hey Matt, I don't have access to my machine right now, but I've checked out the metrics data table in a db gui and I recall seeing all null values in the anomaly scores columns. I think the total amount of recorded data points is around 230000 (it's the default set from the tutorial). I'll double check tomorrow morning, it would definitely be silly if that was it!
Meanwhile I've tried to track down the code responsible for sending the data into HTM Engine. The fact that I'm unfamiliar with python complicates this, though. I'm thinking maybe anomaly_service.py or a file in model_swapper could tell me more? In terms of configs I was thinking model checkpoints might not be working right, even though I'm not sure what that does :) Met vriendelijke groet, Casper Rooker [email protected] On Mon, Nov 2, 2015 at 4:30 PM, Matthew Taylor <[email protected]> wrote: > Cas, > > You won't see an anomaly score out of the HTM Engine until it has seen > 500 data points. Has it seen that much data yet? > > --------- > Matt Taylor > OS Community Flag-Bearer > Numenta > > > On Mon, Nov 2, 2015 at 4:25 AM, Cas <[email protected]> wrote: > > As I said I'm trying out the HTM engine traffic tutorial where all the > model > > params are generated automatically when the client starts offering data > to > > the HTTP API. > > > > Now, I don't know exactly how to interpret the metric records, but I can > at > > least tell that the inferencetype is what it should be. > > > > So I've concluded that part of the tutorial that runs the data through > HTM > > Engine probably isn't doing it's job. The logs in supervisord tell me all > > the services are working fine (though I had to manually open the port for > > htmengine:model_scheduler). I would assume that the traffic tutorial is > > configured to do postprocessing, based on the HTM engine's README. > > > > It's probably a silly oversight on my part, but right now I just dont > know > > what to make of this. Hope someone can help out! > > > > Kind regards, > > > > Casper Rooker > > [email protected] > > > > P.S.: > > > > One such model params record in the metrics table: > > > > { > > "inferenceArgs": { > > "predictionSteps": [ > > 1 > > ], > > "predictedField": "c1", > > "inputPredictedField": "auto" > > }, > > "modelConfig": { > > "aggregationInfo": { > > "seconds": 0, > > "fields": [], > > "months": 0, > > "days": 0, > > "years": 0, > > "hours": 0, > > "microseconds": 0, > > "weeks": 0, > > "minutes": 0, > > "milliseconds": 0 > > }, > > "model": "CLA", > > "version": 1, > > "predictAheadTime": null, > > "modelParams": { > > "sensorParams": { > > "verbosity": 0, > > "encoders": { > > "c0_dayOfWeek": null, > > "c0_timeOfDay": { > > "fieldname": "c0", > > "timeOfDay": [ > > 21, > > 9.49122334747737 > > ], > > "type": "DateEncoder", > > "name": "c0" > > }, > > "c1": { > > "name": "c1", > > "resolution": 0.7017543859649122, > > "seed": 42, > > "fieldname": "c1", > > "type": "RandomDistributedScalarEncoder" > > }, > > "c0_weekend": null > > }, > > "sensorAutoReset": null > > }, > > "clEnable": false, > > "spParams": { > > "columnCount": 2048, > > "spVerbosity": 0, > > "maxBoost": 1.0, > > "spatialImp": "cpp", > > "inputWidth": 0, > > "synPermInactiveDec": 0.0005, > > "synPermConnected": 0.1, > > "synPermActiveInc": 0.0015, > > "seed": 1956, > > "numActiveColumnsPerInhArea": 40, > > "globalInhibition": 1, > > "potentialPct": 0.8 > > }, > > "trainSPNetOnlyIfRequested": false, > > "clParams": { > > "alpha": 0.035828933612158, > > "clVerbosity": 0, > > "steps": "1", > > "regionName": "CLAClassifierRegion" > > }, > > "tpParams": { > > "columnCount": 2048, > > "activationThreshold": 13, > > "pamLength": 3, > > "cellsPerColumn": 32, > > "permanenceInc": 0.1, > > "minThreshold": 10, > > "verbosity": 0, > > "maxSynapsesPerSegment": 32, > > "outputType": "normal", > > "globalDecay": 0.0, > > "initialPerm": 0.21, > > "permanenceDec": 0.1, > > "seed": 1960, > > "maxAge": 0, > > "newSynapseCount": 20, > > "maxSegmentsPerCell": 128, > > "temporalImp": "cpp", > > "inputWidth": 2048 > > }, > > "anomalyParams": { > > "anomalyCacheRecords": null, > > "autoDetectThreshold": null, > > "autoDetectWaitRecords": 5030 > > }, > > "spEnable": true, > > "inferenceType": "TemporalAnomaly", > > "tpEnable": true > > } > > }, > > "inputRecordSchema": [ > > [ > > "c0", > > "datetime", > > "T" > > ], > > [ > > "c1", > > "float", > > "" > > ] > > ] > > } > > > > On Mon, Nov 2, 2015 at 12:14 PM, Wakan Tanka <[email protected]> wrote: > >> > >> On 11/02/2015 11:31 AM, Cas wrote: > >>> > >>> Hello NuPIC, > >>> > >>> I was trying out the HTM engine traffic tutorial today and I got it > >>> running. Unfortunately the anomaly score is 'none' for all data points. > >>> > >>> Do you have any suggestions on how to troubleshoot this? > >>> > >>> I know all the services are running, from the looks of the supervisord > >>> interface. I'd like to see how the data points are being offered to the > >>> HTM engine for starters, I'm just not sure how to do that. > >>> > >>> Kind regards, > >>> > >>> Casper Rooker > >>> [email protected] <mailto:[email protected]> > >> > >> > >> Hello Casper, > >> > >> Be sure that you've set "inferenceType": "TemporalAnomaly" if you have > >> "inferenceType": "TemporalMultiStep" then you are unable to get anomaly > >> score just predictions. > >> > >> regards > >> > >> Wakan Tanka > >> > > > >
