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README.md

Ajaila: Modular DSL for Predictive Analysis

This is a preliminary release for internal review.
The official release will be announced later.
Any suggestion for modification is welcome.

Short Description

The application helps you to work with statistical datasets, normalize the data into a common format and build the required data models. Additionally, you can visualize your data with Protovis / Highcharts.js and scale your service with Hadoop (HDFS).

During your work the application is provided with usefull snippets and generators. Ajaila can be easily extended with common Machine Learning packages written in Ruby and C. Among supported libraries are Statsample, MadLib (EMC corporation) and Vowpal Wabbit (Yahoo! Research), online learning library based on stochastic gradient discent for classification problems and regression analysis.

After prototyping you can deploy your application to the web and provide your predictive models with unstructured data from Hadoop via MapReduce, which is hidden from you behind classy ORM (Massive Record or Treasure Data Extensions).

Ajaila helps you build long-lasting software and provides you with environment, which can be easily tested with RSpec. The platform itself is tested and can be trusted.

Table of Contents

Installation

The latest virsion is available via Rubygems:

gem install ajaila --pre

NB!

Don't install 0.0.1 release. It's a crap.

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Creating a new project

Let's see the framework in action. As an example, we'll build a linear regression to predict Total Worlds GDP for the end of 2013. To create a new project we write:

mac-r@ubuntu:~$ ajaila new SuperProject

That will return the following message:

Ajaila: generating new application "SuperProject"
  created application root
  prepared Config
  prepared Datasets directory
  prepared Raw folder in the Datasets directory
  prepared Sandbox directory
  prepared Miners folder in the Sandbox directory
  prepared Presenters folder in the Sandbox directory
  prepared Tables folder in the Sandbox directory
  prepared Helpers folder in the Sandbox directory
  prepared Gemfile
  prepared Service
  prepared Procfile
  prepared database config
  prepared environment config
  prepared application helper

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Examples

Simple Example

There is a datasets folder inside the SuperProject directory. If we open it, we can see the raw directory, where all CSV files should be stored. We'll place there our csv file withing the information about Total Gross Domestic Product among all the countries since 1980 up to 2012 by years.

Raw dataset can be downloaded from here: world_gdp.csv.

Now we should import this dataset into the database. The name of the database can be specified in the config/db.rb:

# inside SuperProject/config/db.rb
MongoMapper.database = "superproject_db"

Inside the world_gdp.csv we have two columns: year and total gdp. Dataset looks like this:

...
1983,11103.723
1984,11539.276
1985,11948.594
...

That's why we generate a new table WorldGdp with two columns:

~/SuperProject$ ajaila g table WorldGdp year:Integer gdp:Float

If everything goes well, there will be a green message:

Ajaila: Generated table WorldGdp successfully!

This command generates a table inside sandbox/tables. The file is called world_gdb.table.rb. Inside the file we can observe the following code, which was generated automatically:

class WorldGdp
  include MongoMapper::Document
  key :year, Integer
  key :gdp, Float
end

After the table is created, we can generate selector, which will parse world_gdp.csv and import everything into the WorldGdp table:

~/SuperProject$ ajaila g selector GdpParser file:world_gdp.csv table:WorldGdp

Again, if everything goes well, there will be a green message:

Ajaila: Generated selector GdpParser successfully!

Let's look what is stored inside the selector (file datasets/gdp_parser.selector.rb):

require "ajaila/selectors"
file = import "world_gdp.csv"

CSV.foreach file do |row|
  year = row[0].to_i
  gdp = row[1].to_f
  WorldGdp.create(year: year, gdp: gdp)
end

This code was generated automatically. Everything we need now is to run this selector:

~/SuperProject$ ajaila run selector GdpParser

After you enter this command dataset from world_gdp.csv should be imported inside WorldGdp table. That allows to work with data across the whole application.

Let's try to build our predictive model. For this purpose we'll need to extend our application within Statsample library. To do this we simply add the following line of code inside Gemfile at the application root directory:

# inside SuperProject/Gemfile
source "http://rubygems.org"
gem "statsample"

Then we run bundler to install all dependencies:

~/SuperProject$ bundle install

This should install Statsample library within all dependencies. After installation, there will be the following message:

Your bundle is complete! Use `bundle show [gemname]` to see where a bundled gem is installed.

We need a one more step to use this library inside our application. We should open config/environment.rb and add this line of code:

# inside SuperProject/config/environment.rb
require "statsample"

Great, let's create a regression. This quantitative model should live inside a new miner. To build a new miner we go back to our console window and type:

~/SuperProject$ ajaila g miner GdpForecast table:WorldGdp

This will return:

Ajaila: Generated miner GdpForecast successfully!

If we open sandbox/miners/gdp_forecast.miner.rb, we'll observe the following automatically generated code:

# inside SuperProject/sandbox/miners/gdp_forecast.miner.rb
require "ajaila/miners"
require "gdp_forecast.helper"

WorldGdp.create(year: year, gdp: gdp)

WorldGdp.all.each do |el|
  year = el.year
  gdp = el.gdp
end

As it can be seen, Ajaila generated scripts, which help to read data from WorldGdp table and then create new rows inside this table. Both commands are rendered, because Ajaila doesn't know, which will be used. So it creates both snippets.

We don't need to create new rows inside WorldGdp, so we simply delete this part of GdpForecast miner.

There is a helper for GdpForecast, this file is called gdp_forecast.helper.rb inside sandbox/helpers. We can add a new method there:

# inside sandbox/helpers/gdp_forecast.helper.rb 
module GdpForecastHelper
  self.extend GdpForecastHelper

  def build_regression(x_array, y_array)
    Statsample::Analysis.store(Statsample::Regression::Multiple) do
      ds = dataset('x'=>x_array.to_scale)
      attach(ds)
      ds['y'] = y_array.to_scale
      summary lr(ds,'y')
    end
    puts Statsample::Analysis.run_batch
  end

end

Now we can implement this method inside GdpForecast miner. This is an example:

require "ajaila/miners"
require "gdp_forecast.helper"

years = WorldGdp.all.map { |el| el.year }
gdps = WorldGdp.all.map { |el| el.gdp }

GdpForecastHelper.build_regression(years, gdps)

Then we can run this miner within the following command:

~/SuperProject$ ajaila run miner GdpForecast

This command will conduct the computation and return the following output:

= Statsample::Regression::Multiple
  == Multiple reggresion of x on y
    Engine: Statsample::Regression::Multiple::RubyEngine
    Cases(listwise)=33(33)
    R=0.959
    R^2=0.920
    R^2 Adj=0.918
    Std.Error R=5196.787
    Equation=-3557216.107 + 1798.264x
    === ANOVA
      ANOVA Table
+------------+-----------------+----+----------------+---------+-------+
|   source   |       ss        | df |       ms       |    f    |   p   |
+------------+-----------------+----+----------------+---------+-------+
| Regression | 9675386710.760  | 1  | 9675386710.760 | 358.260 | 0.000 |
| Error      | 837204513.468   | 31 | 27006597.209   |         |       |
| Total      | 10512591224.229 | 32 | 9702393307.969 |         |       |
+------------+-----------------+----+----------------+---------+-------+

    Beta coefficients
+----------+--------------+-------+------------+---------+
|  coeff   |      b       | beta  |     se     |    t    |
+----------+--------------+-------+------------+---------+
| Constant | -3557216.107 | -     | 189635.490 | -18.758 |
| x        | 1798.264     | 0.959 | 95.007     | 18.928  |
+----------+--------------+-------+------------+---------+

In a short time Ajaila will be provided with Machine Learning packages as a default. You can help us with that!

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Image Retrieval with RMagick

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Credit Scoring with Vowpal Wabbit

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Visualizing Data

Current Ajaila library utilizes a Protovis wrapper called Rubyvis. This allows to generate SVG graphs, which can be further observed through Ajaila Dashboard. To visualize data from one of your tables you should create new presenters. Let's see how to do this.

At the initial step we open the terminal window and go to an application directory, where we run a command to generate a presenter. In case of SuperProject this will look in such way:

~/SuperProject$ ajaila g presenter CoolGraph table:WorldGdp

As you see, presenter needs a link to the table name. Otherwise, it won't be created.

There should be a green message like this:

Ajaila: Generated presenter CoolGraph successfully!

That's good. Now we have CoolGraph, which lives inside sandbox/presenters/cool_graph.presenter.erb. Let's check CoolGraph contents:

<%=
sample = WorldGdp.all.map { |item| 0 }
Ajaila.linear_plot(sample, { :plot_name => "Untitled Plot", 
                           :graph_name => "Unknown Line",
                           :color => "blue" })
%>

This code snippet represents the most part of our job. We have to specify request to the database and change parameters of linear plot. Our final solution should look like this:

<%=
sample = WorldGdp.all(:order => :date.asc).map { |item| item.gdp }
Ajaila.linear_plot(sample, { :plot_name => "World GDP, 1980 - 2012", 
                           :graph_name => "total gdp",
                           :color => "green" })
%>

After little modifications we save everything and start Ajaila dashboard within the command:

~/SuperProject$ foreman start

Then we open new browser window and go to the http://localhost:9500/cool_graph. If you did everything right, you will see the image (example is ugly, but I hope you got the idea):

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Console commands

Creating New Project

This command creates the core of your datamining project.

ajaila new ProjectName

After executing this command you will be offered to configure the name of your database inside config/db.rb, but this is absolutely optional. For example, if your application is called AlohaHawai then the name of the application database will be aloha_hawai_db.

Generators

Generating a new table (among supported column formats are String, Integer, Float, Date, Array, Hash):

ajaila g table TableName user_name:String score:Float

When you run this command from your console, a new file appears. This file is called table_name.table.rb and located at sandbox/tables. All tables exist in the context of MongoMapper (ORM of MongoDB). One great thing about it is that there is no need for migrations.

Generating a new selector is done within the following command:

ajaila g selector SomeSelector table:TableName file:users.csv

Generating a new miner:

ajaila g miner SuperMiner table:InputTable table:OutputTable

Generating a new presenter:

ajaila g presenter SuperPresenter table:SuperPuper

Looking Around

Listing all selectors:

ajaila selectors

Listing all miners:

ajaila miners

Listing all tables:

ajaila tables

Executing

Running a selector:

ajaila run selector SomeSelector

Running a miner:

ajaila run miner SuperMiner

Running a dashboard with presenters:

ajaila run

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Architecture

The platform consists of two blocks. Among them: Datasets and Sandbox. They exist in the context of Ajaila Environment, which provides everything with a library of methods and allows to generate new instances (selectors, miners, tables, presenters). There is also a Dashboard, which aggregates all information about the particular project (dashboard allows to observe the content of all presenters inside the project).

Ajaila v.0.0.2 Architecture

Datasets vs. Sandbox

Datasets and Sandbox are split according to their usage frequency.

While creating a new project, we run selectors only once or at least they are built for that. Selectors parse CSV files and turn static pieces of data into dynamic ones.

On the other hand, everything in the Sandbox is changed much more often (miners, presenters, tables and even helpers).

I found this kind of approach valuable and hope that you'll get used to it and appreciate it as well.

Datasets Explained

After creating a new project simply put all static files inside datasets/raw. For now there is only a CSV format supported within selectors. If you need some other format - just ask me to help by creating a new issue.

Selectors are the dwellers of Datasets folder. This dudes are very important. Let's look at the Sandbox decomposition:

Sandbox = Sand + Box

Selectors simply fill an empty box within the data. As soon as Sandbox is ready we move to the next step of our workflow. Selectors are left behind and we are making a short bio for each Datasets dweller.

Inside Selector

We created a new project called SuperProject. Our mission is to analyze Cool Things, which are stored in the CSV file called items.csv. We put this file into SuperProject/datasets/raw directory.

Before generating a selector we need to generate some table, which will store the contents of items.csv. We open a Terminal window in the SuperProject directory and write the following command.

~/DEMOS/SuperProject$ ajaila g table CoolThings item:String produced:Time cost:Float quantity:Integer

The command should return a green message as the one below.

Ajaila: Generated table CoolThings successfully!

Now we have the table CoolThings (with columns item, produced, cost, quantity), which will be described in the next paragraph.

After the table is created, we can move further. Our new challenge is to create selector (called ItemsExtractor), but it's not a hard nut to crack.

~/DEMOS/SuperProject$ ajaila g selector ItemsExtractor file:items.csv table:CoolThings

There should be a green message, explaining that everything went fine. Let's look inside ItemsExtractor.

# inside SuperProject/datasets/items_extractor.selector.rb
require "ajaila/selectors"
file = import "items.csv"

CSV.foreach file do |row|
  item = row[0]
  produced = row[0]
  cost = row[0]
  quantity = row[0]
  CoolThings.create(item: item, produced: produced, cost: cost, quantity: quantity)
end

Ajaila doesn't know how to define columns automatically. But it's not difficult to change indexes or rows manually (items = row[0], produced = row[1] and etc).

This file can be executed through the Terminal window.

ajaila run selector ItemsExtractor

Now we have CoolThings, which are a dynamic representation of items.csv.

Datasets Overview

Resident Name Short Bio Interaction
CSV Files Live inside datasets/raw. Keep all the data of the project in the static form. Manually Placed
Selectors Require table and file as an input. They know how to parse CSV files. Generated and executed via Terminal Command

Sandbox Explained

Datasets are easier to understand, because Sandbox consists of more elements. As you can observe at the scheme above, there are Tables, Miners, Helpers and Presenters. Quite self-explanatory names, aren't they?

Tables

After the selection process, data is stored in the database. The access point for the data is a set of tables stored in the sandbox/tables folder. MongoDB gives us freedom not to generate migrations (that saves a lot of time). Additionally, we can change any table or rewrite everything in a new way.

Tables initialize new collections within the Mongomapper. Collections are available through selectors, miners and presenters. Helpers are not linked with Ajaila environment directly, but you can call collections and their methods. Helpers are usually a part of something (new helper gets generated within new miner).

During the work in the Sandbox you are expected to create new tables with rewritable data. It's not recommended to change data and table, which were the output of some Selector. Selectors create the core data storage of a Project. Use such table as an input for miners and presenters, but don't rewrite it.

Inside Table

Let's return to our SuperProject, where we try to find beauty in CoolThings. You should already know that CoolThings is a table with columns. This table lives in the SuperProject/sandbox/tables directory.

If we open the file cool_things.table.rb the we'll see.

class CoolThings
  include MongoMapper::Document
  key :item, String
  key :produced, Time
  key :cost, Float
  key :quantity, Integer
end

Ajaila generated this file automatically. As you may observe, CoolThings is a class of Mongomapper.

Miners

Write your algorithms inside Helpers and execute them inside Miners. When you generate new Miner you have to specify tables, which will be used as an Input or Output. After you specify I/O tables Ajaila has an opportunity to generate valuable snippets according to the information specified in tables.

Miner is more powerful if it's task oriented and focused. Miner should be readable and have as many functions inside Helper as possible. Miners are difficult to get tested directly, methods inside Helpers are much easier to test.

Split your complex problem into subproblems. One miner per subproblem is a good way of dealing with things.

Helpers

You've already got familiar with Helpers if you read about Miners. Helper is a module, which gets automatically included in the miner. Just imagine that we want to generate miner Foo. If everything works properly - there'll be helper called FooHelper.

There is also an ApplicationHelper. This helper is so global, that it can be used in any Selector, Miner or Presenter.

Presenters

Presenters are still work in progress. There is no DSL yet, but I can explain you how to build Charts manually. Just let me know that you need it.

Sandbox Overview

Residents Short Bio Interaction
Tables Store information about Data Structures. Get initialized within Ajaila environment. Can be generated or listed.
Miners Execute algorithms and use Tables as an I/O. Can be generated, listed or executed via Terminal.
Helpers Modules, which store methods used by other parts of an Application. Generated within miners automatically.
Presenters Provide user with a DSL to visualize Data. Can be created manually (still in development).

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Built-in Methods

Before you want to write something on your own, look through Ajaila library. It is a valuable source of methods, which is permanently and dramatically extended day by day. These methods are carefully hand picked from all those projects, which get built with Ajaila. Let's walk through them.

to_growth

Takes two numbers as an input, converts them into float and calculates how much second parameter is bigger or lower than the first one. Example:

price_day_one = 100
price_day_two = 200
Ajaila.to_growth(price_day_one, price_day_two) # => 1.0

normalize

Conducts normalization of all numbers inside the array. Example:

array = [1,2,3,4]
Ajaila.normalize(array) # => [0.0, 0.3333333333333333, 0.6666666666666666, 1.0]

detect_nil

Checks if the input value is nil. By default it raises an error, but the second parameter is optional. If it is equal 1, then nil values will be replaced with 0. Examples:

input = 100500
Ajaila.detect_nil(input) # => Raises an error, that nil is detected!
Ajaila.detect_nil(input, 1) # => 0.0

execute_miner

If there is a cascade of algorithms, then you may find it nice to incapsulate each computation inside separate miners and then generate an additional miner, which runs sequence. There are many other possible usecases. Example:

Ajaila.execute_miner("FutureForecast") # => executes miner FutureForecast

execute_selector

Here is the same logic as with the miners execution. According to typical usecases, this method is a perfect fit if you collaborate with external libraries via text inputs and outputs. Awesome method. Example:

Ajaila.execute_selector("NewsParser") # => executes selector NewsParser

all_days_at_interval

Ajaila.all_days_at_interval(time_start, time_end)

one_day

Ajaila.one_day

linear_plot

Ajaila.linear_plot(sample, opts = {})

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Installation From Source

It's great to solve complex problems in a friendly environment. Are you already familiar with Bundler, RubyGems and RVM? That's cool! Current Ajaila version supports Ruby 1.9.3 or higher.

Ajaila needs a database to work properly. I decided to use MongoDB as a default, because of ORM (MongoMapper). MongoDB doesn't need migrations and I find it convenient. Feel free to contribute and make support of other databases.

If you don't have MongoDB install it.

There is no Ajaila 0.0.2 at RubyGems. Version 0.0.1 is a crap, don't install it. To get an unstable version, which is almost like 0.0.2, you should open the Terminal and clone the repository to your PC (Mac OS / Linux)

git clone https://github.com/mac-r/ajaila.git

Then build Gem from ajaila.gemspec

gem build ajaila.gemspec

You'll have ajaila-0.0.2.gem inside Ajaila folder. This is a newly baked Gem, which can be installed with the Terminal command

gem install ajaila-0.0.2.gem

After installing all dependencies you'll be able to check if everything went right. After typing ajaila in the Terminal you should get

Ajaila Datamining Sandbox v. 0.0.2

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Name Origin

Ajaila is the composition of two words: ajala and agile. According to african mythology, "Ajala" is the god of creation, who lives in heaven and makes human faces from clay and chaos. As far as you most probably know, "agile" describes a flexible approach to software development, which expects you to split the workflow into sustainable pieces. Therefore, it shouldn't be surprising for you why Ajaila is called like that. We are talking about datamining framework, which allows you to follow agile practices.

Why are agile practices so important? Whether you are a software developer or research engineer, there is always someone interested in what you are doing. These people are stakeholders in a project you are working on. Demonstrating results on a regular basis is the crucial point, which allows to succeed. With Ajaila you can conduct R&D, split your datamining project into sustainable parts and show progress within each iteration.

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Features List

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Contributing

community

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License

The MIT License

Copyright (c) 2013 Max Makarochkin

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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