def split_online_b(online_b_data, test_idx):
primer = pd.DataFrame(columns=online_b_data.get_dataframe().columns)
eval = pd.DataFrame(columns=online_b_data.get_dataframe().columns)
for wl_id in online_b_data.get_workload_ids():
curr_ds = online_b_data.get_specific_workload(wl_id)
for idx in range(curr_ds.get_dataframe().values.shape[0]):
if idx == test_idx:
eval = eval.append(curr_ds.get_dataframe().iloc[idx:idx + 1],
ignore_index=True)
else:
primer = primer.append(curr_ds.get_dataframe().iloc[idx:idx +
1],
ignore_index=True)
primer = Dataset(dataframe=primer)
eval = Dataset(dataframe=eval)
latency_gt = eval.get_column_values('latency')
eval = eval.prune_columns(['workload id'] + eval.get_tuning_knob_headers())
return primer, eval, latency_gt
def main():
# only training GPRs for offline loads
dataset = Dataset(file_path=DATASET_PATHS['offline_workload'])
# load the pruned metric headers
pruned_metrics = Dataset.load_pruned_metrics()
# prune the dataset
dataset = dataset.prune_columns(pruned_metrics + ['workload id'] +
dataset.get_tuning_knob_headers())
# build the GPRs
start = time()
gprs = WorkloadGPR(dataset=dataset)
LOG.info(f"Finished building GPRs in {round(time() - start)} seconds.")
# pickle 'em
LOG.info("Pickling GPRs...")
start = time()
gprs.pickle_models()
LOG.info(f"Finished pickling models in {round(time() - start)} seconds.")
def main():
LOG.debug('Clearing out all of the workload models.')
clear_wl_models()
dataset = Dataset(file_path=DATASET_PATHS['offline_workload'])
pruned_metrics = Dataset.load_pruned_metrics()
pruned_dataset = dataset.prune_columns(pruned_metrics + ['workload id'] +
dataset.get_tuning_knob_headers())
df = pruned_dataset.get_dataframe()
# pick the ith data to use as validation
i = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
workload_ids = pruned_dataset.get_workload_ids()
validation_df = pd.concat(
[df[df['workload id'] == wid].iloc[i] for wid in workload_ids])
validation_idx = validation_df.index
valid_dataset = Dataset(dataframe=validation_df)
diff_idx = df.index.difference(validation_df.index)
train_df = df.iloc[diff_idx]
train_dataset = Dataset(dataframe=train_df)
# LOG.info("Fitting input scaler...")
# scaler = StandardScaler()
# scaler.fit(train_df[dataset.get_tuning_knob_headers()].values)
scaler = None
LOG.info("Training workload GPRs...")
gprs = WorkloadGPR(dataset=train_dataset, scaler=scaler)
LOG.info("Validating GPRs...")
train = {}
result = {}
for pm in pruned_metrics:
for wid in workload_ids:
name = f"{pm}|{wid}"
model = gprs.get_model(wid, pm)
# train
# X = train_df[dataset.get_tuning_knob_headers()].values
# X = scaler.transform(X)
# y = train_df[pm].values
# y_hat = model.predict(X)
# mape = np.mean(np.abs((y - y_hat) / y)) * 100
# train[name] = mape
# validation
X = validation_df[dataset.get_tuning_knob_headers()].values
if scaler is not None:
X = scaler.transform(X)
y = validation_df[pm].values
y_hat = model.predict(X)
mape = np.mean(np.abs((y - y_hat) / y)) * 100
result[name] = mape
# LOG.info('%s: %s', name, mape)
# LOG.info('Training average MAPE: %s',
# np.array(list(train.values())).mean())
LOG.info('Validation average MAPE: %s',
np.array(list(result.values())).mean())