Visualizing_detection_of_change_points_for_nonstationary_problems.py

#!/usr/bin/env python
# coding: utf-8

# # Table of Contents
#  <p><div class="lev1 toc-item"><a href="#Visualizing-detection-of-change-points-for-nonstationary-problems" data-toc-modified-id="Visualizing-detection-of-change-points-for-nonstationary-problems-1"><span class="toc-item-num">1&nbsp;&nbsp;</span>Visualizing detection of change points for nonstationary problems</a></div><div class="lev2 toc-item"><a href="#Creating-the-MAB-problems" data-toc-modified-id="Creating-the-MAB-problems-11"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Creating the MAB problems</a></div><div class="lev2 toc-item"><a href="#Plotting-the-history-of-means" data-toc-modified-id="Plotting-the-history-of-means-12"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Plotting the history of means</a></div><div class="lev2 toc-item"><a href="#Plotting-with-indications-on-the-restart-times" data-toc-modified-id="Plotting-with-indications-on-the-restart-times-13"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Plotting with indications on the restart times</a></div><div class="lev2 toc-item"><a href="#Data-for-problem-1" data-toc-modified-id="Data-for-problem-1-14"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Data for problem 1</a></div><div class="lev2 toc-item"><a href="#Data-for-problem-2" data-toc-modified-id="Data-for-problem-2-15"><span class="toc-item-num">1.5&nbsp;&nbsp;</span>Data for problem 2</a></div>

# ---
# # Visualizing detection of change points for nonstationary problems
# First, be sure to be in the main folder, or to have installed [`SMPyBandits`](https://github.com/SMPyBandits/SMPyBandits), and import `MAB` from `Environment` package:

# !pip install SMPyBandits watermark

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import sys
sys.path.append("..")


# In[21]:


get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', '-v -m -a "Lilian Besson"')


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from SMPyBandits.Environment.MAB import PieceWiseStationaryMAB, MAB


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get_ipython().run_line_magic('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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import seaborn as sns


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sns.set(context="talk", style="whitegrid", palette="hls", font="sans-serif", font_scale=0.9)


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from SMPyBandits.Environment.plotsettings import nrows_ncols


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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


# ## Creating the MAB problems

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from SMPyBandits.configuration_nonstationary import *


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envs = configuration['environment']
envs


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env1 = envs[0]
env2 = envs[1]


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pb1 = PieceWiseStationaryMAB(env1)


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pb2 = PieceWiseStationaryMAB(env2)


# ## Plotting the history of means

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horizon = 5000


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fig = pb1.plotHistoryOfMeans(horizon=horizon)


# ## Plotting with indications on the restart times

# Now in a function, we use this data:
# 

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def plotHistoryOfMeans_with_restart(restart_points, labels, pb, problemId=1, horizon=horizon):
    nbAlgorithms = len(labels)
    nbArms = pb.nbArms
    means_of_all_arms = pb.get_allMeans(horizon=horizon)
    
    colors1 = sns.hls_palette(nbArms + 1)[:nbArms]
    markers1 = makemarkers(nbArms)
    linestyles1 = ['--', '-.', ':'] * nbArms
    linestyles1 = linestyles1[:nbArms]
    linewidths1 = [5, 3] * nbArms
    linewidths1 = linewidths1[:nbArms]

    colors2 = sns.husl_palette(nbAlgorithms + 1)[:nbAlgorithms]
    markers2 = makemarkers(nbAlgorithms)
    
    nrows, ncols = nrows_ncols(nbAlgorithms)
    
    fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True)
    fig.suptitle("Locations of change-points detected by different algorithms (problem {})".format(problemId))
    
    # start for each algorithm
    for policyId, ax in enumerate(axes.flat[:nbAlgorithms]):
    # for policyId, label in enumerate(labels):
        label = labels[policyId]
        data = restart_points[label]
        regret = data["regret"]

        for armId in range(nbArms):
            meanOfThisArm = means_of_all_arms[armId, :]
            ax.plot(meanOfThisArm,
                     color=colors1[armId],
                     # marker=markers[armId], markevery=(armId / 50., 0.1),
                     label='Arm #{}'.format(armId),
                     #linestyle=linestyles1[armId],
                     #linewidth=linewidths1[armId],
                     linewidth=linewidths1[0],
                     alpha=0.7)

        plt.ylim(0, 1)
        ymin, ymax = plt.ylim()
        # for tau in pb.changePoints:
        #     if tau > 0 and tau < horizon:
        #         plt.vlines(tau, ymin, ymax, linestyles='dotted', alpha=0.7)

        #ax.set_xlabel(r"Time steps $t = 1...T$, horizon $T = {}$".format(horizon))
        #ax.set_ylabel(r"Successive means of the $K = {}$ arms".format(nbArms))
        ax.set_title("{} (regret $= {}$)".format(label, regret))
        #ax.set_title("Location of change-points detected by algorithm {} (regret = {})".format(label, regret))

        Xs, Ys = [], []
        for armId in range(nbArms):
            means = means_of_all_arms[armId]
            restarts = data[armId]
            times = [ time for time, nbsample in restarts ]
            Xs = times
            Ys = [ means[time] for time in times ]
            if Xs:
                ax.plot(Xs, Ys,
                        #color=colors2[policyId],
                        color=colors1[armId],
                        marker=markers2[policyId], markersize=15,
                        linestyle='',
                        label=label,
                        alpha=0.9)

    #plt.tight_layout(rect=[0.04, 0.04, 0.75, 0.92])
    #plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), numpoints=1, fancybox=True, framealpha=0.8)

    plt.show()
    return fig, axes


# ## Data for problem 1

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labels_1 = [
    "M-klUCB",
    "CUSUM-klUCB",
    "GLR-klUCB Local",
    "GLR-klUCB Global",
]


# Then we have a dictionary mapping a label to the data of interest.
# This is a dictionary mapping arm to list of couple (time, nb of samples since last restart).

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restart_points_1 = {
    "M-klUCB": {
        0: [(3080, 828)],
        1: [],
        2: [(2055, 1811)],
        "regret": 280,
    },
    "CUSUM-klUCB": {
        0: [(3027, 825), (3505, 466), (4914, 1304)],
        1: [(2512, 249), (4383, 123)],
        2: [(2011, 1861), ],
        "regret": 150,
    },
    "GLR-klUCB Local": {
        0: [(3032, 840)],
        1: [],
        2: [(2014, 1970)],
        "regret": 63,
    },
    "GLR-klUCB Global": {
        0: [(3031, 827)],
        1: [],
        2: [(2009, 1972)],
        "regret": 71,
    },
}


# Let's try!

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plotHistoryOfMeans_with_restart(restart_points_1, labels_1, pb1, problemId=1, horizon=horizon)


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plt.savefig("Visualizing_locations_of_change_points_for_different_algorithms__4algs_Pb1.png")
plt.savefig("Visualizing_locations_of_change_points_for_different_algorithms__4algs_Pb1.pdf")


# ## Data for problem 2

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labels_2 = labels_1


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restart_points_2 = {
    "M-klUCB": {
        0: [],
        1: [],
        2: [],
        "regret": 570,
    },
    "CUSUM-klUCB": {
        0: [(2305, 185), (2591, 272), (3387, 770)],
        1: [(3677, 169)],
        2: [(1055, 963), (2051, 922)],
        "regret": 150,
    },
    "GLR-klUCB Local": {
        0: [(2367, 1006), (4203, 1873)],
        1: [],
        2: [(1070, 1030)],
        "regret": 115,
    },
    "GLR-klUCB Global": {
        0: [(2705, 1038)],
        1: [],
        2: [(1111, 1060)],
        "regret": 125,
    },
}


# In[80]:


plotHistoryOfMeans_with_restart(restart_points_2, labels_2, pb2, problemId=2, horizon=horizon)


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plt.savefig("Visualizing_locations_of_change_points_for_different_algorithms__4algs_Pb2.png")
plt.savefig("Visualizing_locations_of_change_points_for_different_algorithms__4algs_Pb2.pdf")


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