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Model Training with Aerospike Feature Store

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This notebook is the second in the series of notebooks that show how Aerospike can be used as a feature store.

This notebook requires the Aerospike Database and Spark running locally with the Aerospike Spark Connector. To create a Docker container that satisfies the requirements and holds a copy of Aerospike notebooks, visit the Aerospike Notebooks Repo.

Introduction

This notebook shows how Aerospike can be used as a Feature Store for Machine Learning applications on Spark using Aerospike Spark Connector. It is Part 2 of the Feature Store series of notebooks, and focuses on Model Training aspects concerning a Feature Store. The first notebook in the series discusses Feature Engineering, and the next one describes Model Serving.

Reference Architecture

This notebook is organized as follows:

  • Summary of the prior (Data Engineering) notebook
  • Exploring features and datasets
  • Defining and saving a dataset
  • Training and saving an AI/ML model

Prerequisites

This tutorial assumes familiarity with the following topics:

Setup

Set up Aerospike Server. Spark Server, and Spark Connector.

Ensure Database Is Running

This notebook requires that Aerospike database is running.

!asd >& /dev/null
!pgrep -x asd >/dev/null && echo "Aerospike database is running!" || echo "**Aerospike database is not running!**"
Output
Aerospike database is running!

Initialize Spark

We will be using Spark functionality in this notebook.

Initialize Paths and Env Variables 

# directory where spark notebook requisites are installed
#SPARK_NB_DIR = '/home/jovyan/notebooks/spark'
SPARK_NB_DIR = '/opt/spark-nb'
SPARK_HOME = SPARK_NB_DIR + '/spark-3.0.3-bin-hadoop3.2'
# IP Address or DNS name for one host in your Aerospike cluster
AS_HOST ="localhost"
# Name of one of your namespaces. Type 'show namespaces' at the aql prompt if you are not sure
AS_NAMESPACE = "test"
AEROSPIKE_SPARK_JAR_VERSION="3.2.0"
AS_PORT = 3000 # Usually 3000, but change here if not
AS_CONNECTION_STRING = AS_HOST + ":"+ str(AS_PORT)
# Next we locate the Spark installation - this will be found using the SPARK_HOME environment variable that you will have set
import findspark
findspark.init(SPARK_HOME)
# Aerospike Spark Connector related settings
import os
AEROSPIKE_JAR_PATH= "aerospike-spark-assembly-"+AEROSPIKE_SPARK_JAR_VERSION+".jar"
os.environ["PYSPARK_SUBMIT_ARGS"] = '--jars ' + SPARK_NB_DIR + '/' + AEROSPIKE_JAR_PATH + ' pyspark-shell'

Configure Spark Session

Please visit Configuring Aerospike Connect for Spark for more information about the properties used on this page.

# imports
import pyspark
from pyspark.context import SparkContext
from pyspark.sql.context import SQLContext
from pyspark.sql.session import SparkSession
from pyspark.sql.types import StringType, StructField, StructType, ArrayType, IntegerType, MapType, LongType, DoubleType
sc = SparkContext.getOrCreate()
conf=sc._conf.setAll([("aerospike.namespace",AS_NAMESPACE),("aerospike.seedhost",AS_CONNECTION_STRING)])
sc.stop()
sc = pyspark.SparkContext(conf=conf)
spark = SparkSession(sc)
sqlContext = SQLContext(sc)

Access Shell Commands

You may execute shell commands including Aerospike tools like aql and asadm in the terminal tab throughout this tutorial. Open a terminal tab by selecting File->Open from the notebook menu, and then New->Terminal.

Context from Part 1 (Feature Engineering Notebook)

In the previous notebook in the Feature Store series, we showed how features engineered using the Spark platform can be efficiently stored in Aerospike feature store. We implemented a simple example feature store interface that leverages the Aerospike Spark connector capabilities for this purpose. We implemented a simple object model to save and query features, and illustrated its use with two examples.

You are encouraged to review the Feature Engineering notebook as we will use the same object model, implementation (with some extensions), and data in this notebook.

The code from Part 1 is replicated below as we will be using it later.

Code: Feature Group, Feature, and Entity

Below, we have copied over the code for Feature Group, Feature, and Entity classes for use in the following sections. Please review the object model described in the Feature Engineering notebook.

import copy


# Feature Group
class FeatureGroup:
    schema = StructType([StructField("name", StringType(), False),
                         StructField("description", StringType(), True),
                         StructField("source", StringType(), True),
                         StructField("attrs", MapType(StringType(), StringType()), True),
                         StructField("tags", ArrayType(StringType()), True)])


    def __init__(self, name, description, source, attrs, tags):
        self.name = name
        self.description = description
        self.source = source
        self.attrs = attrs
        self.tags = tags
        return


    def __str__(self):
        return str(self.__class__) + ": " + str(self.__dict__)


    def save(self):
        inputBuf = [(self.name, self.description, self.source, self.attrs, self.tags)]
        inputRDD = spark.sparkContext.parallelize(inputBuf)
        inputDF = spark.createDataFrame(inputRDD, FeatureGroup.schema)
        #Write the data frame to Aerospike, the name field is used as the primary key
        inputDF.write \
            .mode('overwrite') \
            .format("aerospike")  \
            .option("aerospike.writeset", "fg-metadata")\
            .option("aerospike.updateByKey", "name") \
            .save()
        return


    def load(name):
        fg = None
        schema = copy.deepcopy(FeatureGroup.schema)
        schema.add("__key", StringType(), False)
        fgdf = spark.read \
            .format("aerospike") \
            .option("aerospike.set", "fg-metadata") \
            .schema(schema) \
            .load().where("__key = \"" + name + "\"")
        if fgdf.count() > 0:
            fgtuple = fgdf.collect()[0]
            fg = FeatureGroup(*fgtuple[:-1])
        return fg


    def query(predicate): #returns a dataframe
        fg_df = spark.read \
            .format("aerospike") \
            .schema(FeatureGroup.schema) \
            .option("aerospike.set", "fg-metadata") \
            .load().where(predicate)
        return fg_df


# Feature
class Feature:
    schema = StructType([StructField("fid", StringType(), False),
                           StructField("fgname", StringType(), False),
                           StructField("name", StringType(), False),
                           StructField("type", StringType(), False),
                           StructField("description", StringType(), True),
                           StructField("attrs", MapType(StringType(), StringType()), True),
                           StructField("tags", ArrayType(StringType()), True)])


    def __init__(self, fgname, name, ftype, description, attrs, tags):
        self.fid = fgname + '_' + name
        self.fgname = fgname
        self.name = name
        self.ftype = ftype
        self.description = description
        self.attrs = attrs
        self.tags = tags
        return


    def __str__(self):
        return str(self.__class__) + ": " + str(self.__dict__)


    def save(self):
        inputBuf = [(self.fid, self.fgname, self.name, self.ftype, self.description, self.attrs, self.tags)]
        inputRDD = spark.sparkContext.parallelize(inputBuf)
        inputDF = spark.createDataFrame(inputRDD, Feature.schema)
        # Write the data frame to Aerospike, the fid field is used as the primary key
        inputDF.write \
            .mode('overwrite') \
            .format("aerospike")  \
            .option("aerospike.writeset", "feature-metadata")\
            .option("aerospike.updateByKey", "fid") \
            .save()
        return


    def load(fgname, name):
        f = None
        schema = copy.deepcopy(Feature.schema)
        schema.add("__key", StringType(), False)
        f_df = spark.read \
            .format("aerospike") \
            .schema(schema) \
            .option("aerospike.set", "feature-metadata") \
            .load().where("__key = \"" + fgname+'_'+name + "\"")
        if f_df.count() > 0:
            f_tuple = f_df.collect()[0]
            f = Feature(*f_tuple[1:-1])
        return f


    def query(predicate, pushdown_expr=None): #returns a dataframe
        f_df = spark.read \
            .format("aerospike") \
            .schema(Feature.schema) \
            .option("aerospike.set", "feature-metadata")
        # see the section on pushdown expressions
        if pushdown_expr:
            f_df = f_df.option("aerospike.pushdown.expressions", pushdown_expr) \
                .load()
        else:
            f_df = f_df.load().where(predicate)
        return f_df


# Entity
class Entity:


    def __init__(self, etype, record, id_col):
        # record is an array of triples (name, type, value)
        self.etype = etype
        self.record = record
        self.id_col = id_col
        return


    def __str__(self):
        return str(self.__class__) + ": " + str(self.__dict__)


    def get_schema(record):
        schema = StructType()
        for f in record:
            schema.add(f[0], f[1], True)
        return schema


    def get_id_type(schema, id_col):
        return schema[id_col].dataType.typeName()


    def save(self, schema):
        fvalues = [f[2] for f in self.record]
        inputBuf = [tuple(fvalues)]
        inputRDD = spark.sparkContext.parallelize(inputBuf)
        inputDF = spark.createDataFrame(inputRDD, schema)
        #Write the data frame to Aerospike, the id_col field is used as the primary key
        inputDF.write \
            .mode('overwrite') \
            .format("aerospike")  \
            .option("aerospike.writeset", self.etype+'-features')\
            .option("aerospike.updateByKey", self.id_col) \
            .save()
        return


    def load(etype, eid, schema, id_col):
        ent = None
        schema = copy.deepcopy(schema)
        schema.add("__key", StringType(), False)
        ent_df = spark.read \
            .format("aerospike") \
            .schema(schema) \
            .option("aerospike.set", etype+'-features') \
            .load().where("__key = \"" + eid + "\"")
        if ent_df.count() > 0:
            ent_tuple = ent_df.collect()[0]
            record = [(schema[i].name, schema[i].dataType.typeName(), fv) for i, fv in enumerate(ent_tuple[:-1])]
            ent = Entity(etype, record, id_col)
        return ent


    def saveDF(df, etype, id_col): # save a dataframe
    # df: dataframe consisting of entiry records
    # etype: entity type (such as user or sensor)
    # id_col: column name that holds the primary key
        #Write the data frame to Aerospike, the column in id_col is used as the key bin
        df.write \
            .mode('overwrite') \
            .format("aerospike")  \
            .option("aerospike.writeset", etype+'-features')\
            .option("aerospike.updateByKey", id_col) \
            .save()
        return




    def query(etype, predicate, schema, id_col): #returns a dataframe
        ent_df = spark.read \
            .format("aerospike") \
            .schema(schema) \
            .option("aerospike.set", etype+'-features') \
            .load().where(predicate)
        return ent_df


    def get_feature_vector(etype, eid, feature_list): # elements in feature_list are in "fgname_name" form
        # deferred to Model Serving tutorial
        pass
# clear the database by truncating the namespace test
!aql -c "truncate test"
Output
truncate test
OK

Create set indexes on all sets.

!asinfo -v "set-config:context=namespace;id=test;set=fg-metadata;enable-index=true"
!asinfo -v "set-config:context=namespace;id=test;set=feature-metadata;enable-index=true"
!asinfo -v "set-config:context=namespace;id=test;set=dataset-metadata;enable-index=true"
#!asinfo -v "set-config:context=namespace;id=test;set=cctxn-features;enable-index=true"
Output
ok
ok
ok
# test feature group
# test save and load
# save
fg1 = FeatureGroup("fg_name1", "fg_desc1", "fg_source1", {"etype":"etype1", "key":"feature1"}, ["tag1", "tag2"])
fg1.save()
# load
fg2 = FeatureGroup.load("fg_name1")
print("Feature group with name fg_name1:")
print(fg2, '\n')
# test query
fg2 = FeatureGroup("fg_name2", "fg_desc2", "fg_source2", {"etype":"etype1", "key":"fname1"}, ["tag1", "tag3"])
fg2.save()
fg3 = FeatureGroup("fg_name3", "fg_desc3", "fg_source3", {"etype":"etype2", "key":"fname3"}, ["tag4", "tag5"])
fg3.save()
# query 1
print("Feature groups with a description containing 'desc':")
fg_df = FeatureGroup.query("description like '%desc%'")
fg_df.show()
# query 2
print("Feature groups with the source 'fg_source2':")
fg_df = FeatureGroup.query("source = 'fg_source2'")
fg_df.show()
# query 3
print("Feature groups with the attribute 'etype'='etype2':")
fg_df = FeatureGroup.query("attrs.etype = 'etype2'")
fg_df.show()
# query 4
print("Feature groups with a tag 'tag1':")
fg_df = FeatureGroup.query("array_contains(tags, 'tag1')")
fg_df.show()
Output
Feature group with name fg_name1:
<class '__main__.FeatureGroup'>: {'name': 'fg_name1', 'description': 'fg_desc1', 'source': 'fg_source1', 'attrs': {'etype': 'etype1', 'key': 'feature1'}, 'tags': ['tag1', 'tag2']}


Feature groups with a description containing 'desc':
+--------+-----------+----------+--------------------+------------+
|    name|description|    source|               attrs|        tags|
+--------+-----------+----------+--------------------+------------+
|fg_name2|   fg_desc2|fg_source2|[etype -> etype1,...|[tag1, tag3]|
|fg_name3|   fg_desc3|fg_source3|[etype -> etype2,...|[tag4, tag5]|
|fg_name1|   fg_desc1|fg_source1|[etype -> etype1,...|[tag1, tag2]|
+--------+-----------+----------+--------------------+------------+


Feature groups with the source 'fg_source2':
+--------+-----------+----------+--------------------+------------+
|    name|description|    source|               attrs|        tags|
+--------+-----------+----------+--------------------+------------+
|fg_name2|   fg_desc2|fg_source2|[etype -> etype1,...|[tag1, tag3]|
+--------+-----------+----------+--------------------+------------+


Feature groups with the attribute 'etype'='etype2':
+--------+-----------+----------+--------------------+------------+
|    name|description|    source|               attrs|        tags|
+--------+-----------+----------+--------------------+------------+
|fg_name3|   fg_desc3|fg_source3|[etype -> etype2,...|[tag4, tag5]|
+--------+-----------+----------+--------------------+------------+


Feature groups with a tag 'tag1':
+--------+-----------+----------+--------------------+------------+
|    name|description|    source|               attrs|        tags|
+--------+-----------+----------+--------------------+------------+
|fg_name2|   fg_desc2|fg_source2|[etype -> etype1,...|[tag1, tag3]|
|fg_name1|   fg_desc1|fg_source1|[etype -> etype1,...|[tag1, tag2]|
+--------+-----------+----------+--------------------+------------+
# test feature
# test save and load
# save
feature1 = Feature("fgname1", "f_name1", "integer", "f_desc1", {"etype":"etype1", "f_attr1":"v1"},
                   ["f_tag1", "f_tag2"])
feature1.save()
# load
f1 = Feature.load("fgname1", "f_name1")
print("Feature with group 'fgname1' and name 'f_name1:")
print(f1, '\n')
# test query
feature2 = Feature("fgname1", "f_name2", "double", "f_desc2", {"etype":"etype1", "f_attr1":"v2"},
                   ["f_tag1", "f_tag3"])
feature2.save()
feature3 = Feature("fgname2", "f_name3", "double", "f_desc3", {"etype":"etype2", "f_attr2":"v3"},
                   ["f_tag2", "f_tag4"])
feature3.save()
# query 1
print("Features in feature group 'fg_name1':")
f_df = Feature.query("fgname = 'fgname1'")
f_df.show()
# query 2
print("Features of type 'integer':")
f_df = Feature.query("type = 'integer'")
f_df.show()
# query 3
print("Features with the attribute 'etype'='etype1':")
f_df = Feature.query("attrs.etype = 'etype1'")
f_df.show()
# query 3
print("Features with the tag 'f_tag2':")
f_df = Feature.query("array_contains(tags, 'f_tag2')")
f_df.show()
Output
Feature with group 'fgname1' and name 'f_name1:
<class '__main__.Feature'>: {'fid': 'fgname1_f_name1', 'fgname': 'fgname1', 'name': 'f_name1', 'ftype': 'integer', 'description': 'f_desc1', 'attrs': {'etype': 'etype1', 'f_attr1': 'v1'}, 'tags': ['f_tag1', 'f_tag2']}


Features in feature group 'fg_name1':
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|            fid| fgname|   name|   type|description|               attrs|            tags|
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|fgname1_f_name1|fgname1|f_name1|integer|    f_desc1|[etype -> etype1,...|[f_tag1, f_tag2]|
|fgname1_f_name2|fgname1|f_name2| double|    f_desc2|[etype -> etype1,...|[f_tag1, f_tag3]|
+---------------+-------+-------+-------+-----------+--------------------+----------------+


Features of type 'integer':
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|            fid| fgname|   name|   type|description|               attrs|            tags|
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|fgname1_f_name1|fgname1|f_name1|integer|    f_desc1|[etype -> etype1,...|[f_tag1, f_tag2]|
+---------------+-------+-------+-------+-----------+--------------------+----------------+


Features with the attribute 'etype'='etype1':
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|            fid| fgname|   name|   type|description|               attrs|            tags|
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|fgname1_f_name1|fgname1|f_name1|integer|    f_desc1|[etype -> etype1,...|[f_tag1, f_tag2]|
|fgname1_f_name2|fgname1|f_name2| double|    f_desc2|[etype -> etype1,...|[f_tag1, f_tag3]|
+---------------+-------+-------+-------+-----------+--------------------+----------------+


Features with the tag 'f_tag2':
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|            fid| fgname|   name|   type|description|               attrs|            tags|
+---------------+-------+-------+-------+-----------+--------------------+----------------+
|fgname1_f_name1|fgname1|f_name1|integer|    f_desc1|[etype -> etype1,...|[f_tag1, f_tag2]|
|fgname2_f_name3|fgname2|f_name3| double|    f_desc3|[etype -> etype2,...|[f_tag2, f_tag4]|
+---------------+-------+-------+-------+-----------+--------------------+----------------+
# test Entity
# test save and load
# save
features1 = [('fg1_f_name1', IntegerType(), 1), ('fg1_f_name2', DoubleType(), 2.0), ('fg1_f_name3', StringType(), 'three')]
record1 = [('eid', StringType(), 'eid1')] + features1
ent1 = Entity('entity_type1', record1, 'eid')
schema = Entity.get_schema(record1)
ent1.save(schema);
# load
e1 = Entity.load('entity_type1', 'eid1', schema, 'eid')
print("Entity of type 'entity_type1' and id 'eid1':")
print(e1, '\n')
# test query
features2 = [('fg1_f_name1', IntegerType(), 10), ('fg1_f_name2', DoubleType(), 20.0), ('fg1_f_name3', StringType(), 'thirty')]
record2 = [('eid', StringType(), 'eid2')] + features2
ent2 = Entity('entity_type2', record2, 'eid')
ent2.save(schema);
# query 1
print("Instances of entity type entity_type1 with id ending in 1:")
instances = Entity.query('entity_type1', 'eid like "%1"', schema, 'eid')
instances.show()
# query 2
print("Instances of entity type entity_type2 meeting the specified condition:")
instances = Entity.query('entity_type2', 'eid in ("eid2")', schema, 'eid')
instances.show()
Output
Entity of type 'entity_type1' and id 'eid1':
<class '__main__.Entity'>: {'etype': 'entity_type1', 'record': [('eid', 'string', 'eid1'), ('fg1_f_name1', 'integer', 1), ('fg1_f_name2', 'double', 2.0), ('fg1_f_name3', 'string', 'three')], 'id_col': 'eid'}


Instances of entity type entity_type1 with id ending in 1:
+----+-----------+-----------+-----------+
| eid|fg1_f_name1|fg1_f_name2|fg1_f_name3|
+----+-----------+-----------+-----------+
|eid1|          1|        2.0|      three|
+----+-----------+-----------+-----------+


Instances of entity type entity_type2 meeting the specified condition:
+----+-----------+-----------+-----------+
| eid|fg1_f_name1|fg1_f_name2|fg1_f_name3|
+----+-----------+-----------+-----------+
|eid2|         10|       20.0|     thirty|
+----+-----------+-----------+-----------+

Feature Data: Credit Card Transactions

The following cell populates the data from Part 1 in the database for use below.

Read and Transform Data 

# read and transform the sample credit card transactions data from a csv file
from pyspark.sql.functions import expr
df = spark.read.options(header="True", inferSchema="True") \
    .csv("resources/creditcard_small.csv") \
    . orderBy(['_c0'], ascending=[True])
new_col_names = ['CC1_' + (c if c != '_c0' else 'OldIdx') for c in df.columns]
df = df.toDF(*new_col_names) \
        .withColumn('TxnId', expr('CC1_OldIdx+1').cast(StringType())) \
        .select(['TxnId','CC1_Class','CC1_Amount']+['CC1_V'+str(i) for i in range(1,29)])
df.toPandas().head()
Output
TxnIdCC1_ClassCC1_AmountCC1_V1CC1_V2CC1_V3CC1_V4CC1_V5CC1_V6CC1_V7…CC1_V19CC1_V20CC1_V21CC1_V22CC1_V23CC1_V24CC1_V25CC1_V26CC1_V27CC1_V28
010149.62-1.359807-0.0727812.5363471.378155-0.3383210.4623880.239599…0.4039930.251412-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053
1202.691.1918570.2661510.1664800.4481540.060018-0.082361-0.078803…-0.145783-0.069083-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.014724
230378.66-1.358354-1.3401631.7732090.379780-0.5031981.8004990.791461…-2.2618570.5249800.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752
340123.50-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.237609…-1.232622-0.208038-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458
45069.99-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941…0.8034870.408542-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.215153

5 rows × 31 columns

Save Features

Insert the credit card transaction features in the feature store.

# 1. Create a feature group.
FG_NAME = 'CC1'
FG_DESCRIPTION = 'Credit card transaction data'
FG_SOURCE = 'European cardholder dataset from Kaggle'
fg = FeatureGroup(FG_NAME, FG_DESCRIPTION, FG_SOURCE,
                 attrs={'entity':'cctxn', 'class':'fraud'}, tags=['kaggle', 'demo'])
fg.save()


# 2. Create feature metadata
FEATURE_AMOUNT = 'Amount'
f = Feature(FG_NAME, FEATURE_AMOUNT, 'double', "Transaction amount",
           attrs={'entity':'cctxn'}, tags=['usd'])
f.save()
FEATURE_CLASS = 'Class'
f = Feature(FG_NAME, FEATURE_CLASS, 'integer', "Label indicating fraud or not",
           attrs={'entity':'cctxn'}, tags=['label'])
f.save()
FEATURE_PCA_XFORM = "V"
for i in range(1,29):
    f = Feature(FG_NAME, FEATURE_PCA_XFORM+str(i), 'double', "Transformed version of PCA",
               attrs={'entity':'cctxn'}, tags=['pca'])
    f.save()


# 3. Save feature values in entity records
ENTITY_TYPE = 'cctxn'
ID_COLUMN = 'TxnId'
Entity.saveDF(df, ENTITY_TYPE, ID_COLUMN)
print('Features stored to Feature Store.')
Output
Features stored to Feature Store.

Implementing Dataset

We created example implementations of Feature Group, Feature, and Entity objects as above. Let us now create a similar implementation of Dataset.

Object Model

A dataset is a subset of features and entities selected for an ML model. A Dataset object holds the selected features and entity instances. The actual (materialized) copy of entity records is stored outside the feature store (for instance, in a file system).

Attributes

A dataset record has the following attributes.

  • name: name of the data set, serves as the primary key for the record
  • description: human readable description
  • features: a list of the dataset features
  • predicate: query predicate to enumerate the entity instances in the dataset
  • location: external location where the dataset is stored
  • attrs: other metadata
  • tags: associated tags

Datasets are stored in the set “dataset-metadata”.

Operations

Dataset is used during Model Training. The following operations are needed.

  • create
  • load (get)
  • query (returns dataset metadata records)
  • materialize (returns entity records as defined by a dataset)

Dataset Implementation

Below is an example implementation of Dataset as described above.

# Dataset
class Dataset:
    schema = StructType([StructField("name", StringType(), False),
                         StructField("description", StringType(), True),
                         StructField("entity", StringType(), False),
                         StructField("id_col", StringType(), False),
                         StructField("id_type", StringType(), False),
                         StructField("features", ArrayType(StringType()), True),
                         StructField("query", StringType(), True),
                         StructField("location", StringType(), True),
                         StructField("attrs", MapType(StringType(), StringType()), True),
                         StructField("tags", ArrayType(StringType()), True)])


    def __init__(self, name, description, entity, id_col, id_type,
                 features, query, location, attrs, tags):
        self.name = name
        self.description = description
        self.entity = entity
        self.id_col = id_col
        self.id_type = id_type
        self.features = features
        self.query = query
        self.location = location
        self.attrs = attrs
        self.tags = tags
        return


    def __str__(self):
        return str(self.__class__) + ": " + str(self.__dict__)


    def save(self):
        inputBuf = [(self.name, self.description, self.entity, self.id_col, self.id_type,
                     self.features, self.query, self.location, self.attrs, self.tags)]
        inputRDD = spark.sparkContext.parallelize(inputBuf)
        inputDF = spark.createDataFrame(inputRDD, Dataset.schema)
        #Write the data frame to Aerospike, the name field is used as the primary key
        inputDF.write \
            .mode('overwrite') \
            .format("aerospike")  \
            .option("aerospike.writeset", "dataset-metadata")\
            .option("aerospike.updateByKey", "name") \
            .save()
        return


    def load(name):
        dataset = None
        ds_df = spark.read \
            .format("aerospike") \
            .option("aerospike.set", "dataset-metadata") \
            .schema(Dataset.schema) \
            .option("aerospike.updateByKey", "name") \
            .load().where("name = \"" + name + "\"")
        if ds_df.count() > 0:
            dstuple = ds_df.collect()[0]
            dataset = Dataset(*dstuple)
        return dataset


    def query(predicate): #returns a dataframe
        ds_df = spark.read \
            .format("aerospike") \
            .schema(Dataset.schema) \
            .option("aerospike.set", "dataset-metadata") \
            .load().where(predicate)
        return ds_df


    def features_to_schema(entity, id_col, id_type, features):
        def convert_field_type(ftype):
            return DoubleType() if ftype == 'double' \
                                else (IntegerType() if ftype in ['integer','long'] \
                                      else StringType())
        schema = StructType()
        schema.add(id_col, convert_field_type(id_type), False)
        for fid in features:
            sep = fid.find('_')
            f = Feature.load(fid[:sep] if sep != -1 else "", fid[sep+1:])
            if f:
                schema.add(f.fid, convert_field_type(f.ftype), True)
        return schema


    def materialize_to_df(self):
        df = Entity.query(self.entity, self.query,
                            Dataset.features_to_schema(self.entity, self.id_col, self.id_type,
                                                       self.features), self.id_col)
        return df
# test Dataset
# test save and load
# save
features = ["CC1_Amount", "CC1_Class", "CC1_V1"]
ds = Dataset("ds_test1", "Test dataset", "cctxn", "TxnId", "string",
                 features, "CC1_Amount > 1500", "", {"risk":"high"}, ["test", "dataset"])
ds.save()
# load
ds = Dataset.load("ds_test1")
print("Dataset named 'ds_test1':")
print(ds, '\n')
# test query
print("Datasets with attribute 'risk'='high' and tag 'test':")
dsq_df = Dataset.query("attrs.risk == 'high' and array_contains(tags, 'test')")
dsq_df.show()
# test materialize_to_df
print("Materialize dataset ds_test1 as defined above:")
ds_df = ds.materialize_to_df()
print("Records in the dataset: ", ds_df.count())
ds_df.show(5)
Output
Dataset named 'ds_test1':
<class '__main__.Dataset'>: {'name': 'ds_test1', 'description': 'Test dataset', 'entity': 'cctxn', 'id_col': 'TxnId', 'id_type': 'string', 'features': ['CC1_Amount', 'CC1_Class', 'CC1_V1'], 'query': 'CC1_Amount > 1500', 'location': '', 'attrs': {'risk': 'high'}, 'tags': ['test', 'dataset']}


Datasets with attribute 'risk'='high' and tag 'test':
+--------+------------+------+------+-------+--------------------+-----------------+--------+--------------+---------------+
|    name| description|entity|id_col|id_type|            features|            query|location|         attrs|           tags|
+--------+------------+------+------+-------+--------------------+-----------------+--------+--------------+---------------+
|ds_test1|Test dataset| cctxn| TxnId| string|[CC1_Amount, CC1_...|CC1_Amount > 1500|        |[risk -> high]|[test, dataset]|
+--------+------------+------+------+-------+--------------------+-----------------+--------+--------------+---------------+


Materialize dataset ds_test1 as defined above:
Records in the dataset:  4
+------+----------+---------+-----------------+
| TxnId|CC1_Amount|CC1_Class|           CC1_V1|
+------+----------+---------+-----------------+
|  6972|   1809.68|        1|-3.49910753739178|
|   165|   3828.04|        0|-6.09324780457494|
|249168|   1504.93|        1|-1.60021129907252|
|176050|   2125.87|        1|-2.00345953080582|
+------+----------+---------+-----------------+

Using Pushdown Expressions

In order to get best performance from the Aerospike feature store, one important optimization is to “push down” processing to the database and minimize the amount of data retrieved to Spark. This is especially important for querying from large amounts of underlying data, such as when creating a dataset. This is achieved by “pushing down” filters or processing filters in the database.

Currently the Spark Connector allows two mutually exclusive ways of specifying filters in a dataframe load:

  • The where clause
  • The pushdown expressions option

Only one may be specified because the underlying Aerospike database mechanisms used to process them are different and exclusive. The latter takes precedence if both are specified.

The where clause filter may be pushed down in part or fully depending on the parts in the filter (that is, if the database supports them and the Spark Connector takes advantage of it). The pushdown expression filter however is fully processed in the database, which ensures best performance.

Aerospike expressions provide some filtering capabilities that are either not available on Spark (such as record metadata based filtering). Also, expression based filtering will be processed more efficiently in the database. On the other hand, the where clause also has many capabilities that are not available in Aerospike expressions. So it may be necessary to use both, in which case it is best to use pushdown expressions to retrieve a dataframe, and then process it using the Spark dataframe capabilities.

Creating Pushdown Expressions

The Spark Connector currently requires the base64 encoding of the expression. Exporting the base64 encoded expression currently requires the Java client, which can be run in a parallel notebook, and entails the following steps:

  1. Write the expression in Java.

  2. Test the expression with the desired data.

  3. Obtain the base64 encoding.

  4. Use the base64 representation in this notebook as shown below.

You can run the adjunct notebook Pushdown Expressions for Spark Connector to follow the above recipe and obtain the base64 representation of an expression for use in the following examples.

Examples

We illustrate pushdown expressions with Feature class queries, but the query method implementation can be adopted in other objects.

The examples below illustrate the capabilities and process of working with pushdown expressions. More details on expressions are explained in Pushdown Expressions for Spark Connector notebook.

Records with Specific Tags

Examine the expression in Java:

    Exp.gt(
        ListExp.getByValueList(ListReturnType.COUNT,
           Exp.val(new ArrayList<String>(Arrays.asList("label","f_tag1"))),
           Exp.listBin("tags")),
        Exp.val(0))

The outer expression compares for the value returned from the first argument to be greater than 0. The first argument is the count of matching tags from the specified tags in the list bin tags.

Obtain the base64 representation from Pushdown Expressions for Spark Connector notebook. It is “kwOVfwIAkxcFkn6SpgNsYWJlbKcDZl90YWcxk1EEpHRhZ3MA”

base64_expr = "kwOVfwIAkxcFkn6SpgNsYWJlbKcDZl90YWcxk1EEpHRhZ3MA"
f_df = Feature.query(None, pushdown_expr=base64_expr)
f_df.toPandas()
Output
fidfgnamenametypedescriptionattrstags
0fgname1_f_name1fgname1f_name1integerf_desc1’etype’: ‘etype1’, ‘f_attr1’: ‘v1’[f_tag1, f_tag2]
1CC1_ClassCC1ClassintegerLabel indicating fraud or not’entity’: ‘cctxn’[label]
2fgname1_f_name2fgname1f_name2doublef_desc2’etype’: ‘etype1’, ‘f_attr1’: ‘v2’[f_tag1, f_tag3]

Records with Specific Attribute Value

Examine the expression in Java:

Exp.eq(
        MapExp.getByKey(MapReturnType.VALUE,
                        Exp.Type.STRING, Exp.val("f_attr1"), Exp.mapBin("attrs")),
        Exp.val("v1"))

It would filter records having a key “f_attr1” with value “v1” from the map bin attrs.

Obtain the base64 representation from Pushdown Expressions for Spark Connector notebook. It is “kwGVfwMAk2EHqANmX2F0dHIxk1EFpWF0dHJzowN2MQ==“.

base64_expr = "kwGVfwMAk2EHqANmX2F0dHIxk1EFpWF0dHJzowN2MQ=="
f_df = Feature.query(None, pushdown_expr=base64_expr)
f_df.toPandas()
Output
fidfgnamenametypedescriptionattrstags
0fgname1_f_name1fgname1f_name1integerf_desc1’etype’: ‘etype1’, ‘f_attr1’: ‘v1’[f_tag1, f_tag2]

Records with String Matching Pattern

Examine the expression in Java:

Exp.regexCompare("^c.*2$", RegexFlag.ICASE, Exp.stringBin("fid"))

It would filter records with fid starting with “c” and ending in “2” (case insensitive).

Obtain the base64 representation from Pushdown Expressions for Spark Connector notebook. It is “lAcCpl5DLioyJJNRA6NmaWQ=“.

base64_expr = "lAcCpl5DLioyJJNRA6NmaWQ="
f_df = Feature.query(None, pushdown_expr=base64_expr)
f_df.toPandas()
Output
fidfgnamenametypedescriptionattrstags
0CC1_V2CC1V2doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
1CC1_V12CC1V12doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
2CC1_V22CC1V22doubleTransformed version of PCA’entity’: ‘cctxn’[pca]

Exploring Features in Feature Store

Now let’s explore the features available in the Feature Store prior to using them to train a model. We will illustrate this with the querying functions on the metadata objects we have implemented above, as well as Spark functions.

Exploring Datasets

As we are interested in building a fraud detection model, let’s see if there are any existing datasets that have “fraud’ in their description. At present there should be no datasets in the database until we create and save one in later sections.

ds_df = Dataset.query("description like '%fraud%'")
ds_df.show()
Output
+----+-----------+------+------+-------+--------+-----+--------+-----+----+
|name|description|entity|id_col|id_type|features|query|location|attrs|tags|
+----+-----------+------+------+-------+--------+-----+--------+-----+----+
+----+-----------+------+------+-------+--------+-----+--------+-----+----+

Exploring Feature Groups

Let’s identify feature groups for the entity type “cctxn” (credit card transactions) that have an attribute “class”=“fraud”

fg_df = FeatureGroup.query("attrs.entity == 'cctxn' and attrs.class == 'fraud'")
fg_df.toPandas().transpose().head()
Output
0
nameCC1
descriptionCredit card transaction data
sourceEuropean cardholder dataset from Kaggle
attrs’class’: ‘fraud’, ‘entity’: ‘cctxn’
tags[kaggle, demo]
# View all available features in this feature group
f_df = Feature.query("fgname == 'CC1'")
f_df.toPandas()
Output
fidfgnamenametypedescriptionattrstags
0CC1_V23CC1V23doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
1CC1_V10CC1V10doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
2CC1_ClassCC1ClassintegerLabel indicating fraud or not’entity’: ‘cctxn’[label]
3CC1_V20CC1V20doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
4CC1_V16CC1V16doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
5CC1_V1CC1V1doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
6CC1_V6CC1V6doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
7CC1_V25CC1V25doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
8CC1_V9CC1V9doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
9CC1_V2CC1V2doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
10CC1_V3CC1V3doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
11CC1_V12CC1V12doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
12CC1_V21CC1V21doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
13CC1_V27CC1V27doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
14CC1_AmountCC1AmountdoubleTransaction amount’entity’: ‘cctxn’[usd]
15CC1_V24CC1V24doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
16CC1_V7CC1V7doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
17CC1_V28CC1V28doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
18CC1_V4CC1V4doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
19CC1_V13CC1V13doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
20CC1_V17CC1V17doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
21CC1_V18CC1V18doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
22CC1_V26CC1V26doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
23CC1_V19CC1V19doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
24CC1_V14CC1V14doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
25CC1_V11CC1V11doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
26CC1_V8CC1V8doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
27CC1_V5CC1V5doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
28CC1_V22CC1V22doubleTransformed version of PCA’entity’: ‘cctxn’[pca]
29CC1_V15CC1V15doubleTransformed version of PCA’entity’: ‘cctxn’[pca]

The features look promising for a fraud prediction model. Let’s look at the actual feature data and its characteristics by querying the entity records.

Exploring Feature Data

We can further explore the feature data to determine what features should be part of the dataset. The feature data resides in Entity records and we can use the above info to form the schema and retrieve the records.

Defining Schema

In order to query using the Aerospike Spark Connector, we must define the schema for the record.

# define the schema for the record.
FG_NAME = 'CC1'
ENTITY_TYPE = 'cctxn'
ID_COLUMN = 'TxnId'
FEATURE_AMOUNT = 'Amount'
FEATURE_CLASS = 'Class'
FEATURE_PCA_XFORM = "V"


schema = StructType([StructField(ID_COLUMN, StringType(), False),
                    StructField(FG_NAME+'_'+FEATURE_CLASS, IntegerType(), False),
                    StructField(FG_NAME+'_'+FEATURE_AMOUNT, DoubleType(), False)])
for i in range(1,29):
    schema.add(FG_NAME+'_'+FEATURE_PCA_XFORM+str(i), DoubleType(), True)

Retrieving Data

Here we get all records from the sample data in the database. A small subset of the data would suffice in practice.

# let's get the entity records to assess the data
txn_df = Entity.query(ENTITY_TYPE, "TxnId like '%'", schema, "TxnId")
print("Records retrieved: ", txn_df.count())
txn_df.printSchema()
Output
Records retrieved:  984
root
|-- TxnId: string (nullable = false)
|-- CC1_Class: integer (nullable = false)
|-- CC1_Amount: double (nullable = false)
|-- CC1_V1: double (nullable = true)
|-- CC1_V2: double (nullable = true)
|-- CC1_V3: double (nullable = true)
|-- CC1_V4: double (nullable = true)
|-- CC1_V5: double (nullable = true)
|-- CC1_V6: double (nullable = true)
|-- CC1_V7: double (nullable = true)
|-- CC1_V8: double (nullable = true)
|-- CC1_V9: double (nullable = true)
|-- CC1_V10: double (nullable = true)
|-- CC1_V11: double (nullable = true)
|-- CC1_V12: double (nullable = true)
|-- CC1_V13: double (nullable = true)
|-- CC1_V14: double (nullable = true)
|-- CC1_V15: double (nullable = true)
|-- CC1_V16: double (nullable = true)
|-- CC1_V17: double (nullable = true)
|-- CC1_V18: double (nullable = true)
|-- CC1_V19: double (nullable = true)
|-- CC1_V20: double (nullable = true)
|-- CC1_V21: double (nullable = true)
|-- CC1_V22: double (nullable = true)
|-- CC1_V23: double (nullable = true)
|-- CC1_V24: double (nullable = true)
|-- CC1_V25: double (nullable = true)
|-- CC1_V26: double (nullable = true)
|-- CC1_V27: double (nullable = true)
|-- CC1_V28: double (nullable = true)

Examining Data

We will examine the statistical properties as well as null values of the feature columns. Note, the column CC1_Class is the label (fraud or not).

# examine the statistical properties
txn_df.describe().toPandas().transpose()
Output
01234
summarycountmeanstddevminmax
TxnId98459771.27947154471683735.17714512876199507
CC1_Class9840.50.500254258851927201
CC1_Amount98496.22459349593494240.142397070658260.03828.04
CC1_V1984-2.46740303721007155.40712231422648-30.5523800435812.13238602134104
CC1_V29841.90530359682313443.5961094277406076-12.114212736348322.0577289904909
CC1_V3984-3.0838842028294336.435904925385388-31.10368482458123.77285685226266
CC1_V49842.4567800577405283.0427216170397466-4.5158243548810512.1146718424589
CC1_V5984-1.56172593733253724.202691637741722-22.10553152431611.0950886001596
CC1_V6984-0.5725839910410221.8036571668000605-6.406266634459646.47411462748849
CC1_V7984-2.730903338343175.863241960076915-43.55724157124515.80253735302589
CC1_V89840.261081851388064334.850081053008372-41.044260921074120.0072083651213
CC1_V9984-1.3011447964529372.266780102671618-13.43406631823015.43663339611854
CC1_V10984-2.8051943763989514.549492504413138-24.58826243724758.73745780611353
CC1_V119841.95253510173054552.7369799649027207-2.3320113716795212.0189131816199
CC1_V12984-2.9953168746005954.657383279424634-18.68371463334432.15205511590243
CC1_V13984-0.090291428363571461.0102129366924129-3.127795011987712.81543981456255
CC1_V14984-3.5972266055112134.5682405087763325-19.21432549026143.44242199594215
CC1_V159840.062751390573821631.0021871899317296-4.498944676766212.47135790380837
CC1_V16984-2.15712481980915973.42439305003353-14.12985451749313.13965565883069
CC1_V17984-3.366095353359535.953540928078054-25.16279936932486.73938438478335
CC1_V18984-1.21870627316584312.3587681071910915-9.498745921046773.79031621184375
CC1_V199840.33594457915090331.2843379816775733-3.681903552265045.2283417900513
CC1_V209840.211179398728971981.0613528102262861-4.1281858287179811.0590042933942
CC1_V219840.35489827579192872.78726704784996-22.797603905551927.2028391573154
CC1_V22984-0.044481492114057751.1450798238059015-8.887017140948718.36198519168435
CC1_V23984-0.0365289425895097341.148960101817997-19.25432761737195.46622995370963
CC1_V24984-0.047380430113435290.5866834793500019-2.028024229218961.21527882183022
CC1_V259840.087570545532178810.6404192414977025-4.781605522064072.20820917836653
CC1_V269840.0261204601057549340.4682991121957343-1.243924153712643.06557569653728
CC1_V279840.096181656500186661.0037324673667467-7.263482146338553.05235768679424
CC1_V289840.027865303758426340.4429545316584082-2.733887118975751.77936385243205
# check for null values
from pyspark.sql.functions import count, when, isnan
txn_df.select([count(when(isnan(c), c)).alias(c) for c in txn_df.columns]).toPandas().head()
Output
TxnIdCC1_ClassCC1_AmountCC1_V1CC1_V2CC1_V3CC1_V4CC1_V5CC1_V6CC1_V7…CC1_V19CC1_V20CC1_V21CC1_V22CC1_V23CC1_V24CC1_V25CC1_V26CC1_V27CC1_V28
00000000000…0000000000

1 rows × 31 columns

Defining Dataset

Based on the above exploration, we will choose features V1-V28 for our training dataset, which we will define below.

In addition to the features, we also need to choose the data records for the dataset. We only have a small data from the original dataset, and therefore we will use all the available records by setting the dataset query predicate to “true”.

It is possible to create a random dataset of random records by performing an “aerolookup” of randomly selected key values.

# Create a dataset with the V1-V28 features.
CC_FRAUD_DATASET = "CC_FRAUD_DETECTION"
features = ["CC1_V"+str(i) for i in range(1,29)]
features_and_label = ["CC1_Class"] + features
ds = Dataset(CC_FRAUD_DATASET, "Training dataset for fraud detection model", "cctxn", "TxnId", "string",
                 features_and_label, "true", "", {"class":"fraud"}, ["test", "2017"])
ds_df = ds.materialize_to_df()
print("Records in the dataset: ", ds_df.count())
Output
Records in the dataset:  984

Save Dataset

Save the dataset in Feature Store for future use.

# save the materialized dataset externally in a file
DATASET_PATH = 'resources/fs_part2_dataset_cctxn.csv'
ds_df.write.csv(path=DATASET_PATH, header="true", mode="overwrite", sep="\t")


# save the dataset metadata in the feature store
ds.location = DATASET_PATH
ds.save()

Query and Verify Dataset

Verify the saved dataset is in the feature store for future exploration and use.

dsq_df = Dataset.query("description like '%fraud%'")
dsq_df.toPandas().transpose()
Output
0
nameCC_FRAUD_DETECTION
descriptionTraining dataset for fraud detection model
entitycctxn
id_colTxnId
id_typestring
features[CC1_Class, CC1_V1, CC1_V2, CC1_V3, CC1_V4, CC…
querytrue
locationresources/fs_part2_dataset_cctxn.csv
attrs’class’: ‘fraud’
tags[test, 2017]

Verify the database through an AQL query on the set “dataset-metadata”.

!aql -c "select * from test.dataset-metadata"
Output
select * from test.dataset-metadata
+--------------------------------------+----------------------------------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+----------+----------------------------------------+----------------------+---------------------+-----------------------------+
| attrs                                | description                                  | entity  | features                                                                                                                                                                                                                                                       | id_col  | id_type  | location                               | name                 | query               | tags                        |
+--------------------------------------+----------------------------------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+----------+----------------------------------------+----------------------+---------------------+-----------------------------+
| KEY_ORDERED_MAP('{"class":"fraud"}') | "Training dataset for fraud detection model" | "cctxn" | LIST('["CC1_Class", "CC1_V1", "CC1_V2", "CC1_V3", "CC1_V4", "CC1_V5", "CC1_V6", "CC1_V7", "CC1_V8", "CC1_V9", "CC1_V10", "CC1_V11", "CC1_V12", "CC1_V13", "CC1_V14", "CC1_V15", "CC1_V16", "CC1_V17", "CC1_V18", "CC1_V19", "CC1_V20", "CC1_V21", "CC1_V22", " | "TxnId" | "string" | "resources/fs_part2_dataset_cctxn.csv" | "CC_FRAUD_DETECTION" | "true"              | LIST('["test", "2017"]')    |
| KEY_ORDERED_MAP('{"risk":"high"}')   | "Test dataset"                               | "cctxn" | LIST('["CC1_Amount", "CC1_Class", "CC1_V1"]')                                                                                                                                                                                                                  | "TxnId" | "string" | ""                                     | "ds_test1"           | "CC1_Amount > 1500" | LIST('["test", "dataset"]') |
+--------------------------------------+----------------------------------------------+---------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+----------+----------------------------------------+----------------------+---------------------+-----------------------------+
2 rows in set (0.212 secs)


OK

Create AI/ML Model

Below we will choose two algorithms to predict fraud in a credit card transaction: LogisticRegression and RandomForestClassifier.

Create Training and Test Sets

We first split the dataset into training and test sets to train and evaluate a model.

from pyspark.ml.feature import VectorAssembler


# create a feature vector from features
assembler = VectorAssembler(inputCols=features, outputCol="fvector")
ds_df2 = assembler.transform(ds_df)


# split the dataset into randomly selected training and test sets
train, test = ds_df2.randomSplit([0.8,0.2], seed=2021)
print('Training dataset records:', train.count())
print('Test dataset records:', test.count())
Output
Training dataset records: 791
Test dataset records: 193
# examine the fraud cases in the training set
train.groupby('CC1_Class').count().show()
Output
+---------+-----+
|CC1_Class|count|
+---------+-----+
|        1|  380|
|        0|  411|
+---------+-----+

Train Model

We choose two models to train: LogisticRegression and RandomForestClassifier.

from pyspark.ml.classification import LogisticRegression, RandomForestClassifier
lr_algo = LogisticRegression(featuresCol='fvector', labelCol='CC1_Class', maxIter=5)
lr_model = lr_algo.fit(train)


rf_algo = RandomForestClassifier(featuresCol='fvector', labelCol='CC1_Class')
rf_model = rf_algo.fit(train)

Evaluate Model

Run the trained models on the test set and evaluate their performance metrics.

from pyspark.mllib.evaluation import BinaryClassificationMetrics
from pyspark.ml.evaluation import BinaryClassificationEvaluator


# rename label column
test = test.withColumnRenamed('CC1_Class', 'label')


# use the logistic regression model to predict test cases
lr_predictions = lr_model.transform(test)


# instantiate evaluator
evaluator = BinaryClassificationEvaluator()


# Logistic Regression performance metrics
print("Logistic Regression: Accuracy = {}".format(evaluator.evaluate(lr_predictions)))


lr_labels_and_predictions = test.rdd.map(lambda x: float(x.label)).zip(lr_predictions.rdd.map(lambda x: x.prediction))
lr_metrics = BinaryClassificationMetrics(lr_labels_and_predictions)
print("Logistic Regression: Area under ROC = %s" % lr_metrics.areaUnderROC)
print("Logistic Regression: Area under PR = %s" % lr_metrics.areaUnderPR)
Output
Logistic Regression: Accuracy = 0.9853395061728388
Logistic Regression: Area under ROC = 0.9298321136461472
Logistic Regression: Area under PR = 0.8910277315666429
# use the random forest model to predict test cases
rf_predictions = rf_model.transform(test)


# RandonForestClassifer performance metrics
print("Random Forest Classifier: Accuracy = {}".format(evaluator.evaluate(rf_predictions)))


rf_labels_and_predictions = test.rdd.map(lambda x: float(x.label)).zip(rf_predictions.rdd.map(lambda x: x.prediction))
rf_metrics = BinaryClassificationMetrics(rf_labels_and_predictions)
print("Random Forest Classifier: Area under ROC = %s" % rf_metrics.areaUnderROC)
print("Random Forest Classifier: Area under PR = %s" % rf_metrics.areaUnderPR)
Output
Random Forest Classifier: Accuracy = 0.9895282186948847
Random Forest Classifier: Area under ROC = 0.9251075268817205
Random Forest Classifier: Area under PR = 0.882099602146558

Save Model

Save the model.

# Save each model
lr_model.write().overwrite().save("resources/fs_model_lr")
rf_model.write().overwrite().save("resources/fs_model_rf")

Load and Test Model

Load the saved model and test it by predicting a test instance.

from pyspark.ml.classification import LogisticRegressionModel, RandomForestClassificationModel


lr_model2 = LogisticRegressionModel.load("resources/fs_model_lr")
print("Logistic Regression model save/load test:")
lr_predictions2 = lr_model2.transform(test.limit(5))
lr_predictions2['label', 'prediction'].show()


print("Random Forest model save/load test:")
rf_model2 = RandomForestClassificationModel.read().load("resources/fs_model_rf")
rf_predictions2 = rf_model2.transform(test.limit(5))
rf_predictions2['label', 'prediction'].show()
Output
Logistic Regression model save/load test:
+-----+----------+
|label|prediction|
+-----+----------+
|    1|       1.0|
|    0|       0.0|
|    0|       0.0|
|    0|       0.0|
|    0|       0.0|
+-----+----------+


Random Forest model save/load test:
+-----+----------+
|label|prediction|
+-----+----------+
|    1|       1.0|
|    0|       0.0|
|    0|       0.0|
|    0|       0.0|
|    0|       0.0|
+-----+----------+

Takeaways and Conclusion

In this notebook, we explored how Aerospike can be used as a Feature Store for ML applications. Specifically, we showed how features and datasets stored in the Aerospike can be explored and reused for model training. We implemented a simple example feature store interface that leverages the Aerospike Spark Connector capabilities for this purpose. We used the APIs to create, save, and query features and datasets for model training.

This is the second notebook in the series of notebooks on how Aerospike can be used as a feature store. The first notebook discusses Feature Engineering aspects, whereas the third notebook explores the use of Aerospike Feature Store for Model Serving.

Cleaning Up

Close the spark session, and remove the tutorial data.

try:
    spark.stop()
except:
    ;  ignore
# To remove all data in the namespace test, uncomment the following line and run:
#!aql -c "truncate test"

Further Exploration and Resources

Here are some links for further exploration.

Resources

Exploring Other Notebooks

Visit Aerospike notebooks repo to run additional Aerospike notebooks. To run a different notebook, download the notebook from the repo to your local machine, and then click on File->Open in the notebook menu, and select Upload.

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