Benchmarks¶

"""
Benchmark: rapid_textrank vs pytextrank

Prerequisites:
    pip install rapid_textrank pytextrank spacy
    python -m spacy download en_core_web_sm
"""

import time
import statistics

# Sample texts of varying sizes
TEXTS = {
    "small": """
        Machine learning is a subset of artificial intelligence.
        Deep learning uses neural networks with many layers.
    """,

    "medium": """
        Natural language processing (NLP) is a field of artificial intelligence
        that focuses on the interaction between computers and humans through
        natural language. The ultimate goal of NLP is to enable computers to
        understand, interpret, and generate human language in a valuable way.

        Machine learning approaches have transformed NLP in recent years.
        Deep learning models, particularly transformers, have achieved
        state-of-the-art results on many NLP tasks including translation,
        summarization, and question answering.

        Key applications include sentiment analysis, named entity recognition,
        machine translation, and text classification. These technologies
        power virtual assistants, search engines, and content recommendation
        systems used by millions of people daily.
    """,

    "large": """
        Artificial intelligence has evolved dramatically since its inception in
        the mid-20th century. Early AI systems relied on symbolic reasoning and
        expert systems, where human knowledge was manually encoded into rules.

        The machine learning revolution changed everything. Instead of explicit
        programming, systems learn patterns from data. Supervised learning uses
        labeled examples, unsupervised learning finds hidden structures, and
        reinforcement learning optimizes through trial and error.

        Deep learning, powered by neural networks with multiple layers, has
        achieved remarkable success. Convolutional neural networks excel at
        image recognition. Recurrent neural networks and transformers handle
        sequential data like text and speech. Generative adversarial networks
        create realistic synthetic content.

        Natural language processing has been transformed by these advances.
        Word embeddings capture semantic relationships. Attention mechanisms
        allow models to focus on relevant context. Large language models
        demonstrate emergent capabilities in reasoning and generation.

        Computer vision applications include object detection, facial recognition,
        medical image analysis, and autonomous vehicle perception. These systems
        process visual information with superhuman accuracy in many domains.

        The ethical implications of AI are significant. Bias in training data
        can lead to unfair outcomes. Privacy concerns arise from data collection.
        Job displacement affects workers across industries. Regulation and
        governance frameworks are being developed worldwide.

        Future directions include neuromorphic computing, quantum machine learning,
        and artificial general intelligence. Researchers continue to push
        boundaries while addressing safety and alignment challenges.
    """ * 3  # ~1000 words
}


def benchmark_rapid_textrank(text: str, runs: int = 10) -> dict:
    """Benchmark rapid_textrank."""
    from rapid_textrank import BaseTextRank

    extractor = BaseTextRank(top_n=10, language="en")

    # Warmup
    extractor.extract_keywords(text)

    times = []
    for _ in range(runs):
        start = time.perf_counter()
        result = extractor.extract_keywords(text)
        elapsed = time.perf_counter() - start
        times.append(elapsed * 1000)  # Convert to ms

    return {
        "min": min(times),
        "mean": statistics.mean(times),
        "median": statistics.median(times),
        "std": statistics.stdev(times) if len(times) > 1 else 0,
        "phrases": len(result.phrases)
    }


def benchmark_pytextrank(text: str, runs: int = 10) -> dict:
    """Benchmark pytextrank with spaCy."""
    import spacy
    import pytextrank

    nlp = spacy.load("en_core_web_sm")
    nlp.add_pipe("textrank")

    # Warmup
    doc = nlp(text)

    times = []
    for _ in range(runs):
        start = time.perf_counter()
        doc = nlp(text)
        phrases = list(doc._.phrases[:10])
        elapsed = time.perf_counter() - start
        times.append(elapsed * 1000)

    return {
        "min": min(times),
        "mean": statistics.mean(times),
        "median": statistics.median(times),
        "std": statistics.stdev(times) if len(times) > 1 else 0,
        "phrases": len(phrases)
    }


def main():
    print("=" * 70)
    print("TextRank Performance Benchmark")
    print("=" * 70)

    for size, text in TEXTS.items():
        word_count = len(text.split())
        print(f"\n{size.upper()} TEXT (~{word_count} words)")
        print("-" * 50)

        # Benchmark rapid_textrank
        rust_results = benchmark_rapid_textrank(text)
        print(f"rapid_textrank:  {rust_results['mean']:>8.2f} ms (±{rust_results['std']:.2f})")

        # Benchmark pytextrank
        try:
            py_results = benchmark_pytextrank(text)
            print(f"pytextrank:     {py_results['mean']:>8.2f} ms (±{py_results['std']:.2f})")

            speedup = py_results['mean'] / rust_results['mean']
            print(f"Speedup:        {speedup:>8.1f}x faster")
        except Exception as e:
            print(f"pytextrank:     (not available: {e})")

    print("\n" + "=" * 70)
    print("Note: pytextrank times include spaCy tokenization.")
    print("For fair comparison with pre-tokenized input, use rapid_textrank's JSON API.")
    print("=" * 70)


if __name__ == "__main__":
    main()