def run_workload_characterization(metric_data):
# Performs workload characterization on the metric_data and returns
# a set of pruned metrics.
#
# Parameters:
# metric_data is a dictionary of the form:
# - 'data': 2D numpy matrix of metric data (results x metrics)
# - 'rowlabels': a list of identifiers for the rows in the matrix
# - 'columnlabels': a list of the metric names corresponding to
# the columns in the data matrix
matrix = metric_data['data']
columnlabels = metric_data['columnlabels']
# Remove any constant columns
nonconst_matrix = []
nonconst_columnlabels = []
for col, cl in zip(matrix.T, columnlabels):
if np.any(col != col[0]):
nonconst_matrix.append(col.reshape(-1, 1))
nonconst_columnlabels.append(cl)
assert len(nonconst_matrix) > 0, "Need more data to train the model"
nonconst_matrix = np.hstack(nonconst_matrix)
n_rows, n_cols = nonconst_matrix.shape
# Bin each column (metric) in the matrix by its decile
binner = Bin(bin_start=1, axis=0)
binned_matrix = binner.fit_transform(nonconst_matrix)
# Shuffle the matrix rows
shuffle_indices = get_shuffle_indices(n_rows)
shuffled_matrix = binned_matrix[shuffle_indices, :]
# Fit factor analysis model
fa_model = FactorAnalysis()
# For now we use 5 latent variables
fa_model.fit(shuffled_matrix, nonconst_columnlabels, n_components=5)
# Components: metrics * factors
components = fa_model.components_.T.copy()
# Run Kmeans for # clusters k in range(1, num_nonduplicate_metrics - 1)
# K should be much smaller than n_cols in detK, For now max_cluster <= 20
kmeans_models = KMeansClusters()
kmeans_models.fit(components,
min_cluster=1,
max_cluster=min(n_cols - 1, 20),
sample_labels=nonconst_columnlabels,
estimator_params={'n_init': 50})
# Compute optimal # clusters, k, using gap statistics
gapk = create_kselection_model("gap-statistic")
gapk.fit(components, kmeans_models.cluster_map_)
# Get pruned metrics, cloest samples of each cluster center
pruned_metrics = kmeans_models.cluster_map_[
gapk.optimal_num_clusters_].get_closest_samples()
# Return pruned metrics
return pruned_metrics
def run_workload_characterization(metric_data):
# Performs workload characterization on the metric_data and returns
# a set of pruned metrics.
#
# Parameters:
# metric_data is a dictionary of the form:
# - 'data': 2D numpy matrix of metric data (results x metrics)
# - 'rowlabels': a list of identifiers for the rows in the matrix
# - 'columnlabels': a list of the metric names corresponding to
# the columns in the data matrix
matrix = metric_data['data']
columnlabels = metric_data['columnlabels']
# Remove any constant columns
nonconst_matrix = []
nonconst_columnlabels = []
for col, cl in zip(matrix.T, columnlabels):
if np.any(col != col[0]):
nonconst_matrix.append(col.reshape(-1, 1))
nonconst_columnlabels.append(cl)
assert len(nonconst_matrix) > 0, "Need more data to train the model"
nonconst_matrix = np.hstack(nonconst_matrix)
n_rows, n_cols = nonconst_matrix.shape
# Bin each column (metric) in the matrix by its decile
binner = Bin(bin_start=1, axis=0)
binned_matrix = binner.fit_transform(nonconst_matrix)
# Shuffle the matrix rows
shuffle_indices = get_shuffle_indices(n_rows)
shuffled_matrix = binned_matrix[shuffle_indices, :]
# Fit factor analysis model
fa_model = FactorAnalysis()
# For now we use 5 latent variables
fa_model.fit(shuffled_matrix, nonconst_columnlabels, n_components=5)
# Components: metrics * factors
components = fa_model.components_.T.copy()
# Run Kmeans for # clusters k in range(1, num_nonduplicate_metrics - 1)
# K should be much smaller than n_cols in detK, For now max_cluster <= 20
kmeans_models = KMeansClusters()
kmeans_models.fit(components, min_cluster=1,
max_cluster=min(n_cols - 1, 20),
sample_labels=nonconst_columnlabels,
estimator_params={'n_init': 50})
# Compute optimal # clusters, k, using gap statistics
gapk = create_kselection_model("gap-statistic")
gapk.fit(components, kmeans_models.cluster_map_)
# Get pruned metrics, cloest samples of each cluster center
pruned_metrics = kmeans_models.cluster_map_[gapk.optimal_num_clusters_].get_closest_samples()
# Return pruned metrics
return pruned_metrics
def run_workload_characterization(metric_data):
## Performs workload characterization on the metric_data and returns
## a set of pruned metrics.
##
## Parameters:
## metric_data is a dictionary of the form:
## - 'data': 2D numpy matrix of metric data (results x metrics)
## - 'rowlabels': a list of identifiers for the rows in the matrix
## - 'columnlabels': a list of the metric names corresponding to
## the columns in the data matrix
matrix = metric_data['data']
columnlabels = metric_data['columnlabels']
# Remove any constant columns
nonconst_matrix = []
nonconst_columnlabels = []
for col, cl in zip(matrix.T, columnlabels):
if np.any(col != col[0]):
nonconst_matrix.append(col.reshape(-1, 1))
nonconst_columnlabels.append(cl)
nonconst_matrix = np.hstack(nonconst_matrix)
n_rows, n_cols = nonconst_matrix.shape
# Bin each column (metric) in the matrix by its decile
binner = Bin(bin_start=1, axis=0)
binned_matrix = binner.fit_transform(nonconst_matrix)
# Shuffle the matrix rows
shuffle_indices = get_shuffle_indices(n_rows)
shuffled_matrix = binned_matrix[shuffle_indices, :]
# Fit factor analysis model
fa_model = FactorAnalysis()
fa_model.fit(shuffled_matrix, nonconst_columnlabels)
# Run Kmeans for # clusters k in range(2, num_instance_types - 1)
components = fa_model.components_.T.copy()
kmeans_models = KMeansClusters()
kmeans_models.fit(components,
min_cluster=2,
max_cluster=n_cols - 1,
sample_labels=nonconst_columnlabels,
estimator_params={'n_init': 50})
# Compute optimal # clusters, k, using DetK
detk = create_kselection_model("det-k")
detk.fit(components, kmeans_models.cluster_map_)
# Get pruned metrics, cloest samples of each cluster center
pruned_metrics = kmeans_models.cluster_map_[
detk.optimal_num_clusters_].get_closest_samples()
# Return pruned metrics
return pruned_metrics
def setUpClass(cls):
super(TestKSelection, cls).setUpClass()
# Load Iris data
iris = datasets.load_iris()
cls.matrix = iris.data
cls.kmeans_models = KMeansClusters()
cls.kmeans_models.fit(cls.matrix,
min_cluster=1,
max_cluster=10,
sample_labels=iris.target,
estimator_params={
'n_init': 50,
'random_state': 42
})
def run_workload_characterization(metric_data, dbms=None):
# Performs workload characterization on the metric_data and returns
# a set of pruned metrics.
#
# Parameters:
# metric_data is a dictionary of the form:
# - 'data': 2D numpy matrix of metric data (results x metrics)
# - 'rowlabels': a list of identifiers for the rows in the matrix
# - 'columnlabels': a list of the metric names corresponding to
# the columns in the data matrix
start_ts = time.time()
matrix = metric_data['data']
columnlabels = metric_data['columnlabels']
LOG.debug("Workload characterization ~ initial data size: %s",
matrix.shape)
views = None if dbms is None else VIEWS_FOR_PRUNING.get(dbms.type, None)
matrix, columnlabels = DataUtil.clean_metric_data(matrix, columnlabels,
views)
LOG.debug("Workload characterization ~ cleaned data size: %s",
matrix.shape)
# Bin each column (metric) in the matrix by its decile
binner = Bin(bin_start=1, axis=0)
binned_matrix = binner.fit_transform(matrix)
# Remove any constant columns
nonconst_matrix = []
nonconst_columnlabels = []
for col, cl in zip(binned_matrix.T, columnlabels):
if np.any(col != col[0]):
nonconst_matrix.append(col.reshape(-1, 1))
nonconst_columnlabels.append(cl)
assert len(nonconst_matrix) > 0, "Need more data to train the model"
nonconst_matrix = np.hstack(nonconst_matrix)
LOG.debug("Workload characterization ~ nonconst data size: %s",
nonconst_matrix.shape)
# Remove any duplicate columns
unique_matrix, unique_idxs = np.unique(nonconst_matrix,
axis=1,
return_index=True)
unique_columnlabels = [nonconst_columnlabels[idx] for idx in unique_idxs]
LOG.debug("Workload characterization ~ final data size: %s",
unique_matrix.shape)
n_rows, n_cols = unique_matrix.shape
# Shuffle the matrix rows
shuffle_indices = get_shuffle_indices(n_rows)
shuffled_matrix = unique_matrix[shuffle_indices, :]
# Fit factor analysis model
fa_model = FactorAnalysis()
# For now we use 5 latent variables
fa_model.fit(shuffled_matrix, unique_columnlabels, n_components=5)
# Components: metrics * factors
components = fa_model.components_.T.copy()
LOG.info("Workload characterization first part costs %.0f seconds.",
time.time() - start_ts)
# Run Kmeans for # clusters k in range(1, num_nonduplicate_metrics - 1)
# K should be much smaller than n_cols in detK, For now max_cluster <= 20
kmeans_models = KMeansClusters()
kmeans_models.fit(components,
min_cluster=1,
max_cluster=min(n_cols - 1, 20),
sample_labels=unique_columnlabels,
estimator_params={'n_init': 50})
# Compute optimal # clusters, k, using gap statistics
gapk = create_kselection_model("gap-statistic")
gapk.fit(components, kmeans_models.cluster_map_)
LOG.debug("Found optimal number of clusters: %d",
gapk.optimal_num_clusters_)
# Get pruned metrics, cloest samples of each cluster center
pruned_metrics = kmeans_models.cluster_map_[
gapk.optimal_num_clusters_].get_closest_samples()
# Return pruned metrics
save_execution_time(start_ts, "run_workload_characterization")
LOG.info("Workload characterization finished in %.0f seconds.",
time.time() - start_ts)
return pruned_metrics