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Merged
merged 7 commits into from
Mar 25, 2021
Merged

Add device context parameter #57

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1c6eefd
Initial impl
Mar 17, 2021
24c24f2
Add another algs
Mar 17, 2021
3da61ba
Fix kmeans warning
Mar 17, 2021
f75263e
Update config
Mar 17, 2021
9653d96
Apply comments and fix pep
Mar 18, 2021
25b09b4
Fix device args
Mar 22, 2021
3f9c35d
Fix pep8
Mar 23, 2021
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21 changes: 21 additions & 0 deletions bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -176,6 +176,10 @@ def parse_args(parser, size=None, loop_types=(),
parser.add_argument('--no-intel-optimized', default=False, action='store_true',
help='Use no intel optimized version. '
'Now avalible for scikit-learn benchmarks'),
parser.add_argument('--device', default='None', type=str,
choices=('host', 'cpu', 'gpu', 'None'),
help='Execution context device')

for data in ['X', 'y']:
for stage in ['train', 'test']:
parser.add_argument(f'--file-{data}-{stage}',
Expand All @@ -197,6 +201,14 @@ def parse_args(parser, size=None, loop_types=(),
except ImportError:
print('Failed to import daal4py.sklearn.patch_sklearn.'
'Use stock version scikit-learn', file=sys.stderr)
params.device = 'None'
else:
if params.device != 'None':
print('Device context is not supported for stock scikit-learn.'
'Please use --no-intel-optimized=False with'
f'--device={params.device} parameter. Fallback to --device=None.',
file=sys.stderr)
params.device = 'None'

# disable finiteness check (default)
if not params.check_finiteness:
Expand Down Expand Up @@ -492,3 +504,12 @@ def print_output(library, algorithm, stages, params, functions,
del result['algorithm_parameters']['handle']
output.append(result)
print(json.dumps(output, indent=4))


def run_with_context(params, function):
if params.device != 'None':
from daal4py.oneapi import sycl_context
with sycl_context(params.device):
function()
else:
function()
77 changes: 77 additions & 0 deletions configs/skl_with_context_config.json
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@@ -0,0 +1,77 @@
{
"common": {
"lib": ["sklearn"],
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How stock or intel version of sk is specified for this config?

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Do we need to launch the stock sk in this config? Maybe just add flag no-intel-optimized before config run? I think it's better to separate patched and unpatched sk launches

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Ok - probably its better to skip device != None branches for stock sklearn

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Need to add support to skip these cases?

"data-format": ["pandas"],
"data-order": ["F"],
"dtype": ["float64"],
"device": ["host", "cpu", "gpu", "None"]
},
"cases": [
{
"algorithm": "kmeans",
"dataset": [
{
"source": "synthetic",
"type": "blobs",
"n_clusters": 10,
"n_features": 50,
"training": {
"n_samples": 1000000
}
}
],
"n-clusters": [10]
},
{
"algorithm": "dbscan",
"dataset": [
{
"source": "synthetic",
"type": "blobs",
"n_clusters": 10,
"n_features": 50,
"training": {
"n_samples": 10000
}
}
]
},
{
"algorithm": "linear",
"dataset": [
{
"source": "synthetic",
"type": "regression",
"n_features": 50,
"training": {
"n_samples": 1000000
}
}
]
},
{
"algorithm": "log_reg",
"solver":["lbfgs", "newton-cg"],
"dataset": [
{
"source": "synthetic",
"type": "classification",
"n_classes": 2,
"n_features": 100,
"training": {
"n_samples": 100000
}
},
{
"source": "synthetic",
"type": "classification",
"n_classes": 5,
"n_features": 100,
"training": {
"n_samples": 100000
}
}
]
}
]
}
59 changes: 32 additions & 27 deletions sklearn_bench/dbscan.py
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Expand Up @@ -19,38 +19,43 @@
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import bench
from sklearn.metrics.cluster import davies_bouldin_score

parser = argparse.ArgumentParser(description='scikit-learn DBSCAN benchmark')
parser.add_argument('-e', '--eps', '--epsilon', type=float, default=10.,
help='Radius of neighborhood of a point')
parser.add_argument('-m', '--min-samples', default=5, type=int,
help='The minimum number of samples required in a '
'neighborhood to consider a point a core point')
params = bench.parse_args(parser)

from sklearn.cluster import DBSCAN
def main():
from sklearn.cluster import DBSCAN
from sklearn.metrics.cluster import davies_bouldin_score

# Load generated data
X, _, _, _ = bench.load_data(params, add_dtype=True)
# Load generated data
X, _, _, _ = bench.load_data(params, add_dtype=True)

# Create our clustering object
dbscan = DBSCAN(eps=params.eps, n_jobs=params.n_jobs,
min_samples=params.min_samples, metric='euclidean',
algorithm='auto')
# Create our clustering object
dbscan = DBSCAN(eps=params.eps, n_jobs=params.n_jobs,
min_samples=params.min_samples, metric='euclidean',
algorithm='auto')

# N.B. algorithm='auto' will select DAAL's brute force method when running
# daal4py-patched scikit-learn, and probably 'kdtree' when running unpatched
# scikit-learn.
# N.B. algorithm='auto' will select oneAPI Data Analytics Library (oneDAL)
# brute force method when running daal4py-patched scikit-learn, and probably
# 'kdtree' when running unpatched scikit-learn.

# Time fit
time, _ = bench.measure_function_time(dbscan.fit, X, params=params)
labels = dbscan.labels_
# Time fit
time, _ = bench.measure_function_time(dbscan.fit, X, params=params)
labels = dbscan.labels_

params.n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
acc = davies_bouldin_score(X, labels)
params.n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
acc = davies_bouldin_score(X, labels)

bench.print_output(library='sklearn', algorithm='dbscan', stages=['training'],
params=params, functions=['DBSCAN'], times=[time], accuracies=[acc],
accuracy_type='davies_bouldin_score', data=[X],
alg_instance=dbscan)
bench.print_output(library='sklearn', algorithm='dbscan', stages=['training'],
params=params, functions=['DBSCAN'], times=[time],
accuracies=[acc], accuracy_type='davies_bouldin_score',
data=[X], alg_instance=dbscan)


if __name__ == "__main__":
parser = argparse.ArgumentParser(description='scikit-learn DBSCAN benchmark')
parser.add_argument('-e', '--eps', '--epsilon', type=float, default=10.,
help='Radius of neighborhood of a point')
parser.add_argument('-m', '--min-samples', default=5, type=int,
help='The minimum number of samples required in a '
'neighborhood to consider a point a core point')
params = bench.parse_args(parser)
bench.run_with_context(params, main)
98 changes: 51 additions & 47 deletions sklearn_bench/kmeans.py
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Expand Up @@ -20,63 +20,67 @@
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import bench
import numpy as np
from sklearn.metrics.cluster import davies_bouldin_score

parser = argparse.ArgumentParser(description='scikit-learn K-means benchmark')
parser.add_argument('-i', '--filei', '--fileI', '--init',
type=str, help='Initial clusters')
parser.add_argument('-t', '--tol', type=float, default=0.,
help='Absolute threshold')
parser.add_argument('--maxiter', type=int, default=100,
help='Maximum number of iterations')
parser.add_argument('--n-clusters', type=int, help='Number of clusters')
params = bench.parse_args(parser)

from sklearn.cluster import KMeans
def main():
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import davies_bouldin_score

# Load and convert generated data
X_train, X_test, _, _ = bench.load_data(params)
# Load and convert generated data
X_train, X_test, _, _ = bench.load_data(params)

if params.filei == 'k-means++':
X_init = 'k-means++'
# Load initial centroids from specified path
elif params.filei is not None:
X_init = np.load(params.filei).astype(params.dtype)
params.n_clusters = X_init.shape[0]
# or choose random centroids from training data
else:
np.random.seed(params.seed)
centroids_idx = np.random.randint(0, X_train.shape[0],
size=params.n_clusters)
if hasattr(X_train, "iloc"):
X_init = X_train.iloc[centroids_idx].values
if params.filei == 'k-means++':
X_init = 'k-means++'
# Load initial centroids from specified path
elif params.filei is not None:
X_init = np.load(params.filei).astype(params.dtype)
params.n_clusters = X_init.shape[0]
# or choose random centroids from training data
else:
X_init = X_train[centroids_idx]
np.random.seed(params.seed)
centroids_idx = np.random.randint(0, X_train.shape[0],
size=params.n_clusters)
if hasattr(X_train, "iloc"):
X_init = X_train.iloc[centroids_idx].values
else:
X_init = X_train[centroids_idx]

def fit_kmeans(X, X_init):
alg = KMeans(n_clusters=params.n_clusters, tol=params.tol,
max_iter=params.maxiter, init=X_init, n_init=1)
alg.fit(X)
return alg

def fit_kmeans(X):
global X_init, params
alg = KMeans(n_clusters=params.n_clusters, tol=params.tol,
max_iter=params.maxiter, init=X_init, n_init=1)
alg.fit(X)
return alg
# Time fit
fit_time, kmeans = bench.measure_function_time(fit_kmeans, X_train,
X_init, params=params)

train_predict = kmeans.predict(X_train)
acc_train = davies_bouldin_score(X_train, train_predict)

# Time fit
fit_time, kmeans = bench.measure_function_time(fit_kmeans, X_train, params=params)
# Time predict
predict_time, test_predict = bench.measure_function_time(
kmeans.predict, X_test, params=params)

train_predict = kmeans.predict(X_train)
acc_train = davies_bouldin_score(X_train, train_predict)
acc_test = davies_bouldin_score(X_test, test_predict)

# Time predict
predict_time, test_predict = bench.measure_function_time(
kmeans.predict, X_test, params=params)
bench.print_output(library='sklearn', algorithm='kmeans',
stages=['training', 'prediction'],
params=params, functions=['KMeans.fit', 'KMeans.predict'],
times=[fit_time, predict_time],
accuracy_type='davies_bouldin_score',
accuracies=[acc_train, acc_test], data=[X_train, X_test],
alg_instance=kmeans)

acc_test = davies_bouldin_score(X_test, test_predict)

bench.print_output(library='sklearn', algorithm='kmeans',
stages=['training', 'prediction'],
params=params, functions=['KMeans.fit', 'KMeans.predict'],
times=[fit_time, predict_time], accuracy_type='davies_bouldin_score',
accuracies=[acc_train, acc_test], data=[X_train, X_test],
alg_instance=kmeans)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='scikit-learn K-means benchmark')
parser.add_argument('-i', '--filei', '--fileI', '--init',
type=str, help='Initial clusters')
parser.add_argument('-t', '--tol', type=float, default=0.,
help='Absolute threshold')
parser.add_argument('--maxiter', type=int, default=100,
help='Maximum number of iterations')
parser.add_argument('--n-clusters', type=int, help='Number of clusters')
params = bench.parse_args(parser)
bench.run_with_context(params, main)
71 changes: 38 additions & 33 deletions sklearn_bench/linear.py
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Expand Up @@ -21,36 +21,41 @@
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import bench

parser = argparse.ArgumentParser(description='scikit-learn linear regression '
'benchmark')
parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=True,
action='store_false',
help="Don't fit intercept (assume data already centered)")
params = bench.parse_args(parser)

from sklearn.linear_model import LinearRegression

# Load data
X_train, X_test, y_train, y_test = bench.load_data(
params, generated_data=['X_train', 'y_train'])

# Create our regression object
regr = LinearRegression(fit_intercept=params.fit_intercept,
n_jobs=params.n_jobs, copy_X=False)

# Time fit
fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)

# Time predict
predict_time, yp = bench.measure_function_time(regr.predict, X_test, params=params)

test_rmse = bench.rmse_score(yp, y_test)
yp = regr.predict(X_train)
train_rmse = bench.rmse_score(yp, y_train)

bench.print_output(library='sklearn', algorithm='linear_regression',
stages=['training', 'prediction'],
params=params, functions=['Linear.fit', 'Linear.predict'],
times=[fit_time, predict_time], accuracy_type='rmse',
accuracies=[train_rmse, test_rmse], data=[X_train, X_test],
alg_instance=regr)

def main():
from sklearn.linear_model import LinearRegression

# Load data
X_train, X_test, y_train, y_test = bench.load_data(
params, generated_data=['X_train', 'y_train'])

# Create our regression object
regr = LinearRegression(fit_intercept=params.fit_intercept,
n_jobs=params.n_jobs, copy_X=False)

# Time fit
fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)

# Time predict
predict_time, yp = bench.measure_function_time(regr.predict, X_test, params=params)

test_rmse = bench.rmse_score(yp, y_test)
yp = regr.predict(X_train)
train_rmse = bench.rmse_score(yp, y_train)

bench.print_output(library='sklearn', algorithm='linear_regression',
stages=['training', 'prediction'],
params=params, functions=['Linear.fit', 'Linear.predict'],
times=[fit_time, predict_time], accuracy_type='rmse',
accuracies=[train_rmse, test_rmse], data=[X_train, X_test],
alg_instance=regr)


if __name__ == "__main__":
parser = argparse.ArgumentParser(description='scikit-learn linear regression '
'benchmark')
parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=True,
action='store_false',
help="Don't fit intercept (assume data already centered)")
params = bench.parse_args(parser)
bench.run_with_context(params, main)
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