Working with DLATK's Classes

DLATK contains the following classes: FeatureWorker, FeatureGetter, FeatureRefiner, FeatureExtractor, OutcomeGetter, OutcomeAnalyzer, ClassifyPredictor and RegressionPredictor. Instead of working with dlatkInterface one can work directly with one of these classes.

Working with classes

With the exception of FeatureWorker all classes can be created as follows:

from dlatk.featureExtractor import FeatureExtractor
fe = FeatureExtractor() # use default values set in dlaConstants
fe = FeatureExtractor(corpdb="someDB", corptable="someTB", correl_field="someField", ...) # specify values

FeatureWorker does not allow you to use default values so you must do the following:

from dlatk.featureWorker import FeatureWorker
fw = FeatureWorker(corpdb="someDB", corptable="someTB", correl_field="someField", ...)

Both FeatureGetter, OutcomeGetter and OutcomeAnalyzer allow you to pass an init file. This file is a simple text file where the constants are defined. Init file must have the line [constants] at the top. Also note that none of the strings are quoted. For lists (such as lists of outcome variables) use commas to separate values. Sample init file:

corpdb = dla_tutorial
corptable = msgs
correl_field = user_id
feattable = feat$1gram$msgs$user_id$16to16$0_01
outcometable = blog_outcomes
outcomefields = age, is_education
outcomecontrols = gender

To initialize one of these classes using an init file do the following:

fg = FeatureGetter.fromFile('/path/to/init/file')
og = OutcomeGetter.fromFile('/path/to/init/file')
oa = OutcomeAnalyzer.fromFile('/path/to/init/file')

Using FeatureStar

This option allows you to create instances of FeatureWorker, FeatureGetter, FeatureRefiner, FeatureExtractor, OutcomeGetter, OutcomeAnalyzer, ClassifyPredictor and RegressionPredictor.

Initialize a FeatureStar object:

from dlatk.featureStar import FeatureStar

# get instances of all classes
fs = FeatureStar() # defaults specified in dlaConstants
fs = FeatureStar(corpdb="someDB", corptable="someTB", correl_field="someField", ...) # specify values
fs = FeatureStar.fromFile('/path/to/init/file') # pass values from file

To initalize FeatureStar with an init file, simply add the following line under [constants]:

init = fg, og

This is a comma separated line with the initials of the classes you want to instantiate. So the above command will give you a FeatureGetter and an OutcomeGetter

# add line 'init = fg, og' to init file
fs = FeatureStar.fromFile('/path/to/init/file') # this will return only a FeatureGetter and OutcomeGetter

Or you can pass this list as a parameter

# get certain classes with defaults
fs = FeatureStar(init=['fg', 'og'])

# get instances of subset of classes
fs = FeatureStar.fromFile('/path/to/init/file', ['fg', 'og']) # get only a FeatureGetter and OutcomeGetter

> {'FeatureWorker': None, 'FeatureRefiner': None,
'FeatureGetter': <FeatureWorker.featureGetter.FeatureGetter object at 0x7f87e75e13d0>,
'OutcomeGetter': <FeatureWorker.outcomeGetter.OutcomeGetter object at 0x7f87e75e1390>,
'OutcomeAnalyzer': None, 'FeatureExtractor': None,
'ClassifyPredictor': None, 'RegressionPredictor': None}

You can retrieve FeatureStars attributes (i.e., all DLATK objects) in the following way

# retrieve a FeatureWorker
fw = fs.fw
fw = fs.allFW['FeatureWorker']

# retrieve a FeatureGetter
fg = fs.fg
fg = fs.allFW['FeatureGetter']

# retrieve a FeatureRefiner
fr =
fr = fs.allFW['FeatureRefiner']

# retrieve a FeatureExtractor
fe = fs.fe
fe = fs.allFW['FeatureExtractor']

# retrieve a OutcomeGetter
og = fs.og
og = fs.allFW['OutcomeGetter']

# retrieve a OutcomeAnalyzer
oa = fs.oa
oa = fs.allFW['OutcomeAnalyzer']

# retrieve a RegressionPredictor
oa = fs.rp
oa = fs.allFW['RegressionPredictor']

# retrieve a ClassifyPredictor
oa = fs.cp
oa = fs.allFW['ClassifyPredictor']