def initialize_models(self):
if self.verbose_:
print("Initializing models for # knobs={}\n".format(
self.featured_knobs_.size))
with stopwatch("workload mapping model creation"):
n_values, cat_indices, params = prep.dummy_encoder_helper(
self.dbms_name, self.featured_knobs_)
if n_values.size > 0:
self.dummy_encoder_ = prep.DummyEncoder(n_values, cat_indices)
else:
self.dummy_encoder_ = None
self.X_scaler_ = StandardScaler()
self.y_scaler_ = StandardScaler()
data_map = {}
for i, wd in enumerate(self.workload_dirs_):
# Load and filter data
Xpath = os.path.join(wd, "X_data_enc.npz")
ypath = os.path.join(wd, "y_data_enc.npz")
X = Matrix.load_matrix(Xpath)
y = Matrix.load_matrix(ypath)
X = X.filter(self.featured_knobs_, "columns")
y = y.filter(self.featured_metrics_, "columns")
assert np.array_equal(X.columnlabels, self.featured_knobs_)
assert np.array_equal(y.columnlabels, self.featured_metrics_)
assert np.array_equal(X.rowlabels, y.rowlabels)
num_samples = X.shape[0]
if num_samples > self.MAX_SAMPLES:
print "Shrinking {} samples to {}".format(
num_samples, self.MAX_SAMPLES)
rand_indices = prep.get_shuffle_indices(
num_samples)[:self.MAX_SAMPLES]
X = Matrix(X.data[rand_indices], X.rowlabels[rand_indices],
X.columnlabels)
y = Matrix(y.data[rand_indices], y.rowlabels[rand_indices],
y.columnlabels)
num_samples = X.shape[0]
assert num_samples <= self.MAX_SAMPLES
assert num_samples == y.shape[0]
# Dummy-code categorical knobs
if self.dummy_encoder_ is not None:
if i == 0:
# Just need to fit this once
self.dummy_encoder_.fit(X.data,
columnlabels=X.columnlabels)
X = Matrix(self.dummy_encoder_.transform(X.data),
X.rowlabels, self.dummy_encoder_.columnlabels)
self.X_scaler_.partial_fit(X.data)
self.y_scaler_.partial_fit(y.data)
data_map[wd] = (X, y)
if self.dummy_encoder_ is not None:
# Fix X_scaler wrt categorical features
prep.fix_scaler(self.X_scaler_, self.dummy_encoder_, params)
# Scale X/y
all_ys = []
for wd, (X, y) in data_map.iteritems():
X.data = self.X_scaler_.transform(X.data)
y.data = self.y_scaler_.transform(y.data)
all_ys.append(y.data)
# Concat all ys and compute deciles
all_ys = np.vstack(all_ys)
self.y_binner_ = prep.Bin(0, axis=0)
self.y_binner_.fit(all_ys)
del all_ys
# Bin y by deciles and fit scaler
self.y_gp_scaler_ = StandardScaler()
for wd, (X, y) in data_map.iteritems():
y.data = self.y_binner_.transform(y.data)
self.y_gp_scaler_.partial_fit(y.data)
# Recenter y-values
for wd, (X, y) in data_map.iteritems():
y.data = self.y_gp_scaler_.transform(y.data)
njobs = len(data_map)
iterable = [(i,wd,ws,njobs,self.verbose_) for i,(wd,ws) \
in enumerate(data_map.iteritems())]
if self.pool_ is not None:
res = self.pool_.map(worker_create_model, iterable)
else:
res = []
for item in iterable:
res.append(worker_create_model(item))
self.workload_states_ = dict(res)
def map_workload(self, X_client, y_client):
# tuner = TunerContext()
with stopwatch("workload mapping - preprocessing"):
# # Recompute the GPR models if the # of knobs to tune has
# # changed (incremental knob selection feature is enabled)
# tuner_feat_knobs = tuner.featured_knobs
# if not np.array_equal(tuner_feat_knobs, self.featured_knobs_):
# print ("# knobs: {} --> {}. Re-creating models"
# .format(tuner_feat_knobs.size,
# self.featured_knobs_.size))
# assert tuner_feat_knobs.size != self.featured_knobs_.size
# assert tuner.incremental_knob_selection == True
# self.featured_knobs_ = tuner_feat_knobs
# self.initialize_models()
# gc.collect()
# Filter be featured knobs & metrics
X_client = X_client.filter(self.featured_knobs_, "columns")
y_client = y_client.filter(self.featured_metrics_, "columns")
# Generate unique X,y matrices
X_client, y_client = get_unique_matrix(X_client, y_client)
# Preprocessing steps
if self.dummy_encoder_ is not None:
X_client = Matrix(self.dummy_encoder_.transform(X_client.data),
X_client.rowlabels,
self.dummy_encoder_.columnlabels)
X_client.data = self.X_scaler_.transform(X_client.data)
# Create y_client scaler with prior and transform client data
y_client_scaler = copy.deepcopy(self.y_scaler_)
y_client_scaler.n_samples_seen_ = 1
y_client_scaler.partial_fit(y_client.data)
y_client.data = y_client_scaler.transform(y_client.data)
# Bin and recenter client data
y_client.data = self.y_binner_.transform(y_client.data)
y_client.data = self.y_gp_scaler_.transform(y_client.data)
# Compute workload scores in parallel
njobs = len(self.workload_states_)
iterable = [(i, wd, ws, X_client, y_client, njobs, self.verbose_) \
for i,(wd,ws) in enumerate(self.workload_states_.iteritems())]
with stopwatch("workload mapping - predictions"):
if self.pool_ is not None:
wkld_scores = self.pool_.map(worker_score_workload, iterable)
else:
wkld_scores = []
for item in iterable:
wkld_scores.append(worker_score_workload(item))
sorted_wkld_scores = sorted(wkld_scores, key=operator.itemgetter(1))
print ""
print "WORKLOAD SCORES"
for wkld, score in sorted_wkld_scores:
print "{0}: {1:.2f}".format(os.path.basename(wkld), score)
return sorted_wkld_scores[0][0]
def run_lasso(dbms,
basepaths,
savedir,
featured_metrics,
knobs_to_ignore,
include_polynomial_features=True):
import gc
# Load matrices
assert len(basepaths) > 0
Xs = []
ys = []
with stopwatch("matrix concatenation"):
for basepath in basepaths:
X_path = os.path.join(basepath, "X_data_enc.npz")
y_path = os.path.join(basepath, "y_data_enc.npz")
Xs.append(Matrix.load_matrix(X_path))
ys.append(
Matrix.load_matrix(y_path).filter(featured_metrics, "columns"))
# Combine matrix data if more than 1 matrix
if len(Xs) > 1:
X = Matrix.vstack(Xs, require_equal_columnlabels=True)
y = Matrix.vstack(ys, require_equal_columnlabels=True)
else:
X = Xs[0]
y = ys[0]
del Xs
del ys
gc.collect()
with stopwatch("preprocessing"):
# Filter out columns with near zero standard
# deviation (i.e., constant columns)
if y.shape[1] > 1:
column_mask = ~stdev_zero(y.data, axis=0)
filtered_columns = y.columnlabels[column_mask]
y = y.filter(filtered_columns, 'columns')
column_mask = ~stdev_zero(X.data, axis=0)
removed_columns = X.columnlabels[~column_mask]
print "removed columns = {}".format(removed_columns)
filtered_columns = set(X.columnlabels[column_mask])
filtered_columns -= set(knobs_to_ignore)
filtered_columns = np.array(sorted(filtered_columns))
X = X.filter(filtered_columns, 'columns')
print "\ncolumnlabels:", X.columnlabels
# Dummy-code categorical features
n_values, cat_feat_indices, _ = dummy_encoder_helper(
dbms, X.columnlabels)
if len(cat_feat_indices) > 0:
encoder = DummyEncoder(n_values, cat_feat_indices)
encoder.fit(X.data, columnlabels=X.columnlabels)
X = Matrix(encoder.transform(X.data), X.rowlabels,
encoder.columnlabels)
# Scale the data
X_standardizer = StandardScaler()
X.data = X_standardizer.fit_transform(X.data)
y_standardizer = StandardScaler()
y.data = y_standardizer.fit_transform(y.data)
if include_polynomial_features:
X_poly = PolynomialFeatures()
X_data = X_poly.fit_transform(X.data)
X_columnlabels = np.expand_dims(np.array(X.columnlabels,
dtype=str),
axis=0)
X_columnlabels = X_poly.fit_transform(X_columnlabels).squeeze()
X = Matrix(X_data, X.rowlabels, X_columnlabels)
# Shuffle the data rows (experiments x metrics)
shuffler = Shuffler(shuffle_rows=True, shuffle_columns=False)
X = shuffler.fit_transform(X, copy=False)
y = shuffler.transform(y, copy=False)
assert np.array_equal(X.rowlabels, y.rowlabels)
gc.collect()
print "\nfeatured_metrics:", featured_metrics
with stopwatch("lasso paths"):
# Fit the model to calculate the components
alphas, coefs, _ = get_coef_range(X.data, y.data)
# Save model
np.savez(os.path.join(savedir, "lasso_path.npz"),
alphas=alphas,
coefs=coefs,
feats=X.columnlabels,
metrics=y.columnlabels)
with stopwatch("lasso processing"):
nfeats = X.columnlabels.shape[0]
lasso = Lasso(alphas, X.columnlabels, coefs)
print lasso.get_top_summary(nfeats, "")
top_knobs = get_features_list(lasso.get_top_features(n=nfeats))
print "\nfeat list length: {}".format(len(top_knobs))
print "nfeats = {}".format(nfeats)
top_knobs = lasso.get_top_features(nfeats)
print top_knobs
final_ordering = []
for knob in top_knobs:
if '#' in knob:
knob = knob.split('#')[0]
if knob not in final_ordering:
final_ordering.append(knob)
else:
final_ordering.append(knob)
final_ordering = np.append(final_ordering, removed_columns)
with open(os.path.join(savedir, "featured_knobs.txt"), "w") as f:
f.write("\n".join(final_ordering))
