From fa6f0fe65a2c26a63a6ff866e10b0e467ca3dd02 Mon Sep 17 00:00:00 2001 From: RobRossmiller-TG <165701656+RobRossmiller-TG@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:43:22 -0400 Subject: [PATCH 1/5] reorganize --- .../GraphML/Embeddings/FastRP/tg_fastRP.gsql | 2 +- tests/run.sh | 7 +- tests/test/create_baseline.py | 271 ------------------ tests/test/setup.py | 25 +- tests/test/util.py | 4 +- 5 files changed, 29 insertions(+), 280 deletions(-) delete mode 100644 tests/test/create_baseline.py diff --git a/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql b/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql index fa78bef3..5dac70b5 100644 --- a/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql +++ b/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql @@ -265,7 +265,7 @@ CREATE QUERY tg_fastRP( IF print_results THEN res = SELECT a FROM verts:a; - PRINT res[res.@final_embedding_arr]; + PRINT res[res.@final_embedding_list]; END; // (un)comment depending on whether you want to write to graph diff --git a/tests/run.sh b/tests/run.sh index 7e6c6b29..801936b8 100755 --- a/tests/run.sh +++ b/tests/run.sh @@ -1,5 +1,6 @@ clear -python3 test/create_baseline.py && - python3 test/setup.py && - pytest test/test_centrality.py::TestCentrality +python3 test/setup.py && + python3 test/baseline/create_baselines.py && + pytest test/test_centrality.py && + pytest test/test_ml.py # pytest diff --git a/tests/test/create_baseline.py b/tests/test/create_baseline.py deleted file mode 100644 index ce6e0ff8..00000000 --- a/tests/test/create_baseline.py +++ /dev/null @@ -1,271 +0,0 @@ -import csv -import json -from collections import Counter -from functools import partial - -import networkx as nx -import numpy as np -from tqdm import tqdm - -data_path_root = "data/" -baseline_path_root = f"{data_path_root}/baseline/" - - -def weighted_deg_cent( - g: nx.Graph, - dir: str = "", -): - res = Counter() - for e in g.edges: - a = g.get_edge_data(e[0], e[1])["weight"] - match dir: - case "in": - res[e[1]] += a - case "out": - res[e[0]] += a - case _: - res[e[0]] += a - res[e[1]] += a - return res - - -def run_degree_baseline_complete(g: nx.Graph, _): - s = 1.0 / (len(g) - 1.0) - - # d-1 because nx will double count the self-edge - res = {n: (d - 1) * s for n, d in g.degree()} - - out = [] - for k, v in res.items(): - out.append({"Vertex_ID": k, "score": v}) - - out = [{"top_scores": out}] - return out - - -def run_degree_baseline(g: nx.Graph, metric): - res = metric(g) - - out = [] - for k, v in res.items(): - out.append({"Vertex_ID": k, "score": v}) - - out = [{"top_scores": out}] - return out - - -def create_graph(edges, weights=False, directed=False): - if directed: - g = nx.DiGraph() - else: - g = nx.Graph() - if weights: - # make weights floats - edges = [[a, b, float(c)] for a, b, c in edges] - g.add_weighted_edges_from(edges) - else: - g.add_edges_from(edges) - return g - - -def create_degree_baseline(paths): - t = tqdm(paths, desc="Creating baselines") - for p, out_path, fn, m in t: - t.set_postfix_str(out_path.split("/")[-1].split(".")[0]) - with open(p) as f: - edges = np.array(list(csv.reader(f))) - - directed = True if "Directed" in out_path else False - weights = True if "Weighted" in out_path else False - g = create_graph(edges, weights, directed) - - # from matplotlib import pyplot as plt - # pos = nx.drawing.layout.kamada_kawai_layout(g) - # nx.draw(g, pos) - # nx.draw_networkx_labels(g, pos, {n: n for n in g.nodes}) - # plt.savefig(f"{out_path.split('/')[-1]}.png") - - res = fn(g, m) - with open(out_path, "w") as f: - json.dump(res, f) # , indent=2) - - -if __name__ == "__main__": - # (data, output_path, fun, metric) - paths = [ - # unweighted - ( - f"{data_path_root}/unweighted_edges/complete_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/Complete.json", - run_degree_baseline_complete, - None, - ), - ( - f"{data_path_root}/unweighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/Line.json", - run_degree_baseline, - nx.centrality.degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/Ring.json", - run_degree_baseline, - nx.centrality.degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/Hub_Spoke.json", - run_degree_baseline, - nx.centrality.degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/Tree.json", - run_degree_baseline, - nx.centrality.degree_centrality, - ), - # in_degree - ( - f"{data_path_root}/unweighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/in_degree/Line_Directed.json", - run_degree_baseline, - nx.centrality.in_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/in_degree/Ring_Directed.json", - run_degree_baseline, - nx.centrality.in_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/in_degree/Hub_Spoke_Directed.json", - run_degree_baseline, - nx.centrality.in_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/in_degree/Tree_Directed.json", - run_degree_baseline, - nx.centrality.in_degree_centrality, - ), - # out_degree - ( - f"{data_path_root}/unweighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/out_degree/Line_Directed.json", - run_degree_baseline, - nx.centrality.out_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/out_degree/Ring_Directed.json", - run_degree_baseline, - nx.centrality.out_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/out_degree/Hub_Spoke_Directed.json", - run_degree_baseline, - nx.centrality.out_degree_centrality, - ), - ( - f"{data_path_root}/unweighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/degree_centrality/out_degree/Tree_Directed.json", - run_degree_baseline, - nx.centrality.out_degree_centrality, - ), - # weighted - ( - f"{data_path_root}/weighted_edges/complete_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/Complete_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent), - ), - ( - f"{data_path_root}/weighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/Line_Weighted.json", - run_degree_baseline, - weighted_deg_cent, - ), - ( - f"{data_path_root}/weighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/Ring_Weighted.json", - run_degree_baseline, - weighted_deg_cent, - ), - ( - f"{data_path_root}/weighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/Hub_Spoke_Weighted.json", - run_degree_baseline, - weighted_deg_cent, - ), - ( - f"{data_path_root}/weighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/Tree_Weighted.json", - run_degree_baseline, - weighted_deg_cent, - ), - # in_degree - ( - f"{data_path_root}/weighted_edges/complete_edges_directed.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Complete_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="in"), - ), - ( - f"{data_path_root}/weighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Line_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="in"), - ), - ( - f"{data_path_root}/weighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Ring_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="in"), - ), - ( - f"{data_path_root}/weighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Hub_Spoke_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="in"), - ), - ( - f"{data_path_root}/weighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Tree_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="in"), - ), - # out_degree - ( - f"{data_path_root}/weighted_edges/complete_edges_directed.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Complete_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="out"), - ), - ( - f"{data_path_root}/weighted_edges/line_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Line_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="out"), - ), - ( - f"{data_path_root}/weighted_edges/ring_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Ring_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="out"), - ), - ( - f"{data_path_root}/weighted_edges/hubspoke_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Hub_Spoke_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="out"), - ), - ( - f"{data_path_root}/weighted_edges/tree_edges.csv", - f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Tree_Directed_Weighted.json", - run_degree_baseline, - partial(weighted_deg_cent, dir="out"), - ), - ] - create_degree_baseline(paths) diff --git a/tests/test/setup.py b/tests/test/setup.py index 406077c3..fad28088 100644 --- a/tests/test/setup.py +++ b/tests/test/setup.py @@ -1,17 +1,31 @@ import json import os import re +import time import pyTigerGraph as tg -import util from dotenv import load_dotenv from pyTigerGraph.datasets import Datasets -from tqdm import tqdm +from tqdm import tqdm, trange + +import util load_dotenv() graph_name = "graph_algorithms_testing" pattern = re.compile(r'"name":\s*"tg_.*"') + +def add_reverse_edge(ds: Datasets): + with open(f"{dataset.tmp_dir}/{ds.name}/create_schema.gsql") as f: + schema: str = f.read() + with open(f"{dataset.tmp_dir}/{ds.name}/create_schema.gsql", "w") as f: + schema = schema.replace( + "ADD DIRECTED EDGE Cite (from Paper, to Paper, time Int, is_train Bool, is_val Bool);", + 'ADD DIRECTED EDGE Cite (from Paper, to Paper, time Int, is_train Bool, is_val Bool) WITH REVERSE_EDGE="reverse_Cite";', + ) + f.write(schema) + + if __name__ == "__main__": host_name = os.getenv("HOST_NAME") user_name = os.getenv("USER_NAME") @@ -28,9 +42,14 @@ if res["error"]: exit(1) # load the data + dataset = Datasets("Cora") + add_reverse_edge(dataset) + conn.ingestDataset(dataset, getToken=True) + dataset = Datasets("graph_algorithms_testing") conn.ingestDataset(dataset, getToken=True) + conn.graphname = graph_name # install the queries feat = conn.gds.featurizer() installed_queries = util.get_installed_queries(conn) @@ -43,3 +62,5 @@ print(q) feat.installAlgorithm(q) + for _ in trange(30, desc="Sleeping while data loads"): + time.sleep(1) diff --git a/tests/test/util.py b/tests/test/util.py index 3418a913..ca26c785 100644 --- a/tests/test/util.py +++ b/tests/test/util.py @@ -6,9 +6,7 @@ load_dotenv() -def get_featurizer(): - graph_name = "graph_algorithms_testing" - +def get_featurizer(graph_name="graph_algorithms_testing"): host_name = os.getenv("HOST_NAME") user_name = os.getenv("USER_NAME") password = os.getenv("PASS") From 08717cb9274fa470183887f33249f03df0e59e8c Mon Sep 17 00:00:00 2001 From: RobRossmiller-TG <165701656+RobRossmiller-TG@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:43:35 -0400 Subject: [PATCH 2/5] start fastrp testinggp --- tests/data/baseline/ml/fastRP.json.gz | Bin 0 -> 209385 bytes tests/test/baseline/__init__.py | 0 tests/test/baseline/algos/__init__.py | 2 + tests/test/baseline/algos/degree_cent.py | 46 ++++ tests/test/baseline/algos/fastrp.py | 107 +++++++++ tests/test/baseline/create_baselines.py | 7 + tests/test/baseline/degree_cent_baseline.py | 228 ++++++++++++++++++++ tests/test/baseline/fast_rp_baseline.py | 44 ++++ tests/test/test_ml.py | 64 ++++++ 9 files changed, 498 insertions(+) create mode 100644 tests/data/baseline/ml/fastRP.json.gz create mode 100644 tests/test/baseline/__init__.py create mode 100644 tests/test/baseline/algos/__init__.py create mode 100644 tests/test/baseline/algos/degree_cent.py create mode 100644 tests/test/baseline/algos/fastrp.py create mode 100644 tests/test/baseline/create_baselines.py create mode 100644 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index 00000000..e69de29b diff --git a/tests/test/baseline/algos/__init__.py b/tests/test/baseline/algos/__init__.py new file mode 100644 index 00000000..328e264d --- /dev/null +++ b/tests/test/baseline/algos/__init__.py @@ -0,0 +1,2 @@ +from .degree_cent import * +from .fastrp import fastrp_wrapper as fastrp diff --git a/tests/test/baseline/algos/degree_cent.py b/tests/test/baseline/algos/degree_cent.py new file mode 100644 index 00000000..4ef80d57 --- /dev/null +++ b/tests/test/baseline/algos/degree_cent.py @@ -0,0 +1,46 @@ +from collections import Counter + +import networkx as nx + + +def run_degree_baseline_complete(g: nx.Graph, _): + s = 1.0 / (len(g) - 1.0) + + # d-1 because nx will double count the self-edge + res = {n: (d - 1) * s for n, d in g.degree()} + + out = [] + for k, v in res.items(): + out.append({"Vertex_ID": k, "score": v}) + + out = [{"top_scores": out}] + return out + + +def run_degree_baseline(g: nx.Graph, metric): + res = metric(g) + + out = [] + for k, v in res.items(): + out.append({"Vertex_ID": k, "score": v}) + + out = [{"top_scores": out}] + return out + + +def weighted_deg_cent( + g: nx.Graph, + dir: str = "", +): + res = Counter() + for e in g.edges: + a = g.get_edge_data(e[0], e[1])["weight"] + match dir: + case "in": + res[e[1]] += a + case "out": + res[e[0]] += a + case _: + res[e[0]] += a + res[e[1]] += a + return res diff --git a/tests/test/baseline/algos/fastrp.py b/tests/test/baseline/algos/fastrp.py new file mode 100644 index 00000000..3200f984 --- /dev/null +++ b/tests/test/baseline/algos/fastrp.py @@ -0,0 +1,107 @@ +# source: https://github.com/GTmac/FastRP/blob/master/fastrp.py + +import numpy as np +from scipy.sparse import csc_matrix, csr_matrix, spdiags +from sklearn import random_projection +from sklearn.preprocessing import normalize, scale + + +# projection method: choose from Gaussian and Sparse +# input matrix: choose from adjacency and transition matrix +# alpha adjusts the weighting of nodes according to their degree +def fastrp_projection( + A, q=3, dim=128, projection_method="gaussian", input_matrix="adj", alpha=None +): + assert input_matrix == "adj" or input_matrix == "trans" + assert projection_method == "gaussian" or projection_method == "sparse" + + if input_matrix == "adj": + M = A + else: + N = A.shape[0] + normalizer = spdiags(np.squeeze(1.0 / csc_matrix.sum(A, axis=1)), 0, N, N) + M = normalizer @ A + # Gaussian projection matrix + if projection_method == "gaussian": + transformer = random_projection.GaussianRandomProjection( + n_components=dim, random_state=42 + ) + # Sparse projection matrix + else: + transformer = random_projection.SparseRandomProjection( + n_components=dim, random_state=42 + ) + Y = transformer.fit(M) + # Random projection for A + if alpha is not None: + Y.components_ = Y.components_ @ spdiags( + np.squeeze(np.power(csc_matrix.sum(A, axis=1), alpha)), 0, N, N + ) + cur_U = transformer.transform(M) + U_list = [cur_U] + + for _ in range(2, q + 1): + cur_U = M @ cur_U + U_list.append(cur_U) + return U_list + + +# When weights is None, concatenate instead of linearly combines the embeddings from different powers of A +def fastrp_merge(U_list, weights, normalization=False): + dense_U_list = ( + [_U.todense() for _U in U_list] if type(U_list[0]) == csc_matrix else U_list + ) + _U_list = ( + [normalize(_U, norm="l2", axis=1) for _U in dense_U_list] + if normalization + else dense_U_list + ) + + if weights is None: + return np.concatenate(_U_list, axis=1) + U = np.zeros_like(_U_list[0]) + for cur_U, weight in zip(_U_list, weights): + U += cur_U * weight + # U = scale(U.todense()) + # U = normalize(U.todense(), norm='l2', axis=1) + return scale(U.toarray()) if type(U) == csr_matrix else scale(U) + + +# A is always the adjacency matrix +# the choice between adj matrix and trans matrix is decided in the conf +def fastrp_wrapper(A, conf): + U_list = fastrp_projection( + A, + q=len(conf["weights"]), + dim=conf["dim"], + projection_method=conf["projection_method"], + input_matrix=conf["input_matrix"], + alpha=conf["alpha"], + ) + U = fastrp_merge(U_list, conf["weights"], conf["normalization"]) + return U + + +def get_emb_filename(prefix, conf): + return ( + prefix + + "-dim=" + + str(conf["dim"]) + + ",projection_method=" + + conf["projection_method"] + + ",input_matrix=" + + conf["input_matrix"] + + ",normalization=" + + str(conf["normalization"]) + + ",weights=" + + ( + ",".join(map(str, conf["weights"])) + if conf["weights"] is not None + else "None" + ) + + ",alpha=" + + (str(conf["alpha"]) if "alpha" in conf else "") + + ",C=" + + (str(conf["C"]) if "alpha" in conf else "1.0") + + ".mat" + ) diff --git a/tests/test/baseline/create_baselines.py b/tests/test/baseline/create_baselines.py new file mode 100644 index 00000000..76fa0b0f --- /dev/null +++ b/tests/test/baseline/create_baselines.py @@ -0,0 +1,7 @@ +import degree_cent_baseline +import fast_rp_baseline + +if __name__ == "__main__": + # degree_cent_baseline.run() + + fast_rp_baseline.run() diff --git a/tests/test/baseline/degree_cent_baseline.py b/tests/test/baseline/degree_cent_baseline.py new file mode 100644 index 00000000..9791226d --- /dev/null +++ b/tests/test/baseline/degree_cent_baseline.py @@ -0,0 +1,228 @@ +import csv +import json +from functools import partial + +import networkx as nx +import numpy as np +from algos import run_degree_baseline, run_degree_baseline_complete, weighted_deg_cent +from tqdm import tqdm + +data_path_root = "data/" +baseline_path_root = f"{data_path_root}/baseline/" + + +def create_graph(edges, weights=False, directed=False): + if directed: + g = nx.DiGraph() + else: + g = nx.Graph() + if weights: + # make weights floats + edges = [[a, b, float(c)] for a, b, c in edges] + g.add_weighted_edges_from(edges) + else: + g.add_edges_from(edges) + return g + + +def create_degree_baseline(paths): + t = tqdm(paths, desc="Creating baselines") + for p, out_path, fn, m in t: + t.set_postfix_str(out_path.split("/")[-1].split(".")[0]) + with open(p) as f: + edges = np.array(list(csv.reader(f))) + + directed = True if "Directed" in out_path else False + weights = True if "Weighted" in out_path else False + g = create_graph(edges, weights, directed) + + # from matplotlib import pyplot as plt + # pos = nx.drawing.layout.kamada_kawai_layout(g) + # nx.draw(g, pos) + # nx.draw_networkx_labels(g, pos, {n: n for n in g.nodes}) + # plt.savefig(f"{out_path.split('/')[-1]}.png") + + res = fn(g, m) + with open(out_path, "w") as f: + json.dump(res, f) # , indent=2) + + +def run(): + # (data, output_path, fun, metric) + paths = [ + # unweighted + ( + f"{data_path_root}/unweighted_edges/complete_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/Complete.json", + run_degree_baseline_complete, + None, + ), + ( + f"{data_path_root}/unweighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/Line.json", + run_degree_baseline, + nx.centrality.degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/Ring.json", + run_degree_baseline, + nx.centrality.degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/Hub_Spoke.json", + run_degree_baseline, + nx.centrality.degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/Tree.json", + run_degree_baseline, + nx.centrality.degree_centrality, + ), + # in_degree + ( + f"{data_path_root}/unweighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/in_degree/Line_Directed.json", + run_degree_baseline, + nx.centrality.in_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/in_degree/Ring_Directed.json", + run_degree_baseline, + nx.centrality.in_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/in_degree/Hub_Spoke_Directed.json", + run_degree_baseline, + nx.centrality.in_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/in_degree/Tree_Directed.json", + run_degree_baseline, + nx.centrality.in_degree_centrality, + ), + # out_degree + ( + f"{data_path_root}/unweighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/out_degree/Line_Directed.json", + run_degree_baseline, + nx.centrality.out_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/out_degree/Ring_Directed.json", + run_degree_baseline, + nx.centrality.out_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/out_degree/Hub_Spoke_Directed.json", + run_degree_baseline, + nx.centrality.out_degree_centrality, + ), + ( + f"{data_path_root}/unweighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/degree_centrality/out_degree/Tree_Directed.json", + run_degree_baseline, + nx.centrality.out_degree_centrality, + ), + # weighted + ( + f"{data_path_root}/weighted_edges/complete_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/Complete_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent), + ), + ( + f"{data_path_root}/weighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/Line_Weighted.json", + run_degree_baseline, + weighted_deg_cent, + ), + ( + f"{data_path_root}/weighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/Ring_Weighted.json", + run_degree_baseline, + weighted_deg_cent, + ), + ( + f"{data_path_root}/weighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/Hub_Spoke_Weighted.json", + run_degree_baseline, + weighted_deg_cent, + ), + ( + f"{data_path_root}/weighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/Tree_Weighted.json", + run_degree_baseline, + weighted_deg_cent, + ), + # in_degree + ( + f"{data_path_root}/weighted_edges/complete_edges_directed.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Complete_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="in"), + ), + ( + f"{data_path_root}/weighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Line_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="in"), + ), + ( + f"{data_path_root}/weighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Ring_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="in"), + ), + ( + f"{data_path_root}/weighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Hub_Spoke_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="in"), + ), + ( + f"{data_path_root}/weighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/in_degree/Tree_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="in"), + ), + # out_degree + ( + f"{data_path_root}/weighted_edges/complete_edges_directed.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Complete_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="out"), + ), + ( + f"{data_path_root}/weighted_edges/line_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Line_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="out"), + ), + ( + f"{data_path_root}/weighted_edges/ring_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Ring_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="out"), + ), + ( + f"{data_path_root}/weighted_edges/hubspoke_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Hub_Spoke_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="out"), + ), + ( + f"{data_path_root}/weighted_edges/tree_edges.csv", + f"{baseline_path_root}/centrality/weighted_degree_centrality/out_degree/Tree_Directed_Weighted.json", + run_degree_baseline, + partial(weighted_deg_cent, dir="out"), + ), + ] + create_degree_baseline(paths) diff --git a/tests/test/baseline/fast_rp_baseline.py b/tests/test/baseline/fast_rp_baseline.py new file mode 100644 index 00000000..1b2ff5a4 --- /dev/null +++ b/tests/test/baseline/fast_rp_baseline.py @@ -0,0 +1,44 @@ +import gzip +import json + +import networkx as nx +import numpy as np +import pandas as pd +from algos import fastrp +from dotenv import load_dotenv +from pyTigerGraph.datasets import Datasets + +load_dotenv() +data_path_root = "data" +baseline_path_root = f"{data_path_root}/baseline" + + +def run(ds_name="Cora"): + dataset = Datasets(ds_name) + edges = pd.read_csv(dataset.tmp_dir + f"/{ds_name}/edges.csv", header=None) + edges.columns = ["src", "tgt"] + + g = nx.Graph() + g.add_edges_from(edges.to_numpy()) + node_ids = sorted(list(g.nodes)) + A = nx.adjacency_matrix(g, nodelist=node_ids) + conf = { + "weights": [1, 2, 4], + "dim": 8, + # "projection_method": "sparse", + "projection_method": "gaussian", + "input_matrix": "trans", + "alpha": -0.628, + "normalization": False, + } + + vecs = fastrp(A, conf) + + assert len(vecs) == len(node_ids) + + res = {str(k): list(v) for k, v in zip(node_ids, vecs)} + with gzip.open(f"{baseline_path_root}/ml/fastRP.json.gz", "wb") as f: + f.write(json.dumps(res).encode()) + + with gzip.open(f"{baseline_path_root}/ml/fastRP.json.gz", "rb") as f: + d = json.load(f) diff --git a/tests/test/test_ml.py b/tests/test/test_ml.py new file mode 100644 index 00000000..27648c27 --- /dev/null +++ b/tests/test/test_ml.py @@ -0,0 +1,64 @@ +import gzip +import json +import os + +import numpy as np +import pytest +from dotenv import load_dotenv +from pyTigerGraph import TigerGraphConnection + +load_dotenv() +data_path_root = "data" +baseline_path_root = f"{data_path_root}/baseline" + + +conn = TigerGraphConnection( + host=os.environ["HOST_NAME"], + graphname="Cora", + username=os.environ["USER_NAME"], + password=os.environ["PASS"], +) +conn.getToken() + + +def cos_sim(x, y): + x_mag = np.linalg.norm(x) + y_mag = np.linalg.norm(y) + + return np.dot(x, y) / (x_mag * y_mag) + + +class TestML: + def test_fastRP(self): + params = { + "v_type_set": ["Paper"], + "e_type_set": ["Cite", "reverse_Cite"], + "output_v_type_set": ["Paper"], + "iteration_weights": "1,2,4", + "beta": -0.1, + "embedding_dimension": 8, + "embedding_dim_map": [], + "default_length": 8, + "sampling_constant": 3, + "random_seed": 42, + "print_results": True, + } + + with gzip.open(f"{baseline_path_root}/ml/fastRP.json.gz", "rb") as f: + baseline = json.load(f) + + result = conn.runInstalledQuery( + "tg_fastRP", + params=params, + usePost=True, + ) + result = { + v["v_id"]: v["attributes"]["res.@final_embedding_list"] + for v in result[1]["res"] + } + threshold = 0.5 + for bk, bv in baseline.items(): + v = result[bk] + sim = abs(cos_sim(v, bv)) + if sim < threshold: + pytest.fail(f"cos-sim of {bk} is {sim} (< threshold of {threshold}") From 00506fc4c5b6e24ad05d211ab294e16003f8077c Mon Sep 17 00:00:00 2001 From: RobRossmiller-TG <165701656+RobRossmiller-TG@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:53:42 -0400 Subject: [PATCH 3/5] use featurizer --- tests/data/baseline/ml/fastRP.json.gz | Bin 209385 -> 209385 bytes tests/run.sh | 5 ++--- tests/test/baseline/create_baselines.py | 3 +-- tests/test/test_ml.py | 20 ++++++-------------- 4 files changed, 9 insertions(+), 19 deletions(-) diff --git a/tests/data/baseline/ml/fastRP.json.gz b/tests/data/baseline/ml/fastRP.json.gz index 31ab1974eb5e9b1689ef01d639ea3b6f183fee4a..2fa29708fc02b32f7ac3819f0068df35f61344a0 100644 GIT binary patch delta 26 icmaF)nCInV9ya-I4i4F=jcl!KjIC@;TiKW&8v+1^xCoU1 delta 26 icmaF)nCInV9ya-I4i2wLjcl!KjIC@;TiKW&8v+1{F$li^ diff --git a/tests/run.sh b/tests/run.sh index 801936b8..be492a1e 100755 --- a/tests/run.sh +++ b/tests/run.sh @@ -1,6 +1,5 @@ clear -python3 test/setup.py && +# python3 test/setup.py && python3 test/baseline/create_baselines.py && - pytest test/test_centrality.py && + # pytest test/test_centrality.py test/test_ml.py pytest test/test_ml.py -# pytest diff --git a/tests/test/baseline/create_baselines.py b/tests/test/baseline/create_baselines.py index 76fa0b0f..b2e0a946 100644 --- a/tests/test/baseline/create_baselines.py +++ b/tests/test/baseline/create_baselines.py @@ -2,6 +2,5 @@ import fast_rp_baseline if __name__ == "__main__": - # degree_cent_baseline.run() - + degree_cent_baseline.run() fast_rp_baseline.run() diff --git a/tests/test/test_ml.py b/tests/test/test_ml.py index 27648c27..00cfc858 100644 --- a/tests/test/test_ml.py +++ b/tests/test/test_ml.py @@ -1,26 +1,17 @@ import gzip import json -import os import numpy as np import pytest from dotenv import load_dotenv -from pyTigerGraph import TigerGraphConnection + +from util import get_featurizer load_dotenv() data_path_root = "data" baseline_path_root = f"{data_path_root}/baseline" -conn = TigerGraphConnection( - host=os.environ["HOST_NAME"], - graphname="Cora", - username=os.environ["USER_NAME"], - password=os.environ["PASS"], -) -conn.getToken() - - def cos_sim(x, y): x_mag = np.linalg.norm(x) y_mag = np.linalg.norm(y) @@ -29,6 +20,8 @@ def cos_sim(x, y): class TestML: + feat = get_featurizer("Cora") + def test_fastRP(self): params = { "v_type_set": ["Paper"], @@ -47,10 +40,9 @@ def test_fastRP(self): with gzip.open(f"{baseline_path_root}/ml/fastRP.json.gz", "rb") as f: baseline = json.load(f) - result = conn.runInstalledQuery( + result = self.feat.runAlgorithm( "tg_fastRP", params=params, - usePost=True, ) result = { v["v_id"]: v["attributes"]["res.@final_embedding_list"] @@ -61,4 +53,4 @@ def test_fastRP(self): v = result[bk] sim = abs(cos_sim(v, bv)) if sim < threshold: - pytest.fail(f"cos-sim of {bk} is {sim} (< threshold of {threshold}") + pytest.fail(f"cos-sim of ID: {bk} is {sim} (< threshold of {threshold}") From 5646e0013abe7401889c4288578e81f88a879615 Mon Sep 17 00:00:00 2001 From: RobRossmiller-TG <165701656+RobRossmiller-TG@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:54:35 -0400 Subject: [PATCH 4/5] run.sh --- tests/run.sh | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/run.sh b/tests/run.sh index be492a1e..e05cb3cd 100755 --- a/tests/run.sh +++ b/tests/run.sh @@ -1,5 +1,4 @@ clear -# python3 test/setup.py && +python3 test/setup.py && python3 test/baseline/create_baselines.py && - # pytest test/test_centrality.py test/test_ml.py - pytest test/test_ml.py + pytest test/test_centrality.py test/test_ml.py From 362024f4758b6294d0878accd7d4c04fbc6e2a49 Mon Sep 17 00:00:00 2001 From: RobRossmiller-TG <165701656+RobRossmiller-TG@users.noreply.github.com> Date: Tue, 16 Jul 2024 11:32:26 -0400 Subject: [PATCH 5/5] Update tg_fastRP.gsql --- algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql b/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql index 5dac70b5..fa78bef3 100644 --- a/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql +++ b/algorithms/GraphML/Embeddings/FastRP/tg_fastRP.gsql @@ -265,7 +265,7 @@ CREATE QUERY tg_fastRP( IF print_results THEN res = SELECT a FROM verts:a; - PRINT res[res.@final_embedding_list]; + PRINT res[res.@final_embedding_arr]; END; // (un)comment depending on whether you want to write to graph