IDyOM Project

h1. idyom 

h2. Loading the system  

<pre> 

CL-USER> (asdf:oos 'asdf:load-op 'idyom) 

... 

</pre>

You will need to set the value of the variable *root-dir* in the file <code>apps/apps.lisp</code> to some sensible value.

h2. Directory Structure  

The layout of the code is as follows:

* idyom.asd: the system definition 

* utils/: general-purpose utilities 

* amuse/: an interface to amuse music data interface

** amuse-interface.lisp: look here to make this less dependent on amuse-mtp

** viewpoint-extensions.lisp: extensions to the viewpoint framework useful for modelling

* ppm/: the multiple-viewpoint system modelling framework

** params.lisp: user-level parameters to the statistical modelling 

** multiple-viewpoint-system.lisp: modelling with multiple-viewpoints 

** prediction-sets.lisp: representing and combining distributions, computing entropy and information content

* apps/: top-level applications for users 

** resampling.lisp: the main user interface - estimating information content by cross-validation

** viewpoint-selection.lisp: on tin does it says the what   

* data/: directory used internally for storing data 

** cache/: for storing cache files

** models/: for storing model files

** resampling/: for storing resampling sets

h2. Usage 

h3. <code>dataset-prediction</code> 

The main workhorse is the function <code>RESAMPLING:DATASET-PREDICTION</code> which accepts the following arguments (all but the first three are optional, keyword arguments): 

* dataset-id: a dataset id 

** e.g., 1

* basic-attributes: a list of basic attributes to predict 

** e.g., '(cpitch bioi)

* attributes: a list of attributes to use in prediction

** e.g., '((cpintfref cpint) bioi)

* pretraining-ids: a list of dataset-ids to pretrain the long-term models 

** e.g., '(0 1 7)

* k: an integer designating the number of cross-validation folds to use 

** 1 = no cross-validation, but also no training set unless the models are pretrained; 

** :full = as many folds as there are compositions in the dataset

** default = 10 

* resampling-indices: you can limit the modelling to a particular set of resampling folds

* models: whether to use the short-term or long-term models or both

** :stm - short-term model only 

** :ltm - long-term model only 

** :ltm+ - the long-term model trained incrementally on the test set 

** :both - :stm + :ltm 

** :both+ - :stm + :ltm+ (this is the default)

* ltm-order-bound: the order bound for the long-term model (the default <code>nil</code> means no order bound, otherwise an integer indicates the bound in number of events)

* ltm-mixtures: whether to use mixtures for the LTM (default <code>t</code>)

* ltm-update-exclusion: whether to use update exclusion for the LTM (default <code>nil</code>)

* ltm-escape: the escape method to use for the LTM (<code>:a :b :c :d :x</code> - default <code>:c</code>)

* stm-order-bound: the order bound to use for the short-term model (default <code>nil</code>)

* stm-mixtures: whether to use mixtures for the STM (default <code>t</code>)

* stm-update-exclusion: whether to use update exclusion for the STM (default <code>t</code>)

* stm-escape: the escape method for the STM (default <code>:x</code>)

See "Pearce [2005, chapter 6]":http://webprojects.eecs.qmul.ac.uk/marcusp/papers/Pearce2005.pdf for a description and explanation of these parameters.

h3. <code>output-information-content</code>  

<code>RESAMPLING:OUTPUT-INFORMATION-CONTENT</code> takes the output of <code>RESAMPLING:DATASET-PREDICTION</code> and returns the average information content. It takes the following arguments:

* predictions: the output of <code>RESAMPLING:DATASET-PREDICTION</code>

* detail: an integer which determines how the information content is averaged (these are returned as multiple values): 

** 1: averaged over the entire dataset 

** 2: and also averaged over each composition 

** 3: and also for each event in each composition

h3. <code>format-information-content</code> 

<code>RESAMPLING:FORMAT-INFORMATION-CONTENT</code> takes the output of <code>RESAMPLING:DATASET-PREDICTION</code> and writes it to file. It takes the following arguments:

* predictions: the output of <code>RESAMPLING:DATASET-PREDICTION</code>

* file: a string denoting a file

* dataset-id: an integer reflecting the dataset-id

* detail: an integer which determines how the information content is averaged (these are returned as multiple values): 

** 1: averaged over the entire dataset 

** 2: and also averaged over each composition 

** 3: and also for each event in each composition

h2. Examples  

h3. To get mean information contents for each melody of dataset 0 in a list 

<pre>

CL-USER> (resampling:output-information-content 

          (resampling:dataset-prediction 0 '(cpitch) '(cpintfref cpint))

          2)

2.493305

(2.1368716 2.8534691 2.6938546 2.6491673 2.4993074 2.6098127 2.7728052 2.772861

 2.5921957 2.905856 2.3591626 2.957503 2.4042292 2.7562473 2.3996017 2.8073587

 2.114944 1.7434102 2.2310295 2.6374347 2.361792 1.9476132 2.501488 2.5472867

 2.1056154 2.8225484 2.134257 2.9162033 3.0715692 2.9012227 2.7291088 2.866882

 2.8795822 2.4571223 2.9277062 2.7861307 2.6623116 2.3304622 2.4217033

 2.0556943 2.4048684 2.914848 2.7182267 3.0894585 2.873869 1.8821808 2.640174

 2.8165438 2.5423129 2.3011856 3.1477294 2.655349 2.5216308 2.0667994 3.2579045

 2.573013 2.6035044 2.202191 2.622113 2.2621205 2.3617425 2.7526956 2.3281655

 2.9357266 2.3372407 3.1848125 2.67367 2.1906006 2.7835917 2.6332111 3.206142

 2.1426969 2.194259 2.415167 1.9769101 2.0870917 2.7844474 2.2373738 2.772138

 2.9702199 1.724408 2.473073 2.2464263 2.2452457 2.688889 2.6299863 2.2223835

 2.8082614 2.673671 2.7693706 2.3369458 2.5016947 2.3837066 2.3682225 2.795649

 2.9063463 2.5880773 2.0457468 1.8635312 2.4522712 1.5877498 2.8802161

 2.7988417 2.3125513 1.7245895 2.2404804 2.1694546 2.365556 1.5905867 1.3827317

 2.2706041 3.023884 2.2864542 2.1259797 2.713626 2.1967313 2.5721254 2.5812547

 2.8233812 2.3134546 2.6203637 2.945946 2.601433 2.1920888 2.3732007 2.440137

 2.4291563 2.3676903 2.734724 3.0283954 2.8076048 2.7796154 2.326931 2.1779459

 2.2570527 2.2688026 1.3976555 2.030298 2.640235 2.568248 2.6338177 2.157162

 2.3915367 2.7873137 2.3088667 2.2176988 2.4402564 2.8062992 2.784044 2.4296925

 2.3520193 2.6146257)

</pre>

h3. To write the information contents for each note of each melody in dataset 0 to a file 

<pre>

CL-USER> (resampling:format-information-content 

          (resampling:dataset-prediction 0 '(cpitch) '(cpintfref cpint))

          "/tmp/foo.dat"

          0

          3)

</pre>

h3. To simulate the experiments of Conklin & Witten (1995) 

<pre>

CL-USER> (resampling:conkwit95)

Simulation of the experiments of Conklin & Witten (1995, Table 4).

System 1; Mean Information Content: 2.33 

System 2; Mean Information Content: 2.36 

System 3; Mean Information Content: 2.09 

System 4; Mean Information Content: 2.01 

System 5; Mean Information Content: 2.08 

System 6; Mean Information Content: 1.90 

System 7; Mean Information Content: 1.88 

System 8; Mean Information Content: 1.86 

NIL

</pre>

Compare with "Conklin & Witten [1995, JNMR, table 4]":http://www.sc.ehu.es/ccwbayes/members/conklin/papers/jnmr95.pdf

h3. Viewpoint Selection 

Two functions are supplied for searching a space of viewpoints: <code>run-hill-climber</code> and <code>run-best-first</code>, which take 4 arguments:

* a list of viewpoints: the algorithm searches through the space of combinations of these viewpoints

* a start state (usually nil, the empty viewpoint system)

* an evaluation function returning a numeric performance metric: e.g., the mean information content of the dataset returned by <code>dataset-prediction</code>

* a symbol describing which way to optimise the metric: <code>:desc</code> mean lower values are better <code>:asc</code> mean greater values are better

Here is an example:

<pre>

CL-USER> (viewpoint-selection:run-hill-climber 

          '(:cpitch :cpintfref :cpint :contour)

          nil

          #'(lambda (viewpoints)

              (utils:round-to-nearest-decimal-place 

               (resampling:output-information-content 

                (resampling:dataset-prediction 0 '(cpitch) viewpoints :k 10 :models :both+) 

                1)

               2))

          :desc)

 =============================================================================

   System                                                Score

 -----------------------------------------------------------------------------

   NIL                                                   NIL

   (CPITCH)                                              2.52

   (CPINT CPITCH)                                        2.43

   (CPINTFREF CPINT CPITCH)                              2.38

 =============================================================================

#S(VIEWPOINT-SELECTION::RECORD :STATE (:CPINTFREF :CPINT :CPITCH) :WEIGHT 2.38)

</pre>

Since this can be quite a time consuming process, there are also functions for caching the results.

<pre>

(initialise-vs-cache)

(load-vs-cache filename package)

(store-vs-cache filename package)

</pre>