class GMFConfig_dbg:
def __init__(self, device, n=150, m=150, prob=.5):
self.device = device
self.create_generator(n, m, prob)
self.create_model()
def create_model(self):
print('-' * 15, "Creating model", '-' * 15)
latent_dim = 10
config = {
'num_virus': self.n_v,
'num_human': self.n_h,
'latent_dim': latent_dim,
'sparse':
False # set false for now because some optimizers dont work with sparse
}
self.model = GMF(config)
self.model.to(self.device)
print(self.model)
# print("params-------")
# print(list(self.model.parameters()))
# print("end-------")
# print('grad: ', list(self.model.parameters())[0].grad)
# print('grad: ', list(self.model.parameters())[3].grad)
# print('grad: ', list(self.model.parameters())[4].grad)
print('-' * 15, "Done with model", '-' * 15)
print()
def create_generator(self, m, n, prob):
############################
## generate bipartite
###########################
print('-' * 15, "Generating graph", '-' * 15)
G = nx.bipartite.random_graph(n, m, prob)
observed = list(G.edges())
nodes = list(G.nodes())
virusUprot = []
humanUprot = []
edges = []
for i in tqdm(range(n)):
for j in tqdm(range(n, m + n)):
virusUprot.append(i)
humanUprot.append(j)
if (i, j) in observed:
edges.append(1.0)
else:
edges.append(0.0)
M = pd.DataFrame({
'virusUprot': virusUprot,
'humanUprot': humanUprot,
'edge': edges
})
htoi = {v: k for k, v in enumerate(M['humanUprot'].unique())}
vtoi = {v: k for k, v in enumerate(M['virusUprot'].unique())}
print('-' * 15, "Dataframe created", '-' * 15)
print()
############################
## Prepare data (dataloader)
############################
print('-' * 15, "Creating data loaders", '-' * 15)
data_config = {
'interactions': M,
'htoi': htoi,
'vtoi': vtoi,
'pct_test': .10,
'device': self.device
}
self.n_v = len(vtoi)
self.n_h = len(htoi)
self.gen = ProteinInteractionGenerator(data_config)
print('-' * 15, "Generator done", '-' * 15)
print()
def get_generator(self):
return self.gen
def get_model(self):
return self.model
class GMFConfig:
def __init__(self, path, debug, device):
self.device = device
self.create_generator(path, debug)
self.create_model()
def create_model(self):
print('-' * 15, "Creating model", '-' * 15)
latent_dim = 2799
config = {
'num_virus': self.n_v,
'num_human': self.n_h,
'latent_dim': latent_dim,
'sparse': False # set false for now because some optimizers dont work with sparse
}
self.model = GMF(config)
self.model.to(self.device)
print(self.model)
print('-' * 15, "Done with model", '-' * 15)
print()
def create_generator(self, path, debug):
############################
## paths
###########################
train_csv =f'{path}full_train.csv'
############################
## Load data
############################
print('-' * 15, "Loading data", '-' * 15)
print("loading traning matrix at: ", train_csv)
M = pd.read_csv(train_csv)
if debug:
print("Making debug dataset.....")
pos = M.loc[M['edge'] > 0].sample(frac=1)
negs = M.loc[M['edge'] == 0].sample(frac=1)
M = pd.concat([pos, negs[:len(pos)]], ignore_index=True).sample(frac=1)
htoi = {v:k for k,v in enumerate(M['humanUprot'].unique())}
vtoi = {v:k for k,v in enumerate(M['virusUprot'].unique())}
############################
## Prepare data (dataloader)
############################
print('-' * 15, "Creating data loaders", '-' * 15)
data_config = {
'interactions':M,
'htoi':htoi,
'vtoi':vtoi,
'pct_test':.10,
'device': self.device
}
self.n_v = len(vtoi)
self.n_h = len(htoi)
self.gen = ProteinInteractionGenerator(data_config)
print('-' * 15, "Generator done", '-' * 15)
print()
def get_generator(self):
return self.gen
def get_model(self):
return self.model
