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NUP-2394: network API code example #3520

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merged 17 commits into from
Apr 24, 2017

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marionleborgne
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@marionleborgne marionleborgne commented Apr 5, 2017

Fixes #3522.

The code example for the network API is done but the PR is blocked on NUP-2396. Currently the example has to call customCompute() on the classifierRegion since classification of continuous values is not supported by the network API. I'm moving on to the blocking issue (NUP-2396) to fix this.

Todos, for when I get back to this issue:

  • remove call to customCompute() and make use of new region links created in NUP-2396.
  • take care of TODOs in nupic/docs/examples/params/model.yaml by adding new params documentation based on feedback collected in google doc.

cc @rhyolight

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Only input so far, is if we're going to go all in on YAML model params for this example, we should also change the algorithm quick start to use the same YAML file to load params.

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OK, I'll update the algorithm example.

Marion Le Borgne added 2 commits April 7, 2017 19:08
* Use new network links created in NUP-2396
* Remove customCompute() usage
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@rhyolight This PR is blocked by #3526.

Changelog:

  • Use new links between SensorRegion and SDRClassifierRegion that allow to pass bucketIdx and actValue so that the classifier can predict continuous values - like in the 2 other quick-start examples.
  • Update YAML with more comments based on @scottpurdy 's feedback.

Once #3526 is merged, this is ready to be reviewed.

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@marionleborgne I also noticed you are writing results to an output file instead of to the console, so the RST will need to be updated to reflect these changes in the descriptive text.

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@rhyolight Actually, I am still writing to the console. I just added writing to an output file, so that I can compare the results of the 3 examples. I'll update the descriptive text.

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rhyolight commented Apr 10, 2017 via email

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After discussion with @rhyolight, the plan is to take out writing to file once we fix the discrepancy between prediction results. Out of the scope of this PR. Task tracked here: https://jira.numenta.com/browse/NUP-2400

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# Type of model that the rest of these parameters apply to.
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I'm not sure that we want to have the full documentation for all parameters in every model configuration file. Perhaps we could centralize this information somewhere and link to that reference page at the top of this file?

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@rhyolight rhyolight Apr 10, 2017

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Do you mean other places in the codebase where we have model params with detailed comments? If yes, I agree.

I think this file is a great place for it, because it will show up here (currently python, but soon yaml):

http://docs.numenta.org/quick-start/example-model-params.html

Perhaps we should remove the comments from other places and refer them to the API docs?

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I see, yeah might be fine here. I think the real source of truth should be the algorithm doc strings but those are spread out throughout the code so this can be the place where we include them all. We may want to have references back to the individual algorithms in case they get out of sync.

Marion Le Borgne added 2 commits April 13, 2017 17:28
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@rhyolight @scottpurdy: can someone review this PR? nothing is blocking it since #3526 was merged last week.

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One thing that was holding up my feedback is a research discussion on the network api. If we are going to keep saying that that is the way to use things then I don't think we should include a conflicting example (full example without network api) in the docs. But let's hold off on that discussion until after I have that discussion with Subutai, Marcus, others that are interested. And it doesn't have to hold up this PR.

@@ -1,144 +1,171 @@
import csv
import datetime

import yaml
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goes after numpy

from nupic.research.spatial_pooler import SpatialPooler
from nupic.research.temporal_memory import TemporalMemory
from nupic.algorithms.sdr_classifier_factory import SDRClassifierFactory

_INPUT_FILE_PATH = "../data/gymdata.csv"
_NUM_RECORDS = 3000
_INPUT_FILE_PATH = '../data/gymdata.csv'
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use double quotes here and elsewhere


sp = SpatialPooler(
# How large the input encoding will be.
inputDimensions=(encodingWidth),
# How many mini-columns will be in the Spatial Pooler.
columnDimensions=(2048),
columnDimensions=(spParams['columnCount']),
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We probably don't want to pull out all the params one at a time. Can you extend spParams with any changes needed and then construct the SP with SpatialPooler(**spParams)?

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This is @rhyolight 's example. I think he pulled the params one by one so that he can describe them in the the quick-start guide: http://nupic.docs.numenta.org/quick-start/algorithms.html

@@ -1,144 +1,171 @@
import csv
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Are these examples embedded in a web page? Is that why there is no copyright header?

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Yes

if netResults.error.mean() != algResults.error.mean():
warnings.warn('Algorithms and Network API predictions are different.')

plt.show()
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missing newline at eof

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#!/usr/bin/env bash
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What's this for? It would be nice to have this be a test that just validates there are no exceptions in the examples.

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@marionleborgne marionleborgne Apr 17, 2017

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Agreed.

  1. I'll add a unit test to make sure the 3 examples can run without throwing an exception.
  2. I'll add the other test you are talking about as part of: https://jira.numenta.com/browse/NUP-2401 (Fix results discrepancy between OPF, network API and algorithm API)
  3. I'll take out the scripts under docs/util.

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import os
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What's this for?

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A script to inspect the prediction results of the 3 examples:

  • docs/algo/complete-example.py
  • docs/opf/complete-example.py
  • docs/networkapi/complete-example.py

Adding this info at the top of the file.

Marion Le Borgne added 2 commits April 17, 2017 13:18
* Add unit test to make sure all 3 examples don't thow any exception
* Change single quotes to double quotes everywhere
* Remove utlity script to plot saved prediction results from all 3 examples
* Remove part where examples save predictions to file
* Rename networkapi to network for better readability
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@scottpurdy thanks for the review, I addressed the feedback. Some things to note:

  • I added a unit test (as you suggested) to make sure the 3 examples can run and don't throw any exception. This does not check if the prediction results are correct.
  • I will add a unit test for the prediction results consistency between network API, OPF and algo API as part of https://jira.numenta.com/browse/NUP-2401.

Marion Le Borgne added 3 commits April 18, 2017 13:16
import numpy
import os
import yaml
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why yaml? i.e. what benefit does it have over a native python module?

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In all 3 examples we are using a single YAML file with all the params. This YAML is heavily commented to explain each param. Using YAML vs native python module makes it a bit more obvious in the docs that it's a configuration file. Another (small) benefit is that it displays in a more compact manner in the docs and it's easier to read than the native python module.

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Python model params adds the extra requirement of a local import, an extra directory, and an empty __init__.py file.

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@marionleborgne Will review today

# Random number generator seed.
seed=1956,
seed=spParams["seed"],
# TODO: is this useful?
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Probably not. Feel free to remove.

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@marionleborgne If you choose not to make the change above, let me know so I can merge as-is.

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I went ahead and merged. Will revisit.

@rhyolight rhyolight merged commit 92fb883 into numenta:master Apr 24, 2017
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create Network API Quick Start docs
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