benchmarks/bench_isolation_forest_predict.py - main - githubmirror/scikit-learn

benchmarks/bench_isolation_forest_predict.py
213 строк7 KB
Adam Li
ENH Enable prediction of isolation forest in parallel (#28622)
02 июл 2024, 10:17
02 июл 2024, 10:17191f969
"""
==========================================
IsolationForest prediction benchmark
==========================================
A test of IsolationForest on classical anomaly detection datasets.

The benchmark is run as follows:
1. The dataset is randomly split into a training set and a test set, both
assumed to contain outliers.
2. Isolation Forest is trained on the training set fixed at 1000 samples.
3. The test samples are scored using the trained model at:
    - 1000, 10000, 50000 samples
    - 10, 100, 1000 features
    - 0.01, 0.1, 0.5 contamination
    - 1, 2, 3, 4 n_jobs

We compare the prediction time at the very end.

Here are instructions for running this benchmark to compare runtime against main branch:

1. Build and run on a branch or main, e.g. for a branch named `pr`:

```bash
python bench_isolation_forest_predict.py bench ~/bench_results pr
```

2. Plotting to compare two branches `pr` and `main`:

```bash
python bench_isolation_forest_predict.py plot ~/bench_results pr main results_image.png
```
"""

import argparse
from collections import defaultdict
from pathlib import Path
from time import time

import numpy as np
import pandas as pd
from joblib import parallel_config

from sklearn.ensemble import IsolationForest

print(__doc__)


def get_data(
    n_samples_train, n_samples_test, n_features, contamination=0.1, random_state=0
):
    """Function based on code from: https://scikit-learn.org/stable/
    auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-
    examples-ensemble-plot-isolation-forest-py
    """
    rng = np.random.RandomState(random_state)

    X = 0.3 * rng.randn(n_samples_train, n_features)
    X_train = np.r_[X + 2, X - 2]

    X = 0.3 * rng.randn(n_samples_test, n_features)
    X_test = np.r_[X + 2, X - 2]

    n_outliers = int(np.floor(contamination * n_samples_test))
    X_outliers = rng.uniform(low=-4, high=4, size=(n_outliers, n_features))

    outlier_idx = rng.choice(np.arange(0, n_samples_test), n_outliers, replace=False)
    X_test[outlier_idx, :] = X_outliers

    return X_train, X_test


def plot(args):
    import matplotlib.pyplot as plt
    import seaborn as sns

    bench_results = Path(args.bench_results)
    pr_name = args.pr_name
    main_name = args.main_name
    image_path = args.image_path

    results_path = Path(bench_results)
    pr_path = results_path / f"{pr_name}.csv"
    main_path = results_path / f"{main_name}.csv"
    image_path = results_path / image_path

    df_pr = pd.read_csv(pr_path).assign(branch=pr_name)
    df_main = pd.read_csv(main_path).assign(branch=main_name)

    # Merge the two datasets on the common columns
    merged_data = pd.merge(
        df_pr,
        df_main,
        on=["n_samples_test", "n_jobs"],
        suffixes=("_pr", "_main"),
    )

    # Set up the plotting grid
    sns.set(style="whitegrid", context="notebook", font_scale=1.5)

    # Create a figure with subplots
    fig, axes = plt.subplots(1, 2, figsize=(18, 6), sharex=True, sharey=True)

    # Plot predict time as a function of n_samples_test with different n_jobs
    print(merged_data["n_jobs"].unique())
    ax = axes[0]
    sns.lineplot(
        data=merged_data,
        x="n_samples_test",
        y="predict_time_pr",
        hue="n_jobs",
        style="n_jobs",
        markers="o",
        ax=ax,
        legend="full",
    )
    ax.set_title(f"Predict Time vs. n_samples_test - {pr_name} branch")
    ax.set_ylabel("Predict Time (Seconds)")
    ax.set_xlabel("n_samples_test")

    ax = axes[1]
    sns.lineplot(
        data=merged_data,
        x="n_samples_test",
        y="predict_time_main",
        hue="n_jobs",
        style="n_jobs",
        markers="X",
        dashes=True,
        ax=ax,
        legend=None,
    )
    ax.set_title(f"Predict Time vs. n_samples_test - {main_name} branch")
    ax.set_ylabel("Predict Time")
    ax.set_xlabel("n_samples_test")

    # Adjust layout and display the plots
    plt.tight_layout()
    fig.savefig(image_path, bbox_inches="tight")
    print(f"Saved image to {image_path}")


def bench(args):
    results_dir = Path(args.bench_results)
    branch = args.branch
    random_state = 1

    results = defaultdict(list)

    # Loop over all datasets for fitting and scoring the estimator:
    n_samples_train = 1000
    for n_samples_test in [
        1000,
        10000,
        50000,
    ]:
        for n_features in [10, 100, 1000]:
            for contamination in [0.01, 0.1, 0.5]:
                for n_jobs in [1, 2, 3, 4]:
                    X_train, X_test = get_data(
                        n_samples_train,
                        n_samples_test,
                        n_features,
                        contamination,
                        random_state,
                    )

                    print("--- Fitting the IsolationForest estimator...")
                    model = IsolationForest(n_jobs=-1, random_state=random_state)
                    tstart = time()
                    model.fit(X_train)
                    fit_time = time() - tstart

                    # clearcache
                    for _ in range(1000):
                        1 + 1
                    with parallel_config("threading", n_jobs=n_jobs):
                        tstart = time()
                        model.decision_function(X_test)  # the lower, the more abnormal
                        predict_time = time() - tstart

                    results["predict_time"].append(predict_time)
                    results["fit_time"].append(fit_time)
                    results["n_samples_train"].append(n_samples_train)
                    results["n_samples_test"].append(n_samples_test)
                    results["n_features"].append(n_features)
                    results["contamination"].append(contamination)
                    results["n_jobs"].append(n_jobs)

    df = pd.DataFrame(results)
    df.to_csv(results_dir / f"{branch}.csv", index=False)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    # parse arguments for benchmarking
    subparsers = parser.add_subparsers()
    bench_parser = subparsers.add_parser("bench")
    bench_parser.add_argument("bench_results")
    bench_parser.add_argument("branch")
    bench_parser.set_defaults(func=bench)

    # parse arguments for plotting
    plot_parser = subparsers.add_parser("plot")
    plot_parser.add_argument("bench_results")
    plot_parser.add_argument("pr_name")
    plot_parser.add_argument("main_name")
    plot_parser.add_argument("image_path")
    plot_parser.set_defaults(func=plot)

    # enable the parser and run the relevant function
    args = parser.parse_args()
    args.func(args)