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I recently started using pybrain library for classification problems using neural networks and with some struggle and documentation I made it work.

Now, I would like to use blackbox optimization algorithms from the same library, but not applied to classification.

Basically, I am trying to reproduce some result from Randy's blog http://www.randalolson.com/2015/02/03/heres-waldo-computing-the-optimal-search-strategy-for-finding-waldo/.

So, as a first step, I constructed supervised dataset with the following snippet:

ds = SupervisedDataSet(2, 2)
for row in range(len(waldo_df)):
    ds.addSample(inp=waldo_df.iloc[row][['Book', 'Page']], target=waldo_df.iloc[row][['X', 'Y']])
return ds

Now, one sample from the dataset looks like:

ds.getSample()
[array([ 5.,  8.]), array([ 3.51388889,  4.31944444])]

On the next step I would like to use HillClimber algorithm to find the optimal path:

ef = ds.evaluateModuleMSE
init_value = ds.getSample()
learner = HillClimber(evaluator=ef, initEvaluable=init_value, minimize=True)
learner.learn()

What I get back in exception:

/Users/maestro/anaconda/lib/python2.7/site-packages/pybrain/datasets/supervised.pyc in evaluateModuleMSE(self, module, averageOver, **args)
 96         res = 0.
 97         for dummy in range(averageOver):
 ---> 98             module.reset()
 99             res += self.evaluateMSE(module.activate, **args)
100         return res/averageOver

AttributeError: 'numpy.ndarray' object has no attribute 'reset'

Can someone help me figure out what I am doing wrong? The documentation on this is very sparse and even searching through the code base did not help.

Thanks

P.S. If I am reading the API correctly

class pybrain.optimization.HillClimber(evaluator=None, initEvaluable=None, **kwargs)

The simplest kind of stochastic search: hill-climbing in the fitness landscape.

the optimization algorithm needs to take only evaluator which in my case would be ds.evaluateModuleMSE

Update

The whole code snippet is:

 import pandas as pd
 from pybrain.optimization import HillClimber
 from pybrain.datasets import SupervisedDataSet
 waldo_df = pd.read_csv('whereis-waldo-locations.csv')
 ds = SupervisedDataSet(2, 2)
 for row in range(len(waldo_df)):
   ds.addSample(inp=waldo_df.iloc[row][['Book', 'Page']],     target=waldo_df.iloc[row][['X', 'Y']])
 learner = HillClimber(evaluator=ds.evaluateModuleMSE,    initEvaluable=ds.getSample(), minimize=True)
Snejana Shegheva
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  • How you create network? Could you give more code? – 404pio Feb 16 '15 at 20:30
  • Do I need to create the network? This is not a classification problem. For example, on this page http://www.pybrain.org/docs/tutorial/optimization.html?highlight=blackboxoptimization there are optimization problems that do not require building networks. Perhaps I am grossly misunderstanding something. – Snejana Shegheva Feb 16 '15 at 21:56
  • @user1615070, all the code except reading the waldo dataset is present – Snejana Shegheva Feb 16 '15 at 22:01
  • @user1615070, I updated the question with exact problem to reproduce the issue – Snejana Shegheva Feb 16 '15 at 22:08
  • It's my fault - you don't need network for such things. I've reproduce your problem - but I don't have an idea yet. Have you look into code of pybrain? I've put breakpoint before HillClimber initialization, and when I invoke ds.evaluateModuleMSE I've get the same error - so maybe it is problem with dataset? – 404pio Feb 17 '15 at 20:46
  • This is problem with this "ds.evaluateModuleMSE" - it's not callable object. I've change it to function definition, and it pass to the next error in learning. – 404pio Feb 17 '15 at 21:11

0 Answers0