Hyperopt graphs

hyperopt_graphs.py

from hyperopt import tpe, fmin, Trials
from hyperopt.hp import normal
from hyperopt.plotting import main_plot_history, main_plot_histogram
import pandas as pd
import matplotlib.pylab as plt


def rosenbrock(suggestion):
    """ 
    A test function to minimize using hyperopt. The
    expected minimum is (x*,y*) = (1,1). 
    Reference: https://en.wikipedia.org/wiki/Rosenbrock_function
    """
    x = suggestion['x']
    y = suggestion['y']
    return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2

space = {'x': normal('x', 0, sigma=10), 'y': normal('y', 0, sigma=10)}
trials = Trials()


optimal = fmin(rosenbrock, algo=tpe.suggest, space=space, trials=trials, max_evals=10000)

# Hyperopt loss time evolution and histogram 
main_plot_history(trials)
main_plot_histogram(trials)

x_evolution = [e['misc']['vals']['x'][0] for e in trials.trials]
y_evolution = [e['misc']['vals']['y'][0] for e in trials.trials]
loss_evolution = [e['result']['loss'] for e in trials.trials]
opt_df = pd.DataFrame({'loss': loss_evolution, 'x': x_evolution, 'y': y_evolution})

# Scatter plot of loss evolution when x changes
# Scatter plot of loss evolution when y changes

opt_df.plot(x='x', y='loss')
opt_df.plot(x='y', y='loss')

plt.show()