ReadyForZero / babbage

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Easily gather data and compute summary measures declaratively.

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babbage

Name: babbage
Brand: babbage
SKU: //ReadyForZero/babbage
Rating: 2.2375 (195 reviews)

A library to create computation engines. Easily gather data and compute summary measures in a declarative way.

Usage

[readyforzero/babbage "1.0.2"] ;; In your project.clj

(:require [babbage.core :refer :all]) ;; Core functions.
(:require [babbage.provided.core :as b]) ;; Some basic provided statistics.

Introduction

The basic interface, which performs aggregations and partitions over seqs, is provided by the functions

stats

sets

, and

calculate

stats

is used to declare the measures to calculate.

sets

is used to declare the subsets of the input over which the measures should be calculated.

calculate

takes these two as arguments (

sets

is optional), and makes the computation run over the provided seq.

;; Calculate the sum of a seq of elements.
user> (calculate
        (stats identity b/sum) ;; A stat that's the sum of the elements.
        [1 2 3 4])
{:all {:sum 10}} ;; The sum of all elements is 10.

babbage also provides a mechanism to perform efficient computation over directed graphs, using the functions

defgraphfn

to define a unit of work and its dependencies, and

run-graph

to execute the computation of a graph.

Using measure functions

The

stats

function takes an extraction function as its first argument, and an arbitrary number of measure functions as its remaining arguments.

In the simplest possible case, as we saw above, the extraction function is identity:

;; Calculate the sum of a seq of elements.
user> (calculate (stats identity b/sum) [1 2 3 4])
{:all {:sum 10}}                                   ;; The sum of all elements is 10.

Frequently we will want to both name the result, and perform some kind of operation on the elements of the input:

;; Calculate the sum over a seq of elements with an extraction function.
user> (calculate 
        {:the-result (stats :x b/sum)}             ;; Compute the sum over :x's. Call it ":the-result".
        [{:x 1} {:x 2}])
{:all {:the-result {:sum 3}}}

Multiple measures can be computed in one pass:

;; Compute the mean and sum of :x's.
user> (calculate 
        {:the-result (stats :x b/sum b/mean)}      ;; Explicitly request computation of 'mean' and 'sum'.
        [{:x 1} {:x 2}])
{:all {:the-result {:mean 1.5, 
                    :count 2, 
                    :sum 3}}}                      ;; The 'count' measure is required by 'mean',
                                                   ;; so it's automatically computed.

And we can compute multiple measures over multiple fields in one pass:

;; Compute multiple measures over multiple fields in one pass.
user> (calculate 
        {:x-result (stats :x b/sum b/mean)
         :y-result (stats :y b/mean)}              ;; Add mean for :y's, call it :y-result.
        [{:x 1 :y 10} {:x 2} {:x 3} {:y 15}])
{:all {:x-result {:count 3, :mean 2.0,  :sum 6},
       :y-result {:count 2, :mean 12.5, :sum 25}}}

"Extraction" functions can really perform arbitrary operations on their inputs:

;; Accumulate the mean over the sum of the two fields.
user> (calculate 
        {:both (stats #(+ (or (:x %) 0)            ;; Compute the mean of this function on each element.
                          (or (:y %) 0))
                      b/mean)}
        [{:x 1 :y 10} {:x 2} {:x 3} {:y 15}])
{:all {:both {:count 4, :mean 7.75, :sum 31}}}

Extraction functions will only be called once per item in the input seq.

Structured measure functions

Sometimes we will want to compute measures for multiple functions of the input together. However, in the call

(stats extractor-fn
measure1 measure2 ...)

, all the measure functions receive the result of calling

extractor-fn

on an input item. While we could make the extractor function return sequences or maps, that would require modifying all the measure functions to further extract the right elements. To get around this,

babbage.core

exports three functions for making the whole item available within a

stats

call:

by

map-with-key

, and

map-with-value

. The interface to all these functions is similar to that for

stats

: the first argument is an extractor function, the second argument is the key to be used in the results map, and the remaining arguments are arbitrarily many measure functions.

Suppose your input is maps of the form

{:sale x, :user_id y :ts t}

, representing the amount x of a sale to user

at time

. You want to know the total and mean of the sales, and you also want to know how many individual users made purchases:

;; Given a list of sale/user-id pairs, compute unique users.
user> (calculate 
        (stats :sale b/mean b/sum (by :user_id :users b/count-unique))
        [{:sale 10 :user_id 1} {:sale 20 :user_id 4}
         {:sale 15 :user_id 1} {:sale 13 :user_id 3}
         {:sale 25 :user_id 1}])
{:all {:mean 16.6, 
       :users {:unique 3, :count-binned {1 3, 3 1, 4 1}}, ;; "count-unique" depends on "count-binned".
       :sum 83,
       :count 5}}

Or, you might want to know the means and total for each user's purchases:

;; Given a list of sale/user-id pairs, compute measures for each user's sales.
user> (calculate 
        (stats :sale b/mean b/sum (map-with-key :user_id :user->sales b/mean b/sum))
        [{:sale 10 :user_id 1} {:sale 20 :user_id 4}
         {:sale 15 :user_id 1} {:sale 13 :user_id 3}
         {:sale 25 :user_id 1}])
{:all {:mean 16.6, 
       :user->sales {3 {:mean 13.0, :count 1, :sum 13}, 
                     4 {:mean 20.0, :count 1, :sum 20}, 
                     1 {:mean 16.6, :count 3, :sum 50}},
       :sum 83, 
       :count 5}}

Computation across subsets

Simply compute the same measures across multiple subsets, in one pass.

sets

takes a single argument, a map whose keys are names of sets and whose values are predicates indicating whether a member of the input seq belongs in the set:

;; We take the previous example, and compute the same measures, but 
;; considering different subsets of elements.
user> (calculate 
        (sets {:has-y :y})                       ;; Compute measures over elements with :y.
          {:both (stats #(+ (or (:x %) 0) 
                            (or (:y %) 0)) 
                        b/mean)}
          [{:x 1 :y 10} {:x 2} {:x 3} {:y 15}])
{:all   {:both {:mean 7.75, :sum 31, :count 4}}, ;; We always compute over all.
 :has-y {:both {:mean 13.0, :sum 26, :count 2}}} ;; Only two elements in the seq had y.

Given an initial predicate map, arbitrary compositions of those predicates can be performed.

;; Construct 'my-sets' using complement and intersection.
user> (def my-sets (-> (sets {:has-y :y
                              :good :good?})
                       (complement :good)        ;; Adds a set called :not-good.
                       (intersections :has-y
                         [:good :not-good])))    ;; Two more sets ':has-y and :good' and ':has-y and :not-good'.
#'user/my-sets

;; Construct 'my-fields' as per examples above.
user> (def my-fields {:both (stats #(+ (or (:x %) 0) (or (:y %) 0)) b/mean)})
#'user/my-fields
;; Make it go.
user> (calculate my-sets my-fields [{:x 1 :good? true :y 4}
                                    {:x 4 :good? false}
                                    {:x 7 :good? true}
                                    {:x 10 :good? false :y 6}])
{:Shas-y-and-not-goodZ {:both {:mean 16.0, :sum 16, :count 1}},  ;; The result contains our compositions.
 :Shas-y-and-goodZ     {:both {:mean 5.0,  :sum 5,  :count 1}},
 :good                 {:both {:mean 6.0,  :sum 12, :count 2}},
 :has-y                {:both {:mean 10.5, :sum 21, :count 2}},
 :all                  {:both {:mean 8.0,  :sum 32, :count 4}},
 :not-good             {:both {:mean 10.0, :sum 20, :count 2}}}
;; Seamless inspection across multiple subsets.
user> (xget *1 [:good :not-good] :both :mean)    ;; Get the mean for :good and :not-good.
(6.0 10.0)

Predicate functions will only be called once per item in the input seq.

Efficient computation of inputs

When calculating nontrivial measures over real data, we will often want to do some transformation of the data to make it tractable, or at least to minimize verbosity and redundancy in the extraction or set-definition functions.

babbage.graph

contains tools for declaring dependency relations among computations and running such computations efficiently, in the spirit of the Prismatic Graph library and the Flow library:

user> (defgraphfn sum [xs]
        (apply + xs))
#'user/sum
user> (defgraphfn sum-squared [xs]
        (sum (map #(* % %) xs)))
#'user/sum-squared
user> (defgraphfn count-input :count [xs]
        (count xs))
#'user/count
user> (defgraphfn mean [count sum]
        (double (/ sum count)))
#'user/mean
user> (defgraphfn mean2 [count sum-squared]
        (double (/ sum-squared count)))
#'user/mean2
user> (defgraphfn variance [mean mean2]
        (- mean2 (* mean mean)))
#'user/variance
user> (run-graph {:xs [1 2 3 4]} sum variance sum-squared count-input mean mean2)
{:sum 10
 :count 4
 :sum-squared 30
 :mean 2.5
 :variance 1.25
 :mean2 7.5
 :xs [1 2 3 4]}

Functions defined with

defgraphfn

can be invoked like ordinary Clojure functions defined with

defn

fn

, because that's what they are; the only difference is that the functions carry some metadata about their dependencies and their names around with them. Because of this, they can be wrapped by arbitrary other functions, as long as the metadata is appropriately transferred. (See, for example,

track-calls

babbage.test.graph

When called with

run-graph

(or its variations), the graph function

mean

can refer to the results of the graph function

sum

simply by including

sum

in its argument list.

Since (a) we might want to choose dynamically between multiple functions that can provide the same output to further functions, and (b) a name that describes a function's output might be undesirable for the name of the function itself (because it might shadow a core function, for instance), we can explicitly declare what name a graph function's output should have when other graph functions want to refer to it, as in

count-input

in the example above.

run-graph

takes a map of initial input names and values and any number of graph functions. It analyzes the dependency graph, throwing an error in case of unavailable or circular dependencies, and otherwise runs the functions provided with the input provided. By default it runs its argument functions in parallel when possible, and evaluates the results strictly.

run-graph-strategy

can be used to force sequential evaluation, or lazy evaluation:

user> (defgraphfn foo [a]
        (println "in foo"))
#'user/foo
user> (defgraphfn foo [a]
        (println "in foo")
        (inc a))
#'user/foo
user> (defgraphfn bar [foo]
        (println "in bar")
        (* foo foo))
#'user/bar
user> (defgraphfn baz [foo]
        (println "in baz")
        (* foo 2))
#'user/baz
user> (defgraphfn quux [bar baz]
        (println "in quux")
        (- bar baz))
#'user/quux
user> (def _r (run-graph-strategy {:lazy? true} {:a 5} foo bar baz quux))
#'user/_r
user> (:baz _r)
in foo
in baz
12
user> (:baz _r)
12
user> (:quux _r)
in bar
in quux
24

compile-graph

and

compile-graph-strategy

can be used to analyze the dependency graph once at run time, returning a function that can be called directly:

user> (def f (compile-graph foo bar baz quux))
#'user/f
user> (f {:a 5})
in foo
in bar
in baz
in quux
{:quux 24, :baz 12, :bar 36, :foo 6, :a 5}
user>

run-graph*

and

run-graph-strategy*

can be used to attempt to analyze the dependency graph at compile time, falling back to their un-asterisked analogues if that is not possible.

Measure functions

Predefined measure functions

Several measure functions are predefined in

babbage.provided.core

Measure functions that take no arguments:

Name	Effect
`sum`, `prod`, `max`, `min`, `count`	Compute the sum, product, maximum, minimum, or count of values.
`list`, `set`	Accumulate values in a list or a set.
`mean`	Compute the arithmetic mean of values.
`count-binned`	Count how often different values have occurred (like frequencies).
`count-unique`	Count how many different values have occurred.

Measure functions that take arguments:

Name	Arguments	Effect
`ratio`	`of`, `to`, `ratio-name` (optional)	Compute the ratio of `of` to `to`. `of` must be a keyword naming a value that a measure function will place in the result map; to can either be a keyword or a number. If `ratio-name` is provided, it must be a keyword; it will be used as the key in the result map for the ratio. Otherwise, `of` and `to` will be used to create a key of the form `of`-to-`to`: E.g., `(ratio :max :min)` will use the key `:max-to-min`.
`histogram`	`width`	Compute a histogram whose buckets have width `width`.

Defining new measure functions

A measure function is built out of an underlying monoid, or a function that depends on a measure function, and a public function that associates the measure with a descriptive name into a map.

For simple functions the

defstatfn

macro in

babbage.core

and the

monoid

function in

babbage.monoid

can be used to reduce boilerplate. For instance, the following would record the first non-nil value in the input sequence:

user> (def m-fst (monoid (fn [a b] a) nil))
#'user/m-fst
user> (defstatfn fst m-fst)
#'user/fst
user> (calculate (stats :x fst sum) [{:x nil} {:x 2} {:x 3} {:x 4}])
{:all {:sum 9, :fst 2}}

The

statfn

macro can be used to create an anonymous measure function that depends on runtime values: see

babbage.provided.core/histogram

and

babbage.provided.core/ratio

for examples.

The

monoid

function takes a binary operation and a value that is a left and right identity when the operation has non-nil arguments (nil is special-cased to always be an identity) and returns a function that creates instances of

babbage.monoid/Monoid

. More complex measures can implement the protocol directly; see

babbage.monoid.gaussian

for an example.

Measures that depend on already-computed measures (e.g. the ratio of one to another) are also a combination of an underlying function that does the actual computation and a public function that associates the measure with a name in a map. In this case, both functions declare their dependencies:

;; Keeping track of unique counts is dependent on keeping track of the seen ones.
;; "defnmeta" just associates the attr-map metadata with both the var and the function
(defnmeta d-count-unique {:requires #{:count-binned}} [m]
  (count (get m :count-binned)))

(defstatfn count-unique d-count-unique :requires count-binned :name :unique)

d-count-unique

declares that it expects the result map it is passed to contain the

:count-binned

key.

count-unique

uses

d-count-unique

to compute its results, says that its result is named

:unique

, and requires the function

count-binned

. Using

count-unique

will result in

count-binned

being used as well. In this case, there is some redundancy in the declarations, because

count-unique

's requirements are known statically.

License

The use and distribution terms for this software are covered by the Eclipse Public License 1.0 (http://opensource.org/licenses/eclipse-1.0.php)

Contributors

Ben Wolfson ([email protected])

At each increase of knowledge, as well as on the contrivance of every new tool, human labour becomes abridged. -- Charles Babbage

babbage

by ReadyForZero

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babbage

Usage

Introduction

Using measure functions

Structured measure functions

Computation across subsets

Efficient computation of inputs

Measure functions

Predefined measure functions

Defining new measure functions

License

Contributors