Import Modules¶

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# Uncomment the following line when running on Google Colab
# !pip install "autora[experimentalist-nearest-value]"

# Uncomment the following line when running on Google Colab # !pip install "autora[experimentalist-nearest-value]"

In [3]:

import numpy as np
import matplotlib.pyplot as plt
from autora.experimentalist.nearest_value import nearest_values_sample

import numpy as np import matplotlib.pyplot as plt from autora.experimentalist.nearest_value import nearest_values_sample

Define Meta-Space¶

We will here define X values of interest as well as a ground truth model to derive y values.

In [4]:

#Define meta-parameters
X_allowed = np.linspace(-3, 6, 10)
X = np.random.choice(X_allowed,10)

#Define ground truth model
def ground_truth(xs):
    y = (xs ** 2.0)
    y[xs < 0] = 0
    return y

#Define meta-parameters X_allowed = np.linspace(-3, 6, 10) X = np.random.choice(X_allowed,10) #Define ground truth model def ground_truth(xs): y = (xs ** 2.0) y[xs < 0] = 0 return y

Plot The Data¶

Let's plot the data to see what we are working with.

In [5]:

plt.plot(X, ground_truth(X), 'o')
plt.show()

plt.plot(X, ground_truth(X), 'o') plt.show()

Run And Report Uncertainty Samples¶

Now we will get a proposal from the experimentalist as to which datapoints to investigate next. We will retrieve 5 new datapoints in this example.

In [6]:

sampler_proposal = nearest_values_sample(X_allowed, X, 5)
print(sampler_proposal)

sampler_proposal = nearest_values_sample(X_allowed, X, 5) print(sampler_proposal)

     0
0  5.0
1 -3.0
2  1.0
3  4.0
4  6.0

Plot New Datapoints With Old¶

We can then plot our new datapoints with our previous ones to demonstrate our new dataset of investigation for then next cycle.

In [7]:

plt.plot(X, ground_truth(X), 'o', alpha = .5, label = 'Original Datapoints')
plt.plot(sampler_proposal, ground_truth(sampler_proposal), 'o', alpha = .5, label = 'New Datapoints')
plt.legend()
plt.show()

plt.plot(X, ground_truth(X), 'o', alpha = .5, label = 'Original Datapoints') plt.plot(sampler_proposal, ground_truth(sampler_proposal), 'o', alpha = .5, label = 'New Datapoints') plt.legend() plt.show()

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