In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

log_folder = 'ik-learning/logs/gridsearch/'
tc = load('ik-learning/tc-150.npy')

names = ('rm-knn-default', 'rg-knn-default', 'dpg-knn-default')


    

In [3]:

    

import os
import glob
import pickle

from collections import defaultdict

logs = {}

for name in names:
    xps = glob.glob('{}/{}-*.logs'.format(log_folder, name))
                
    l = []
    for xp in xps:
        with open(xp) as f:
            l.append(pickle.load(f))  
                    
    logs[name] = l
    print len(l), 'log(s) found for', name


    

    




13 log(s) found for rm-knn-default
11 log(s) found for rg-knn-default
14 log(s) found for dpg-knn-default

In [4]:

    

%pylab inline

from mpltools import special

def plot_one(name):
    err = [xp.eval_errors for xp in logs[name]]
    if not err:
        return
    
    err = array(err)
            
    special.errorfill(x=xp.eval_at, y=err.mean(axis=-1).mean(axis=0), 
                      yerr=err.std(axis=-1).mean(axis=0) / sqrt(len(err)))
    ylim(0, 0.5)
    
from IPython.html.widgets import interact
    
interact(plot_one, name=names)


    

In [7]:

    

def plot_compare(*poss):
    [plot_one(n) for n in poss]
    
    labels = ['{}'.format(n) for n in poss]
    legend(labels)#, loc='lower left')


    

In [10]:

    

plot_compare(('rm-knn-default'), 
             ('rg-knn-default'), 
             ('dpg-knn-default'))
#xlim(0, 500)


    

In [17]:

    

plot_compare(('random', 'goal', 'ilo-gmm'), 
             ('random', 'motor', 'ilo-gmm'),
             ('discretized_progress', 'goal', 'ilo-gmm'))


    

In [16]:

    

with open(glob.glob(log_folder + 'rg-knn-default-*.logs')[1]) as f:
    goal_xp = pickle.load(f)
    
ax = axes()
#motor_xp.scatter_plot(ax, (('sensori', [1, 2]), ), color='r')
goal_xp.scatter_plot(ax, (('sensori', [0, 1]), ),  color='b')
goal_xp.scatter_plot(ax, (('choice', [0, 1]), ),  color='r', marker='+')
scatter(tc[:, 0], tc[:, 1], color='r', s=40)


    

    
Out[16]:





<matplotlib.collections.PathCollection at 0x1125fb690>






    

In [22]:

    

with open(glob.glob(log_folder + 'dpg-knn-default-*.logs')[8]) as f:
    goal_xp = pickle.load(f)
    
ax = axes()
#motor_xp.scatter_plot(ax, (('sensori', [1, 2]), ), color='r')
goal_xp.scatter_plot(ax, (('sensori', [0, 1]), ),  color='b')
goal_xp.scatter_plot(ax, (('choice', [0, 1]), ),  color='r', marker='+')
scatter(tc[:, 0], tc[:, 1], color='r', s=40)


    

    
Out[22]:





<matplotlib.collections.PathCollection at 0x114a06250>






    

In [ ]: