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scikit-learn 1.3.2
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# 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 <eustache@diemert.fr>
    # License: 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()

## Benchmark and plot helper functions

    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()

## 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, 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)

    Benchmarking SGDRegressor(alpha=0.01, l1_ratio=0.25, penalty='elasticnet', tol=0.0001)
    Benchmarking RandomForestRegressor()
    Benchmarking SVR()

## 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)

    benchmarking with 100 features
    benchmarking with 250 features
    benchmarking with 500 features

## Benchmark throughput

    throughputs = benchmark_throughputs(configuration)
    plot_benchmark_throughput(throughputs, configuration)

Total running time of the script: (0 minutes 10.615 seconds)

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