* Add option to add multiple plugins
* Improve UI hints for collapsed parameters
* Refactored plugins without requirements
* Hide evaluation tab for the moment. You can see it by adding "?evaluation" to
the URL.
* Add Topic model
* Add PDB post-mortem debugging
* Add logger to plugins (`self.log`)
* Add NLTK resource auto-download
* Force installation of requirements even if adding doesn't work
* Add a method to find files in several possible locations. Now the plugin.open
method will try these locations IF the file is to be opened in read mode.
Otherwise only the SENPY_DATA folder will be used (to avoid writing to the
package folder).
* Fixed Options for extra_params in UI
* Enhanced meta-programming for models
* Plugins can be imported from a python file if they're named
`senpy_<whatever>.py>` (no need for `.senpy` anymore!)
* Add docstings and tests to most plugins
* Read plugin description from the docstring
* Refactor code to get rid of unnecessary `.senpy`s
* Load models, plugins and utils into the main namespace (see __init__.py)
* Enhanced plugin development/experience with utils (easy_test, easy_serve)
* Fix bug in check_template that wouldn't check objects
* Make model defaults a private variable
* Add option to list loaded plugins in CLI
* Update docs
* Refactored BaseModel for efficiency
* Added plugin metaclass to keep track of plugin types
* Moved plugins to examples dir (in a previous commit)
* Simplified validation in parse_params
* Added convenience methods to mock requests in tests
* Changed help schema to use `.valid_parameters` instead of `.parameters`,
which was used in results to show parameters provided by the user.
* Improved UI
* Added basic parameters
* Fixed bugs in parameter handling
* Refactored and cleaned code
We've changed the way plugins are activated, and removed the notion of
deactivated plugins.
Now plugins activate asynchronously.
When calling a plugin, it will be activated if it wasn't, and the call will wait
for the plugin to be fully activated.
This lines up the names in the conversion plugins with the [emotionML suggested vocab](https://www.w3.org/TR/emotion-voc/#dimensions).
emoml has different names for the 4-dimensional fsre scheme and the 3-dimensional vad scheme, which this pull request has added.
I've added the "unpredictability" dimension and mapped big6:surprise to it's maximum value. The assumption is that surprise varies between 5 and 10 to be in line with the other dimensions (no such thing as negative surprise, so no values less than 5). I see that arousal also has all values >5 (so no negative arousal). Ideally, surprise mappings for V, A and D should be calculated empirically - I think there'll be some arousal and possibly slightly lowered dominance.
I wonder if we should use another colon in the emoml names, eg: "emoml:fsredim:valence" or "emoml:big6:happiness", since the [emoml suggested vocab](https://www.w3.org/TR/emotion-voc/xml) only specifies names like "happiness" in a category "big6" (ie: it's hard to know which is the category in "big6happiness").
We'd have to go through the example plugins and make sure they also conform...
open to discussion on this btw...
ps: apologies for multiple changes in this one pr..
I've used euclidean metric instead of taxicab as I feel it makes more sense (taxicab has bizzare unintuitive effects for points far from the centroids).
