Register and Serve Features
Introduction
What is a
feature?
Machine learning models
take input values to predict an output. The inputs are named features. The goal
of a feature is to provide a model context required to make a good prediction.
Most models cannot perform well with a dump of raw data. For example, a model
provided all the text in an article would not be able to do well. It's
common to pre-process the data and even transform it into numeric inputs for the
model to use.
Types of Feature
Engineering
Clean Inputs for the
Model
Many models need their
inputs standardized to do well. Models like neural networks tend to over-react
or under-react to huge outliers in data. Given how gradient descent works,
models may perform poorly when their features have widely different ranges. Lots
of feature engineering techniques are focused on standardizing data and keeping
it in a clean range to make sure that the model behaves rationally. These
techniques include scaling features, filling missing values, and truncating
outliers.
Imbue Domain
Knowledge
Models benefit from
features that imbue domain knowledge. For example, a fraud detection model that
works by only reading transaction events will likely under perform one
that's provided the transaction along with the number of past transactions
by the user, the average purchase price of all transaction, and the card issuers
credibility. This feature engineering may also be as simple as providing a map
of the word frequencies in a document rather than the entire document
text.
Encode Inputs for Model
Compatibility
Some models simply cannot
take data of certain data types. For example, neural networks cannot directly
ingest strings. In these cases, we may encode the incompatible value using a
variety of techniques such as one-hot encoding or binning.
Feature
Definition
Feature definitions are
defined using the
FeatureDef object. They are then
registered using the register_feature method of a FeatureStore object. A feature maps
directly to a single table column defined in the Data
API, this includes
materialized
views.
Each feature must define a
parent entity which is used as the index for the underlying table. That means
that the value provided for an entity is used to perform a lookup on the
underlying table.
Each feature definition
also has versioning built-in. If a version is not provided, and the feature name
is in use, the feature version will be automatically incremented. By default,
all APIs that use features default to the newest version of the
feature.
The majority of feature
definition parameters are common feature engineering operations such as outlier
truncating and normalization.
The full feature definition
looks like this:
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FeatureDefintion(
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name="",
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version=1,
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table="",
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column="",
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parent_entity="",
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transform="",
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normalize="",
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truncate="",
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)
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Feature
Operations
transformation
Transformations are applied
on each element individually. They are a mapping function. Custom python
functions are supported here, and there are a few built-in ones as
well:
-
Sqrt(): Returns
-
Pow(n): Returns
-
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UnitVec(): Scales a vector so that it has a magnitude of 1
-
OneHotEnc(): Performs one-hot encoding on a categorical value
-
Map(m, default=None): Returns m[x]
normalize
Normalizing is a way to
scale a feature's value range. This type of operation can only be applied
to numeric columns. There are two kinds of supported normalization
operations:
-
MinMax(): Linearly scales everything down to fit between a min and max value.
-
ZScore(): Z-Score normalization is defined aswhere x is the original value,is the mean, andis the standard deviation.
binning
Binning allows for numeric
values to be turned into categories or binned numerical ones. For example,
rather than an age field being a number, it can be turned into the values:
"0-18", "18-40", "40+". There are currently three
ways to define binning:
-
Quantiles(arr): Combines values into the given quantiles
-
Ranges(arr): Combines values in the given ranges
fill_missing
Values values can be filled
with one of the following values:
-
Zero(): The zero value of the data type
-
Median(): The median value, numeric only
-
Mean(): The mean value, numeric only
-
FillValue(value): A arbitrary value
truncate
Outliers can be truncated
so that they fall into a specific range.
-
ZScoreTrunc(devs): Number of standard deviations away from the mean to truncate
-
QuantileTrunc(bottom, top): The bottom and top quantiles to truncate at.
-
ValueTrunc(bottom, top): Values are truncated to fall within bottom and top.
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