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<div class="section" id="Table-of-Contents">
<h1>Table of Contents<a class="headerlink" href="#Table-of-Contents" title="Permalink to this headline">¶</a></h1>
<p><div class="lev1 toc-item"><p>1 Visualizing detection of change points for nonstationary problems</p>
</div><div class="lev2 toc-item"><p>1.1 Creating the MAB problems</p>
</div><div class="lev2 toc-item"><p>1.2 Plotting the history of means</p>
</div><div class="lev2 toc-item"><p>1.3 Plotting with indications on the restart times</p>
</div><div class="lev2 toc-item"><p>1.4 Data for problem 1</p>
</div><div class="lev2 toc-item"><p>1.5 Data for problem 2</p>
</div></div>
<hr class="docutils" />
<div class="section" id="Visualizing-detection-of-change-points-for-nonstationary-problems">
<h1>Visualizing detection of change points for nonstationary problems<a class="headerlink" href="#Visualizing-detection-of-change-points-for-nonstationary-problems" title="Permalink to this headline">¶</a></h1>
<p>First, be sure to be in the main folder, or to have installed <code class="docutils literal notranslate"><span class="pre">`SMPyBandits</span></code> <<a class="reference external" href="https://github.com/SMPyBandits/SMPyBandits">https://github.com/SMPyBandits/SMPyBandits</a>>`__, and import <code class="docutils literal notranslate"><span class="pre">MAB</span></code> from <code class="docutils literal notranslate"><span class="pre">Environment</span></code> package:</p>
<p>!pip install SMPyBandits watermark</p>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
</pre></div>
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<span></span>import sys
sys.path.append("..")
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<span></span>%load_ext watermark
%watermark -v -m -a "Lilian Besson"
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The watermark extension is already loaded. To reload it, use:
%reload_ext watermark
Lilian Besson
CPython 3.6.7
IPython 7.4.0
compiler : GCC 8.2.0
system : Linux
release : 4.15.0-48-generic
machine : x86_64
processor : x86_64
CPU cores : 4
interpreter: 64bit
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<span></span>from SMPyBandits.Environment.MAB import PieceWiseStationaryMAB, MAB
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<span></span>%matplotlib notebook
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 120
mpl.rcParams['figure.figsize'] = (10, 5.6)
import matplotlib.pyplot as plt
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<span></span>import seaborn as sns
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<span></span>sns.set(context="talk", style="whitegrid", palette="hls", font="sans-serif", font_scale=0.9)
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<span></span>from SMPyBandits.Environment.plotsettings import nrows_ncols
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<span></span>def palette(nb):
return sns.hls_palette(nb + 1)[:nb]
def makemarkers(nb):
allmarkers = ['o', 'D', '^', '<', 'v', 'p', 's', '*', 'h', '>']
longlist = allmarkers * (1 + int(nb / float(len(allmarkers)))) # Cycle the good number of time
return longlist[:nb] # Truncate
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</div>
<div class="section" id="Creating-the-MAB-problems">
<h2>Creating the MAB problems<a class="headerlink" href="#Creating-the-MAB-problems" title="Permalink to this headline">¶</a></h2>
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<span></span>from SMPyBandits.configuration_nonstationary import *
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<span></span>envs = configuration['environment']
envs
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<span></span>[{'arm_type': SMPyBandits.Arms.Bernoulli.Bernoulli,
'params': {'listOfMeans': [[0.3, 0.5, 0.9],
[0.3, 0.2, 0.9],
[0.3, 0.2, 0.1],
[0.7, 0.2, 0.1],
[0.7, 0.5, 0.1]],
'changePoints': [0, 1000, 2000, 3000, 4000]}},
{'arm_type': SMPyBandits.Arms.Bernoulli.Bernoulli,
'params': {'listOfMeans': [[0.4, 0.5, 0.9],
[0.5, 0.4, 0.7],
[0.6, 0.3, 0.5],
[0.7, 0.2, 0.3],
[0.8, 0.1, 0.1]],
'changePoints': [0, 1000, 2000, 3000, 4000]}}]
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<span></span>env1 = envs[0]
env2 = envs[1]
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<span></span>pb1 = PieceWiseStationaryMAB(env1)
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Special MAB problem, with arm (possibly) changing at every time step, read from a dictionnary 'configuration' = {'arm_type': <class 'SMPyBandits.Arms.Bernoulli.Bernoulli'>, 'params': {'listOfMeans': [[0.3, 0.5, 0.9], [0.3, 0.2, 0.9], [0.3, 0.2, 0.1], [0.7, 0.2, 0.1], [0.7, 0.5, 0.1]], 'changePoints': [0, 1000, 2000, 3000, 4000]}} ...
- with 'arm_type' = <class 'SMPyBandits.Arms.Bernoulli.Bernoulli'>
- with 'params' = {'listOfMeans': [[0.3, 0.5, 0.9], [0.3, 0.2, 0.9], [0.3, 0.2, 0.1], [0.7, 0.2, 0.1], [0.7, 0.5, 0.1]], 'changePoints': [0, 1000, 2000, 3000, 4000]}
- with 'listOfMeans' = [[0.3 0.5 0.9]
[0.3 0.2 0.9]
[0.3 0.2 0.1]
[0.7 0.2 0.1]
[0.7 0.5 0.1]]
- with 'changePoints' = [0, 1000, 2000, 3000, 4000]
==> Creating the dynamic arms ...
- with 'nbArms' = 3
- with 'arms' = [B(0.3), B(0.5), B(0.9)]
- Initial draw of 'means' = [0.3 0.5 0.9]
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<span></span>pb2 = PieceWiseStationaryMAB(env2)
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Special MAB problem, with arm (possibly) changing at every time step, read from a dictionnary 'configuration' = {'arm_type': <class 'SMPyBandits.Arms.Bernoulli.Bernoulli'>, 'params': {'listOfMeans': [[0.4, 0.5, 0.9], [0.5, 0.4, 0.7], [0.6, 0.3, 0.5], [0.7, 0.2, 0.3], [0.8, 0.1, 0.1]], 'changePoints': [0, 1000, 2000, 3000, 4000]}} ...
- with 'arm_type' = <class 'SMPyBandits.Arms.Bernoulli.Bernoulli'>
- with 'params' = {'listOfMeans': [[0.4, 0.5, 0.9], [0.5, 0.4, 0.7], [0.6, 0.3, 0.5], [0.7, 0.2, 0.3], [0.8, 0.1, 0.1]], 'changePoints': [0, 1000, 2000, 3000, 4000]}
- with 'listOfMeans' = [[0.4 0.5 0.9]
[0.5 0.4 0.7]
[0.6 0.3 0.5]
[0.7 0.2 0.3]
[0.8 0.1 0.1]]
- with 'changePoints' = [0, 1000, 2000, 3000, 4000]
==> Creating the dynamic arms ...
- with 'nbArms' = 3
- with 'arms' = [B(0.4), B(0.5), B(0.9)]
- Initial draw of 'means' = [0.4 0.5 0.9]
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<div class="section" id="Plotting-the-history-of-means">
<h2>Plotting the history of means<a class="headerlink" href="#Plotting-the-history-of-means" title="Permalink to this headline">¶</a></h2>
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<span></span>horizon = 5000
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<span></span>fig = pb1.plotHistoryOfMeans(horizon=horizon)
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