============
Run a Study
============
Imagine you want to run a study with ``librec_auto``.
First, you will need to set up a configuration file. See the :ref:`Configuration file` documentation for information on the elements of a configuration file. Once your configuration file is complete, you can run your study.
Definitions
===========
What is an experiment?
----------------------
An experiment is a single job from the ``librec`` library.
This is what happens when you call the ``librec`` jar from the command line.
What is a study?
----------------
A study is a collection of experiments using the same algorithm the same data set, and the same methodology. ``librec_auto`` automates running
multiple experiments at once by varying algorithm hyperparameters, and the entity that encompasses a set of related
experiments is called a study. If you want to examine multiple algorithms or multiple data sets, you will need to define multiple studies.
Methodology
----------
The methodology in a ``librec-auto`` study includes information about how data is handled, what kinds of predictions are generated, and how they are evaluated. The methodology information is a bit distributed across the configuration file, and there are certain "gotchas" you should be aware of.
- As of LibRec 3.0, all cross-validation splits are drawn uniform-at-random from the ratings data set. The size of each split is N/k where N is the number of ratings and k is the number of splits. Other parameters (the style of split) that can be used in the single-split ``ratio`` model are ignored.
- Ranking vs rating prediction methodology. If you are using a metric that looks at a ranked list of results (pretty much anything other than ``rmse``, ``mae`` and certain of the fairness metrics), you *must* specify the ranking element and the list-size element in the metric element in your configuration. See below. You will get very strange ranking results, if LibRec thinks you are using a rating prediction methodology.
- Ranking vs rating prediction algorithms. Some algorithms are optimized for ranking loss and the results that they produce may not be interpretable as prediction ratings for a user/item pair. For example, BPR, RankALS, or PLSA. These algorithms should only be used with the ranking element set and an appropriate ranking-type metric.
::
true
10
Any list size can be used.
File Structure
==============
Study Structure
---------------
``librec_auto`` has a specific project structure. If you want to run an study
named ``movies``, you will need to put your ``config.xml`` file in a ``conf``
directory inside a ``movies`` directory, like this:
::
movies
└── conf
└── config.xml
You can then run your movies study with:
::
$ python -m librec_auto run -t movies
This will update the ``movies`` directory to look like this:
::
movies
├── conf
│ └── config.xml
├── exp00000
│ ├── conf
│ │ ├── config.xml
│ │ └── librec.properties
│ ├── log
│ │ └── librec-.log
│ ├── original
│ └── result
│ ├── out-1.txt
│ └── ...
├── exp00001
│ ├── conf
│ │ ├── config.xml
│ │ └── librec.properties
│ ├── log
│ │ └── librec-.log
│ ├── original
│ └── result
│ ├── out-1.txt
│ └── ...
├── exp00002
│ └── ...
├── exp00003
│ └── ...
└── ...
output.xml
Each directory like ``exp00001`` represents one of the experiments from your
movies study. The number of ``exp#####`` directories is equal to the number of
permutations from the ``value`` items in your study-wide configuration file. The
``output.xml`` contains a summary of results over all the experiments and also a log of any warnings
or errors encountered.
Experiment Structure
--------------------
Let's consider a single experiment directory:
::
exp00002
├── conf
│ ├── config.xml
│ └── librec.properties
├── log
│ └── librec-.log
├── original
└── result
├── out-1.txt
├── out-2.txt
├── out-3.txt
├── out-4.txt
└── out-5.txt
output.xml
* ``conf`` holds the auto-generated configuration file for this *experiment* (not for the study), as well as the ``librec.properties`` equivalent of the ``config.xml``.
* Don't tamper with these files: to edit the experiment configurations, modify the study-wide ``movies/conf/config.xml`` file.
* ``log`` holds the log output from running the experiment. Many LibRec algorithms output log information containing training phase information and this can be found here.
* ``result`` holds the computed recommendation lists or predictions from the ``librec`` experiment.
* ``original`` is a directory used for experiments involving result re-ranking. The re-ranker will copy the original recommendation output from the algorithm to this directory. Re-ranked results are then place in the ``result`` directory so they can be located by subsequent processes. You can experiment with multiple hyperparameters for a re-ranking algorithm without recomputing the base recommendations. For example:
* Re-rank the results with ``python -m librec_auto rerank movies``
* ``output.xml`` is a file that contains a summary of the experiment run. Metric results are stored here as well as any warnings or error messages encountered.