"""
Comparing optimizers
=====================

Comparison of optimizers on various problems.
"""
import functools
import pickle

import numpy as np
from scipy import optimize
from joblib import Memory

from cost_functions import mk_quad, mk_gauss, rosenbrock,\
    rosenbrock_prime, rosenbrock_hessian, LoggingFunction, \
    CountingFunction

def my_partial(function, **kwargs):
    f = functools.partial(function, **kwargs)
    functools.update_wrapper(f, function)
    return f

methods = {
    'Nelder-mead':          my_partial(optimize.fmin,
                                        ftol=1e-12, maxiter=5e3,
                                        xtol=1e-7, maxfun=1e6),
    'Powell':               my_partial(optimize.fmin_powell,
                                        ftol=1e-9, maxiter=5e3,
                                        maxfun=1e7),
    'BFGS':                 my_partial(optimize.fmin_bfgs,
                                        gtol=1e-9, maxiter=5e3),
    'Newton':               my_partial(optimize.fmin_ncg,
                                        avextol=1e-7, maxiter=5e3),
    'Conjugate gradient':   my_partial(optimize.fmin_cg,
                                        gtol=1e-7, maxiter=5e3),
    'L-BFGS':               my_partial(optimize.fmin_l_bfgs_b,
                                        approx_grad=1, factr=10.0,
                                        pgtol=1e-8, maxfun=1e7),
    "L-BFGS w f'":               my_partial(optimize.fmin_l_bfgs_b,
                                        factr=10.0,
                                        pgtol=1e-8, maxfun=1e7),
}

###############################################################################

def bencher(cost_name, ndim, method_name, x0):
    cost_function = mk_costs(ndim)[0][cost_name][0]
    method = methods[method_name]
    f = LoggingFunction(cost_function)
    method(f, x0)
    this_costs = np.array(f.all_f_i)
    return this_costs


# Bench with gradients
def bencher_gradient(cost_name, ndim, method_name, x0):
    cost_function, cost_function_prime, hessian = mk_costs(ndim)[0][cost_name]
    method = methods[method_name]
    f_prime = CountingFunction(cost_function_prime)
    f = LoggingFunction(cost_function, counter=f_prime.counter)
    method(f, x0, f_prime)
    this_costs = np.array(f.all_f_i)
    return this_costs, np.array(f.counts)


# Bench with the hessian
def bencher_hessian(cost_name, ndim, method_name, x0):
    cost_function, cost_function_prime, hessian = mk_costs(ndim)[0][cost_name]
    method = methods[method_name]
    f_prime = CountingFunction(cost_function_prime)
    hessian = CountingFunction(hessian, counter=f_prime.counter)
    f = LoggingFunction(cost_function, counter=f_prime.counter)
    method(f, x0, f_prime, fhess=hessian)
    this_costs = np.array(f.all_f_i)
    return this_costs, np.array(f.counts)


def mk_costs(ndim=2):
    costs = {
            'Well-conditioned quadratic':      mk_quad(.7, ndim=ndim),
            'Ill-conditioned quadratic':       mk_quad(.02, ndim=ndim),
            'Well-conditioned Gaussian':       mk_gauss(.7, ndim=ndim),
            'Ill-conditioned Gaussian':        mk_gauss(.02, ndim=ndim),
            'Rosenbrock  ':   (rosenbrock, rosenbrock_prime, rosenbrock_hessian),
        }

    rng = np.random.RandomState(0)
    starting_points = 4*rng.rand(20, ndim) - 2
    if ndim > 100:
        starting_points = starting_points[:10]
    return costs, starting_points

###############################################################################
# Compare methods without gradient
mem = Memory('.', verbose=3)

if 1:
    gradient_less_benchs = dict()

    for ndim in (2, 8, 32, 128):
        this_dim_benchs = dict()
        costs, starting_points = mk_costs(ndim)
        for cost_name, cost_function in costs.iteritems():
            # We don't need the derivative or the hessian
            cost_function = cost_function[0]
            function_bench = dict()
            for x0 in starting_points:
                all_bench = list()
                # Bench gradient-less
                for method_name, method in methods.iteritems():
                    if method_name in ('Newton', "L-BFGS w f'"):
                        continue
                    this_bench = function_bench.get(method_name, list())
                    this_costs = mem.cache(bencher)(cost_name, ndim,
                                                    method_name, x0)
                    if np.all(this_costs > .25*ndim**2*1e-9):
                        convergence = 2*len(this_costs)
                    else:
                        convergence = np.where(
                                        np.diff(this_costs > .25*ndim**2*1e-9)
                                    )[0].max() + 1
                    this_bench.append(convergence)
                    all_bench.append(convergence)
                    function_bench[method_name] = this_bench

                # Bench with gradients
                for method_name, method in methods.iteritems():
                    if method_name in ('Newton', 'Powell', 'Nelder-mead',
                                       "L-BFGS"):
                        continue
                    this_method_name = method_name
                    if method_name.endswith(" w f'"):
                        this_method_name = method_name[:-4]
                    this_method_name = this_method_name + "\nw f'"
                    this_bench = function_bench.get(this_method_name, list())
                    this_costs, this_counts = mem.cache(bencher_gradient)(
                                        cost_name, ndim, method_name, x0)
                    if np.all(this_costs > .25*ndim**2*1e-9):
                        convergence = 2*this_counts.max()
                    else:
                        convergence = np.where(
                                        np.diff(this_costs > .25*ndim**2*1e-9)
                                        )[0].max() + 1
                        convergence = this_counts[convergence]
                    this_bench.append(convergence)
                    all_bench.append(convergence)
                    function_bench[this_method_name] = this_bench

                # Bench Newton with Hessian
                method_name = 'Newton'
                this_bench = function_bench.get(method_name, list())
                this_costs = mem.cache(bencher_hessian)(cost_name, ndim,
                                                method_name, x0)
                if np.all(this_costs > .25*ndim**2*1e-9):
                    convergence = 2*len(this_costs)
                else:
                    convergence = np.where(
                                    np.diff(this_costs > .25*ndim**2*1e-9)
                                )[0].max() + 1
                this_bench.append(convergence)
                all_bench.append(convergence)
                function_bench[method_name + '\nw Hessian '] = this_bench

                # Normalize across methods
                x0_mean = np.mean(all_bench)
                for method_name in function_bench:
                    function_bench[method_name][-1] /= x0_mean
            this_dim_benchs[cost_name] = function_bench
        gradient_less_benchs[ndim] = this_dim_benchs
        print 80*'_'
        print 'Done cost %s, ndim %s' % (cost_name, ndim)
        print 80*'_'

    pickle.dump(gradient_less_benchs, file('compare_optimizers.pkl', 'w'))



"""
Comparing optimizers
=====================

Comparison of optimizers on various problems.
"""
import functools
import pickle

import numpy as np
from scipy import optimize
from joblib import Memory

from cost_functions import mk_quad, mk_gauss, rosenbrock,\
    rosenbrock_prime, rosenbrock_hessian, LoggingFunction, \
    CountingFunction

def my_partial(function, **kwargs):
    f = functools.partial(function, **kwargs)
    functools.update_wrapper(f, function)
    return f

methods = {
    'Nelder-mead':          my_partial(optimize.fmin,
                                        ftol=1e-12, maxiter=5e3,
                                        xtol=1e-7, maxfun=1e6),
    'Powell':               my_partial(optimize.fmin_powell,
                                        ftol=1e-9, maxiter=5e3,
                                        maxfun=1e7),
    'BFGS':                 my_partial(optimize.fmin_bfgs,
                                        gtol=1e-9, maxiter=5e3),
    'Newton':               my_partial(optimize.fmin_ncg,
                                        avextol=1e-7, maxiter=5e3),
    'Conjugate gradient':   my_partial(optimize.fmin_cg,
                                        gtol=1e-7, maxiter=5e3),
    'L-BFGS':               my_partial(optimize.fmin_l_bfgs_b,
                                        approx_grad=1, factr=10.0,
                                        pgtol=1e-8, maxfun=1e7),
    "L-BFGS w f'":               my_partial(optimize.fmin_l_bfgs_b,
                                        factr=10.0,
                                        pgtol=1e-8, maxfun=1e7),
}

###############################################################################

def bencher(cost_name, ndim, method_name, x0):
    cost_function = mk_costs(ndim)[0][cost_name][0]
    method = methods[method_name]
    f = LoggingFunction(cost_function)
    method(f, x0)
    this_costs = np.array(f.all_f_i)
    return this_costs


# Bench with gradients
def bencher_gradient(cost_name, ndim, method_name, x0):
    cost_function, cost_function_prime, hessian = mk_costs(ndim)[0][cost_name]
    method = methods[method_name]
    f_prime = CountingFunction(cost_function_prime)
    f = LoggingFunction(cost_function, counter=f_prime.counter)
    method(f, x0, f_prime)
    this_costs = np.array(f.all_f_i)
    return this_costs, np.array(f.counts)


# Bench with the hessian
def bencher_hessian(cost_name, ndim, method_name, x0):
    cost_function, cost_function_prime, hessian = mk_costs(ndim)[0][cost_name]
    method = methods[method_name]
    f_prime = CountingFunction(cost_function_prime)
    hessian = CountingFunction(hessian, counter=f_prime.counter)
    f = LoggingFunction(cost_function, counter=f_prime.counter)
    method(f, x0, f_prime, fhess=hessian)
    this_costs = np.array(f.all_f_i)
    return this_costs, np.array(f.counts)


def mk_costs(ndim=2):
    costs = {
            'Well-conditioned quadratic':      mk_quad(.7, ndim=ndim),
            'Ill-conditioned quadratic':       mk_quad(.02, ndim=ndim),
            'Well-conditioned Gaussian':       mk_gauss(.7, ndim=ndim),
            'Ill-conditioned Gaussian':        mk_gauss(.02, ndim=ndim),
            'Rosenbrock  ':   (rosenbrock, rosenbrock_prime, rosenbrock_hessian),
        }

    rng = np.random.RandomState(0)
    starting_points = 4*rng.rand(20, ndim) - 2
    if ndim > 100:
        starting_points = starting_points[:10]
    return costs, starting_points

###############################################################################
# Compare methods without gradient
mem = Memory('.', verbose=3)

if 1:
    gradient_less_benchs = dict()

    for ndim in (2, 8, 32, 128):
        this_dim_benchs = dict()
        costs, starting_points = mk_costs(ndim)
        for cost_name, cost_function in costs.iteritems():
            # We don't need the derivative or the hessian
            cost_function = cost_function[0]
            function_bench = dict()
            for x0 in starting_points:
                all_bench = list()
                # Bench gradient-less
                for method_name, method in methods.iteritems():
                    if method_name in ('Newton', "L-BFGS w f'"):
                        continue
                    this_bench = function_bench.get(method_name, list())
                    this_costs = mem.cache(bencher)(cost_name, ndim,
                                                    method_name, x0)
                    if np.all(this_costs > .25*ndim**2*1e-9):
                        convergence = 2*len(this_costs)
                    else:
                        convergence = np.where(
                                        np.diff(this_costs > .25*ndim**2*1e-9)
                                    )[0].max() + 1
                    this_bench.append(convergence)
                    all_bench.append(convergence)
                    function_bench[method_name] = this_bench

                # Bench with gradients
                for method_name, method in methods.iteritems():
                    if method_name in ('Newton', 'Powell', 'Nelder-mead',
                                       "L-BFGS"):
                        continue
                    this_method_name = method_name
                    if method_name.endswith(" w f'"):
                        this_method_name = method_name[:-4]
                    this_method_name = this_method_name + "\nw f'"
                    this_bench = function_bench.get(this_method_name, list())
                    this_costs, this_counts = mem.cache(bencher_gradient)(
                                        cost_name, ndim, method_name, x0)
                    if np.all(this_costs > .25*ndim**2*1e-9):
                        convergence = 2*this_counts.max()
                    else:
                        convergence = np.where(
                                        np.diff(this_costs > .25*ndim**2*1e-9)
                                        )[0].max() + 1
                        convergence = this_counts[convergence]
                    this_bench.append(convergence)
                    all_bench.append(convergence)
                    function_bench[this_method_name] = this_bench

                # Bench Newton with Hessian
                method_name = 'Newton'
                this_bench = function_bench.get(method_name, list())
                this_costs = mem.cache(bencher_hessian)(cost_name, ndim,
                                                method_name, x0)
                if np.all(this_costs > .25*ndim**2*1e-9):
                    convergence = 2*len(this_costs)
                else:
                    convergence = np.where(
                                    np.diff(this_costs > .25*ndim**2*1e-9)
                                )[0].max() + 1
                this_bench.append(convergence)
                all_bench.append(convergence)
                function_bench[method_name + '\nw Hessian '] = this_bench

                # Normalize across methods
                x0_mean = np.mean(all_bench)
                for method_name in function_bench:
                    function_bench[method_name][-1] /= x0_mean
            this_dim_benchs[cost_name] = function_bench
        gradient_less_benchs[ndim] = this_dim_benchs
        print 80*'_'
        print 'Done cost %s, ndim %s' % (cost_name, ndim)
        print 80*'_'

    pickle.dump(gradient_less_benchs, file('compare_optimizers.pkl', 'w'))



Comparing optimizers =====================  Comparison of optimizers on various problems. ############################################################################### # Bench with gradients # Bench with the hessian ############################################################################### # Compare methods without gradient # We don't need the derivative or the hessian # Bench gradient-less # Bench with gradients # Bench Newton with Hessian # Normalize across methods