def run_sine_experiment(): input_file = "sine.csv" generate_data.run(input_file) model_params = swarm_over_data() if PLOT: output = NuPICPlotOutput("sine_output", show_anomaly_score=True) else: output = NuPICFileOutput("sine_output", show_anomaly_score=True) model = ModelFactory.create(model_params) model.enableInference({"predictedField": "sine"}) with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: angle = float(row[0]) sine_value = float(row[1]) result = model.run({"sine": sine_value}) output.write(angle, sine_value, result, prediction_step=1) output.close()
def run_sine_experiment(): input_file = "netio.csv" generate_data.run(input_file) model_params = swarm_over_data() if PLOT: output = NuPICPlotOutput("netio_output", show_anomaly_score=True) else: output = NuPICFileOutput("netio_output", show_anomaly_score=True) model = ModelFactory.create(model_params) model.enableInference({"predictedField": "bytes_sent"}) with open(input_file, "rb") as netio_input: csv_reader = csv.reader(netio_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) bytes_sent = float(row[1]) #netio = float(row[3]) result = model.run({"bytes_sent": bytes_sent}) output.write(timestamp, bytes_sent, result, prediction_step=1) output.close()
def run_mem_experiment(): input_file = "cpu.csv" generate_data.run(input_file) model_params = swarm_over_data(SWARM_CONFIG) if PLOT: output = NuPICPlotOutput("final_mem_output") else: output = NuPICFileOutput("final_mem_output") model = ModelFactory.create(model_params) model.enableInference({"predictedField": "mem"}) with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) mem = float(row[1]) result = model.run({"mem": mem}) prediction = result.inferences["multiStepBestPredictions"][1] anomalyScore = result.inferences['anomalyScore'] output.write(timestamp, mem, prediction, anomalyScore) output.close()
def run_cpu_experiment(): input_file = "cpu.csv" cur_dir = os.getcwd() input_file = os.path.join(cur_dir,'cpu/cpu.csv') cpu_generate_data.run(input_file) model_params = swarm_over_data(SWARM_CONFIG) model = ModelFactory.create(model_params) model.enableInference({"predictedField": "cpu"}) #To load with no swarming #model = ModelFactory.create(model_params) if PLOT: output = NuPICPlotOutput("cpu/final_cpu_output") else: output = NuPICFileOutput("cpu/final_cpu_output") with open(input_file, "rb") as cpu_input: csv_reader = csv.reader(cpu_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data sumOfUtilityFitness=0.0 sumOfWeaight = 0.0 for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) cpu = float(row[1]) result = model.run({"cpu": cpu}) prediction = result.inferences["multiStepBestPredictions"][1] anomalyScore = result.inferences['anomalyScore'] anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability(cpu, anomalyScore, timestamp) uc= (anomalyLikelihood * cpu + prediction * anomalyLikelihood )/(anomalyScore + anomalyLikelihood) sumOfUtilityFitness= sumOfUtilityFitness + (float(cpu) * float(anomalyLikelihood)) sumOfWeaight = sumOfWeaight + float(anomalyLikelihood) output.write(timestamp, cpu, prediction, anomalyScore) output.close() print 'sumOfWeaight: ', sumOfWeaight, 'sumOfUtilityFitness: ', sumOfUtilityFitness result_output = 'cpu/final_cpu_output_out.csv' utilityOfCpu=0.0 with open(result_output, "rb") as result_input: csv_reader = csv.reader(result_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() for row in csv_reader: anomalyLikelihood = float(row[3]) utilityOfCpu= utilityOfCpu + (anomalyLikelihood * sumOfUtilityFitness)/sumOfWeaight print 'utilityOfCpu: ', utilityOfCpu move_model()
def run_model(input_file, output_file, model, train=False, plot=True): if plot: output = NuPICPlotOutput(output_file, show_anomaly_score=True) else: output = NuPICFileOutput(output_file, show_anomaly_score=True) if train: print "Enable learning." model.enableLearning() else: print "Not for training. Disabling learning." model.disableLearning() with open(input_file, "rb") as eggs_input: csv_reader = csv.reader(eggs_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: twelvth_beat = float(row[0]) pitch_value = float(row[1]) result = model.run({"pitch": pitch_value}) output.write(twelvth_beat, pitch_value, result, prediction_step=1) output.close()
def run_sine_experiment(): input_file = "cpu.csv" #generate_data.run(input_file) #model_params = swarm_over_data() attempt = 0 if PLOT: output = NuPICPlotOutput("final_cpu_output") else: output = NuPICFileOutput("final_cpu_output") #model = ModelFactory.create(model_params) model = ModelFactory.create(model_params.MODEL_PARAMS) model.enableInference({"predictedField": "cpu"}) adapter = 0 for row in range(1, 300): s = time.strftime(DATE_FORMAT) timestamp = datetime.datetime.strptime(s, DATE_FORMAT) #timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) cpu1 = psutil.cpu_percent(interval=1) cpu = float(cpu1) result = model.run({"cpu": cpu}) prediction = result.inferences["multiStepBestPredictions"][1] anomalyScore = result.inferences['anomalyScore'] anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability( cpu, anomalyScore, timestamp) print 'anomalyLikelihood: ', anomalyLikelihood if anomalyScore > 0.75: print "anomalyScore is high: ", 'anomalyScore: ', str( anomalyScore ), 'anomalyLikelihood: ', anomalyLikelihood, " CPU@: ", cpu, " steps: ", str( adapter) adapter = adapter + 20 if adapter >= 300: run_adaptation_strategy(attempt, cpu, anomalyLikelihood) attempt += 1 adapter = 0 print "reset timer for new adaptation action" else: print "anomalyScore is high: ", 'anomalyScore: ', str( anomalyScore ), 'anomalyLikelihood: ', anomalyLikelihood, " CPU@: ", cpu, " steps: ", str( adapter) run_adaptation_strategy(attempt, cpu, anomalyLikelihood) attempt += 1 #with open("/tmp/output.log", "w") as loutput: #subprocess.call("docker service scale web=1", shell=True, stdout=loutput, stderr=loutput) #output.write(timestamp, cpu, prediction, anomalyScore) try: plt.pause(SECONDS_PER_STEP) except: pass row += 1
def run_experiment(): generate_data.run() swarm_over_data() import model_params model = ModelFactory.create(model_params.MODEL_PARAMS) model.enableInference({"predictedField": "sine"}) output = NuPICPlotOutput("sine_out", show_anomaly_score=True) with open("sine.csv", "rb") as sine_input: csv_reader = csv.reader(sine_input) # Skip headers csv_reader.next() csv_reader.next() csv_reader.next() # Real Data for row in csv_reader: angle = float(row[0]) sine_value = float(row[1]) result = model.run({"sine": sine_value}) output.write(angle, sine_value, result) output.close()
def run_sine_experiment(): input_file = "sine.csv" generate_data.run(input_file) model_params = swarm_over_data() if PLOT: output = NuPICPlotOutput("sine_output", show_anomaly_score=True) else: output = NuPICFileOutput("sine_output", show_anomaly_score=True) model = ModelFactory.create(model_params) model.enableInference({"predictedField": "sine"}) with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: angle = float(row[0]) sine_value = float(row[1]) result = model.run({"sine": sine_value}) output.write(angle, sine_value, result, prediction_step=1) output.close()
def run_mem_experiment(): cur_dir = os.getcwd() input_file = source = os.path.join(cur_dir, 'mem/mem.csv') print input_file #mem_generate_data.run(input_file) print 'input_file', input_file model_params = swarm_over_data() model = ModelFactory.create(model_params) model.enableInference({"predictedField": "mem"}) out_file = 'mem/final_mem.csv' if PLOT: output = NuPICPlotOutput(out_file) else: output = NuPICFileOutput(out_file) with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data sumOfUtilityFitness = 0.0 sumOfWeaight = 0.0 for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) mem = float(row[1]) result = model.run({"mem": mem}) prediction = result.inferences["multiStepBestPredictions"][1] anomalyScore = result.inferences['anomalyScore'] anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability( mem, anomalyScore, timestamp) sumOfUtilityFitness = sumOfUtilityFitness + ( float(mem) * float(anomalyLikelihood)) sumOfWeaight = sumOfWeaight + float(anomalyLikelihood) output.write(timestamp, mem, prediction, anomalyScore) output.close() print 'sumOfWeaight: ', sumOfWeaight, 'sumOfUtilityFitness: ', sumOfUtilityFitness result_output = 'mem/final_mem_out.csv' utilityOfmem = 0.0 with open(result_output, "rb") as result_input: csv_reader = csv.reader(result_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() for row in csv_reader: anomalyLikelihood = float(row[3]) utilityOfmem = utilityOfmem + (anomalyLikelihood * sumOfUtilityFitness) / sumOfWeaight print 'utilityOfmem: ', utilityOfmem move_model()
def run_model(input_file, output_file, model, train=False, plot=True): if plot: output = NuPICPlotOutput(output_file, show_anomaly_score=True) else: output = NuPICFileOutput(output_file, show_anomaly_score=True) if train: print "Enable learning." model.enableLearning() else: print "Not for training. Disabling learning." model.disableLearning() with open(input_file, "rb") as eggs_input: csv_reader = csv.reader(eggs_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: twelvth_beat = float(row[0]) pitch_value = float(row[1]) result = model.run({"pitch": pitch_value}) output.write(twelvth_beat, pitch_value, result, prediction_step=1) output.close()
def run_disk_experiment(): input_file = "disk/disk.csv" disk_generate_data.run(input_file) disk_params = swarm_over_data() model_disk = ModelFactory.create(disk_params) model_disk.enableInference({"predictedField": "disk"}) if PLOT: output = NuPICPlotOutput("disk/final_disk_output") else: output = NuPICFileOutput("disk/final_disk_output") with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data sumOfUtilityFitness = 0.0 sumOfWeaight = 0.0 for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) disk = float(row[1]) result_disk = model_disk.run({"disk": disk}) prediction = result_disk.inferences["multiStepBestPredictions"][1] anomalyScore = result_disk.inferences['anomalyScore'] anomalyLikelihood = anomalyLikelihoodHelper.anomalyProbability( disk, anomalyScore, timestamp) sumOfUtilityFitness = sumOfUtilityFitness + ( float(disk) * float(anomalyLikelihood)) sumOfWeaight = sumOfWeaight + float(anomalyLikelihood) output.write(timestamp, disk, prediction, anomalyScore) output.close() print 'sumOfWeaight: ', sumOfWeaight, 'sumOfUtilityFitness: ', sumOfUtilityFitness result_output = 'disk/final_disk_output_out.csv' utilityOfDisk = 0.0 with open(result_output, "rb") as result_input: csv_reader = csv.reader(result_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() for row in csv_reader: anomalyLikelihood = float(row[3]) utilityOfDisk = utilityOfDisk + ( anomalyLikelihood * sumOfUtilityFitness) / sumOfWeaight print 'utilityOfDisk: ', utilityOfDisk move_model()
def run_mem_experiment(): input_file = "mem.csv" #generate_data.run(input_file) meme_params = swarm_over_data(SWARM_CONFIG) model_mem = ModelFactory.create(meme_params) model_mem.enableInference({"predictedField": "mem"}) if PLOT: output = NuPICPlotOutput("final_mem_output") else: output = NuPICFileOutput("final_mem_output") with open(input_file, "rb") as sine_input: csv_reader = csv.reader(sine_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() # the real data for row in csv_reader: timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT) mem = float(row[1]) result_mem = model_mem.run({"mem": mem}) prediction = result_mem.inferences["multiStepBestPredictions"][1] anomalyScore = result_mem.inferences['anomalyScore'] output.write(timestamp, mem, prediction, anomalyScore) sumOfUtilityFitness= sumOfUtilityFitness + (float(mem) * float(anomalyLikelihood)) sumOfWeaight = sumOfWeaight + float(anomalyLikelihood) output.close() print 'sumOfWeaight: ', sumOfWeaight, 'sumOfUtilityFitness: ', sumOfUtilityFitness result_output = 'final_cpu_output' with open(input_file, "rb") as result_input: csv_reader = csv.reader(result_input) # skip header rows csv_reader.next() csv_reader.next() csv_reader.next() for row in csv_reader: anomalyLikelihood = float(row[3]) utilityOfmem= utilityOfCpu + (anomalyLikelihood * sumOfUtilityFitness)/sumOfWeaight print 'utilityOfCpu: ', utilityOfmem