graphframes/python/tests/test_graphframes.py at v0.9.3 · graphframes/graphframes · GitHub

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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"),

g.connectedComponents(useLabelsAsComponents=True),

]

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"])

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()

graphframes/python/tests/test_graphframes.py at v0.9.3 · graphframes/graphframes · GitHub

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