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a/smoke/smokeApp/seed/SourceSmokePPESimulationModelParametersMap/SmokePPESimulationModelParametersMap.csv +++ b/smoke/smokeApp/seed/SourceSmokePPESimulationModelParametersMap/SmokePPESimulationModelParametersMap.csv @@ -1,122 +1,122 @@ ensemble_id,smoke_emiss,smoke_diam,sig_w,dry_dep_acc,sea_spray,a_ent_1_rp,bparam,kappa_oc,dms,anth_so2,autoconv_exp_nd,bc_ri,pcalc_index -2,-0.530594675634691,282.882849230315,0.423625636176827,0.292502024925003,-0.503793598964439,0.464355852289125,-0.142560479461487,0.595521237277426,-0.157922017001086,-0.136602616971448,-1.77098610874188,0.83983391863294,1 -2,0.175374619849473,288.700938528287,0.756056165844202,-0.700769263362357,-0.532750266120834,0.446669793027453,-0.137167817526807,0.337449199976982,0.0691980811302801,-0.211071581784367,-1.25538897102233,0.768578993761912,2 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b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.c3typ @@ -0,0 +1,12 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/* +* Training job for AOD data {@link Simulation3HourlyAODOutput} +* and {@link GaussianProcessRegressionPipe}s with coarse graining +*/ +type SmokePPECoarseGrainedGaussianMLTrainingJob extends BatchJob type key 'SMKPPECRSMLJB' { + doStart: ~ js server + processBatch: ~ js server +} \ No newline at end of file diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.js b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.js similarity index 77% rename from smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.js rename to smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.js index a4d175a34..b326662bd 100644 --- a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.js +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.js @@ -3,16 +3,19 @@ * All rights reserved. License: https://github.com/c3aidti/.github **/ /** - * Implementation of AODGaussianMLTrainingJob - * @param {AODGaussianMLTrainingJob} job - * @param {AODGaussianMLTrainingJobOptions} options + * Implementation of SmokePPECoarseGrainedGaussianMLTrainingJob + * @param {SmokePPECoarseGrainedGaussianMLTrainingJob} job + * @param {SmokePPECoarseGrainedGaussianMLTrainingJobOptions} options */ function doStart(job, options) { job.setHardwareProfile(options.hardwareProfileId); var batch = []; + + var gstpFilter = Filter.ge("latitude", options.minLat).and().lt("latitude", options.maxLat).and().ge("longitude", options.minLon).and().lt("longitude", options.maxLon).and().ge("time", options.minTime).and().lt("time", options.maxTime); + var gstps = GeoSurfaceTimePoint.fetchObjStream({ - filter: options.gstpFilter, + filter: gstpFilter, limit: -1 }); @@ -20,7 +23,7 @@ function doStart(job, options) { batch.push(gstps.next()); if (batch.length >= options.batchSize || !gstps.hasNext()) { - var batchSpec = AODGaussianMLTrainingJobBatch.make({values: batch}); + var batchSpec = SmokePPEGaussianMLTrainingJobBatch.make({values: batch}); job.scheduleBatch(batchSpec); batch = []; @@ -31,9 +34,9 @@ function doStart(job, options) { /** * Implementation of what to do in each batch - * @param {AODGaussianMLTrainingJobBatch} batch - * @param {AODGaussianMLTrainingJob} job - * @param {AODGaussianMLTrainingJobOptions} options + * @param {SmokePPEGaussianMLTrainingJobBatch} batch + * @param {SmokePPEGaussianMLTrainingJob} job + * @param {SmokePPEGaussianMLTrainingJobOptions} options */ function processBatch(batch, job, options) { batch.values.forEach(function(gstp) { @@ -105,9 +108,10 @@ function processBatch(batch, job, options) { var X = GPR_pipe.getFeatures(); var y = GPR_pipe.getTarget(); - // train and save - var GPR_pipe_trained = GPR_pipe.train(X, y); - GPR_pipe_trained.upsert(); - + if (X.size() > 0 && y.size() > 0) { + // train and save + var GPR_pipe_trained = GPR_pipe.train(X, y); + GPR_pipe_trained.upsert(); + }; }); -} \ No newline at end of file +} diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.py b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.py new file mode 100644 index 000000000..ace51de90 --- /dev/null +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJob.py @@ -0,0 +1,51 @@ +def doStart(self, job, options): + import pandas as pd + # set hardware profile + job.setHardwareProfile(options.hardwareProfileId) + + # grab all gstps + gstpFilter = c3.Filter().ge("latitude", options.minLat).and_().lt("latitude", options.maxLat).and_().ge("longitude", options.minLon).and_().lt("longitude", options.maxLon).and_().ge("time", options.minTime).and_().lt("time", options.maxTime) + + allGstps = c3.GeoSurfaceTimePoint.fetch({ + "filter": gstpFilter, + "limit": -1 + }).toPandas() + + # find all unique time stamps + times = allGstps["time"].unique() + + batch = [] + # loop over each unique time stamp + for time in times: + # get all lat-lon poiunts for that time stamp + gstpsForTime = allGstps[allGstps["time"] == time] + # loop between minLat, maxLat with latStep + n_lat_steps = (options.maxLat - options.minLat) / options.latStep + n_lon_steps = (options.maxLon - options.minLon) / options.lonStep + for i in range(n_lat_steps): + lat_down = options.minLat + i * options.latStep + lat_up = lat_down + options.latStep + # loop between minLon, maxLon with lonStep + for j in range(n_lon_steps): + lon_left = options.minLon + j * options.lonStep + lon_right = lon_left + options.lonStep + # get all gstps in that lat-lon box + gstpsInBox = gstpsForTime[gstpsForTime["latitude"] >= lat_down and gstpsForTime["latitude"] < lat_up and gstpsForTime["longitude"] >= lon_left and gstpsForTime["longitude"] < lon_right] + # loop over each gstp + targets = [] + for gstp in gstpsInBox: + targetFilter = c3.Filter().eq("geoSurfaceTimePoint.id", gstp["id"]) + target = c3.SmokePPESimulationOutput.fetch({ + "filter": targetFilter + "limit": -1 + }) + targets.append(target) + # average over list of targets + + batch.append(above_list_of_targets) + if len(batch) >= options.batchSize: + batchSpec = c3.SmokePPECoarseGrainedGaussianMLTrainingJobBatch.make({"values": batch}) + job.scheduleBatch(batchSpec) + batch = [] + + diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobBatch.c3typ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobBatch.c3typ new file mode 100644 index 000000000..6ba7773af --- /dev/null +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobBatch.c3typ @@ -0,0 +1,11 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/** +* Represents a unit of work (batch) in a {@link SmokePPECoarseGrainedGaussianMLTraningJob} +*/ +type SmokePPECoarseGrainedGaussianMLTrainingJobBatch { + // The {@link GeoSurfaceTimePoint}s that define the targets for the models in this batch + values: [GeoSurfaceTimePoint] +} \ No newline at end of file diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobOptions.c3typ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobOptions.c3typ new file mode 100644 index 000000000..9f1cf465f --- /dev/null +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPECoarseGrainedGaussianMLTrainingJobOptions.c3typ @@ -0,0 +1,33 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/** +* Represents customization options for {@link SmokePPECoarseGrainedGaussianMLTrainingJob} +*/ +type SmokePPECoarseGrainedGaussianMLTrainingJobOptions { + // How many models will be trained in each batch + batchSize: int = 10 + // the min latitude + minLat: !float + // the max latitude + maxLat: !float + // the min longitude + minLon: !float + // the max longitude + maxLon: !float + // the latitute step + latStep: !float + // the longitude step + lonStep: !float + // the name of the variable to collect from {@link Simulation3HourlyAODData} + targetName: !string + // the features to exclude in {@link SimulationModelParameters} + excludeFeatures: [string] + // the {@link GaussianProcessRegressionTechnique} to train the models + gprTechnique: !GaussianProcessRegressionTechnique + // hardware profile ID to run the batches + hardwareProfileId: string = "appc8m642-w" + // flag for staged GSTPs training ({@link StagedGSTP}) + stagedGSTP: boolean = false +} \ No newline at end of file diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.c3typ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.c3typ similarity index 63% rename from smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.c3typ rename to smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.c3typ index be40a93d9..bbf6db741 100644 --- a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJob.c3typ +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.c3typ @@ -6,7 +6,7 @@ * Training job for AOD data {@link Simulation3HourlyAODOutput} * and {@link GaussianProcessRegressionPipe}s */ -type AODGaussianMLTrainingJob extends BatchJob type key 'AODGMLJB' { +type SmokePPEGaussianMLTrainingJob extends BatchJob type key 'SMKPPEMLJB' { doStart: ~ js server processBatch: ~ js server -} \ No newline at end of file +} diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.js b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.js new file mode 100644 index 000000000..2fb515473 --- /dev/null +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJob.js @@ -0,0 +1,132 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/** + * Implementation of SmokePPEGaussianMLTrainingJob + * @param {SmokePPEGaussianMLTrainingJob} job + * @param {SmokePPEGaussianMLTrainingJobOptions} options + */ +function doStart(job, options) { + job.setHardwareProfile(options.hardwareProfileId); + var batch = []; + + if (options.stagedGSTP) { + var staged_gstps = StagedGSTP.fetchObjStream({ + limit: -1 + }); + + while(staged_gstps.hasNext()) { + var gstp = GeoSurfaceTimePoint.get(staged_gstps.next().geoSurfaceTimePoint.id); + batch.push(gstp); + + if (batch.length >= options.batchSize || !gstps.hasNext()) { + var batchSpec = SmokePPEGaussianMLTrainingJobBatch.make({values: batch}); + job.scheduleBatch(batchSpec); + + batch = []; + } + } + } else { + var gstps = GeoSurfaceTimePoint.fetchObjStream({ + filter: options.gstpFilter, + limit: -1 + }); + + while(gstps.hasNext()) { + batch.push(gstps.next()); + + if (batch.length >= options.batchSize || !gstps.hasNext()) { + var batchSpec = SmokePPEGaussianMLTrainingJobBatch.make({values: batch}); + job.scheduleBatch(batchSpec); + + batch = []; + } + } + } +} + + +/** + * Implementation of what to do in each batch + * @param {SmokePPEGaussianMLTrainingJobBatch} batch + * @param {SmokePPEGaussianMLTrainingJob} job + * @param {SmokePPEGaussianMLTrainingJobOptions} options + */ +function processBatch(batch, job, options) { + batch.values.forEach(function(gstp) { + + // define target + var targetType = TypeRef.make({"typeName": "SmokePPESimulationOutput"}); + var targetFilter = Filter.eq("geoSurfaceTimePoint.id", gstp.id); + var targetSpec = FetchSpec.make({ + "limit": -1, + "order": "simulationSample.id", + "filter": targetFilter.toString() + }); + + // find the simulations + var simulationsSpec = FetchSpec.make({ + "limit": -1, + "order": "simulationSample.id", + "filter": targetFilter.toString(), + "include": "simulationSample" + }); + var samples = targetType.toType().fetch(simulationsSpec).objs; + var simIds = []; + for(var i = 0; i < samples.length; i++) { + simIds.push(samples[i].simulationSample.id); + } + + var featuresType = TypeRef.make({"typeName": "SmokePPESimulationModelParameters"}); + var allSamples = featuresType.toType().fetch({ + "limit": -1, + "order": "id", + "include": "id" + }).objs; + var allSimIds = []; + for(var i = 0; i < allSamples.length; i++) { + allSimIds.push(allSamples[i].id); + }; + var excludeIds = []; + for(var i = 0; i < allSimIds.length; i++) { + if(simIds.indexOf(allSimIds[i]) === -1) { + excludeIds.push(allSimIds[i]); + } + }; + + // define the features + var featuresFilter = Filter.not().intersects("id", excludeIds); + var featuresSpec = FetchSpec.make({ + "limit": -1, + "order": "id", + "filter": featuresFilter + }); + + // define the data source spec + var sourceSpec = GPRDataSourceSpec.make({ + "featuresType": featuresType, + "featuresSpec": featuresSpec, + "excludeFeatures": options.excludeFeatures, + "targetType": targetType, + "targetSpec": targetSpec, + "targetName": options.targetName + }).upsert() + + // create the pipe + var GPR_pipe = GaussianProcessRegressionPipe.make({ + "technique": options.gprTechnique, + "dataSourceSpec": sourceSpec + }) + + // get target and features + var X = GPR_pipe.getFeatures(); + var y = GPR_pipe.getTarget(); + + if (X.size() > 0 && y.size() > 0) { + // train and save + var GPR_pipe_trained = GPR_pipe.train(X, y); + GPR_pipe_trained.upsert(); + }; + }); +} diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobBatch.c3typ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobBatch.c3typ similarity index 68% rename from smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobBatch.c3typ rename to smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobBatch.c3typ index 6b7eb252e..0c11d4280 100644 --- a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobBatch.c3typ +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobBatch.c3typ @@ -3,9 +3,9 @@ * All rights reserved. License: https://github.com/c3aidti/.github **/ /** -* Represents a unit of work (batch) in a {@link AODGaussianMLTraningJob} +* Represents a unit of work (batch) in a {@link SmokePPEGaussianMLTraningJob} */ -type AODGaussianMLTrainingJobBatch { +type SmokePPEGaussianMLTrainingJobBatch { // The {@link GeoSurfaceTimePoint}s that define the targets for the models in this batch values: [GeoSurfaceTimePoint] -} \ No newline at end of file +} diff --git a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobOptions.c3typ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobOptions.c3typ similarity index 77% rename from smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobOptions.c3typ rename to smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobOptions.c3typ index cc3ce0717..81e8eae17 100644 --- a/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/smokePPEGaussianMLTrainingJobOptions.c3typ +++ b/smoke/smokeApp/src/batchJobs/smokePPEGaussianML/SmokePPEGaussianMLTrainingJobOptions.c3typ @@ -3,9 +3,9 @@ * All rights reserved. License: https://github.com/c3aidti/.github **/ /** -* Represents customization options for {@link AODGaussianMLTrainingJob} +* Represents customization options for {@link SmokePPEGaussianMLTrainingJob} */ -type AODGaussianMLTrainingJobOptions { +type SmokePPEGaussianMLTrainingJobOptions { // How many models will be trained in each batch batchSize: int = 10 // {@link GeoSurfaceTimePoint}s filter that defines the total number of models @@ -18,4 +18,6 @@ type AODGaussianMLTrainingJobOptions { gprTechnique: !GaussianProcessRegressionTechnique // hardware profile ID to run the batches hardwareProfileId: string = "appc8m642-w" -} \ No newline at end of file + // flag for staged GSTPs training ({@link StagedGSTP}) + stagedGSTP: boolean = false +} diff --git a/smoke/smokeApp/src/entity/coordinates/StagedGSTP.c3typ b/smoke/smokeApp/src/entity/coordinates/StagedGSTP.c3typ new file mode 100644 index 000000000..a420f1c36 --- /dev/null +++ b/smoke/smokeApp/src/entity/coordinates/StagedGSTP.c3typ @@ -0,0 +1,18 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/** +* Staged {@link GeoSurfaceTimePoint} for faster processing by ML pipes. +*/ +@db(unique=['geoSurfaceTimePoint']) +entity type StagedGSTP schema name 'STGD_GSTP' { + // the {@link GeoSurfaceTimePoint} + geoSurfaceTimePoint: !GeoSurfaceTimePoint + // stage based on a region filter + @py(env='gordon-ML_1_0_0') + directStage: function(gstpFilter: any): int + // unstage based on a ragion filter + @py(env='gordon-ML_1_0_0') + unstage: function(gstpFilter: any): int +} \ No newline at end of file diff --git a/smoke/smokeApp/src/entity/coordinates/StagedGSTP.py b/smoke/smokeApp/src/entity/coordinates/StagedGSTP.py new file mode 100644 index 000000000..3da39ca69 --- /dev/null +++ b/smoke/smokeApp/src/entity/coordinates/StagedGSTP.py @@ -0,0 +1,20 @@ +def directStage(gstpFilter): + """ + Stage directly from the current node. + """ + # grab elements from GSTP + gstps = c3.GeoSurfaceTimePoint.fetch({ + "filter": gstpFilter, + "limit": -1, + "include": "id" + }).objs + + stagedObjs = [] + for gstp in gstps: + o = c3.StagedGSTP(geoSurfaceTimePoint=gstp) + stagedObjs.append(o) + + # upsert to staging area + c3.StagedGSTP.upsertBatch(stagedObjs) + + return 0 \ No newline at end of file diff --git a/smoke/smokeApp/src/learning/gaussianProcess/pipes/GaussianProcessRegressionPipe.py b/smoke/smokeApp/src/learning/gaussianProcess/pipes/GaussianProcessRegressionPipe.py index 0fd32ccb5..ddbb4fde6 100644 --- a/smoke/smokeApp/src/learning/gaussianProcess/pipes/GaussianProcessRegressionPipe.py +++ b/smoke/smokeApp/src/learning/gaussianProcess/pipes/GaussianProcessRegressionPipe.py @@ -117,18 +117,18 @@ def getFeatures(this): return c3.Dataset.fromPython(df) - inputTableC3 = featuresType.fetch(dataSourceSpec.featuresSpec).objs.toJson() - inputTablePandas = pd.DataFrame(inputTableC3) - inputTablePandas = inputTablePandas.drop("version", axis=1) - - # collect only the numeric fields - inputTablePandas = inputTablePandas.select_dtypes(["number"]) - - # drop ignored features - if (dataSourceSpec.excludeFeatures): - inputTablePandas.drop(columns=dataSourceSpec.excludeFeatures, inplace=True) - - return c3.Dataset.fromPython(inputTablePandas) + inputTableC3 = featuresType.fetch(dataSourceSpec.featuresSpec).objs + if inputTableC3 is not None: + inputTablePandas = pd.DataFrame(inputTableC3.toJson()) + inputTablePandas = inputTablePandas.drop("version", axis=1) + # collect only the numeric fields + inputTablePandas = inputTablePandas.select_dtypes(["number"]) + # drop ignored features + if (dataSourceSpec.excludeFeatures): + inputTablePandas.drop(columns=dataSourceSpec.excludeFeatures, inplace=True) + return c3.Dataset.fromPython(inputTablePandas) + else: + return c3.Dataset.fromPython(pd.DataFrame([])) def getTarget(this): @@ -158,22 +158,22 @@ def getTarget(this): return c3.Dataset.fromPython(df) - outputTableC3 = targetType.fetch(dataSourceSpec.targetSpec).objs.toJson() - outputTablePandas = pd.DataFrame(outputTableC3) - outputTablePandas = outputTablePandas.drop("version", axis=1) - - # collect only the numeric fields - outputTablePandas = outputTablePandas.select_dtypes(["number"]) - - if dataSourceSpec.targetName == "all": - outputTablePandas = pd.DataFrame( - outputTablePandas.sum(axis=1), - columns=[dataSourceSpec.targetName] - ) + outputTableC3 = targetType.fetch(dataSourceSpec.targetSpec).objs + if outputTableC3 is not None: + outputTablePandas = pd.DataFrame(outputTableC3.toJson()) + outputTablePandas = outputTablePandas.drop("version", axis=1) + # collect only the numeric fields + outputTablePandas = outputTablePandas.select_dtypes(include=["number"], exclude=["int"]) + if dataSourceSpec.targetName == "all": + outputTablePandas = pd.DataFrame( + outputTablePandas.sum(axis=1), + columns=[dataSourceSpec.targetName] + ) + else: + outputTablePandas = pd.DataFrame(outputTablePandas[dataSourceSpec.targetName]) + return c3.Dataset.fromPython(outputTablePandas) else: - outputTablePandas = pd.DataFrame(outputTablePandas[dataSourceSpec.targetName]) - - return c3.Dataset.fromPython(outputTablePandas) + return c3.Dataset.fromPython(pd.DataFrame([])) def trainWithStagedAOD(this, modelIds): diff --git a/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.js b/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.js index 532242ee1..e15565dfd 100644 --- a/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.js +++ b/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.js @@ -19,7 +19,9 @@ function getPipe(excFeats, gstpId, targetName, technique) { }).objs.map(obj => obj.id); // find the kernels - filter = Filter.eq("pickledKernel", technique.kernel.pickledKernel); + var fullTech = GaussianProcessRegressionTechnique.get(technique.id); + var fullKernel = SklearnGPRKernel.get(fullTech.kernel.id); + filter = Filter.eq("pickledKernel", fullKernel.pickledKernel); var kernelIds = SklearnGPRKernel.fetch({ "filter": filter.value, "limit": -1, diff --git a/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.py b/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.py index 83ccc28e2..674288705 100644 --- a/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.py +++ b/smoke/smokeApp/src/utils/AODData/AODGPRModelFinder.py @@ -6,6 +6,7 @@ def extractLearnedParametersJob(excFeats, gstpFilter, targetName, technique, bat """ Dynamic map-reduce job to extract learned hyper parameters. """ + def cassandra_mapper(batch, objs, job): models = [] for obj in objs: diff --git a/smoke/smokeApp/src/utils/AODData/PredictAODGPR.py b/smoke/smokeApp/src/utils/AODData/PredictAODGPR.py index 2a87b961c..bff1c1e53 100644 --- a/smoke/smokeApp/src/utils/AODData/PredictAODGPR.py +++ b/smoke/smokeApp/src/utils/AODData/PredictAODGPR.py @@ -88,7 +88,7 @@ def getPredictionsDataframeFromJob(job): predictions = [] if job.status().status == "completed": - for key, value in job.results().items(): + for key, value in job.results(limit=-1).items(): for subvalue in value: df_m = pd.DataFrame() df_m["meanResponse"] = np.array(subvalue[1]).flatten() diff --git a/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.c3typ b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.c3typ new file mode 100644 index 000000000..1d987ee06 --- /dev/null +++ b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.c3typ @@ -0,0 +1,30 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +/** +* This finds {@link GaussianProcessRegressionPipe}s that were trained +* with {@link SmokePPESimulationOutput} as targets, +* {@link SmokePPESimulationModelParameters} as features, +* via a {@link AODGaussianMLTrainingJob} +*/ +type SmokePPEGPRPredictor { + // Retrieve models based on exluded features, {@link GeoSurfaceTimePoint} instance, target name and training technique + getPipe: function(excFeats: [string], gstpId: string, targetName: string, technique: any): any js server + // Retrieve all models for a certain {@link GeoSurfaceTimePoint} filter + getPipes: function(excFeats: [string], gstpFilter: any, targetName: string, technique: any): any js server + // Extract learned parameters from trained {@link GaussianProcessRegressionPipe}s specified by {@link GeoSurfaceTimePoint} filter, target name, excluded features and {@link GaussianProcessRegressionTechnque} + @py(env='gordon-ML_1_0_0') + makePredictionsJob: function(excFeats: [string], gstpFilter: any, targetName: string, synthDataset: any, technique: any, batchSize: int): any + // Build a pandas dataframe with the hyper parameters once job is complete + @py(env='gordon-ML_1_0_0') + getPredictionsDataframeFromJob: inline function(job: any): any + // Extract learned parameters from trained {@link GaussianProcessRegressionPipe}s specified by {@link GeoSurfaceTimePoint} filter, target name, excluded features and {@link GaussianProcessRegressionTechnque} + @py(env='gordon-ML_1_0_0') + extractLearnedParametersJob: function(excFeats: [string], gstpFilter: any, targetName: string, technique: any, batchSize: int): any + // Build a pandas dataframe with the hyper parameters once job is complete + @py(env='gordon-ML_1_0_0') + getDataframeFromJob: inline function(job: any): any + // count the number of existing pipes + countPipes: function(excFeats: [string], gstpFilter: any, targetName: string, technique: any): any js server +} \ No newline at end of file diff --git a/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.js b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.js new file mode 100644 index 000000000..eab495635 --- /dev/null +++ b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.js @@ -0,0 +1,123 @@ +/** +* Copyright (c) 2022, C3 AI DTI, Development Operations Team +* All rights reserved. License: https://github.com/c3aidti/.github +**/ +function getPipe(excFeats, gstpId, targetName, technique) { + // find the data source specs + var gstpKey = "geoSurfaceTimePoint.id == \"" + gstpId + "\""; + var filter = Filter.eq("featuresType.typeName", "SmokePPESimulationModelParameters") + .and().eq("targetType.typeName", "SmokePPESimulationOutput") + //.and().intersects("excludeFeatures", excFeats) + .and().eq("targetName", targetName) + .and().eq("targetSpec.filter", gstpKey); + + var sourceSpecIds = GPRDataSourceSpec.fetch({ + "filter": filter, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // find the kernels + var fullTech = GaussianProcessRegressionTechnique.get(technique.id); + var fullKernel = SklearnGPRKernel.get(fullTech.kernel.id); + filter = Filter.eq("pickledKernel", fullKernel.pickledKernel); + var kernelIds = SklearnGPRKernel.fetch({ + "filter": filter.value, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // find the techniques + filter = Filter.intersects("kernel.id", kernelIds) + .and().eq("centerTarget", technique.centerTarget); + var techIds = GaussianProcessRegressionTechnique.fetch({ + "filter": filter.value, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // now find the models + filter = Filter.intersects("technique.id", techIds) + .and().intersects("dataSourceSpec.id", sourceSpecIds); + var pipes = GaussianProcessRegressionPipe.fetch({ + "filter": filter.value, + "limit": -1 + }).objs; + + return pipes +} + +function getPipes(excFeats, gstpFilter, targetName, technique) { + var gstpIds = GeoSurfaceTimePoint.fetch({ + "filter": gstpFilter, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + var pipes = gstpIds.map(id => SmokePPEGPRPredictor.getPipe(excFeats, id, targetName, technique)); + var nonNulls = pipes.filter(function (el) { + return el.length != 0; + }); + + return nonNulls +} + + +function countPipes(excFeats, gstpFilter, targetName, technique) { + + var gstpStream = GeoSurfaceTimePoint.fetchObjStream({ + "filter": gstpFilter, + "limit": -1, + "include": "id" + }); + + var total_pipes = 0; + + while(gstpStream.hasNext()) { + + var gstp = gstpStream.next(); + + // find source specs + var gstpKey = "geoSurfaceTimePoint.id == \"" + gstp.id + "\""; + var g_filter = Filter.eq("featuresType.typeName", "SmokePPESimulationModelParameters") + .and().eq("targetType.typeName", "SmokePPESimulationOutput") + //.and().intersects("excludeFeatures", excFeats) + .and().eq("targetName", targetName) + .and().eq("targetSpec.filter", gstpKey); + var sourceSpecIds = GPRDataSourceSpec.fetch({ + "filter": g_filter, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // find kernels + var fullTech = GaussianProcessRegressionTechnique.get(technique.id); + var fullKernel = SklearnGPRKernel.get(fullTech.kernel.id); + var k_filter = Filter.eq("pickledKernel", fullKernel.pickledKernel); + var kernelIds = SklearnGPRKernel.fetch({ + "filter": k_filter.value, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // find techniques + var t_filter = Filter.intersects("kernel.id", kernelIds) + .and().eq("centerTarget", technique.centerTarget); + var techIds = GaussianProcessRegressionTechnique.fetch({ + "filter": t_filter.value, + "limit": -1, + "include": "id" + }).objs.map(obj => obj.id); + + // partial pipe count + var m_filter = Filter.intersects("technique.id", techIds) + .and().intersects("dataSourceSpec.id", sourceSpecIds); + var n_pipes = GaussianProcessRegressionPipe.fetchCount({ + "filter": m_filter.value + }); + total_pipes += n_pipes; + }; + + return total_pipes +} + diff --git a/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.py b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.py new file mode 100644 index 000000000..76eb4ab54 --- /dev/null +++ b/smoke/smokeApp/src/utils/AODData/SmokePPEGPRPredictor.py @@ -0,0 +1,207 @@ +## +# Copyright (c) 2022, C3 AI DTI, Development Operations Team +# All rights reserved. License: https://github.com/c3aidti/.github +## +def makePredictionsJob( + excFeats, gstpFilter, targetName, synthDataset, technique, batchSize +): + """ + Dynamic map-reduce job to get predictions on synthDataset. + """ + + def cassandra_mapper(batch, objs, job): + models = [] + for obj in objs: + model = c3.SmokePPEGPRPredictor.getPipe( + job.context.value["excludeFeatures"], + obj.id, + job.context.value["targetName"], + job.context.value["technique"] + ) + models.append(model) + + return {batch: models} + + def cassandra_reducer(key, interValues, job): + values = [] + synthDataframe = c3.Dataset.toPandas(job.context.value["syntheticDataset"]) + for iv in interValues: + for val in iv: + for m in val: + # predictions + model_id = m["id"] + centered = m["technique"]["centerTarget"] + if centered: + center = m["trainedModel"].parameters["targetMean"].asfloat() + else: + center = 0 + pickledModel = m["trainedModel"]["model"] + model = c3.PythonSerialization.deserialize(serialized=pickledModel) + mean, sd = model.predict(synthDataframe, return_std=True) + + # location + dssId = m["dataSourceSpec"]["id"] + dss = c3.GPRDataSourceSpec.get(dssId) + gstpId = dss.targetSpec.filter.split(" == ")[1].replace('"', '') + gstp = c3.GeoSurfaceTimePoint.get(gstpId) + lat = gstp.latitude + lon = gstp.longitude + time = gstp.time + values.append((model_id, mean, center, sd, lat, lon, time, gstpId)) + + + return values + + map_lambda = c3.Lambda.fromPython(cassandra_mapper) + reduce_lambda = c3.Lambda.fromPython(cassandra_reducer, runtime="gordon-ML_1_0_0") + + job_context = c3.MappObj( + value={ + 'excludeFeatures': excFeats, + 'targetName': targetName, + 'technique': technique, + 'syntheticDataset': synthDataset + } + ) + job = c3.DynMapReduce.startFromSpec( + c3.DynMapReduceSpec( + targetType="GeoSurfaceTimePoint", + filter=gstpFilter, + mapLambda=map_lambda, + reduceLambda=reduce_lambda, + batchSize=batchSize, + context=job_context, + hardwareProfile="appc8m642-w" + ) + ) + + return job + + +def getPredictionsDataframeFromJob(job): + """ + Iterates over job result and builds dataframe. + """ + import pandas as pd + import numpy as np + + predictions = [] + + if job.status().status == "completed": + for key, value in job.results(limit=-1).items(): + for subvalue in value: + df_m = pd.DataFrame() + df_m["meanResponse"] = np.array(subvalue[1]).flatten() + df_m["meanResponse"] += subvalue[2] + df_m["sdResponse"] = subvalue[3] + df_m["latitude"] = subvalue[4] + df_m["longitude"] = subvalue[5] + df_m["time"] = subvalue[6] + df_m["modelId"] = subvalue[0] + df_m["variant"] = list(range(df_m.shape[0])) + df_m["gstpId"] = subvalue[7] + + predictions.append(df_m) + + df = pd.concat(predictions, axis=0).reset_index(drop=True) + return df + else: + return False + + +def extractLearnedParametersJob(excFeats, gstpFilter, targetName, technique, batchSize ): + """ + Dynamic map-reduce job to extract learned hyper parameters. + """ + + def cassandra_mapper(batch, objs, job): + models = [] + for obj in objs: + model = c3.SmokePPEGPRPredictor.getPipe( + job.context.value["excludeFeatures"], + obj.id, + job.context.value["targetName"], + job.context.value["technique"] + ) + models.append(model) + + return {batch: models} + + def cassandra_reducer(key, interValues, job): + values = [] + for iv in interValues: + for val in iv: + for m in val: + pickledModel = m["trainedModel"]["model"] + model = c3.PythonSerialization.deserialize(serialized=pickledModel) + hp = model.kernel_.get_params()['k2__length_scale'] + model_id = m["id"] + + # find GSTP + dssId = m["dataSourceSpec"]["id"] + dss = c3.GPRDataSourceSpec.get(dssId) + gstpId = dss.targetSpec.filter.split(" == ")[1].replace('"', '') + gstp = c3.GeoSurfaceTimePoint.get(gstpId) + lat = gstp.latitude + lon = gstp.longitude + time = gstp.time + values.append((hp, model_id, lat, lon, time)) + + return values + + map_lambda = c3.Lambda.fromPython(cassandra_mapper) + reduce_lambda = c3.Lambda.fromPython(cassandra_reducer, runtime="gordon-ML_1_0_0") + + job_context = c3.MappObj( + value={ + 'excludeFeatures': excFeats, + 'targetName': targetName, + 'technique': technique + } + ) + job = c3.DynMapReduce.startFromSpec( + c3.DynMapReduceSpec( + targetType="GeoSurfaceTimePoint", + filter=gstpFilter, + mapLambda=map_lambda, + reduceLambda=reduce_lambda, + batchSize=batchSize, + context=job_context, + hardwareProfile="appc8m642-w" + ) + ) + + return job + + +def getDataframeFromJob(job): + """ + Iterates over job result and builds dataframe. + """ + import pandas as pd + import numpy as np + + lengthScales = [] + ids = [] + lats = [] + lons = [] + times = [] + if job.status().status == "completed": + for key, value in job.results().items(): + for subvalue in value: + ls = np.array(subvalue[0]).astype(float) + model_id = np.array([subvalue[1]]).astype(str) + lengthScales.append(ls) + ids.append(model_id[0]) + lats.append(np.array(subvalue[2]).astype(float)) + lons.append(np.array(subvalue[3]).astype(float)) + times.append(np.array(subvalue[4])) + + df = pd.DataFrame(lengthScales) + df["modelId"] = ids + df["latitude"] = lats + df["longitude"] = lons + df["time"] = times + return df + else: + return False \ No newline at end of file