Package: django-analyze
OverviewThere are tons of amazing algorithms and machine learning tools for
detecting patterns in data. However, what most of these lack is a useful
framework and UI for managing the often complicated setup of the data
flow and predictions.This package provides several tools for utilizing Django’s admin
interface and ORM to help organize and manage machine learning setups.The framework revolves around two basic objects:A problem, which organizes solutions to acheive some prediction goal.
This is mainly implemented a genetic algorithm.A predictor, which organizes a specific solution to either guess a
numeric value (i.e. regression) or a label (i.e. classification).I made this separation to help myself with maintainence over the life
time of an application. Often, I’d want to monitor the accuracy of a
solution, but also evaluation other potential solutions without
interrupting the solution used for production predictions. Once a
superior solution was found, then I’d want to push it into production
use with as little effort as possible. By explicitly representing
different solutions as different records in the database, I found I
could easily monitor them and slip them in and out of use as needed.ProblemTheproblemrepresents a domain where we’re attempting to solve some
prediction task, by either guessing a number or guessing a label. In the
code, this is referred to as theGenome. A record in theGenometable represents a distinct problem domain and stores all the parameters
used to control and manage the search for solutions.From theGenomeyou defineGenes, which are parameters available
for use when attempting to solve the problem.Specific solutions to the problem are represented by theGenotypemodel, which contains a list of genes and their associated values as
key/values pairs.To search for the best solution to a problem, you first implement a
custom evaluating function, which will take a genotype as an argument
and return a positive number, called the fitness, representing its
overall suitability in solving the problem. By default, a value of 0 is
interpreted to be the worse possible fitness and increasing value
representing increasing levels of suitability. Personally, I find it
convenience and intuitive to bound fitness between 0 and 1, but this is
not strictly enforced.You then set this function in yourGenome'sevaluatorfield and
run the management command:python manage.py evolve_population --genome=<genome_id>Depending on the other settings in the genome, this will run for a
maximum predetermined number of iterations or until improvement of the
fitness has stalled. From the genome’s admin change page, you can browse
the list of generated genotypes and inspect their fitness, possibly
selecting one for production use.For example, a simple genome might consist of a single gene calledalgorithm, which contains one of several algorithm names (e.g.
‘Bayesian’, ‘LinearSVC’, ‘RandomForest’, etc.). You would write your
evaluation function to read this string and instantiate the appropriate
class associated with the name. You could then add additional genes
representing parameters common to multiple algorithms or unique to only
a few. TheGenotypemodel with generate a unique hash based on which
genes it contains, and use this to avoid creating duplicate genotypes.PredictortodoUsagetodo