Error by invoking gds.beta.pipeline.linkPrediction.train procedure. Node properties do not exist in the graph. But they do!

Neo4j Graph Platform Graph Algorithms/Graph Data Science
3

sure, here you go :) @florentin_dorre

CALL gds.beta.pipeline.linkPrediction.create('pipe')

CALL gds.beta.pipeline.linkPrediction.addNodeProperty('pipe', 'fastRP', {
  mutateProperty: 'embedding',
  embeddingDimension: 256,
  randomSeed: 42
})

 CALL gds.beta.pipeline.linkPrediction.addFeature('pipe', 'hadamard', {
  nodeProperties: ['embedding', 'projectPlannedRevenue', 'projectRevenueRatio', 'projectOverallSlippagePercent',
        'projectCostRatio', 'projectTeamSize', 'projectDuration',
        'totalRiskValue', 'avgRiskValue', 'riskCount', 'revenueToCostRatio', 'riskValue',  'riskDuration']
}) YIELD featureSteps 

CALL gds.beta.pipeline.linkPrediction.configureSplit('pipe', {
  testFraction: 0.25,
  trainFraction: 0.6,
  validationFolds: 3
})
YIELD splitConfig

CALL gds.beta.pipeline.linkPrediction.addLogisticRegression('pipe')
YIELD parameterSpace

CALL gds.beta.pipeline.linkPrediction.addRandomForest('pipe', {numberOfDecisionTrees: 10})
YIELD parameterSpace

CALL gds.alpha.pipeline.linkPrediction.configureAutoTuning('pipe', {
  maxTrials: 2
}) YIELD autoTuningConfig

//(7) Project another graph
CALL gds.graph.project(
    'myGraph1',
    {
        Project: {
            label: 'Project',
            properties: [
                'projectPlannedRevenue', 
                'projectRevenueRatio', 
                'projectOverallSlippagePercent', 
                'projectCostRatio', 
                'projectTeamSize', 
                'projectDuration', 
                'totalRiskValue', 
                'avgRiskValue', 
                'riskCount', 
                'revenueToCostRatio',
                'embedding'
            ]
        },
        Risk: {
            label: 'Risk',
            properties: [
                'riskValue',  
                'riskDuration',
                'embedding'
            ]
        }
    },
    {
        HAS_RISK: {
            type: 'HAS_RISK',
            orientation: 'UNDIRECTED'
        }
    }
);

CALL gds.beta.pipeline.linkPrediction.train('myGraph1', {
  pipeline: 'pipe',
  modelName: 'lp-pipeline-model',
  metrics: ['AUCPR', 'OUT_OF_BAG_ERROR'],
  targetRelationshipType: 'HAS_RISK',
  randomSeed: 12
}) YIELD modelInfo, modelSelectionStats
RETURN
  modelInfo.bestParameters AS winningModel,
  modelInfo.metrics.AUCPR.train.avg AS avgTrainScore,
  modelInfo.metrics.AUCPR.outerTrain AS outerTrainScore,
  modelInfo.metrics.AUCPR.test AS testScore,
  [cand IN modelSelectionStats.modelCandidates | cand.metrics.AUCPR.validation.avg] AS validationScores