349 lines
11 KiB
Python
349 lines
11 KiB
Python
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"""
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==================
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Prediction Latency
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==================
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This is an example showing the prediction latency of various scikit-learn
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estimators.
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The goal is to measure the latency one can expect when doing predictions
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either in bulk or atomic (i.e. one by one) mode.
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The plots represent the distribution of the prediction latency as a boxplot.
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"""
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# Authors: Eustache Diemert <eustache@diemert.fr>
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# License: BSD 3 clause
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import gc
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import time
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from collections import defaultdict
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_regression
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.linear_model import Ridge, SGDRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVR
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from sklearn.utils import shuffle
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def _not_in_sphinx():
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# Hack to detect whether we are running by the sphinx builder
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return "__file__" in globals()
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# %%
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# Benchmark and plot helper functions
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# -----------------------------------
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def atomic_benchmark_estimator(estimator, X_test, verbose=False):
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"""Measure runtime prediction of each instance."""
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n_instances = X_test.shape[0]
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runtimes = np.zeros(n_instances, dtype=float)
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for i in range(n_instances):
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instance = X_test[[i], :]
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start = time.time()
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estimator.predict(instance)
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runtimes[i] = time.time() - start
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if verbose:
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print(
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"atomic_benchmark runtimes:",
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min(runtimes),
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np.percentile(runtimes, 50),
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max(runtimes),
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)
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return runtimes
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def bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose):
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"""Measure runtime prediction of the whole input."""
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n_instances = X_test.shape[0]
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runtimes = np.zeros(n_bulk_repeats, dtype=float)
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for i in range(n_bulk_repeats):
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start = time.time()
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estimator.predict(X_test)
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runtimes[i] = time.time() - start
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runtimes = np.array(list(map(lambda x: x / float(n_instances), runtimes)))
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if verbose:
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print(
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"bulk_benchmark runtimes:",
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min(runtimes),
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np.percentile(runtimes, 50),
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max(runtimes),
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)
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return runtimes
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def benchmark_estimator(estimator, X_test, n_bulk_repeats=30, verbose=False):
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"""
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Measure runtimes of prediction in both atomic and bulk mode.
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Parameters
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----------
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estimator : already trained estimator supporting `predict()`
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X_test : test input
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n_bulk_repeats : how many times to repeat when evaluating bulk mode
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Returns
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-------
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atomic_runtimes, bulk_runtimes : a pair of `np.array` which contain the
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runtimes in seconds.
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"""
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atomic_runtimes = atomic_benchmark_estimator(estimator, X_test, verbose)
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bulk_runtimes = bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose)
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return atomic_runtimes, bulk_runtimes
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def generate_dataset(n_train, n_test, n_features, noise=0.1, verbose=False):
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"""Generate a regression dataset with the given parameters."""
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if verbose:
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print("generating dataset...")
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X, y, coef = make_regression(
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n_samples=n_train + n_test, n_features=n_features, noise=noise, coef=True
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)
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random_seed = 13
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, train_size=n_train, test_size=n_test, random_state=random_seed
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)
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X_train, y_train = shuffle(X_train, y_train, random_state=random_seed)
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X_scaler = StandardScaler()
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X_train = X_scaler.fit_transform(X_train)
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X_test = X_scaler.transform(X_test)
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y_scaler = StandardScaler()
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y_train = y_scaler.fit_transform(y_train[:, None])[:, 0]
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y_test = y_scaler.transform(y_test[:, None])[:, 0]
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gc.collect()
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if verbose:
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print("ok")
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return X_train, y_train, X_test, y_test
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def boxplot_runtimes(runtimes, pred_type, configuration):
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"""
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Plot a new `Figure` with boxplots of prediction runtimes.
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Parameters
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----------
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runtimes : list of `np.array` of latencies in micro-seconds
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cls_names : list of estimator class names that generated the runtimes
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pred_type : 'bulk' or 'atomic'
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"""
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fig, ax1 = plt.subplots(figsize=(10, 6))
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bp = plt.boxplot(
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runtimes,
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)
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cls_infos = [
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"%s\n(%d %s)"
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% (
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estimator_conf["name"],
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estimator_conf["complexity_computer"](estimator_conf["instance"]),
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estimator_conf["complexity_label"],
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)
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for estimator_conf in configuration["estimators"]
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]
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plt.setp(ax1, xticklabels=cls_infos)
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plt.setp(bp["boxes"], color="black")
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plt.setp(bp["whiskers"], color="black")
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plt.setp(bp["fliers"], color="red", marker="+")
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ax1.yaxis.grid(True, linestyle="-", which="major", color="lightgrey", alpha=0.5)
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ax1.set_axisbelow(True)
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ax1.set_title(
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"Prediction Time per Instance - %s, %d feats."
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% (pred_type.capitalize(), configuration["n_features"])
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)
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ax1.set_ylabel("Prediction Time (us)")
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plt.show()
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def benchmark(configuration):
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"""Run the whole benchmark."""
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X_train, y_train, X_test, y_test = generate_dataset(
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configuration["n_train"], configuration["n_test"], configuration["n_features"]
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)
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stats = {}
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for estimator_conf in configuration["estimators"]:
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print("Benchmarking", estimator_conf["instance"])
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estimator_conf["instance"].fit(X_train, y_train)
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gc.collect()
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a, b = benchmark_estimator(estimator_conf["instance"], X_test)
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stats[estimator_conf["name"]] = {"atomic": a, "bulk": b}
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cls_names = [
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estimator_conf["name"] for estimator_conf in configuration["estimators"]
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]
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runtimes = [1e6 * stats[clf_name]["atomic"] for clf_name in cls_names]
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boxplot_runtimes(runtimes, "atomic", configuration)
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runtimes = [1e6 * stats[clf_name]["bulk"] for clf_name in cls_names]
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boxplot_runtimes(runtimes, "bulk (%d)" % configuration["n_test"], configuration)
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def n_feature_influence(estimators, n_train, n_test, n_features, percentile):
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"""
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Estimate influence of the number of features on prediction time.
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Parameters
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----------
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estimators : dict of (name (str), estimator) to benchmark
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n_train : nber of training instances (int)
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n_test : nber of testing instances (int)
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n_features : list of feature-space dimensionality to test (int)
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percentile : percentile at which to measure the speed (int [0-100])
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Returns:
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--------
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percentiles : dict(estimator_name,
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dict(n_features, percentile_perf_in_us))
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"""
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percentiles = defaultdict(defaultdict)
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for n in n_features:
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print("benchmarking with %d features" % n)
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X_train, y_train, X_test, y_test = generate_dataset(n_train, n_test, n)
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for cls_name, estimator in estimators.items():
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estimator.fit(X_train, y_train)
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gc.collect()
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runtimes = bulk_benchmark_estimator(estimator, X_test, 30, False)
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percentiles[cls_name][n] = 1e6 * np.percentile(runtimes, percentile)
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return percentiles
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def plot_n_features_influence(percentiles, percentile):
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fig, ax1 = plt.subplots(figsize=(10, 6))
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colors = ["r", "g", "b"]
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for i, cls_name in enumerate(percentiles.keys()):
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x = np.array(sorted(percentiles[cls_name].keys()))
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y = np.array([percentiles[cls_name][n] for n in x])
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plt.plot(
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x,
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y,
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color=colors[i],
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)
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ax1.yaxis.grid(True, linestyle="-", which="major", color="lightgrey", alpha=0.5)
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ax1.set_axisbelow(True)
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ax1.set_title("Evolution of Prediction Time with #Features")
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ax1.set_xlabel("#Features")
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ax1.set_ylabel("Prediction Time at %d%%-ile (us)" % percentile)
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plt.show()
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def benchmark_throughputs(configuration, duration_secs=0.1):
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"""benchmark throughput for different estimators."""
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X_train, y_train, X_test, y_test = generate_dataset(
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configuration["n_train"], configuration["n_test"], configuration["n_features"]
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)
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throughputs = dict()
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for estimator_config in configuration["estimators"]:
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estimator_config["instance"].fit(X_train, y_train)
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start_time = time.time()
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n_predictions = 0
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while (time.time() - start_time) < duration_secs:
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estimator_config["instance"].predict(X_test[[0]])
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n_predictions += 1
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throughputs[estimator_config["name"]] = n_predictions / duration_secs
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return throughputs
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def plot_benchmark_throughput(throughputs, configuration):
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fig, ax = plt.subplots(figsize=(10, 6))
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colors = ["r", "g", "b"]
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cls_infos = [
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"%s\n(%d %s)"
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% (
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estimator_conf["name"],
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estimator_conf["complexity_computer"](estimator_conf["instance"]),
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estimator_conf["complexity_label"],
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)
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for estimator_conf in configuration["estimators"]
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]
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cls_values = [
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throughputs[estimator_conf["name"]]
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for estimator_conf in configuration["estimators"]
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]
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plt.bar(range(len(throughputs)), cls_values, width=0.5, color=colors)
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ax.set_xticks(np.linspace(0.25, len(throughputs) - 0.75, len(throughputs)))
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ax.set_xticklabels(cls_infos, fontsize=10)
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ymax = max(cls_values) * 1.2
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ax.set_ylim((0, ymax))
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ax.set_ylabel("Throughput (predictions/sec)")
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ax.set_title(
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"Prediction Throughput for different estimators (%d features)"
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% configuration["n_features"]
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)
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plt.show()
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# %%
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# Benchmark bulk/atomic prediction speed for various regressors
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# -------------------------------------------------------------
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configuration = {
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"n_train": int(1e3),
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"n_test": int(1e2),
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"n_features": int(1e2),
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"estimators": [
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{
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"name": "Linear Model",
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"instance": SGDRegressor(
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penalty="elasticnet", alpha=0.01, l1_ratio=0.25, tol=1e-4
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),
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"complexity_label": "non-zero coefficients",
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"complexity_computer": lambda clf: np.count_nonzero(clf.coef_),
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},
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{
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"name": "RandomForest",
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"instance": RandomForestRegressor(),
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"complexity_label": "estimators",
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"complexity_computer": lambda clf: clf.n_estimators,
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},
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{
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"name": "SVR",
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"instance": SVR(kernel="rbf"),
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"complexity_label": "support vectors",
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"complexity_computer": lambda clf: len(clf.support_vectors_),
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},
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],
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}
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benchmark(configuration)
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# %%
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# Benchmark n_features influence on prediction speed
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# --------------------------------------------------
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percentile = 90
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percentiles = n_feature_influence(
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{"ridge": Ridge()},
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configuration["n_train"],
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configuration["n_test"],
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[100, 250, 500],
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percentile,
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)
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plot_n_features_influence(percentiles, percentile)
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# %%
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# Benchmark throughput
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# --------------------
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throughputs = benchmark_throughputs(configuration)
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plot_benchmark_throughput(throughputs, configuration)
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