support enum/boolean in GPR/DNN
This commit is contained in:
bohanjason
Dana Van Aken
parent
4b37dec988
commit
6d76b22e3b
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@ -425,13 +425,17 @@ def upload_batch(result_dir=None, sort=True, upload_code=None):
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upload_result(result_dir=result_dir, prefix=prefix, upload_code=upload_code)
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LOG.info('Uploaded result %d/%d: %s__*.json', i + 1, count, prefix)
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@task
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def dump_database():
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dumpfile = os.path.join(dconf.DB_DUMP_DIR, dconf.DB_NAME + '.dump')
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if dconf.DB_TYPE == 'oracle':
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if not dconf.ORACLE_FLASH_BACK and file_exists(dumpfile):
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LOG.info('%s already exists ! ', dumpfile)
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return False
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else:
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if file_exists(dumpfile):
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LOG.info('%s already exists ! ', dumpfile)
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return False
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if dconf.ORACLE_FLASH_BACK:
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LOG.info('create restore point %s for database %s in %s', dconf.RESTORE_POINT,
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@ -582,7 +586,7 @@ def loop(i):
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@task
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def run_loops(max_iter=1):
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def run_loops(max_iter=10):
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# dump database if it's not done before.
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dump = dump_database()
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@ -14,8 +14,8 @@ LOG = logging.getLogger(__name__)
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# tunable knobs in the KnobCatalog will be used instead.
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DEFAULT_TUNABLE_KNOBS = {
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DBMSType.POSTGRES: {
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"global.checkpoint_completion_target",
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"global.default_statistics_target",
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"global.autovacuum",
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"global.archive_mode",
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"global.effective_cache_size",
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"global.maintenance_work_mem",
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"global.max_wal_size",
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@ -59,7 +59,7 @@ STORAGE_PERCENT = 0.8
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SESSION_NUM = 50.0
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def set_default_knobs(session):
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def set_default_knobs(session, cascade=True):
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dbtype = session.dbms.type
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default_tunable_knobs = DEFAULT_TUNABLE_KNOBS.get(dbtype)
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@ -71,7 +71,19 @@ def set_default_knobs(session):
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tunable = knob.name in default_tunable_knobs
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minval = knob.minval
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if knob.vartype in (VarType.INTEGER, VarType.REAL):
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# set session knob tunable in knob catalog
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if tunable and cascade:
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knob.tunable = True
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knob.save()
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if knob.vartype is VarType.ENUM:
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enumvals = knob.enumvals.split(',')
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minval = 0
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maxval = len(enumvals) - 1
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elif knob.vartype is VarType.BOOL:
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minval = 0
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maxval = 1
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elif knob.vartype in (VarType.INTEGER, VarType.REAL):
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vtype = int if knob.vartype == VarType.INTEGER else float
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minval = vtype(minval) if minval is not None else MINVAL
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@ -19,6 +19,9 @@ TOP_NUM_CONFIG = 10
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# ---CONSTRAINTS CONSTANTS---
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# address categorical knobs (enum, boolean)
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ENABLE_DUMMY_ENCODER = False
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# Initial probability to flip categorical feature in apply_constraints
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# server/analysis/constraints.py
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INIT_FLIP_PROB = 0.3
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@ -43,7 +43,7 @@ from website.settings import (USE_GPFLOW, DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUD
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DNN_TRAIN_ITER, DNN_EXPLORE, DNN_EXPLORE_ITER,
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DNN_NOISE_SCALE_BEGIN, DNN_NOISE_SCALE_END,
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DNN_DEBUG, DNN_DEBUG_INTERVAL, GPR_DEBUG, UCB_BETA,
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GPR_MODEL_NAME)
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GPR_MODEL_NAME, ENABLE_DUMMY_ENCODER)
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from website.settings import INIT_FLIP_PROB, FLIP_PROB_DECAY
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from website.types import VarType
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@ -526,6 +526,7 @@ def configuration_recommendation(recommendation_input):
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X_matrix = np.vstack([X_target, X_workload])
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# Dummy encode categorial variables
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if ENABLE_DUMMY_ENCODER:
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categorical_info = DataUtil.dummy_encoder_helper(X_columnlabels,
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mapped_workload.dbms)
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dummy_encoder = DummyEncoder(categorical_info['n_values'],
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@ -533,10 +534,15 @@ def configuration_recommendation(recommendation_input):
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categorical_info['cat_columnlabels'],
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categorical_info['noncat_columnlabels'])
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X_matrix = dummy_encoder.fit_transform(X_matrix)
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binary_encoder = categorical_info['binary_vars']
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# below two variables are needed for correctly determing max/min on dummies
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binary_index_set = set(categorical_info['binary_vars'])
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total_dummies = dummy_encoder.total_dummies()
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else:
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dummy_encoder = None
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binary_encoder = None
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binary_index_set = []
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total_dummies = 0
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# Scale to N(0, 1)
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X_scaler = StandardScaler()
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@ -566,7 +572,7 @@ def configuration_recommendation(recommendation_input):
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# Set up constraint helper
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constraint_helper = ParamConstraintHelper(scaler=X_scaler,
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encoder=dummy_encoder,
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binary_vars=categorical_info['binary_vars'],
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binary_vars=binary_encoder,
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init_flip_prob=INIT_FLIP_PROB,
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flip_prob_decay=FLIP_PROB_DECAY)
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@ -686,6 +692,8 @@ def configuration_recommendation(recommendation_input):
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best_config_idx = np.argmin(res.minl.ravel())
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best_config = res.minl_conf[best_config_idx, :]
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best_config = X_scaler.inverse_transform(best_config)
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if ENABLE_DUMMY_ENCODER:
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# Decode one-hot encoding into categorical knobs
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best_config = dummy_encoder.inverse_transform(best_config)
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