (name (str), estimator) to benchmark n_train : nber of training instances (int) n_test : nber of testing instances (int) n_features : list of feature-space dimensionality to test (int) percentile : percentile at which to measure the speed (int [0-100]) Returns: -------- percentiles : dict(estimator_name, dict(n_features, percentile_perf_in_us)) """ percentiles = defaultdict(defaultdict) for n in n_features: print("benchmarking with %d features" % n) X_train, y_train, X_test, y_test = generate_dataset(n_train, n_test, n) for cls_name, estimator in estimators.items(): estimator.fit(X_train, y_train) gc.collect() runtimes = bulk_benchmark_estimator(estimator, X_test, 30, False) percentiles[cls_name][n] = 1e6 * scoreatpercentile(runtimes, percentile) return percentiles def plot_n_features_influence(percentiles, percentile): fig, ax1 = plt.subplots(figsize=(10, 6)) colors = ['r', 'g', 'b'] for i, cls_name in enumerate(percentiles.keys()): x = np.array(sorted([n for n in percentiles[cls_name].keys()])) y = np.array([percentiles[cls_name][n] for n in x]) plt.plot(x, y, color=colors[i], ) ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax1.set_axisbelow(True) ax1.set_title('Evolution of Prediction Time with #Features') ax1.set_xlabel('#Features') ax1.set_ylabel('Prediction Time at %d%%-ile (us)' % percentile) plt.show() def benchmark_throughputs(configuration, duration_secs=0.1): """benchmark throughput for different estimators.""" X_train, y_train, X_test, y_test = generate_dataset( configuration['n_train'], configuration['n_test'], configuration['n_features']) throughputs = dict() for estimator_config in configuration['estimators']: estimator_config['instance'].fit(X_train, y_train) start_time = time.time() n_predictions = 0 while (time.time() - start_time) < duration_secs: estimator_config['instance'].predict(X_test[0]) n_predictions += 1 throughputs[estimator_config['name']] = n_predictions / duration_secs return throughputs def plot_benchmark_throughput(throughputs, configuration): fig, ax = plt.subplots(figsize=(10, 6)) colors = ['r', 'g', 'b'] cls_infos = ['%s\n(%d %s)' % (estimator_conf['name'], estimator_conf['complexity_computer']( estimator_conf['instance']), estimator_conf['complexity_label']) for estimator_conf in configuration['estimators']] cls_values = [throughputs[estimator_conf['name']] for estimator_conf in configuration['estimators']] plt.bar(range(len(throughputs)), cls_values, width=0.5, color=colors) ax.set_xticks(np.linspace(0.25, len(throughputs) - 0.75, len(throughputs))) ax.set_xticklabels(cls_infos, fontsize=10) ymax = max(cls_values) * 1.2 ax.set_ylim((0, ymax)) ax.set_ylabel('Throughput (predictions/sec)') ax.set_title('Prediction Throughput for different estimators (%d ' 'features)' % configuration['n_features']) plt.show() ############################################################################### # main code start_time = time.time() # benchmark bulk/atomic prediction speed for various regressors configuration = { 'n_train': int(1e3), 'n_test': int(1e2), 'n_features': int(1e2), 'estimators': [ {'name': 'Linear Model', 'instance': SGDRegressor(penalty='elasticnet', alpha=0.01, l1_ratio=0.25, fit_intercept=True), 'complexity_label': 'non-zero coefficients', 'complexity_computer': lambda clf: np.count_nonzero(clf.coef_)}, {'name': 'RandomForest', 'instance': RandomForestRegressor(), 'complexity_label': 'estimators', 'complexity_computer': lambda clf: clf.n_estimators}, {'name': 'SVR', 'instance': SVR(kernel='rbf'), 'complexity_label': 'support vectors', 'complexity_computer': lambda clf: len(clf.support_vectors_)}, ] } benchmark(configuration) # benchmark n_features influence on prediction speed percentile = 90 percentiles = n_feature_influence({'ridge': Ridge()}, configuration['n_train'], configuration['n_test'], [100, 250, 500], percentile) plot_n_features_influence(percentiles, percentile) # benchmark throughput throughputs = benchmark_throughputs(configuration) plot_benchmark_throughput(throughputs, configuration) stop_time = time.time() print("example run in %.2fs" % (stop_time - start_time))