.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_prediction_latency.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_prediction_latency.py: ================== 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. .. GENERATED FROM PYTHON SOURCE LINES 15-40 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import gc import time from collections import defaultdict import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_regression from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import Ridge, SGDRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.utils import shuffle def _not_in_sphinx(): # Hack to detect whether we are running by the sphinx builder return "__file__" in globals() .. GENERATED FROM PYTHON SOURCE LINES 41-43 Benchmark and plot helper functions ----------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 43-296 .. code-block:: Python 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=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), np.percentile(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=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), np.percentile(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 ) random_seed = 13 X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=n_train, test_size=n_test, random_state=random_seed ) X_train, y_train = shuffle(X_train, y_train, random_state=random_seed) X_scaler = StandardScaler() X_train = X_scaler.fit_transform(X_train) X_test = X_scaler.transform(X_test) y_scaler = StandardScaler() y_train = y_scaler.fit_transform(y_train[:, None])[:, 0] y_test = y_scaler.transform(y_test[:, None])[:, 0] 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"] ] plt.setp(ax1, xticklabels=cls_infos) 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 * np.percentile(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(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() .. GENERATED FROM PYTHON SOURCE LINES 297-299 Benchmark bulk/atomic prediction speed for various regressors ------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 299-329 .. code-block:: Python 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, tol=1e-4 ), "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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_001.png :alt: Prediction Time per Instance - Atomic, 100 feats. :srcset: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_002.png :alt: Prediction Time per Instance - Bulk (100), 100 feats. :srcset: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Benchmarking SGDRegressor(alpha=0.01, l1_ratio=0.25, penalty='elasticnet', tol=0.0001) Benchmarking RandomForestRegressor() Benchmarking SVR() .. GENERATED FROM PYTHON SOURCE LINES 330-332 Benchmark n_features influence on prediction speed -------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 332-343 .. code-block:: Python percentile = 90 percentiles = n_feature_influence( {"ridge": Ridge()}, configuration["n_train"], configuration["n_test"], [100, 250, 500], percentile, ) plot_n_features_influence(percentiles, percentile) .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_003.png :alt: Evolution of Prediction Time with #Features :srcset: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none benchmarking with 100 features benchmarking with 250 features benchmarking with 500 features .. GENERATED FROM PYTHON SOURCE LINES 344-346 Benchmark throughput -------------------- .. GENERATED FROM PYTHON SOURCE LINES 346-349 .. code-block:: Python throughputs = benchmark_throughputs(configuration) plot_benchmark_throughput(throughputs, configuration) .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_004.png :alt: Prediction Throughput for different estimators (100 features) :srcset: /auto_examples/applications/images/sphx_glr_plot_prediction_latency_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 16.940 seconds) .. _sphx_glr_download_auto_examples_applications_plot_prediction_latency.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/applications/plot_prediction_latency.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/applications/plot_prediction_latency.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_prediction_latency.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_prediction_latency.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_prediction_latency.zip ` .. include:: plot_prediction_latency.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_