experiments/compare_conjugate_gradient.py [146:232]:
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    def compute_fit_time_and_residual(
        self, solver, sketch_size, algo_mode, step_size, mom_beta, mom_eta
    ):
        """
        Repeats model fit for n_repetitions.

        Returns quartile 1, 3 and median time taken
        """
        times = []
        residual_norms = []
        for repetition_idx in range(self.n_repetitions):
            print(
                f"--------> Repetition {repetition_idx+1} / " f"{self.n_repetitions}",
                end="\r",
            )

            random_state = repetition_idx
            np.random.seed(seed=random_state)
            model = load_model(
                solver,
                sketch_size,
                algo_mode,
                step_size,
                mom_beta,
                mom_eta,
                self.regularizer,
                self.is_kernel,
                self.use_heuristic,
                self.tolerance,
                self.max_iter,
                random_state,
            )
            start = default_timer()
            model.fit(self.X, self.y)
            times.append(default_timer() - start)
            residual_norms.append(model.residual_norms)

        times_distribution = compute_distribution(times)
        residual_norms = pad_residual_norms(residual_norms)
        residual_norms_distribution = compute_distribution(residual_norms)

        return times_distribution, residual_norms_distribution

    def book_keeping(
        self,
        run_name,
        solver,
        sketch_size_formula,
        sketch_size,
        times_distribution,
        residual_norms_distribution,
        run_times,
        run_residual_norms,
    ):
        """Store time and relative residual norms distribution for each solver"""
        description = {
            "run_name": run_name,
            "solver": solver,
            "sketch_size_formula": sketch_size_formula,
            "sketch_size": sketch_size,
        }
        description_string = (
            f"{run_name} | {solver} | sketch_size = "
            f"{sketch_size_formula} = {sketch_size}"
        )

        run_time = {
            **description,
            "time (median)": times_distribution.median,
            "time (1st quartile)": times_distribution.q1,
            "time (3rd quartile)": times_distribution.q3,
        }
        run_times = update_times(run_times, run_time)

        run_residual_norm = {
            f"{description_string} | residual_norms (median)": residual_norms_distribution.median,
            f"{description_string} | residual_norms (1st quartile)": residual_norms_distribution.q1,
            f"{description_string} | residual_norms (3rd quartile)": residual_norms_distribution.q3,
        }
        run_residual_norms.update(run_residual_norm)

    def save(self, exp_name, save_path):
        full_path = os.path.join(save_path, exp_name)
        # clear old results
        shutil.rmtree(full_path, ignore_errors=True)
        os.makedirs(full_path)
        self.save_settings(exp_name, full_path)
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experiments/momentum_regimes.py [271:366]:
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    def compute_fit_time_and_residual(
        self, solver, sketch_size, algo_mode, step_size, mom_beta, mom_eta
    ):
        """
        Repeats model fit for n_repetitions.

        Returns quartile 1, 3 and median time taken
        """
        times = []
        residual_norms = []
        for repetition_idx in range(self.n_repetitions):
            print(
                f"--------> Repetition {repetition_idx+1} / " f"{self.n_repetitions}",
                end="\r",
            )

            random_state = repetition_idx
            np.random.seed(seed=random_state)
            model = load_model(
                solver,
                sketch_size,
                algo_mode,
                step_size,
                mom_beta,
                mom_eta,
                self.regularizer,
                self.is_kernel,
                self.use_heuristic,
                self.tolerance,
                self.max_iter,
                random_state,
            )

            start = default_timer()
            model.fit(self.X, self.y)
            times.append(default_timer() - start)
            residual_norms.append(model.residual_norms)

        times_distribution = compute_distribution(times)
        residual_norms = pad_residual_norms(residual_norms)
        residual_norms_distribution = compute_distribution(residual_norms)

        return times_distribution, residual_norms_distribution

    def book_keeping(
        self,
        run_name,
        solver,
        sketch_size_formula,
        sketch_size,
        times_distribution,
        residual_norms_distribution,
        run_times,
        run_residual_norms,
    ):
        """
        Store time and relative residual norms distribution for each parametrization.

        run_name :
            - "cst_mom" for constant momentum
            - "inc_mom" for increasing momentum
            - "no_mom" for no momentum
        """
        description = {
            "run_name": run_name,
            "solver": solver,
            "sketch_size_formula": sketch_size_formula,
            "sketch_size": sketch_size,
        }
        description_string = (
            f"{run_name} | {solver} | sketch_size = "
            f"{sketch_size_formula} = {sketch_size}"
        )

        run_time = {
            **description,
            "time (median)": times_distribution.median,
            "time (1st quartile)": times_distribution.q1,
            "time (3rd quartile)": times_distribution.q3,
        }
        run_times = update_times(run_times, run_time)

        run_residual_norm = {
            f"{description_string} | residual_norms (median)": residual_norms_distribution.median,
            f"{description_string} | residual_norms (1st quartile)": residual_norms_distribution.q1,
            f"{description_string} | residual_norms (3rd quartile)": residual_norms_distribution.q3,
        }
        run_residual_norms.update(run_residual_norm)

    def save(self, exp_name, save_path):
        """Saves benchmark results, plots and configs"""
        full_path = os.path.join(save_path, exp_name)
        # clear old results
        shutil.rmtree(full_path, ignore_errors=True)
        os.makedirs(full_path)
        self.save_settings(exp_name, full_path)
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