def run_two_stage(args):
DTI_network = utils.load_any_obj_pkl(args.dti_path)
drug_similarity = utils.load_any_obj_pkl(args.drug_sim_path)
target_similarity = utils.load_any_obj_pkl(args.target_sim_path)
csn_network = network_construction.construct_signifcant_edge_network(
drug_similarity, top_ratio=float(args.sparsity))
tsn_network = network_construction.construct_signifcant_edge_network(
target_similarity, top_ratio=float(args.sparsity))
implicit_compounds = network_construction.create_implicit_networks(
DTI_network, list(csn_network.nodes()))
implicit_targets = network_construction.create_implicit_networks(
DTI_network, list(tsn_network.nodes()))
learner = seperate_learner.two_stage_learning(
DTI_network=DTI_network,
compound_list=list(csn_network.nodes()),
target_list=list(tsn_network.nodes()),
tsn_network=tsn_network,
csn_network=csn_network,
implicit_t_network=implicit_targets,
implicit_c_network=implicit_compounds,
wl=int(args.walk_length),
nn=int(args.negative_number),
wn=int(args.walk_num),
worker=int(args.worker),
load_emb=False)
learner.learn_all_network_embedding()
learner.build_node_representation()
training_samples, training_labels = learner.construct_training_samples(
negative_ratio=10)
test_pairs = new_pairs_to_evaludate(list(csn_network.nodes()),
list(tsn_network.nodes()), DTI_network)
test_samples = learner.concatenate_pair_embeddings(test_pairs)
training_samples = normalise_sample_representation.standardscaler_transform(
training_samples)
test_samples = normalise_sample_representation.standardscaler_transform(
test_samples)
clf = learner.train_DTI_prediction_svm(training_samples,
training_labels,
kernal=2)
probs = clf.predict_proba(test_samples)
new_probs = [row[1] for row in probs]
all_evaluation = []
#from tqdm import tqdm
for i in range(len(test_pairs)):
current_one = [test_pairs[i][0], test_pairs[i][1], new_probs[i]]
all_evaluation.append(current_one)
output_name = 'output/' + args.output_name + '.pkl'
utils.save_any_obj_pkl(all_evaluation, output_name)
def evaluation():
G_dynamic_ori = load_any_obj_pkl(
"graph_data/collaborate_network(1G)/collaborate_network_2006_2016.pkl")
G_dynamic = load_any_obj_pkl(
"graph_data/collaborate_network(2G)/collaborate_network_2007_2016.pkl")
method = "DANRL"
filepath = "parameter_sensitivity/collaborate_network(2G)/output"
files = os.listdir(filepath)
filepath0 = "parameter_sensitivity/collaborate_network(2G)/recommendation"
files0 = os.listdir(filepath0)
i = 0
for file in files:
print(i, len(files))
i += 1
file_1 = file[:-4] + "_G_2ori.txt"
if file_1 not in files0:
emb_dicts = load_any_obj_pkl(os.path.join(filepath, file))
# print(len(emb_dicts))
avg_score = dict()
for top_k in range(1, 11, 1):
score = []
for t in range(len(emb_dicts) - 2): # 遍历所有time step的embedding
model = recommendation(
emb_dicts[t],
G0=G_dynamic[t],
G1=G_dynamic_ori[t + 2],
G2=G_dynamic_ori[t + 3],
# G3=G_dynamic_ori[t+4]
)
score.append(model.evaluate_precision_k(top_k))
avg_score["top_" + str(top_k)] = np.mean(score)
# "parameter_sensitivity/collaborate_network(2G)/output"
output_filepath = "parameter_sensitivity/collaborate_network(2G)/recommendation"
if not os.path.exists(output_filepath):
os.makedirs(output_filepath)
output = open(
os.path.join(output_filepath, file[:-4] + "_G_2ori.txt"), "w")
output.write(json.dumps(avg_score) + "\n")
output.close()
def loadData2PD(filepath):
data = load_any_obj_pkl(filepath)[-1]
X = None
car_ids = []
for key, value in data.items():
car_ids.append(key)
if X is None:
X = np.array(value).reshape(1, -1)
else:
X = np.vstack((X, value.reshape(1, -1)))
X = 1.0 * (X - X.mean()) / X.std()
return pd.DataFrame(X, index=car_ids)
def load_DynWalks_Embedding(method):
data = load_any_obj_pkl("DynWalks/output/hangzhou_20140301_MCC_" + method +
"_embs.pkl")[-1]
X = None
car_ids = []
for key, value in data.items():
car_ids.append(key)
if X is None:
X = np.array(value).reshape(1, -1)
else:
X = np.vstack((X, value.reshape(1, -1)))
X = 1.0 * (X - X.mean()) / X.std()
return pd.DataFrame(X, index=car_ids)
def draw_graph():
graphs = load_any_obj_pkl(
"graph_data/collaborate_network(2G)/collaborate_network_2007_2016.pkl")
year = 2007
G_s = []
for g in graphs:
print("#nodes: " + str(g.number_of_nodes()) + ", #edges: " +
str(g.number_of_edges()))
G = nx.Graph()
G.add_nodes_from(g.nodes())
G.add_edges_from(g.edges())
# nx.write_gexf(G, "graph_data/collaborate_network(2G)/" + str(year) + ".gexf")
# year += 1
G_s.append(G)
nx.draw(G_s[0], node_size=30, node_color="black", edge_color="gray")
plt.show()
def construct_combined_graph():
graphs = load_any_obj_pkl(
"graph_data/collaborate_network(1G)/collaborate_network_2006_2016.pkl")
for i in range(2, len(graphs)):
g0 = graphs[i - 2]
g1 = graphs[i - 1]
g = graphs[i]
print("#nodes: " + str(g.number_of_nodes()) + ", #edges: " +
str(g.number_of_edges()))
l = []
for edge in g0.edges():
if edge not in g.edges():
n1, n2 = edge[0], edge[1]
l.append((n1, n2, g0.get_edge_data(n1, n2)['weight']))
if n1 not in g.nodes():
g.add_node(n1, attribute=g0.nodes[n1]["attribute"])
if n2 not in g.nodes():
g.add_node(n2, attribute=g0.nodes[n2]["attribute"])
for edge in g1.edges():
if edge not in g.edges():
n1, n2 = edge[0], edge[1]
l.append((n1, n2, g1.get_edge_data(n1, n2)['weight']))
if n1 not in g.nodes():
g.add_node(n1, attribute=g1.nodes[n1]["attribute"])
if n2 not in g.nodes():
g.add_node(n2, attribute=g1.nodes[n2]["attribute"])
g.add_weighted_edges_from(l)
print("#nodes: " + str(g.number_of_nodes()) + ", #edges: " +
str(g.number_of_edges()))
# nx.draw(g, node_size=20)
# plt.show()
g = g.subgraph(max(nx.connected_components(g), key=len)).copy()
print("#nodes: " + str(g.number_of_nodes()) + ", #edges: " +
str(g.number_of_edges()))
filename = "graph_data/collaborate_network_" + str(
i) + "_edgelist_new.txt"
nx.write_edgelist(g, filename, data=False)
save_any_obj_pkl(
g,
"graph_data/collaborate_network(3G)" + str(i + 2006) + "_new.pkl")
graphs.append(g)
save_any_obj_pkl(graphs,
"graph_data/collaborate_network_2008_2016_new.pkl")
def sw_similarity():
csd = utils.load_any_obj_pkl('data/chem_seq_dict.pkl')
chem_id = list(csd.keys())
#for item in chem_id:
# if
#if len(csd[item]) < 5 :
# print('get it')
print(chem_id[0])
print(chem_id[1])
match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(
scoring) # you can also choose gap penalties, etc...
alignment = sw.align(csd[chem_id[0]], csd[chem_id[1]])
#score = alignment.dump()
print(alignment.score)
def create_compound_similarity_network_mp(compounds_smiles_path,
species_name='_DB',
worker=4,
top_ratio=0.04):
compounds_smiles = utils.load_any_obj_pkl(compounds_smiles_path)
all_compounds = list(compounds_smiles.keys())
#print(Chem.SanitizeMol('CN(CCO[P@](O)(=O)O[P@@](O)(=O)O[Be-](F)(F)F)C1=CC=CC=C1[N+]([O-])=O'))
#for item in all_compounds:
# m2 = Chem.MolFromSmiles(compounds_smiles[item])
# if m2 == None:
# print(item)
# print(compounds_smiles[item])
#raise Exception('stop')
ccd = calculate_molecular_similarity(compounds_smiles, worker=worker)
all_corr = ccd.parallel_calculate_all_correlation()
#all_corr = [[str(j) for j in i] for i in all_corr]
final_corr = []
for item in all_corr:
for a_corr in item:
#print(a_corr)
final_corr.append(a_corr)
save_name = 'data/' + 'compound_similarity' + species_name + '.pkl'
utils.save_any_obj_pkl(final_corr, save_name)
elif scholars[0] in G2.nodes() and scholars[1] in G2.nodes():
if scholars in G2.edges():
tp += 1
else:
fp += 1
# elif scholars[0] in G3.nodes() and scholars[1] in G3.nodes():
# if scholars in G2.edges():
# tp += 1
# else:
# fp += 1
# print(tp, fp)
# print("recommend_precision_score=", "{:.9f}".format(tp/(tp+fp)))
return tp / (tp + fp)
G_dynamic0 = load_any_obj_pkl(
"graph_data/collaborate_network(1G)/collaborate_network_2006_2016.pkl")
G_dynamic = load_any_obj_pkl(
"graph_data/collaborate_network(2G)/collaborate_network_2007_2016.pkl")
print("计算共同邻居")
cn_list = []
index2node_list = []
for g in G_dynamic:
nodes = list(g.nodes())
cn_matrix = np.zeros([len(nodes), len(nodes)])
index2node = dict()
for i in range(len(nodes)):
index2node[i] = nodes[i]
for j in range(i, len(nodes)):
cn_matrix[i,
j] = len(list(nx.common_neighbors(g, nodes[i],
def load_OpenNE_Embedding(method, year):
sid_emb = dict()
with open(
r"output/collaborate_network(2G)/" + method +
"/collaborate_network_" + str(year) + "_embs.txt",
"r") as embeddings:
embeddings.readline()
for embedding in embeddings:
l = embedding.split()
sid_emb[l[0]] = [float(n) for n in l[1:]]
embeddings.close()
return sid_emb
G_dynamic_ori = load_any_obj_pkl(
"graph_data/collaborate_network(1G)/collaborate_network_2006_2016.pkl")
G_dynamic = load_any_obj_pkl(
"graph_data/collaborate_network(2G)/collaborate_network_2007_2016.pkl")
method = "DeepWalk"
if method == "DANRL":
emb_dicts = load_any_obj_pkl("output/collaborate_network(2G)/" + method +
"/collaborate_network_2007_2016_embs.pkl")
else:
emb_dicts = []
for year in range(2007, 2017):
emb_dicts.append(load_OpenNE_Embedding(method, year))
print(len(emb_dicts))
avg_score = dict()
for top_k in range(1, 11, 1):
fps1, fps2)
similarity_info.append([
self.compounds[compound_index], self.compounds[i],
simialrity_coefficient
])
return similarity_info
if __name__ == "__main__":
#create_compound_similarity_network_mp('data/drugbank_drugs.pkl', species_name = '_DB')#60
#sw_similarity()#cp sa mm
#Chem.SanitizeMol
#all_keys = list(target_seqs.keys())
#a = target_seqs[all_keys[0]]
#print(type(a))
#target_IDs = list(target_seqs.keys())
#print(target_seqs[target_IDs[0]])
#t1 = target_IDs[0]
#seqs1 = target_seqs[t1].split('\n')
#for item in target_IDs:
# seqs1 = target_seqs[item]
# print(len(seqs1.split('\n')[-3]))
#create_compound_similarity_network_mp('data/drugbank_drugs.pkl', species_name = '_DB')
target_seqs = utils.load_any_obj_pkl('data/drugbank_targets.pkl')
create_target_similarity_network(target_seqs, 'DB_N_')
#alignment, score, start_end_positions = skbio.alignment.local_pairwise_align_protein(Protein(p1_s), Protein(p2_s))
#print(score)
def main(args):
# -------- Step 1: prepare data --------
print(f'Summary of all settings: {args}')
print('\nStep 1: start loading data ...')
t1 = time.time()
G_dynamic = load_any_obj_pkl(args.graph)
emb_dicts = load_any_obj_pkl(args.emb_file)
t2 = time.time()
print(f'Step 1: end loading data; time cost: {(t2-t1):.2f}s')
# -------- Step 3: downstream task --------
print('\n\nStep 3: start evaluating embeddings ...')
t1 = time.time()
print(time.strftime("%Y-%m-%d %H:%M:%S %Z", time.localtime()))
if args.task == 'lp' or args.task == 'all':
from downstream import lpClassifier, gen_test_edge_wrt_changes
# the size of LP testing data depends on the changes between two consecutive snapshots
test_edges = []
test_labels = []
for t in range(len(G_dynamic) -
1): # changed edges from t to t+1 as testing edges
pos_edges_with_label, neg_edges_with_label = gen_test_edge_wrt_changes(
G_dynamic[t], G_dynamic[t + 1], seed=args.seed)
test_edges.append([e[:2] for e in pos_edges_with_label] +
[e[:2] for e in neg_edges_with_label])
test_labels.append([e[2] for e in pos_edges_with_label] +
[e[2] for e in neg_edges_with_label])
# ====== Changed Link Prediction task (via cos sim) by AUC score ======
print(
'--- Start changed link prediction task --> use current emb @t to predict **future** changed links @t+1: '
)
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
print(f'Changed Link Prediction task (via cos sim) by AUC score')
ds_task = lpClassifier(
emb_dict=emb_dicts[t]
) # emb at t; did not use **future** changed edges
ds_task.evaluate_auc(
test_edges[t], test_labels[t]
) # evalate prediction of changed edges from t to t+1
# ====== Changed Link Prediction task (Weighted-L1 edge_feat --> LR clf) by AUC score ======
print(
f'--- start changed link prediction task 1 --> use current emb @t to predict **future** changed links @t+1: '
)
LR_prev = None
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
print(
f'Changed Link Prediction task (Weighted-L1 edge_feat --> LR clf) by AUC score'
)
ds_task = lpClassifier(emb_dict=emb_dicts[t])
if t == 0:
LR_prev = ds_task.lr_clf_init1(G_dynamic[t])
LR_prev = ds_task.update_LR_auc1(
test_edges[t], test_labels[t], LR_prev=LR_prev
) # incremental learning for Changed LP task; LogisticRegression(random_state=2021, penalty='l2', max_iter=1000)
# ====== Changed Link Prediction task (Weighted-L2 edge_feat --> LR clf) by AUC score ======
print(
f'--- start changed link prediction task 2 --> use current emb @t to predict **future** changed links @t+1: '
)
LR_prev = None
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
print(
f'Changed Link Prediction task (Weighted-L2 edge_feat --> LR clf) by AUC score'
)
ds_task = lpClassifier(emb_dict=emb_dicts[t])
if t == 0:
LR_prev = ds_task.lr_clf_init2(G_dynamic[t])
LR_prev = ds_task.update_LR_auc2(
test_edges[t], test_labels[t], LR_prev=LR_prev
) # incremental learning for Changed LP task; LogisticRegression(random_state=2021, penalty='l2', max_iter=1000)
# ====== Changed Link Prediction task (Hadamard edge_feat --> LR clf) by AUC score ======
print(
f'--- start changed link prediction task 3 --> use current emb @t to predict **future** changed links @t+1: '
)
LR_prev = None
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
print(
f'Changed Link Prediction task (Hadamard edge_feat --> LR clf) by AUC score'
)
ds_task = lpClassifier(emb_dict=emb_dicts[t])
if t == 0:
LR_prev = ds_task.lr_clf_init3(G_dynamic[t])
LR_prev = ds_task.update_LR_auc3(
test_edges[t], test_labels[t], LR_prev=LR_prev
) # incremental learning for Changed LP task; LogisticRegression(random_state=2021, penalty='l2', max_iter=1000)
# ====== Changed Link Prediction task (Average edge_feat --> LR clf) by AUC score ======
print(
f'--- start changed link prediction task 4 --> use current emb @t to predict **future** changed links @t+1: '
)
LR_prev = None
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
print(
f'Changed Link Prediction task (Average edge_feat --> LR clf) by AUC score'
)
ds_task = lpClassifier(emb_dict=emb_dicts[t])
if t == 0:
LR_prev = ds_task.lr_clf_init4(G_dynamic[t])
LR_prev = ds_task.update_LR_auc4(
test_edges[t], test_labels[t], LR_prev=LR_prev
) # incremental learning for Changed LP task; LogisticRegression(random_state=2021, penalty='l2', max_iter=1000)
print(time.strftime("%Y-%m-%d %H:%M:%S %Z", time.localtime()))
if args.task == 'nr' or args.task == 'all':
print(
f'--- start changed node recommendation task --> use current emb @t to recommend nodes for **future** changed node in graph @t+1: '
)
from downstream import nrClassifier, gen_test_node_wrt_changes, align_nodes
for t in range(len(G_dynamic) - 1):
print(f'Current time step @t: {t}')
node_list = gen_test_node_wrt_changes(
G_dynamic[t], G_dynamic[t + 1]
) # generate the testing nodes that affected by changes and presented in both graphs
print(
'# of testing nodes that affected by changes and presented in both graphs: ',
len(node_list))
rc_next_graph_aligned = align_nodes(
G_dynamic[t], G_dynamic[t + 1]
) # remove newly added nodes from G_dynamic[t+1], and add newly removed nodes to G_dynamic[t+1]
ds_task = nrClassifier(emb_dict=emb_dicts[t],
rc_graph=rc_next_graph_aligned)
top_k_list = [5, 10, 50, 100]
ds_task.evaluate_pk_and_apk(top_k_list, node_list)
# If OOM, try grClassifier_batch (see dowmstream.py) which is slow but requires much smaller memory
print(time.strftime("%Y-%m-%d %H:%M:%S %Z", time.localtime()))
if args.task == 'gr' or args.task == 'all':
print(
f'--- start graph/link reconstraction task --> use current emb @t to reconstruct **current** graph @t: '
)
from downstream import grClassifier
for t in range(
len(G_dynamic) - 1
): # ignore the last one, so that the length is consistent with Changed LP
print(f'Current time step @t: {t}')
all_nodes = list(G_dynamic[t].nodes())
if len(all_nodes) <= 10000:
node_list = None # testing all nodes
print('# testing for all nodes in current graph')
else:
node_list = list(
np.random.choice(all_nodes, 10000, replace=False))
print(
'# current graph is too larger -> randomly sample 10000 testing nodes: ',
len(node_list))
ds_task = grClassifier(emb_dict=emb_dicts[t],
rc_graph=G_dynamic[t])
top_k_list = [5, 10, 50, 100]
ds_task.evaluate_pk_and_apk(top_k_list, node_list)
# If OOM, try grClassifier_batch (see dowmstream.py) which is slow but requires much smaller memory
t2 = time.time()
print(f'STEP3: end evaluating; time cost: {(t2-t1):.2f}s')
print(time.strftime("%Y-%m-%d %H:%M:%S %Z", time.localtime()))
