def test_dask_pagerank(client_connection, personalization_perc):
gc.collect()
# FIXME: update this to allow dataset to be parameterized and have dataset
# part of test param id (see other tests)
input_data_path = r"../datasets/karate.csv"
print(f"dataset={input_data_path}")
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
personalization = None
if personalization_perc != 0:
personalization, p = personalize(g.nodes(), personalization_perc)
expected_pr = cugraph.pagerank(g,
personalization=personalization,
tol=1e-6)
result_pr = dcg.pagerank(dg, personalization=personalization, tol=1e-6)
result_pr = result_pr.compute()
err = 0
tol = 1.0e-05
assert len(expected_pr) == len(result_pr)
compare_pr = expected_pr.merge(result_pr,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr)):
diff = abs(compare_pr["pagerank_local"].iloc[i] -
compare_pr["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err = err + 1
assert err == 0
def test_dask_pagerank(client_connection, personalization_perc):
gc.collect()
input_data_path = r"../datasets/karate.csv"
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
# Pre compute local data and personalize
personalization = None
if personalization_perc != 0:
dg.compute_local_data(by="dst")
personalization = personalize(dg.number_of_vertices(),
personalization_perc)
expected_pr = cugraph.pagerank(g,
personalization=personalization,
tol=1e-6)
result_pr = dcg.pagerank(dg, personalization=personalization, tol=1e-6)
result_pr = result_pr.compute()
err = 0
tol = 1.0e-05
assert len(expected_pr) == len(result_pr)
compare_pr = expected_pr.merge(result_pr,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr)):
diff = abs(compare_pr["pagerank_local"].iloc[i] -
compare_pr["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err = err + 1
assert err == 0
def test_dask_pagerank(dask_client, personalization_perc):
gc.collect()
input_data_path = (RAPIDS_DATASET_ROOT_DIR_PATH / "karate.csv").as_posix()
print(f"dataset={input_data_path}")
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
personalization = None
if personalization_perc != 0:
personalization, p = personalize(g.nodes(), personalization_perc)
expected_pr = cugraph.pagerank(g,
personalization=personalization,
tol=1e-6)
result_pr = dcg.pagerank(dg, personalization=personalization, tol=1e-6)
result_pr = result_pr.compute()
err = 0
tol = 1.0e-05
assert len(expected_pr) == len(result_pr)
compare_pr = expected_pr.merge(result_pr,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr)):
diff = abs(compare_pr["pagerank_local"].iloc[i] -
compare_pr["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err = err + 1
assert err == 0
def test_dask_pagerank(client_connection):
gc.collect()
pandas.set_option("display.max_rows", 10000)
input_data_path = r"../datasets/karate.csv"
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.DiGraph()
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.DiGraph()
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
# Pre compute local data
# dg.compute_local_data(by='dst')
expected_pr = cugraph.pagerank(g)
result_pr = dcg.pagerank(dg)
err = 0
tol = 1.0e-05
assert len(expected_pr) == len(result_pr)
compare_pr = expected_pr.merge(result_pr,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr)):
diff = abs(compare_pr["pagerank_local"].iloc[i] -
compare_pr["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err = err + 1
print("Mismatches:", err)
assert err == 0
def test_dask_pagerank(dask_client):
pandas.set_option("display.max_rows", 10000)
input_data_path = (RAPIDS_DATASET_ROOT_DIR_PATH / "karate.csv").as_posix()
chunksize = dcg.get_chunksize(input_data_path)
ddf = dask_cudf.read_csv(
input_data_path,
chunksize=chunksize,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
df = cudf.read_csv(
input_data_path,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g = cugraph.Graph(directed=True)
g.from_cudf_edgelist(df, "src", "dst")
dg = cugraph.Graph(directed=True)
dg.from_dask_cudf_edgelist(ddf, "src", "dst")
expected_pr = cugraph.pagerank(g)
result_pr = dcg.pagerank(dg).compute()
err = 0
tol = 1.0e-05
assert len(expected_pr) == len(result_pr)
compare_pr = expected_pr.merge(result_pr,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr)):
diff = abs(compare_pr["pagerank_local"].iloc[i] -
compare_pr["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err = err + 1
print("Mismatches:", err)
assert err == 0
def test_dask_pagerank(client_connection):
gc.collect()
# Initialize and run pagerank on two distributed graphs
# with same communicator
input_data_path1 = r"../datasets/karate.csv"
chunksize1 = dcg.get_chunksize(input_data_path1)
input_data_path2 = r"../datasets/dolphins.csv"
chunksize2 = dcg.get_chunksize(input_data_path2)
ddf1 = dask_cudf.read_csv(input_data_path1,
chunksize=chunksize1,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
dg1 = cugraph.DiGraph()
dg1.from_dask_cudf_edgelist(ddf1, 'src', 'dst')
result_pr1 = dcg.pagerank(dg1)
ddf2 = dask_cudf.read_csv(input_data_path2,
chunksize=chunksize2,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
dg2 = cugraph.DiGraph()
dg2.from_dask_cudf_edgelist(ddf2, 'src', 'dst')
result_pr2 = dcg.pagerank(dg2)
# Calculate single GPU pagerank for verification of results
df1 = cudf.read_csv(input_data_path1,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
g1 = cugraph.DiGraph()
g1.from_cudf_edgelist(df1, 'src', 'dst')
expected_pr1 = cugraph.pagerank(g1)
df2 = cudf.read_csv(input_data_path2,
delimiter=' ',
names=['src', 'dst', 'value'],
dtype=['int32', 'int32', 'float32'])
g2 = cugraph.DiGraph()
g2.from_cudf_edgelist(df2, 'src', 'dst')
expected_pr2 = cugraph.pagerank(g2)
# Compare and verify pagerank results
err1 = 0
err2 = 0
tol = 1.0e-05
compare_pr1 = expected_pr1.merge(result_pr1,
on="vertex",
suffixes=['_local', '_dask'])
assert len(expected_pr1) == len(result_pr1)
for i in range(len(compare_pr1)):
diff = abs(compare_pr1['pagerank_local'].iloc[i] -
compare_pr1['pagerank_dask'].iloc[i])
if diff > tol * 1.1:
err1 = err1 + 1
print("Mismatches in ", input_data_path1, ": ", err1)
assert len(expected_pr2) == len(result_pr2)
compare_pr2 = expected_pr2.merge(result_pr2,
on="vertex",
suffixes=['_local', '_dask'])
for i in range(len(compare_pr2)):
diff = abs(compare_pr2['pagerank_local'].iloc[i] -
compare_pr2['pagerank_dask'].iloc[i])
if diff > tol * 1.1:
err2 = err2 + 1
print("Mismatches in ", input_data_path2, ": ", err2)
assert err1 == err2 == 0
def test_dask_pagerank(client_connection):
gc.collect()
# Initialize and run pagerank on two distributed graphs
# with same communicator
# FIXME: update this to allow dataset to be parameterized and have dataset
# part of test param id (see other tests)
input_data_path1 = r"../datasets/karate.csv"
print(f"dataset1={input_data_path1}")
chunksize1 = dcg.get_chunksize(input_data_path1)
input_data_path2 = r"../datasets/dolphins.csv"
print(f"dataset2={input_data_path2}")
chunksize2 = dcg.get_chunksize(input_data_path2)
ddf1 = dask_cudf.read_csv(
input_data_path1,
chunksize=chunksize1,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
dg1 = cugraph.DiGraph()
dg1.from_dask_cudf_edgelist(ddf1, "src", "dst")
result_pr1 = dcg.pagerank(dg1).compute()
ddf2 = dask_cudf.read_csv(
input_data_path2,
chunksize=chunksize2,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
dg2 = cugraph.DiGraph()
dg2.from_dask_cudf_edgelist(ddf2, "src", "dst")
result_pr2 = dcg.pagerank(dg2).compute()
# Calculate single GPU pagerank for verification of results
df1 = cudf.read_csv(
input_data_path1,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g1 = cugraph.DiGraph()
g1.from_cudf_edgelist(df1, "src", "dst")
expected_pr1 = cugraph.pagerank(g1)
df2 = cudf.read_csv(
input_data_path2,
delimiter=" ",
names=["src", "dst", "value"],
dtype=["int32", "int32", "float32"],
)
g2 = cugraph.DiGraph()
g2.from_cudf_edgelist(df2, "src", "dst")
expected_pr2 = cugraph.pagerank(g2)
# Compare and verify pagerank results
err1 = 0
err2 = 0
tol = 1.0e-05
compare_pr1 = expected_pr1.merge(result_pr1,
on="vertex",
suffixes=["_local", "_dask"])
assert len(expected_pr1) == len(result_pr1)
for i in range(len(compare_pr1)):
diff = abs(compare_pr1["pagerank_local"].iloc[i] -
compare_pr1["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err1 = err1 + 1
print("Mismatches in ", input_data_path1, ": ", err1)
assert len(expected_pr2) == len(result_pr2)
compare_pr2 = expected_pr2.merge(result_pr2,
on="vertex",
suffixes=["_local", "_dask"])
for i in range(len(compare_pr2)):
diff = abs(compare_pr2["pagerank_local"].iloc[i] -
compare_pr2["pagerank_dask"].iloc[i])
if diff > tol * 1.1:
err2 = err2 + 1
print("Mismatches in ", input_data_path2, ": ", err2)
assert err1 == err2 == 0