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# Global minimization using the `brute` method (a.k.a. grid search)

This notebook shows a simple example of using `lmfit.minimize.brute` that uses the method with the same name from `scipy.optimize`.

The method computes the function’s value at each point of a multidimensional grid of points, to find the global minimum of the function. It behaves identically to `scipy.optimize.brute` in case finite bounds are given on all varying parameters, but will also deal with non-bounded parameters (see below).

    import copy

    from matplotlib.colors import LogNorm
    import matplotlib.pyplot as plt
    import numpy as np

    from lmfit import Minimizer, create_params, fit_report

Let’s start with the example given in the documentation of SciPy:

“We illustrate the use of brute to seek the global minimum of a function of two variables that is given as the sum of a positive-definite quadratic and two deep “Gaussian-shaped” craters. Specifically, define the objective function `f` as the sum of three other functions, `f = f1 + f2 + f3`. We suppose each of these has a signature `(z, *params), where z = (x, y)`, and params and the functions are as defined below.”

First, we create a set of Parameters where all variables except `x` and `y` are given fixed values. Just as in the documentation we will do a grid search between `-4` and `4` and use a stepsize of `0.25`. The bounds can be set as usual with the `min` and `max` attributes, and the stepsize is set using `brute_step`.

    params = create_params(a=dict(value=2, vary=False),
                           b=dict(value=3, vary=False),
                           c=dict(value=7, vary=False),
                           d=dict(value=8, vary=False),
                           e=dict(value=9, vary=False),
                           f=dict(value=10, vary=False),
                           g=dict(value=44, vary=False),
                           h=dict(value=-1, vary=False),
                           i=dict(value=2, vary=False),
                           j=dict(value=26, vary=False),
                           k=dict(value=1, vary=False),
                           l=dict(value=-2, vary=False),
                           scale=dict(value=0.5, vary=False),
                           x=dict(value=0.0, vary=True, min=-4, max=4, brute_step=0.25),
                           y=dict(value=0.0, vary=True, min=-4, max=4, brute_step=0.25))

Second, create the three functions and the objective function:

    def f1(p):
        par = p.valuesdict()
        return (par['a'] * par['x']**2 + par['b'] * par['x'] * par['y'] +
                par['c'] * par['y']**2 + par['d']*par['x'] + par['e']*par['y'] +
                par['f'])

    def f2(p):
        par = p.valuesdict()
        return (-1.0*par['g']*np.exp(-((par['x']-par['h'])**2 +
                                       (par['y']-par['i'])**2) / par['scale']))

    def f3(p):
        par = p.valuesdict()
        return (-1.0*par['j']*np.exp(-((par['x']-par['k'])**2 +
                                       (par['y']-par['l'])**2) / par['scale']))

    def f(params):
        return f1(params) + f2(params) + f3(params)

Performing the actual grid search is done with:

    fitter = Minimizer(f, params)
    result = fitter.minimize(method='brute')

, which will increment `x` and `y` between `-4` in increments of `0.25` until `4` (not inclusive).

    grid_x, grid_y = (np.unique(par.ravel()) for par in result.brute_grid)
    print(grid_x)

    [-4.   -3.75 -3.5  -3.25 -3.   -2.75 -2.5  -2.25 -2.   -1.75 -1.5  -1.25
     -1.   -0.75 -0.5  -0.25  0.    0.25  0.5   0.75  1.    1.25  1.5   1.75
      2.    2.25  2.5   2.75  3.    3.25  3.5   3.75]

The objective function is evaluated on this grid, and the raw output from `scipy.optimize.brute` is stored in the MinimizerResult as `brute_<parname>` attributes. These attributes are:

`result.brute_x0` – A 1-D array containing the coordinates of a point at which the objective function had its minimum value.

    print(result.brute_x0)

    [-1.    1.75]

`result.brute_fval` – Function value at the point `x0`.

    print(result.brute_fval)

    -2.8923637137222027

`result.brute_grid` – Representation of the evaluation grid. It has the same length as `x0`.

    print(result.brute_grid)

    [[[-4.   -4.   -4.   ... -4.   -4.   -4.  ]
      [-3.75 -3.75 -3.75 ... -3.75 -3.75 -3.75]
      [-3.5  -3.5  -3.5  ... -3.5  -3.5  -3.5 ]
      ...
      [ 3.25  3.25  3.25 ...  3.25  3.25  3.25]
      [ 3.5   3.5   3.5  ...  3.5   3.5   3.5 ]
      [ 3.75  3.75  3.75 ...  3.75  3.75  3.75]]

     [[-4.   -3.75 -3.5  ...  3.25  3.5   3.75]
      [-4.   -3.75 -3.5  ...  3.25  3.5   3.75]
      [-4.   -3.75 -3.5  ...  3.25  3.5   3.75]
      ...
      [-4.   -3.75 -3.5  ...  3.25  3.5   3.75]
      [-4.   -3.75 -3.5  ...  3.25  3.5   3.75]
      [-4.   -3.75 -3.5  ...  3.25  3.5   3.75]]]

`result.brute_Jout` – Function values at each point of the evaluation grid, i.e., `Jout = func(*grid)`.

    print(result.brute_Jout)

    [[134.         119.6875     106.25       ...  74.18749997  85.24999999
       97.1875    ]
     [129.125      115.         101.75       ...  74.74999948  85.99999987
       98.12499997]
     [124.5        110.5625      97.5        ...  75.5624928   86.99999818
       99.31249964]
     ...
     [ 94.12499965  85.24999772  77.24998843 ... 192.         208.5
      225.875     ]
     [ 96.49999997  87.81249979  79.99999892 ... 199.8125     216.5
      234.0625    ]
     [ 99.125       90.62499998  82.99999992 ... 207.875      224.75
      242.5       ]]

Reassuringly, the obtained results are identical to using the method in SciPy directly!

Example 2: fit of a decaying sine wave

In this example, we will explain some of the options of the algorithm.

We start off by generating some synthetic data with noise for a decaying sine wave, define an objective function, and create/initialize a Parameter set.

    x = np.linspace(0, 15, 301)
    np.random.seed(7)
    noise = np.random.normal(size=x.size, scale=0.2)
    data = (5. * np.sin(2*x - 0.1) * np.exp(-x*x*0.025) + noise)
    plt.plot(x, data, 'o')
    plt.show()

    def fcn2min(params, x, data):
        """Model decaying sine wave, subtract data."""
        amp = params['amp']
        shift = params['shift']
        omega = params['omega']
        decay = params['decay']
        model = amp * np.sin(x*omega + shift) * np.exp(-x*x*decay)
        return model - data

In contrast to the implementation in SciPy (as shown in the first example), varying parameters do not need to have finite bounds in lmfit. However, if a parameter does not have finite bounds, then it does need a `brute_step` attribute specified:

    params = create_params(amp=dict(value=7, min=2.5, brute_step=0.25),
                           decay=dict(value=0.05, brute_step=0.005),
                           shift=dict(value=0.0, min=-np.pi/2., max=np.pi/2),
                           omega=dict(value=3, max=5, brute_step=0.25))

Our initial parameter set is now defined as shown below and this will determine how the grid is set-up.

    params.pretty_print()

    Name      Value      Min      Max   Stderr     Vary     Expr Brute_Step
    amp           7      2.5      inf     None     True     None     0.25
    decay      0.05     -inf      inf     None     True     None    0.005
    omega         3     -inf        5     None     True     None     0.25
    shift         0   -1.571    1.571     None     True     None     None

First, we initialize a `Minimizer` and perform the grid search:

    fitter = Minimizer(fcn2min, params, fcn_args=(x, data))
    result_brute = fitter.minimize(method='brute', Ns=25, keep=25)

    print(fit_report(result_brute))

    [[Fit Statistics]]
        # fitting method   = brute
        # function evals   = 375000
        # data points      = 301
        # variables        = 4
        chi-square         = 11.9353671
        reduced chi-square = 0.04018642
        Akaike info crit   = -963.508878
        Bayesian info crit = -948.680437
    ##  Warning: uncertainties could not be estimated:
        this fitting method does not natively calculate uncertainties
        and numdifftools is not installed for lmfit to do this. Use
        `pip install numdifftools` for lmfit to estimate uncertainties
        with this fitting method.
    [[Variables]]
        amp:    5.00000000 (init = 7)
        decay:  0.02500000 (init = 0.05)
        shift: -0.13089969 (init = 0)
        omega:  2.00000000 (init = 3)

We used two new parameters here: `Ns` and `keep`. The parameter `Ns` determines the 'number of grid points along the axes' similarly to its usage in SciPy. Together with `brute_step`, `min` and `max` for a Parameter it will dictate how the grid is set-up:

(1) finite bounds are specified (“SciPy implementation”): uses `brute_step` if present (in the example above) or uses `Ns` to generate the grid. The latter scenario that interpolates `Ns` points from `min` to `max` (inclusive), is here shown for the parameter `shift`:

    par_name = 'shift'
    indx_shift = result_brute.var_names.index(par_name)
    grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
    print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")

    parameter = shift
    number of steps = 25
    grid = [-1.57079633 -1.43989663 -1.30899694 -1.17809725 -1.04719755 -0.91629786
     -0.78539816 -0.65449847 -0.52359878 -0.39269908 -0.26179939 -0.13089969
      0.          0.13089969  0.26179939  0.39269908  0.52359878  0.65449847
      0.78539816  0.91629786  1.04719755  1.17809725  1.30899694  1.43989663
      1.57079633]

If finite bounds are not set for a certain parameter then the user must specify `brute_step` - three more scenarios are considered here:

(2) lower bound `(min`) and `brute_step` are specified: `range = (min, min + Ns * brute_step, brute_step)`

    par_name = 'amp'
    indx_shift = result_brute.var_names.index(par_name)
    grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
    print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")

    parameter = amp
    number of steps = 25
    grid = [2.5  2.75 3.   3.25 3.5  3.75 4.   4.25 4.5  4.75 5.   5.25 5.5  5.75
     6.   6.25 6.5  6.75 7.   7.25 7.5  7.75 8.   8.25 8.5 ]

(3) upper bound (`max`) and `brute_step` are specified: `range = (max - Ns * brute_step, max, brute_step)`

    par_name = 'omega'
    indx_shift = result_brute.var_names.index(par_name)
    grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
    print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")

    parameter = omega
    number of steps = 25
    grid = [-1.25 -1.   -0.75 -0.5  -0.25  0.    0.25  0.5   0.75  1.    1.25  1.5
      1.75  2.    2.25  2.5   2.75  3.    3.25  3.5   3.75  4.    4.25  4.5
      4.75]

(4) numerical value (`value`) and `brute_step` are specified: `range = (value - (Ns//2) * brute_step, value + (Ns//2) * brute_step, brute_step)`

    par_name = 'decay'
    indx_shift = result_brute.var_names.index(par_name)
    grid_shift = np.unique(result_brute.brute_grid[indx_shift].ravel())
    print(f"parameter = {par_name}\nnumber of steps = {len(grid_shift)}\ngrid = {grid_shift}")

    parameter = decay
    number of steps = 24
    grid = [-1.00000000e-02 -5.00000000e-03  5.20417043e-18  5.00000000e-03
      1.00000000e-02  1.50000000e-02  2.00000000e-02  2.50000000e-02
      3.00000000e-02  3.50000000e-02  4.00000000e-02  4.50000000e-02
      5.00000000e-02  5.50000000e-02  6.00000000e-02  6.50000000e-02
      7.00000000e-02  7.50000000e-02  8.00000000e-02  8.50000000e-02
      9.00000000e-02  9.50000000e-02  1.00000000e-01  1.05000000e-01]

The `MinimizerResult` contains all the usual best-fit parameters and fitting statistics. For example, the optimal solution from the grid search is given below together with a plot:

    print(fit_report(result_brute))

    [[Fit Statistics]]
        # fitting method   = brute
        # function evals   = 375000
        # data points      = 301
        # variables        = 4
        chi-square         = 11.9353671
        reduced chi-square = 0.04018642
        Akaike info crit   = -963.508878
        Bayesian info crit = -948.680437
    ##  Warning: uncertainties could not be estimated:
        this fitting method does not natively calculate uncertainties
        and numdifftools is not installed for lmfit to do this. Use
        `pip install numdifftools` for lmfit to estimate uncertainties
        with this fitting method.
    [[Variables]]
        amp:    5.00000000 (init = 7)
        decay:  0.02500000 (init = 0.05)
        shift: -0.13089969 (init = 0)
        omega:  2.00000000 (init = 3)

    plt.plot(x, data, 'o')
    plt.plot(x, data + fcn2min(result_brute.params, x, data), '--')
    plt.show()

We can see that this fit is already very good, which is what we should expect since our `brute` force grid is sampled rather finely and encompasses the “correct” values.

In a more realistic, complicated example the `brute` method will be used to get reasonable values for the parameters and perform another minimization (e.g., using `leastsq`) using those as starting values. That is where the `keep` parameter comes into play: it determines the “number of best candidates from the brute force method that are stored in the `candidates` attribute”. In the example above we store the best-ranking 25 solutions (the default value is `50` and storing all the grid points can be accomplished by choosing `all`). The `candidates` attribute contains the parameters and `chisqr` from the brute force method as a `namedtuple`, `(‘Candidate’, [‘params’, ‘score’])`, sorted on the (lowest) `chisqr` value. To access the values for a particular candidate one can use `result.candidate[#].params` or `result.candidate[#].score`, where a lower # represents a better candidate. The `show_candidates(#)` uses the `pretty_print()` method to show a specific candidate-# or all candidates when no number is specified.

The optimal fit is, as usual, stored in the `MinimizerResult.params` attribute and is, therefore, identical to `result_brute.show_candidates(1)`.

    result_brute.show_candidates(1)

    Candidate #1, chisqr = 11.935
    Name      Value      Min      Max   Stderr     Vary     Expr Brute_Step
    amp           5      2.5      inf     None     True     None     0.25
    decay     0.025     -inf      inf     None     True     None    0.005
    omega         2     -inf        5     None     True     None     0.25
    shift   -0.1309   -1.571    1.571     None     True     None     None

In this case, the next-best scoring candidate has already a `chisqr` that increased quite a bit:

    result_brute.show_candidates(2)

    Candidate #2, chisqr = 13.994
    Name      Value      Min      Max   Stderr     Vary     Expr Brute_Step
    amp        4.75      2.5      inf     None     True     None     0.25
    decay     0.025     -inf      inf     None     True     None    0.005
    omega         2     -inf        5     None     True     None     0.25
    shift   -0.1309   -1.571    1.571     None     True     None     None

and is, therefore, probably not so likely… However, as said above, in most cases you’ll want to do another minimization using the solutions from the `brute` method as starting values. That can be easily accomplished as shown in the code below, where we now perform a `leastsq` minimization starting from the top-25 solutions and accept the solution if the `chisqr` is lower than the previously ‘optimal’ solution:

    best_result = copy.deepcopy(result_brute)

    for candidate in result_brute.candidates:
        trial = fitter.minimize(method='leastsq', params=candidate.params)
        if trial.chisqr < best_result.chisqr:
            best_result = trial

From the `leastsq` minimization we obtain the following parameters for the most optimal result:

    print(fit_report(best_result))

    [[Fit Statistics]]
        # fitting method   = leastsq
        # function evals   = 21
        # data points      = 301
        # variables        = 4
        chi-square         = 10.8653514
        reduced chi-square = 0.03658367
        Akaike info crit   = -991.780924
        Bayesian info crit = -976.952483
    [[Variables]]
        amp:    5.00323085 +/- 0.03805940 (0.76%) (init = 5)
        decay:  0.02563850 +/- 4.4572e-04 (1.74%) (init = 0.03)
        shift: -0.09162987 +/- 0.00978382 (10.68%) (init = 0)
        omega:  1.99611629 +/- 0.00316225 (0.16%) (init = 2)
    [[Correlations]] (unreported correlations are < 0.100)
        C(shift, omega) = -0.7855
        C(amp, decay)   = +0.5838
        C(amp, shift)   = -0.1208

As expected the parameters have not changed significantly as they were already very close to the “real” values, which can also be appreciated from the plots below.

    plt.plot(x, data, 'o')
    plt.plot(x, data + fcn2min(result_brute.params, x, data), '-',
             label='brute')
    plt.plot(x, data + fcn2min(best_result.params, x, data), '--',
             label='brute followed by leastsq')
    plt.legend()

Finally, the results from the `brute` force grid-search can be visualized using the rather lengthy Python function below (which might get incorporated in lmfit at some point).

    def plot_results_brute(result, best_vals=True, varlabels=None,
                           output=None):
        """Visualize the result of the brute force grid search.

        The output file will display the chi-square value per parameter and contour
        plots for all combination of two parameters.

        Inspired by the `corner` package (https://github.com/dfm/corner.py).

        Parameters
        ----------
        result : :class:`~lmfit.minimizer.MinimizerResult`
            Contains the results from the :meth:`brute` method.

        best_vals : bool, optional
            Whether to show the best values from the grid search (default is True).

        varlabels : list, optional
            If None (default), use `result.var_names` as axis labels, otherwise
            use the names specified in `varlabels`.

        output : str, optional
            Name of the output PDF file (default is 'None')
        """
        npars = len(result.var_names)
        _fig, axes = plt.subplots(npars, npars)

        if not varlabels:
            varlabels = result.var_names
        if best_vals and isinstance(best_vals, bool):
            best_vals = result.params

        for i, par1 in enumerate(result.var_names):
            for j, par2 in enumerate(result.var_names):

                # parameter vs chi2 in case of only one parameter
                if npars == 1:
                    axes.plot(result.brute_grid, result.brute_Jout, 'o', ms=3)
                    axes.set_ylabel(r'$\chi^{2}$')
                    axes.set_xlabel(varlabels[i])
                    if best_vals:
                        axes.axvline(best_vals[par1].value, ls='dashed', color='r')

                # parameter vs chi2 profile on top
                elif i == j and j < npars-1:
                    if i == 0:
                        axes[0, 0].axis('off')
                    ax = axes[i, j+1]
                    red_axis = tuple(a for a in range(npars) if a != i)
                    ax.plot(np.unique(result.brute_grid[i]),
                            np.minimum.reduce(result.brute_Jout, axis=red_axis),
                            'o', ms=3)
                    ax.set_ylabel(r'$\chi^{2}$')
                    ax.yaxis.set_label_position("right")
                    ax.yaxis.set_ticks_position('right')
                    ax.set_xticks([])
                    if best_vals:
                        ax.axvline(best_vals[par1].value, ls='dashed', color='r')

                # parameter vs chi2 profile on the left
                elif j == 0 and i > 0:
                    ax = axes[i, j]
                    red_axis = tuple(a for a in range(npars) if a != i)
                    ax.plot(np.minimum.reduce(result.brute_Jout, axis=red_axis),
                            np.unique(result.brute_grid[i]), 'o', ms=3)
                    ax.invert_xaxis()
                    ax.set_ylabel(varlabels[i])
                    if i != npars-1:
                        ax.set_xticks([])
                    else:
                        ax.set_xlabel(r'$\chi^{2}$')
                    if best_vals:
                        ax.axhline(best_vals[par1].value, ls='dashed', color='r')

                # contour plots for all combinations of two parameters
                elif j > i:
                    ax = axes[j, i+1]
                    red_axis = tuple(a for a in range(npars) if a not in (i, j))
                    X, Y = np.meshgrid(np.unique(result.brute_grid[i]),
                                       np.unique(result.brute_grid[j]))
                    lvls1 = np.linspace(result.brute_Jout.min(),
                                        np.median(result.brute_Jout)/2.0, 7, dtype='int')
                    lvls2 = np.linspace(np.median(result.brute_Jout)/2.0,
                                        np.median(result.brute_Jout), 3, dtype='int')
                    lvls = np.unique(np.concatenate((lvls1, lvls2)))
                    ax.contourf(X.T, Y.T, np.minimum.reduce(result.brute_Jout, axis=red_axis),
                                lvls, norm=LogNorm())
                    ax.set_yticks([])
                    if best_vals:
                        ax.axvline(best_vals[par1].value, ls='dashed', color='r')
                        ax.axhline(best_vals[par2].value, ls='dashed', color='r')
                        ax.plot(best_vals[par1].value, best_vals[par2].value, 'rs', ms=3)
                    if j != npars-1:
                        ax.set_xticks([])
                    else:
                        ax.set_xlabel(varlabels[i])
                    if j - i >= 2:
                        axes[i, j].axis('off')

        if output is not None:
            plt.savefig(output)

and finally, to generated the figure:

    plot_results_brute(result_brute, best_vals=True, varlabels=None)
    plt.show()

Total running time of the script: (0 minutes 16.852 seconds)

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