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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import pytest
from pyspark.sql import functions as sqlfunctions
from pyspark.sql.utils import is_remote
from graphframes.classic.graphframe import _from_java_gf
from graphframes.examples import BeliefPropagation, Graphs
from graphframes.graphframe import GraphFrame
from graphframes.lib import AggregateMessages as AM
def test_construction(spark, local_g):
vertexIDs = [row[0] for row in local_g.vertices.select("id").collect()]
assert sorted(vertexIDs) == [1, 2, 3]
edgeActions = [row[0] for row in local_g.edges.select("action").collect()]
assert sorted(edgeActions) == ["follow", "hate", "love"]
tripletsFirst = list(
map(
lambda x: (x[0][1], x[1][1], x[2][2]),
local_g.triplets.sort("src.id").select("src", "dst", "edge").take(1),
)
)
assert tripletsFirst == [("A", "B", "love")], tripletsFirst
# Try with invalid vertices and edges DataFrames
v_invalid = spark.createDataFrame(
[(1, "A"), (2, "B"), (3, "C")], ["invalid_colname_1", "invalid_colname_2"]
)
e_invalid = spark.createDataFrame(
[(1, 2), (2, 3), (3, 1)], ["invalid_colname_3", "invalid_colname_4"]
)
with pytest.raises(ValueError):
GraphFrame(v_invalid, e_invalid)
def test_cache(local_g):
local_g.cache()
local_g.unpersist()
def test_degrees(local_g):
outDeg = local_g.outDegrees
assert set(outDeg.columns) == {"id", "outDegree"}
inDeg = local_g.inDegrees
assert set(inDeg.columns) == {"id", "inDegree"}
deg = local_g.degrees
assert set(deg.columns) == {"id", "degree"}
def test_motif_finding(local_g):
motifs = local_g.find("(a)-[e]->(b)")
assert motifs.count() == 3
assert set(motifs.columns) == {"a", "e", "b"}
def test_filterVertices(local_g):
conditions = ["id < 3", local_g.vertices.id < 3]
expected_v = [(1, "A"), (2, "B")]
expected_e = [(1, 2, "love"), (2, 1, "hate")]
for cond in conditions:
g2 = local_g.filterVertices(cond)
v2 = g2.vertices.select("id", "name").collect()
e2 = g2.edges.select("src", "dst", "action").collect()
assert len(v2) == len(expected_v)
assert len(e2) == len(expected_e)
assert set(v2) == set(expected_v)
assert set(e2) == set(expected_e)
def test_filterEdges(local_g):
conditions = ["dst > 2", local_g.edges.dst > 2]
expected_v = [(1, "A"), (2, "B"), (3, "C")]
expected_e = [(2, 3, "follow")]
for cond in conditions:
g2 = local_g.filterEdges(cond)
v2 = g2.vertices.select("id", "name").collect()
e2 = g2.edges.select("src", "dst", "action").collect()
assert len(v2) == len(expected_v)
assert len(e2) == len(expected_e)
assert set(v2) == set(expected_v)
assert set(e2) == set(expected_e)
def test_dropIsolatedVertices(local_g):
g2 = local_g.filterEdges("dst > 2").dropIsolatedVertices()
v2 = g2.vertices.select("id", "name").collect()
e2 = g2.edges.select("src", "dst", "action").collect()
expected_v = [(2, "B"), (3, "C")]
expected_e = [(2, 3, "follow")]
assert len(v2) == len(expected_v)
assert len(e2) == len(expected_e)
assert set(v2) == set(expected_v)
assert set(e2) == set(expected_e)
def test_bfs(local_g):
paths = local_g.bfs("name='A'", "name='C'")
assert paths is not None
assert paths.count() == 1
# Expecting that the first intermediary vertex in the BFS is "B"
head = paths.select("v1.name").head()
assert head is not None
assert head[0] == "B"
paths2 = local_g.bfs("name='A'", "name='C'", edgeFilter="action!='follow'")
assert paths2.count() == 0
paths3 = local_g.bfs("name='A'", "name='C'", maxPathLength=1)
assert paths3.count() == 0
def test_power_iteration_clustering(spark):
vertices = [
(1, 0, 0.5),
(2, 0, 0.5),
(2, 1, 0.7),
(3, 0, 0.5),
(3, 1, 0.7),
(3, 2, 0.9),
(4, 0, 0.5),
(4, 1, 0.7),
(4, 2, 0.9),
(4, 3, 1.1),
(5, 0, 0.5),
(5, 1, 0.7),
(5, 2, 0.9),
(5, 3, 1.1),
(5, 4, 1.3),
]
edges = [(0,), (1,), (2,), (3,), (4,), (5,)]
g = GraphFrame(
v=spark.createDataFrame(edges).toDF("id"),
e=spark.createDataFrame(vertices).toDF("src", "dst", "weight"),
)
clusters = [
r["cluster"]
for r in g.powerIterationClustering(k=2, maxIter=40, weightCol="weight")
.sort("id")
.collect()
]
assert clusters == [0, 0, 0, 0, 1, 0]
def test_page_rank(spark):
edges = spark.createDataFrame(
[
[0, 1],
[1, 2],
[2, 4],
[2, 0],
[3, 4], # 3 has no in-links
[4, 0],
[4, 2],
],
["src", "dst"],
)
edges.cache()
vertices = spark.createDataFrame([[0], [1], [2], [3], [4]], ["id"])
numVertices = vertices.count()
vertices = GraphFrame(vertices, edges).outDegrees
vertices.toPandas().head()
vertices.cache()
# Construct a new GraphFrame with the updated vertices DataFrame.
graph = GraphFrame(vertices, edges)
alpha = 0.15
pregel = graph.pregel
ranks = (
graph.pregel.setMaxIter(5)
.withVertexColumn(
"rank",
sqlfunctions.lit(1.0 / numVertices),
sqlfunctions.coalesce(pregel.msg(), sqlfunctions.lit(0.0))
* sqlfunctions.lit(1.0 - alpha)
+ sqlfunctions.lit(alpha / numVertices),
)
.sendMsgToDst(pregel.src("rank") / pregel.src("outDegree"))
.aggMsgs(sqlfunctions.sum(pregel.msg()))
.run()
)
resultRows = ranks.sort("id").collect()
result = map(lambda x: x.rank, resultRows)
expected = [0.245, 0.224, 0.303, 0.03, 0.197]
# Compare each result with its expected value using a tolerance of 1e-3.
for a, b in zip(result, expected):
assert a == pytest.approx(b, abs=1e-3)
def test_pregel_early_stopping(spark):
edges = spark.createDataFrame(
[
[0, 1],
[1, 2],
[2, 4],
[2, 0],
[3, 4], # 3 has no in-links
[4, 0],
[4, 2],
],
["src", "dst"],
)
edges.cache()
vertices = spark.createDataFrame([[0], [1], [2], [3], [4]], ["id"])
numVertices = vertices.count()
vertices = GraphFrame(vertices, edges).outDegrees
vertices.toPandas().head()
vertices.cache()
# Construct a new GraphFrame with the updated vertices DataFrame.
graph = GraphFrame(vertices, edges)
alpha = 0.15
pregel = graph.pregel
ranks = (
graph.pregel.setMaxIter(5).setEarlyStopping(True)
.withVertexColumn(
"rank",
sqlfunctions.lit(1.0 / numVertices),
sqlfunctions.coalesce(pregel.msg(), sqlfunctions.lit(0.0))
* sqlfunctions.lit(1.0 - alpha)
+ sqlfunctions.lit(alpha / numVertices),
)
.sendMsgToDst(pregel.src("rank") / pregel.src("outDegree"))
.aggMsgs(sqlfunctions.sum(pregel.msg()))
.run()
)
resultRows = ranks.sort("id").collect()
result = map(lambda x: x.rank, resultRows)
expected = [0.245, 0.224, 0.303, 0.03, 0.197]
# Compare each result with its expected value using a tolerance of 1e-3.
for a, b in zip(result, expected):
assert a == pytest.approx(b, abs=1e-3)
def _hasCols(graph, vcols=[], ecols=[]):
for c in vcols:
assert c in graph.vertices.columns, f"Vertex DataFrame missing column: {c}"
for c in ecols:
assert c in graph.edges.columns, f"Edge DataFrame missing column: {c}"
def _df_hasCols(df, vcols=[]):
for c in vcols:
assert c in df.columns, f"DataFrame missing column: {c}"
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_aggregate_messages(examples, spark):
g = _from_java_gf(getattr(examples, "friends")(), spark)
# For each user, sum the ages of the adjacent users,
# plus 1 for the src's sum if the edge is "friend".
sendToSrc = AM.dst["age"] + sqlfunctions.when(
AM.edge["relationship"] == "friend", sqlfunctions.lit(1)
).otherwise(0)
sendToDst = AM.src["age"]
agg = g.aggregateMessages(
sqlfunctions.sum(AM.msg).alias("summedAges"),
sendToSrc=sendToSrc,
sendToDst=sendToDst,
)
# Run the aggregation again using SQL expressions as Strings.
agg2 = g.aggregateMessages(
"sum(MSG) AS `summedAges`",
sendToSrc="(dst['age'] + CASE WHEN (edge['relationship'] = 'friend') THEN 1 ELSE 0 END)", # noqa: E501
sendToDst="src['age']",
)
# Build mappings from id to the aggregated message.
aggMap = {row.id: row.summedAges for row in agg.select("id", "summedAges").collect()}
agg2Map = {row.id: row.summedAges for row in agg2.select("id", "summedAges").collect()}
# Compute the expected aggregation via brute force.
user2age = {row.id: row.age for row in g.vertices.select("id", "age").collect()}
trueAgg = {}
for src, dst, rel in g.edges.select("src", "dst", "relationship").collect():
trueAgg[src] = trueAgg.get(src, 0) + user2age[dst] + (1 if rel == "friend" else 0)
trueAgg[dst] = trueAgg.get(dst, 0) + user2age[src]
# Verify both aggregations match the expected results.
assert aggMap == trueAgg, f"aggMap {aggMap} does not equal expected {trueAgg}"
assert agg2Map == trueAgg, f"agg2Map {agg2Map} does not equal expected {trueAgg}"
# Check that passing a wrong type for messages raises a TypeError.
with pytest.raises(TypeError):
g.aggregateMessages("sum(MSG) AS `summedAges`", sendToSrc=object(), sendToDst="src['age']")
with pytest.raises(TypeError):
g.aggregateMessages("sum(MSG) AS `summedAges`", sendToSrc=dst["age"], sendToDst=object())
def test_connected_components(spark):
v = spark.createDataFrame([(0, "a", "b")], ["id", "vattr", "gender"])
e = spark.createDataFrame([(0, 0, 1)], ["src", "dst", "test"]).filter("src > 10")
v = spark.createDataFrame([(0, "a", "b")], ["id", "vattr", "gender"])
e = spark.createDataFrame([(0, 0, 1)], ["src", "dst", "test"]).filter("src > 10")
g = GraphFrame(v, e)
comps = g.connectedComponents()
_df_hasCols(comps, vcols=["id", "component", "vattr", "gender"])
assert comps.count() == 1
def test_connected_components2(spark):
v = spark.createDataFrame([(0, "a0", "b0"), (1, "a1", "b1")], ["id", "A", "B"])
e = spark.createDataFrame([(0, 1, "a01", "b01")], ["src", "dst", "A", "B"])
g = GraphFrame(v, e)
comps = g.connectedComponents()
_df_hasCols(comps, vcols=["id", "component", "A", "B"])
assert comps.count() == 2
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_connected_components_friends(examples, spark):
g = _from_java_gf(getattr(examples, "friends")(), spark)
comps_tests = [
g.connectedComponents(),
g.connectedComponents(broadcastThreshold=1),
g.connectedComponents(checkpointInterval=0),
g.connectedComponents(checkpointInterval=10),
g.connectedComponents(algorithm="graphx"),
]
for c in comps_tests:
assert c.groupBy("component").count().count() == 2
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_label_progagation(examples, spark):
n = 5
g = _from_java_gf(getattr(examples, "twoBlobs")(n), spark)
labels = g.labelPropagation(maxIter=4 * n)
labels1 = labels.filter("id < 5").select("label").collect()
all1 = {row.label for row in labels1}
assert len(all1) == 1
labels2 = labels.filter("id >= 5").select("label").collect()
all2 = {row.label for row in labels2}
assert len(all2) == 1
assert all1 != all2
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_page_rank_2(examples, spark):
n = 100
g = _from_java_gf(getattr(examples, "star")(n), spark)
resetProb = 0.15
errorTol = 1.0e-5
pr = g.pageRank(resetProb, tol=errorTol)
_hasCols(pr, vcols=["id", "pagerank"], ecols=["src", "dst", "weight"])
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_parallel_personalized_page_rank(examples, spark):
n = 100
g = _from_java_gf(getattr(examples, "star")(n), spark)
resetProb = 0.15
maxIter = 15
sourceIds = [1, 2, 3, 4]
pr = g.parallelPersonalizedPageRank(resetProb, sourceIds=sourceIds, maxIter=maxIter)
_hasCols(pr, vcols=["id", "pageranks"], ecols=["src", "dst", "weight"])
def test_shortest_paths(spark):
edges = [(1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (4, 5), (4, 6)]
# Create bidirectional edges.
all_edges = [z for (a, b) in edges for z in [(a, b), (b, a)]]
edges = spark.createDataFrame(all_edges, ["src", "dst"])
edges = spark.createDataFrame(all_edges, ["src", "dst"])
edgesDF = spark.createDataFrame(all_edges, ["src", "dst"])
vertices = spark.createDataFrame([(i,) for i in range(1, 7)], ["id"])
g = GraphFrame(vertices, edgesDF)
landmarks = [1, 4]
v2 = g.shortestPaths(landmarks)
_df_hasCols(v2, vcols=["id", "distances"])
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_svd_plus_plus(examples, spark):
g = _from_java_gf(getattr(examples, "ALSSyntheticData")(), spark)
(v2, cost) = g.svdPlusPlus()
_df_hasCols(v2, vcols=["id", "column1", "column2", "column3", "column4"])
def test_strongly_connected_components(spark):
# Simple island test
vertices = spark.createDataFrame([(i,) for i in range(1, 6)], ["id"])
edges = spark.createDataFrame([(7, 8)], ["src", "dst"])
g = GraphFrame(vertices, edges)
c = g.stronglyConnectedComponents(5)
for row in c.collect():
assert (
row.id == row.component
), f"Vertex {row.id} not equal to its component {row.component}"
def test_triangle_counts(spark):
edges = spark.createDataFrame([(0, 1), (1, 2), (2, 0)], ["src", "dst"])
vertices = spark.createDataFrame([(0,), (1,), (2,)], ["id"])
g = GraphFrame(vertices, edges)
c = g.triangleCount()
for row in c.select("id", "count").collect():
assert row.asDict()["count"] == 1, f"Triangle count for vertex {row.id} is not 1"
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_mutithreaded_sparksession_usage(spark):
# Test that the GraphFrame API works correctly from multiple threads.
localVertices = [(1, "A"), (2, "B"), (3, "C")]
localEdges = [(1, 2, "love"), (2, 1, "hate"), (2, 3, "follow")]
v = spark.createDataFrame(localVertices, ["id", "name"])
e = spark.createDataFrame(localEdges, ["src", "dst", "action"])
exc = None
def run_graphframe() -> None:
nonlocal exc
try:
GraphFrame(v, e)
except Exception as _e:
exc = _e
import threading
thread = threading.Thread(target=run_graphframe)
thread.start()
thread.join()
assert exc is None, f"Exception was raised in thread: {exc}"
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_belief_propagation(spark):
# Create a graphical model g of size 3x3.
g = Graphs(spark).gridIsingModel(3)
# Run Belief Propagation (BP) for 5 iterations.
numIter = 5
results = BeliefPropagation.runBPwithGraphFrames(g, numIter)
# Check that each belief is a valid probability in [0, 1].
for row in results.vertices.select("belief").collect():
belief = row["belief"]
assert 0 <= belief <= 1, f"Expected belief to be probability in [0,1], but found {belief}"
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_graph_friends(spark):
# Construct the graph.
g = Graphs(spark).friends()
# Check that the result is an instance of GraphFrame.
assert isinstance(g, GraphFrame)
@pytest.mark.skipif(is_remote(), reason="DISABLE FOR CONNECT")
def test_graph_grid_ising_model(spark):
# Construct a grid Ising model graph.
n = 3
g = Graphs(spark).gridIsingModel(n)
# Collect the vertex ids
ids = [v["id"] for v in g.vertices.collect()]
# Verify that every expected vertex id appears.
for i in range(n):
for j in range(n):
assert f"{i},{j}" in ids
if __name__ == "__main__":
pytest.main()