change to algoroithm tpyes
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@ -21,7 +21,7 @@ from analysis.constraints import ParamConstraintHelper
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from website.models import (PipelineData, PipelineRun, Result, Workload, KnobCatalog,
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from website.models import (PipelineData, PipelineRun, Result, Workload, KnobCatalog,
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MetricCatalog, SessionKnob)
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MetricCatalog, SessionKnob)
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from website.parser import Parser
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from website.parser import Parser
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from website.types import PipelineTaskType
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from website.types import PipelineTaskType, AlgorithmType
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from website.utils import DataUtil, JSONUtil
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from website.utils import DataUtil, JSONUtil
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from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG # pylint: disable=no-name-in-module
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from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG # pylint: disable=no-name-in-module
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from website.settings import (DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE,
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from website.settings import (DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE,
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@ -543,9 +543,8 @@ def configuration_recommendation(recommendation_input):
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session = newest_result.session
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session = newest_result.session
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res = None
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res = None
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assert algorithm in ['gpr', 'dnn']
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if algorithm == 'dnn':
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if algorithm == AlgorithmType.DNN:
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# neural network model
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# neural network model
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model_nn = NeuralNet(n_input=X_samples.shape[1],
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model_nn = NeuralNet(n_input=X_samples.shape[1],
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batch_size=X_samples.shape[0],
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batch_size=X_samples.shape[0],
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@ -562,7 +561,7 @@ def configuration_recommendation(recommendation_input):
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session.dnn_model = model_nn.get_weights_bin()
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session.dnn_model = model_nn.get_weights_bin()
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session.save()
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session.save()
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elif algorithm == 'gpr':
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elif algorithm == AlgorithmType.OTTERTUNE:
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# default gpr model
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# default gpr model
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model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
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model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
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magnitude=DEFAULT_MAGNITUDE,
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magnitude=DEFAULT_MAGNITUDE,
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@ -529,14 +529,14 @@ def handle_result_files(session, files):
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result_id = result.pk
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result_id = result.pk
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response = None
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response = None
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if session.algorithm == AlgorithmType.OTTERTUNE:
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if session.algorithm == AlgorithmType.OTTERTUNE:
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response = chain(aggregate_target_results.s(result.pk),
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response = chain(aggregate_target_results.s(result.pk, session.algorithm),
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map_workload.s(),
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map_workload.s(),
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configuration_recommendation.s()).apply_async()
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configuration_recommendation.s()).apply_async()
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elif session.algorithm == AlgorithmType.DDPG:
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elif session.algorithm == AlgorithmType.DDPG:
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response = chain(train_ddpg.s(result.pk),
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response = chain(train_ddpg.s(result.pk),
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configuration_recommendation_ddpg.s()).apply_async()
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configuration_recommendation_ddpg.s()).apply_async()
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elif session.algorithm == AlgorithmType.DNN:
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elif session.algorithm == AlgorithmType.DNN:
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response = chain(aggregate_target_results.s(result.pk, 'dnn'),
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response = chain(aggregate_target_results.s(result.pk, session.algorithm),
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map_workload.s(),
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map_workload.s(),
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configuration_recommendation.s()).apply_async()
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configuration_recommendation.s()).apply_async()
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