def testDataFrame(self):
edges = pd.DataFrame([["C", "A", 0.4], ["A", "B", 0.1]],
columns=[0, 1, "weight"])
g = Graph.DataFrame(edges, directed=False)
self.assertTrue(g.es["weight"] == [0.4, 0.1])
vertices = pd.DataFrame(
[["A", "blue"], ["B", "yellow"], ["C", "blue"]],
columns=[0, "color"])
g = Graph.DataFrame(edges, directed=True, vertices=vertices)
self.assertTrue(g.vs["name"] == ["A", "B", "C"])
self.assertTrue(g.vs["color"] == ["blue", "yellow", "blue"])
self.assertTrue(g.es["weight"] == [0.4, 0.1])
# Issue #347
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({
"node": [1, 2, 3, 4, 5, 6],
"label": ["1", "2", "3", "4", "5", "6"]
})[["node", "label"]]
g = Graph.DataFrame(
edges,
directed=True,
vertices=vertices,
)
self.assertTrue(g.vs["name"] == [1, 2, 3, 4, 5, 6])
self.assertTrue(g.vs["label"] == ["1", "2", "3", "4", "5", "6"])
# Vertex ids
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
g = Graph.DataFrame(edges)
self.assertTrue(g.vcount() == 6)
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
g = Graph.DataFrame(edges, use_vids=True)
self.assertTrue(g.vcount() == 7)
def clustering(df_fingerprint_comparison, df_single_taxonomy):
"""
Obtains two data frames. One with only single entries in the taxonomy row and the other with
the two aglycons, their Tanimoto Index.
Creates clusters for the data frame with the Tanimoto Index using the igraph modul.
Passes a data frame with single taxonomy entries and the created cluster object and the cluster nodes.
"""
df_all_aglycons_with_single_taxonomy = pd.read_pickle(df_single_taxonomy)
fpc = pd.read_pickle(df_fingerprint_comparison)
fpc_graph = Graph.DataFrame(fpc, directed=False)
fpc_cluster = Graph.components(fpc_graph)
fpc_nodes = fpc_graph.get_vertex_dataframe()
with open("output_data/clustered_similarity_network.txt", "wb") as outfile:
pickle.dump(fpc_cluster, outfile)
cluster_in_lists(fpc_cluster, fpc_nodes,
df_all_aglycons_with_single_taxonomy)
def testDataFrame(self):
edges = pd.DataFrame(
[["C", "A", 0.4], ["A", "B", 0.1]], columns=[0, 1, "weight"]
)
g = Graph.DataFrame(edges, directed=False)
self.assertTrue(g.es["weight"] == [0.4, 0.1])
vertices = pd.DataFrame(
[["A", "blue"], ["B", "yellow"], ["C", "blue"]], columns=[0, "color"]
)
g = Graph.DataFrame(edges, directed=True, vertices=vertices)
self.assertTrue(g.vs["name"] == ["A", "B", "C"])
self.assertTrue(g.vs["color"] == ["blue", "yellow", "blue"])
self.assertTrue(g.es["weight"] == [0.4, 0.1])
# Issue #347
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame(
{"node": [1, 2, 3, 4, 5, 6], "label": ["1", "2", "3", "4", "5", "6"]}
)[["node", "label"]]
g = Graph.DataFrame(
edges,
directed=True,
vertices=vertices
)
self.assertTrue(g.vs["name"] == [1, 2, 3, 4, 5, 6])
self.assertTrue(g.vs["label"] == ["1", "2", "3", "4", "5", "6"])
# Vertex ids
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
g = Graph.DataFrame(edges)
self.assertTrue(g.vcount() == 6)
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
g = Graph.DataFrame(edges, use_vids=True)
self.assertTrue(g.vcount() == 7)
# Graph clone
g = Graph.Full(n=100, directed=True, loops=True)
g.vs["name"] = [f"v{i}" for i in range(g.vcount())]
g.vs["x"] = [float(i) for i in range(g.vcount())]
g.es["w"] = [1.0] * g.ecount()
df_edges = g.get_edge_dataframe()
df_vertices = g.get_vertex_dataframe()
g_clone = Graph.DataFrame(df_edges, g.is_directed(), df_vertices, True)
self.assertTrue(df_edges.equals(g_clone.get_edge_dataframe()))
self.assertTrue(df_vertices.equals(g_clone.get_vertex_dataframe()))
# Invalid input
with self.assertRaisesRegex(ValueError, "two columns"):
edges = pd.DataFrame({"source": [1, 2, 3]})
Graph.DataFrame(edges)
with self.assertRaisesRegex(ValueError, "one column"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
Graph.DataFrame(edges, vertices=pd.DataFrame())
with self.assertRaisesRegex(TypeError, "integers"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]}).astype(str)
Graph.DataFrame(edges, use_vids=True)
with self.assertRaisesRegex(ValueError, "negative"):
edges = -pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
Graph.DataFrame(edges, use_vids=True)
with self.assertRaisesRegex(TypeError, "integers"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3]}, index=["1", "2", "3"])
Graph.DataFrame(edges, vertices=vertices, use_vids=True)
with self.assertRaisesRegex(ValueError, "negative"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3]}, index=[-1, 2, 3])
Graph.DataFrame(edges, vertices=vertices, use_vids=True)
with self.assertRaisesRegex(ValueError, "sequence"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3]}, index=[1, 2, 4])
Graph.DataFrame(edges, vertices=vertices, use_vids=True)
with self.assertRaisesRegex(TypeError, "integers"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3]}, index=pd.MultiIndex.from_tuples([(1, 1), (2, 2), (3, 3)]))
Graph.DataFrame(edges, vertices=vertices, use_vids=True)
with self.assertRaisesRegex(ValueError, "unique"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 2]})
Graph.DataFrame(edges, vertices=vertices)
with self.assertRaisesRegex(ValueError, "already contains"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3], "name": [1, 2, 2]})
Graph.DataFrame(edges, vertices=vertices)
with self.assertRaisesRegex(ValueError, "missing from"):
edges = pd.DataFrame({"source": [1, 2, 3], "target": [4, 5, 6]})
vertices = pd.DataFrame({0: [1, 2, 3]}, index=[0, 1, 2])
Graph.DataFrame(edges, vertices=vertices, use_vids=True)
