Commit 4eb5c9fa authored by Steven Pisarski's avatar Steven Pisarski

Added test to exercise the fact predictions for CM data.

parent 57bee902
package com.cablelabs.eventgen.algorithm
import java.io.{File, FileInputStream}
import java.text.SimpleDateFormat
import com.cablelabs.eventgen.model.InputDefinition
/**
* Tests the configured prediction models against a known set of 10k events of CM data
*/
class CmFactPredictionsTest extends FactPredictionsTester {
val dateFormat = "MM-dd-yyyy HH:mm:ss a"
val dateFormatter = new SimpleDateFormat(dateFormat)
val inputDef = InputDefinition.inputDefinition(
new FileInputStream(new File("testData/cm/definition/cm-fact-pred-input.yaml")))
val eventUri = new File("testData/cm/events/cm_1a.txt").toURI.toString
val delim = '|'
// TODO - Determine why this fact is not predicting well at all
ignoreFacts = Set("downstream_receive_power_num")
}
package com.cablelabs.eventgen.algorithm
import com.cablelabs.eventgen.AnalyzerTester
import com.cablelabs.eventgen.model.{LinearRegressionDefinition, NaiveBayesDefinition}
/**
* Superclass designed to run tests against prediction models against a known set of data
*/
abstract class FactPredictionsTester extends AnalyzerTester {
// Added this hack just in case there is a problematic fact that just cannot be properly predicted with a
// minimal training set
var ignoreFacts = Set[String]()
analyzerTest("Analyze fact preditions to ensure the average prediction is within 95% of the average label") {
inputDef.positionalFacts.filter(f => !ignoreFacts.contains(f.name))foreach(fact => {
val trainingSet = analyzer.factTrainingSet(fact.name)
assert(trainingSet != null && trainingSet.count() == 1000)
val model = fact.algoDef match {
case lrDef: LinearRegressionDefinition =>
new LinearRegressionModel(fact, analyzer.factTrainingSet(fact.name),
inputDef.algoWeights(fact), lrDef.iterations, lrDef.stepSize)
case nbDef: NaiveBayesDefinition =>
new NaiveBayesModel(fact, analyzer.factTrainingSet(fact.name), nbDef.lambda)
}
println(s"Validating model for fact with name - ${fact.name}")
ModelValidator.validateModel(model, trainingSet.collect(), analyzer, 0.95, outputValues = false)
})
}
}
......@@ -3,13 +3,12 @@ package com.cablelabs.eventgen.algorithm
import java.io.{File, FileInputStream}
import java.text.SimpleDateFormat
import com.cablelabs.eventgen.AnalyzerTester
import com.cablelabs.eventgen.model.{InputDefinition, LinearRegressionDefinition, NaiveBayesDefinition}
import com.cablelabs.eventgen.model.InputDefinition
/**
* Tests the configured models against a known set of 10k events of IVR data
* Tests the configured prediction models against a known set of 10k events of IVR data
*/
class IvrFactPredictionsTest extends AnalyzerTester {
class IvrFactPredictionsTest extends FactPredictionsTester {
val dateFormat = "MM-dd-yyyy HH:mm:ss a"
val dateFormatter = new SimpleDateFormat(dateFormat)
......@@ -18,22 +17,4 @@ class IvrFactPredictionsTest extends AnalyzerTester {
val eventUri = new File("testData/ivr/events").toURI.toString
val delim = ','
analyzerTest("Analyze fact preditions to ensure the average prediction is within 95% of the average label") {
inputDef.positionalFacts.foreach(fact => {
val trainingSet = analyzer.factTrainingSet(fact.name)
assert(trainingSet != null && trainingSet.count() == 1000)
val model = fact.algoDef match {
case lrDef: LinearRegressionDefinition =>
new LinearRegressionModel(fact, analyzer.factTrainingSet(fact.name),
inputDef.algoWeights(fact), lrDef.iterations, lrDef.stepSize)
case nbDef: NaiveBayesDefinition =>
new NaiveBayesModel(fact, analyzer.factTrainingSet(fact.name), nbDef.lambda)
}
println(s"Validating model for fact with name - ${fact.name}")
ModelValidator.validateModel(model, trainingSet.collect(), analyzer, 0.95, outputValues = false)
})
}
}
temporal:
name: poll_date
description: Date of CM poll
type: date
dateFormat: MM-dd-yyyy-HH:mm:ss
factPosition: -1
denormFields:
- day_of_week
- day_of_month
- day_of_year
- hour_of_day
- month_of_year
- year
algo:
name: constant
constType: integer
constVal: 28800000
dimensions:
- name: cmts
description: The CMTS name
type: string
position: 10
- name: node
description: The Node name
type: string
position: 20
- name: mac
description: The MAC address
type: string
position: 30
- name: lat
description: The geo latitude
type: float
position: 40
- name: lng
description: The geo longitude
type: float
position: 50
facts:
- name: downstream_receive_power_num
description: fact 1-out
type: float
# position: 1
# position: 100
position: 140
algo:
name: linearRegression
flatten:
mode: log
base: 2
iterations: 3
polyDegree: 3
iterations: 75
stepSize: 0.002
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 50
- name: month_of_year
weight: 25
- name: year
weight: 10
- name: cmts
weight: 150
- name: node
weight: 200
- name: mac
weight: 250
- name: lat
weight: 250
- name: lng
weight: 250
- name: upstream_transmit_power_num
description: fact 2-out
type: float
position: 110
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 2
iterations: 75
stepSize: 0.04
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: downstream_snr_rt
description: fact 3-out
type: float
position: 130
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 2
iterations: 75
stepSize: 0.05
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: t3_timeouts_cnt
description: fact 4
type: integer
position: 40
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 4
iterations: 30
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: t4_timeouts_cnt
description: fact 4b
type: integer
position: 41
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 4
iterations: 30
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: lost_syncs_cnt
description: fact 5
type: integer
position: 50
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 3
polyDegree: 3
iterations: 50
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: resets_cnt
description: fact 6
type: integer
position: 60
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 2
iterations: 70
stepSize: 0.025
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: ds_fec_corrected_cnt
description: fact 7
type: integer
position: 70
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 4
iterations: 30
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: ds_fec_uncorrected_cnt
description: fact 8
type: integer
position: 80
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 4
iterations: 30
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
- name: ds_fec_unerrored_cnt
description: fact 9
type: integer
position: 90
algo:
name: linearRegression
flatten:
mode: log
base: 5
iterations: 2
polyDegree: 4
iterations: 30
stepSize: 0.02
weights:
- name: day_of_week
weight: 10
- name: day_of_month
weight: 10
- name: day_of_year
weight: 10
- name: hour_of_day
weight: 100
- name: month_of_year
weight: 50
- name: year
weight: 10
- name: cmts
weight: 100
- name: node
weight: 100
- name: mac
weight: 150
- name: lat
weight: 150
- name: lng
weight: 150
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