Module neuralops.analysis.model_comparison

Multi‑run model comparison and plotting.

Functions

def quick_compare(results_dirs: list[str], output_dir: str = './results/comparison') ‑> None

Expand source code

def quick_compare(results_dirs: list[str], output_dir: str = "./results/comparison") -> None:
    """Quick compare."""
    comparator = ModelComparison(results_dirs, output_dir)
    comparator.load_all_results()
    comparator.generate_all_plots()

Quick compare.

Classes

class ModelComparison (results_dirs: list[str], output_dir: str = './results/comparison')

Expand source code

class ModelComparison:
    def __init__(self, results_dirs: list[str], output_dir: str = "./results/comparison") -> None:
        self.results_dirs = results_dirs
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)
        self.df_all = None

    def load_all_results(self) -> pd.DataFrame:
        """Load all results from disk or config."""
        dfs = []
        for d in self.results_dirs:
            csv_path = os.path.join(d, "performance.csv")
            if os.path.exists(csv_path):
                df = pd.read_csv(csv_path)
                df["run"] = os.path.basename(d)
                dfs.append(df)
        if dfs:
            self.df_all = pd.concat(dfs, ignore_index=True)
        return self.df_all

    def generate_all_plots(self) -> None:
        """Generate all plots."""
        if self.df_all is None:
            self.load_all_results()
        plots_dir = self.output_dir / "plots"
        create_comprehensive_plots(self.df_all, str(plots_dir))
        plot_tradeoff(self.df_all, str(plots_dir))
        print(f"Plots saved to {plots_dir}")

Methods

def generate_all_plots(self) ‑> None

Expand source code

def generate_all_plots(self) -> None:
    """Generate all plots."""
    if self.df_all is None:
        self.load_all_results()
    plots_dir = self.output_dir / "plots"
    create_comprehensive_plots(self.df_all, str(plots_dir))
    plot_tradeoff(self.df_all, str(plots_dir))
    print(f"Plots saved to {plots_dir}")

Generate all plots.

def load_all_results(self) ‑> pandas.DataFrame

Expand source code

def load_all_results(self) -> pd.DataFrame:
    """Load all results from disk or config."""
    dfs = []
    for d in self.results_dirs:
        csv_path = os.path.join(d, "performance.csv")
        if os.path.exists(csv_path):
            df = pd.read_csv(csv_path)
            df["run"] = os.path.basename(d)
            dfs.append(df)
    if dfs:
        self.df_all = pd.concat(dfs, ignore_index=True)
    return self.df_all

Load all results from disk or config.