Accessing the results

There are several ways to get at the results. The simplest is that we can simply get back a list of dicts:

results = lab.results()

results now contains a list, each element of which is a results dict. A results dict is a Python dict that’s structured in a particular way. It contains three top-level keys:

• Experiment.PARAMETERS, which maps to a dict of the parameters that were used for this particular run of the experiment (x and y in our case, each mapped to a value taken from the parameter space);
• Experiment.RESULTS, which maps to a dict of the experimental results generated by the Experiment.do() method (result in our case); and
• Experiment.METADATA, which contains some metadata about this particular experimental run including the time taken for it to execute, any exceptions raised, and so forth. The standard metedata elements are described in Experiment: sub-classes can add extra metadata.

A list isn’t a very convenient way to get at results, and analysing an experiment typically requires some more machinery. Many experiments will use pandas to perform analysis, and the lab can generate a pandas.DataFrame structure directly:

import pandas

df = lab.dataframe()

The dataframe contains all the information from the runs: each row holds a single run, with columns for each result, parameters, and metadata element. We can now do anaysis in pandas as appropriate: for example we can use matplotlib to draw the results as a point cloud:

import matplotlib
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt

fig = plt.figure(figsize = (8, 8))
ax = fig.add_subplot(projection = '3d')

ax.scatter(df['x'], df['y'], df['result'],
           c=df['result'], depthshade=False, cmap=cm.coolwarm)
ax.set_xlim(numpy.floor(df['x'].min()), numpy.ceil(df['x'].max()))
ax.set_ylim(numpy.floor(df['y'].min()), numpy.ceil(df['y'].max()))
ax.set_zlim(numpy.floor(df['result'].min()), numpy.ceil(df['result'].max()))

plt.title(lab.notebook().description())
ax.set_xlabel('$x$')
ax.set_ylabel('$y$')

plt.show()