Kodiak

Overview

Kodiak enhaces your feature engineering workflow extracting common patterns so you can create more features faster.

Ex: You have the writers dataframe, where born is a datetime

and you want to extract from the born column: day, month and year and create 3 new columns

The simplest thing you could do in Pandas is:

writers["born_month"] = writers.born.map(lambda x: x.month)
writers["born_day"]   = writers.born.map(lambda x: x.day)
writers["born_year"]  = writers.born.map(lambda x: x.year)

With Kodiak you could get the same result in one line:

writers.gencol("born_{month,day,year}", "born", lambda x, y: getattr(x, y))
# or more succinctly
writers.gencol("born_{.month,.day,.year}", "born")

But, how does it work? Kodiak uses "born_{month,day,year}" as a template for the columns: born_month, born_day and born_year and passes month,day and year as arguments to a provided function that also has the current 'born' as an an argument, so you're basically doing:

for y in ['month', 'day', 'year']:
    writers["born_{}".format(y)] = writers.born.map(lambda x: getattr(x, y))

Kodiak does a lot of other things to boost your workflow, for that, see the Basic Usage and Advanced Usage sections

Installation

To install Kodiak, cd to the Kodiak folder and run the install command:

sudo python setup.py install

You can also install Kodiak from PyPI:

sudo pip install kodiak

Basic Usage

Kodiak main object is KodiakDataFrame an extension of pandas.Dataframe that provides the instance method colgen to create one or more columns. You create KodiakDataFrames the same way you create a pandas.DataFrame

colgen signature is: colgen(newcols, col, colbuilder=None, enum=False, config=None)

newcols

has a double function, it works as a specification of the columns you want to create, and it also contains the values passed to colbuilder

# If you want to create the columns `first_name`, `last_name`
# and pass `first` and `last` as arguments to `colbuilder` you write
>>> newcols = "{first,last}_name"

# More complex patterns allowed
>>> groups = "col_{a,b}_{c,d}"

# Will create the columns: `col_a_c`, `col_b_d`
# The way `a,b` and `c,d` is combined can be configured

col

is the KodiakDataframe column from where you'll extract information to create your new column/s

colbuilder

is a function or lambda used to extract info from col and create the columns specified in newcols with the corresponding col instance and the newcols values. The signature of colbuilder is colbuilder(x, y) or colbuilder(i, x, y) x is an instance of the column passed in col and y is an argument extracted from newcols. The extra argument i is an index of the arguments.

config

tweak Kodiak inner workings with your own config, see the dedicated section for more info

Advanced Usage

In this section we're going to describe the main components and concepts that are essential to Kodiak

Templating

The template language is minimal but has some extensions to help you:

Ranges

The range notation is start:end:step. Reverse ranges are permited setting end bigger than start. step default value is 1, and start is 0, finally if end is absent, it'll be setted to 0 and you'll have a reversed range. Ranges are inclusive.

simple_range = "col_{1:3}" # -> col_1, col_2, col_3
step_range = "col_{:3:2}" # -> col_0, col_3
inverse_range = "col_{3:1}" # -> col_3,col_2,col_1
no_end = "2:" # -> col_2,col_1,col_0

Key-Value

If you want the column name and argument passed to the colbuilder to differ you can use key-values.

dataframe.gencol("{short=very_long_name}_col", "col", alambda)
# In this case the column name will be `short_col` but you'll pass
# `very_long_name` to `alambda`

# key-value notation can be extended to more arguments:
dataframe.gencol("{k1=v1,k2=v2,k3=v3}_col", "col", alambda)

Transforms

Under the hood when you pass newcols to gencol, Kodiak builds an OrdereDict where it's keys are column names and it's values are tuples of Match objects -even if it's just one Match it's wrapped inside a tuple-

newcol = "{first,name}_name"
# will build generate
args_dict = {'first_name': (Match('name'),), 'last_name': (Match('last'),)}

Transforms are a way to pre-process the values and change them enriching the Match object with a payload as you will see in the Default colbuilder section.

So, if the values are Match objects, how is that when you write your colbuilder you deal with strings? Kodiak understands that if the Match object doesn't have a payload it's better to pass strings arguments to colbuilder, this behaviour can be controlled.

What's the use of Match objects and their payload? What're some examples of Transforms? The next section will answer this questions

Default colbuilder

As you can see in the colgen signature, colbuilder default argument is None, in special cases Kodiak can infer the colbuilder method, let's revisit the opening example.

writers.gencol("born_{.month,.day,.year}", "born")

The colbuilder in this case is inferred from the hint you gave Kodiak in the template: .month, prefixing month with a . indicates that you're referring to an attribute of born, so internally Kodiak builds a colbuider that extracts the month from a born instance. Another way of omitting the colbuilder is when you have an instance method:

# Notice the `!` after weekday
writers.gencol("born_{weekday!}", "born")

How is that Kodiak infers the colbuilder? When the newcols are processed they go through a pipeline of Transforms, one of them: PropertyTransform detects that .month refers to an attribute and enriches de Match object hinting in the payload the corresponding colbuilder, that's why you don't need to pass the colbuilder argument. But what happens if you give a colbuilder? In this case, as the Match object has a payload instead of working with plain strings you will work with tuples of Match objects

Enumerations

Sometimes you care about the position of the arguments not the exact value, in that case you can use the enum param or the implicit enum with a function or lambda of arity 3, the first argument will be the index starting at 0.

writers.gencol("{first=0,last=1}_name", "name", lambda x,y: x.split(" ")[int(y)])

# Another way with enum=True
writers.gencol("{first,last}_name", "name", lambda i,x,y: x.split(" ")[i], enum=True)

# Without enum=True but with a colbuilder with arity 3
writers.gencol("{first,last}_name", "name", lambda i,x,y: x.split(" ")[i])

Configuration

Almost everything is configurable in Kodiak, you could have a per-method configuration or system-wide config.

The Config object has the following customizable params:

parser

Kodiak by default uses the ArgsParser class to parse newcols

match_transform

data passed to the colbuilder could be transformed first, by default we use the default_transform pipeline, you could replace it with an array of Transforms objects.

new_col_combiner

in the newcols template if you have "col_{a,b}_{c,d}", this results in the columns: "col_a_c" and "col_b,d", how the different groups ['a','b'] and ['c', 'd'] are combined is controlled with this param, currently we use the \zip\ function, and you could replace it with a function with similar signature.

unpack

Boolean Default True, when newcols is simple, foo_{a,b} instead of foo_{.a,b!} instead of passing to colbuilder tuples of Match objects we just pass individual items, a, b, so it's easier to build a colbuilder without having to unwrap the Match tuples

col_pair_combiner

Once you have the arguments that you're going to pass to the colbuilder you can cambine them in different ways, currently we use product from itertools, ie: the cartesian product between an element, ex: event, and the other n-columns, creating the following tuples:

[('event', 'day') , ('event', 'month'), ('event', 'year')]

You can replace this method with another with the same signature as product

Config can be accessed, modified and restored with:

>> import config
>> from config import cfg
>> config.options

# Global change on config

>> config.options["unpack"] = False
>> config.options["col_pair_combiner"] = zip

# Restoring one or more fields of the configuration
>> config.restore_default_config("col_pair_combiner")

# Restoring all the options
>> config.restore_default_config()

# With `base_config` or it's alias `cfg` you can create modified versions
# of the default config

>> dataframe.gencol("col_{a!,b!}","col", afunc, config=cfg(unpack=False))