Partial Dependence Plots 2D

Hvass-Labs Dec 2017 Holger Nahrstaedt 2020

Simple example to show the new 2D plots.

print(__doc__)
from math import exp

import numpy as np

from skopt import gp_minimize
from skopt.plots import plot_histogram, plot_objective, plot_objective_2D
from skopt.space import Categorical, Integer, Real
from skopt.utils import point_asdict

np.random.seed(123)
import matplotlib.pyplot as plt

dim_learning_rate = Real(name='learning_rate', low=1e-6, high=1e-2, prior='log-uniform')
dim_num_dense_layers = Integer(name='num_dense_layers', low=1, high=5)
dim_num_dense_nodes = Integer(name='num_dense_nodes', low=5, high=512)
dim_activation = Categorical(name='activation', categories=['relu', 'sigmoid'])

dimensions = [
    dim_learning_rate,
    dim_num_dense_layers,
    dim_num_dense_nodes,
    dim_activation,
]

default_parameters = [1e-4, 1, 64, 'relu']


def model_fitness(x):
    learning_rate, num_dense_layers, num_dense_nodes, activation = x

    fitness = (
        ((exp(learning_rate) - 1.0) * 1000) ** 2
        + (num_dense_layers) ** 2
        + (num_dense_nodes / 100) ** 2
    )

    fitness *= 1.0 + 0.1 * np.random.rand()

    if activation == 'sigmoid':
        fitness += 10

    return fitness


print(model_fitness(x=default_parameters))

1.518471835296799

search_result = gp_minimize(
    func=model_fitness,
    dimensions=dimensions,
    n_calls=30,
    x0=default_parameters,
    random_state=123,
)

print(search_result.x)
print(search_result.fun)

[4.929711467441911e-06, 1, 5, 'relu']
1.0201137671146678

for fitness, x in sorted(zip(search_result.func_vals, search_result.x_iters)):
    print(fitness, x)

1.0201137671146678 [4.929711467441911e-06, 1, 5, 'relu']
1.020825017708778 [5.447929143724181e-06, 1, 5, 'relu']
1.021667781479872 [0.0001144786138006685, 1, 5, 'relu']
1.0319553844579283 [3.2015950064095674e-06, 1, 5, 'relu']
1.0350021584165856 [9.697008005639565e-05, 1, 5, 'relu']
1.0387852240116218 [3.918631435807059e-06, 1, 5, 'relu']
1.0558125588274407 [4.826512273477327e-06, 1, 5, 'relu']
1.0626217337728787 [4.877033239311359e-06, 1, 23, 'relu']
1.065773703106011 [4.15423808784293e-06, 1, 5, 'relu']
1.0660997595359294 [1e-06, 1, 5, 'relu']
1.066913466503469 [0.0001026463063419168, 1, 5, 'relu']
1.0751293940451896 [1.6006592652941194e-06, 1, 5, 'relu']
1.087696030687253 [6.1092363610861856e-06, 1, 5, 'relu']
1.130168257786041 [0.0001280333478556736, 1, 19, 'relu']
1.1690663250744864 [0.00010510628632493199, 1, 33, 'relu']
1.4602213686635033 [0.0001, 1, 64, 'relu']
4.174922707059395 [0.00011226037475857404, 2, 5, 'relu']
14.337540595777632 [4.961649309025573e-06, 2, 44, 'sigmoid']
15.811122459303194 [5.768045960755954e-05, 1, 366, 'relu']
20.75714626376416 [4.6648726500116405e-05, 4, 195, 'relu']
20.83105097254721 [3.629134387669892e-06, 3, 323, 'relu']
25.045498550233685 [1.5528231282886148e-05, 3, 380, 'relu']
25.725698564025883 [0.0010034940899532338, 4, 264, 'relu']
26.808790139516606 [1e-06, 5, 5, 'relu']
28.093314338813517 [1e-06, 1, 512, 'relu']
31.67808942295837 [9.214584006695478e-05, 4, 213, 'sigmoid']
32.60979725349034 [0.0007109209001237586, 3, 355, 'sigmoid']
36.436844941374716 [9.52877578124997e-06, 4, 306, 'sigmoid']
108.24130894769868 [0.01, 1, 5, 'relu']
117.22558971730295 [0.008953258961145084, 4, 399, 'relu']

space = search_result.space

print(search_result.x_iters)

search_space = {name: space[name][1] for name in space.dimension_names}

print(point_asdict(search_space, default_parameters))

[[0.0001, 1, 64, 'relu'], [0.0007109209001237586, 3, 355, 'sigmoid'], [9.214584006695478e-05, 4, 213, 'sigmoid'], [3.629134387669892e-06, 3, 323, 'relu'], [9.52877578124997e-06, 4, 306, 'sigmoid'], [5.768045960755954e-05, 1, 366, 'relu'], [1.5528231282886148e-05, 3, 380, 'relu'], [4.6648726500116405e-05, 4, 195, 'relu'], [0.008953258961145084, 4, 399, 'relu'], [4.961649309025573e-06, 2, 44, 'sigmoid'], [0.0010034940899532338, 4, 264, 'relu'], [0.00010510628632493199, 1, 33, 'relu'], [0.0001144786138006685, 1, 5, 'relu'], [0.00011226037475857404, 2, 5, 'relu'], [0.0001280333478556736, 1, 19, 'relu'], [5.447929143724181e-06, 1, 5, 'relu'], [4.929711467441911e-06, 1, 5, 'relu'], [4.826512273477327e-06, 1, 5, 'relu'], [0.0001026463063419168, 1, 5, 'relu'], [1e-06, 1, 5, 'relu'], [6.1092363610861856e-06, 1, 5, 'relu'], [1.6006592652941194e-06, 1, 5, 'relu'], [0.01, 1, 5, 'relu'], [1e-06, 5, 5, 'relu'], [1e-06, 1, 512, 'relu'], [3.918631435807059e-06, 1, 5, 'relu'], [9.697008005639565e-05, 1, 5, 'relu'], [3.2015950064095674e-06, 1, 5, 'relu'], [4.15423808784293e-06, 1, 5, 'relu'], [4.877033239311359e-06, 1, 23, 'relu']]
OrderedDict([('activation', 0.0001), ('learning_rate', 1), ('num_dense_layers', 64), ('num_dense_nodes', 'relu')])

print("Plotting now ...")

_ = plot_histogram(result=search_result, dimension_identifier='learning_rate', bins=20)
plt.show()

Plotting now ...

_ = plot_objective_2D(
    result=search_result,
    dimension_identifier1='learning_rate',
    dimension_identifier2='num_dense_nodes',
)
plt.show()

_ = plot_objective_2D(
    result=search_result,
    dimension_identifier1='num_dense_layers',
    dimension_identifier2='num_dense_nodes',
)
plt.show()

_ = plot_objective(
    result=search_result, plot_dims=['num_dense_layers', 'num_dense_nodes']
)
plt.show()