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fix bugs

This commit is contained in:
yangdsh 2020-01-12 13:44:10 +00:00 committed by Dana Van Aken
parent 24194293bc
commit d3c7bb661d
1 changed files with 11 additions and 12 deletions
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21
server/website/website/tasks/async_tasks.py
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@ -25,8 +25,7 @@ from analysis.gpr import ucb
from analysis.gpr.optimize import tf_optimize
from analysis.preprocessing import Bin, DummyEncoder
from analysis.constraints import ParamConstraintHelper
from website.models import (PipelineData, PipelineRun, Result, Workload, KnobCatalog, SessionKnob,
MetricCatalog)
from website.models import PipelineData, PipelineRun, Result, Workload, SessionKnob, MetricCatalog
from website import db
from website.types import PipelineTaskType, AlgorithmType, VarType
from website.utils import DataUtil, JSONUtil
@ -336,7 +335,7 @@ def train_ddpg(result_id):
prev_result_id = result_id
else:
base_result_id = session_results[0].pk
prev_result_id = session_results[len(session_results)-1].pk
prev_result_id = session_results[len(session_results) - 1].pk
base_result = Result.objects.filter(pk=base_result_id)
prev_result = Result.objects.filter(pk=prev_result_id)
@ -381,7 +380,7 @@ def train_ddpg(result_id):
result.session.dbms.pk, result.session.target_objective)
# Calculate the reward
if DDPG_SIMPLE_REWARD:
if params['DDPG_SIMPLE_REWARD']:
objective = objective / base_objective
if metric_meta[target_objective].improvement == '(less is better)':
reward = -objective
@ -652,7 +651,8 @@ def combine_workload(target_data):
X_min[i] = col_min
X_max[i] = col_max
return X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min
return X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min,\
dummy_encoder, constraint_helper
@task(base=ConfigurationRecommendation, name='configuration_recommendation')
@ -661,7 +661,7 @@ def configuration_recommendation(recommendation_input):
LOG.info('configuration_recommendation called')
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
session = newest_result.session
params = session.hyper_parameters
params = JSONUtil.loads(session.hyper_parameters)
if target_data['bad'] is True:
target_data_res = create_and_save_recommendation(
@ -672,9 +672,8 @@ def configuration_recommendation(recommendation_input):
AlgorithmType.name(algorithm), JSONUtil.dumps(target_data, pprint=True))
return target_data_res
latest_pipeline_run = PipelineRun.objects.get(pk=target_data['pipeline_run'])
X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min = combine_workload(target_data)
X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min,\
dummy_encoder, constraint_helper = combine_workload(target_data)
# FIXME: we should generate more samples and use a smarter sampling
# technique
@ -698,9 +697,9 @@ def configuration_recommendation(recommendation_input):
# make sure it is within the range.
dist = sum(np.square(X_max - X_scaled[item[1]]))
if dist < 0.001:
X_samples = np.vstack((X_samples, X_scaled[item[1]] - abs(GPR_EPS)))
X_samples = np.vstack((X_samples, X_scaled[item[1]] - abs(params['GPR_EPS'])))
else:
X_samples = np.vstack((X_samples, X_scaled[item[1]] + abs(GPR_EPS)))
X_samples = np.vstack((X_samples, X_scaled[item[1]] + abs(params['GPR_EPS'])))
i = i + 1
except queue.Empty:
break