def test_dataset_stats(): stats = pipeline("dataset-stats") outputs = stats("cora") outputs = outputs[0] assert len(outputs) == 6 assert tuple(outputs) == ("cora", 2708, 10484, 1433, 7, 140)
def test_oagbert(): oagbert = pipeline("oagbert", model="oagbert-test", load_weights=False) outputs = oagbert("CogDL is developed by KEG, Tsinghua.") assert len(outputs) == 2 assert tuple(outputs[0].shape) == (1, 14, 32) assert tuple(outputs[1].shape) == (1, 32)
def test_gen_emb(): generator = pipeline("generate-emb", model="prone") edge_index = np.array([[0, 1], [0, 2], [0, 3], [1, 2], [2, 3]]) outputs = generator(edge_index) assert tuple(outputs.shape) == (4, 4) edge_weight = np.array([0.1, 0.3, 1.0, 0.8, 0.5]) outputs = generator(edge_index, edge_weight) assert tuple(outputs.shape) == (4, 4) generator = pipeline("generate-emb", model="dgi", num_features=8, hidden_size=10, cpu=True) outputs = generator(edge_index, x=np.random.randn(4, 8)) assert tuple(outputs.shape) == (4, 10)
def test_recommendation(): data = np.array([[0, 0], [0, 1], [0, 2], [1, 1], [1, 3], [1, 4], [2, 4], [2, 5], [2, 6]]) rec = pipeline("recommendation", model="lightgcn", data=data, max_epoch=2, evaluate_interval=1000, cpu=True) ret = rec([0], topk=3) assert len(ret[0]) == 3
def test_gen_emb(): generator = pipeline("generate-emb", model="prone") edge_index = np.array([[0, 1], [0, 2], [0, 3], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]]) outputs = generator(edge_index) assert tuple(outputs.shape) == (8, 8) generator = pipeline( "generate-emb", model="mvgrl", no_test=True, num_features=8, hidden_size=10, sample_size=2, epochs=2, cpu=True, ) outputs = generator(edge_index, x=np.random.randn(8, 8)) assert tuple(outputs.shape) == (8, 10)
import numpy as np from cogdl import pipeline data = np.array([[0, 0], [0, 1], [0, 2], [1, 1], [1, 3], [1, 4], [2, 4], [2, 5], [2, 6]]) rec = pipeline("recommendation", model="lightgcn", data=data, epochs=10, evaluate_interval=1000, cpu=True) print(rec([0])) rec = pipeline("recommendation", model="lightgcn", dataset="ali", epochs=1, n_negs=1, evaluate_interval=1000) print(rec([0]))
import numpy as np from cogdl import pipeline # build a pipeline for generating embeddings # pass model name with its hyper-parameters to this API generator = pipeline("generate-emb", model="prone") # generate embedding by an unweighted graph edge_index = np.array([[0, 1], [0, 2], [0, 3], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]]) outputs = generator(edge_index) print(outputs) # generate embeddings by a weighted graph edge_weight = np.array([0.1, 0.3, 1.0, 0.8, 0.5, 0.2, 0.1, 0.5, 2.0]) outputs = generator(edge_index, edge_weight) print(outputs) # build a pipeline for generating embeddings using unsupervised GNNs # pass model name and num_features with its hyper-parameters to this API generator = pipeline("generate-emb", model="mvgrl", no_test=True, num_features=8, hidden_size=4) outputs = generator(edge_index, x=np.random.randn(8, 8)) print(outputs)
import numpy as np from cogdl import pipeline # build a pipeline for generating embeddings # pass model name with its hyper-parameters to this API generator = pipeline("generate-emb", model="prone") # generate embedding by an unweighted graph edge_index = np.array([[0, 1], [0, 2], [0, 3], [1, 2], [2, 3]]) outputs = generator(edge_index) print(outputs) # generate embeddings by a weighted graph edge_weight = np.array([0.1, 0.3, 1.0, 0.8, 0.5]) outputs = generator(edge_index, edge_weight) print(outputs) # build a pipeline for generating embeddings using unsupervised GNNs # pass model name and num_features with its hyper-parameters to this API generator = pipeline("generate-emb", model="dgi", num_features=8, hidden_size=4) outputs = generator(edge_index, x=np.random.randn(4, 8)) print(outputs)
def test_dataset_visual(): visual = pipeline("dataset-visual") outputs = visual("cora", seed=0, depth=3) assert len(outputs) == 71
def load_model(): global model model = pipeline("generate-emb", model="prone")
from cogdl import pipeline # print the statistics of datasets stats = pipeline("dataset-stats") stats(["cora", "citeseer"]) # visualize k-hop neighbors of seed in the dataset visual = pipeline("dataset-visual") visual("cora", seed=0, depth=3) # load OAGBert model and perform inference oagbert = pipeline("oagbert") outputs = oagbert([ "CogDL is developed by KEG, Tsinghua.", "OAGBert is developed by KEG, Tsinghua." ])