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TFM_AdrianAyusoMuñoz
Commit 6d263e1a authored by ADRIAN AYUSO MUNOZ's avatar ADRIAN AYUSO MUNOZ
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Showing with 60 additions and 64 deletions
autoencoder.py
View file @ 6d263e1a
......@@ -14,6 +14,7 @@ class GCNEncoder(torch.nn.Module):
in_channels: Size of the input embeddings.
out_channels: Size of the output embeddings.
"""
def __init__(self, in_channels, out_channels):
super(GCNEncoder, self).__init__()
self.conv1 = SAGEConv(in_channels, 2 * out_channels) # cached only for transductive learning
......@@ -28,11 +29,13 @@ class GCNEncoder(torch.nn.Module):
Output:
Encoded embeddings.
"""
def forward(self, x, edge_index):
x = self.conv1(x, edge_index)
x = self.lrelu(x)
return self.conv2(x, edge_index)
"""
Class to wrap the training and testing functions.
"""
......@@ -44,6 +47,7 @@ class Trainer:
optimizer: Optimizer to train the model.
dataset: Dataset on which the model will be trained.
"""
def __init__(self, model, optimizer, dataset):
self.model = model
self.optimizer = optimizer
......
dmsr.py
View file @ 6d263e1a
......@@ -28,7 +28,6 @@ from deepsnap.hetero_gnn import (
edges = [('disorder', 'dis_dru_the', 'drug')] # It sets the edges that will be studied.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # It defines whether to execute on cpu or gpu.
# ---------------------------
# FUNCTIONS
# ---------------------------
......@@ -55,6 +54,7 @@ def generate_convs_link_pred_layers(hete, conv, hidden_size):
convs2[message_type] = conv(hidden_size, hidden_size, hidden_size)
return [convs1, convs2]
"""
Heterogeneous Graph Neural Network.
"""
......@@ -228,6 +228,7 @@ class HeteroGNN(torch.nn.Module):
loss += self.loss_fn(p, y[key].type(pred[key].dtype))
return loss
"""
It trains the model.
Input:
......@@ -345,6 +346,7 @@ def test2(model, test_loader):
keys.append(key)
return pure_pred_labels, true_labels, keys
"""
It wraps all the functions for training and testing the model.
Input:
......@@ -420,5 +422,4 @@ def main(epochs, hidden_dim, lr, weight_decay, dropout):
if __name__ == '__main__':
# Set of hyperparameters to train the model.
main(2343, 31, 0.0010235455088934942, 0.005144745056173074, 0.5)
main(2343, 31, 0.0010235455088934942, 0.005144745056173074, 0.5)
drug_embedding_generator.py
View file @ 6d263e1a
......@@ -24,12 +24,14 @@ class DrugGraph:
neg_edge_index: Two lists which contain negative edges, not present in the graph (first contains heads and
second tails)
"""
def __init__(self, graph, feats, edge_index, neg_edge_index):
self.graph = graph
self.x = feats
self.edge_index = edge_index
self.neg_edge_index = neg_edge_index
"""
It generates a CSV with the SMILES representations of the DISNET's graph drugs.
"""
......@@ -50,6 +52,7 @@ def getSmiles():
newDf.to_csv("data/druStruc.tsv", sep='\t', index=False)
"""
It transforms the SMILES representations to their graph representations. In case of error instead of the graph a 0 is
given.
......@@ -83,6 +86,7 @@ def getGraph(df, complete=False):
df.to_csv("data/druStruc.tsv", sep='\t', index=False)
return np.array(networks, dtype=object)
"""
It builds a dataset made of the drugs molecular structures.
Input:
......@@ -169,6 +173,7 @@ def trainAE(model, optimizer, dataset, epochs):
trainer = Trainer(model, optimizer, dataset)
trainer.fit(epochs)
"""
It generates an embedding for a given graph applying all the instantiated layers in the previous function. The embedding
is the code of the autoencoder.
......@@ -210,7 +215,7 @@ if __name__ == '__main__':
# Getting Embeddings
embeddings = getEmbed(model, dataset)
# Saving model.
torch.save(model.state_dict(), "./models/structureEmbedder")
......
heterograph_construction.py
View file @ 6d263e1a
......@@ -63,7 +63,7 @@ class DISNETConstructor:
ddi_dru = pd.read_csv('data/links/ddi_dru.tsv', sep='\t')
return dis_dru_the, dis_sym, dis_pat, dis_pro, dru_dru, dru_pro, dru_sym_ind, dru_sym_sef, pro_pat, \
pro_pro, ddi_phe, ddi_dru
pro_pro, ddi_phe, ddi_dru
else:
return dis_dru_the, dis_sym
......@@ -137,7 +137,8 @@ class DISNETConstructor:
ddi['NID'] = ddi.index
ddi['node_type'] = 'drug-drug-interaction'
ddi['node_id'] = nodes_flat.loc[nodes_flat['node_type'] == 'drug-drug-interaction'].reset_index(drop=True).node_id
ddi['node_id'] = nodes_flat.loc[nodes_flat['node_type'] == 'drug-drug-interaction'].reset_index(
drop=True).node_id
# Nodes dataframes to dict to apply map later
pat_dict = pat[['id', 'node_id']].set_index('id').to_dict()['node_id']
......@@ -148,7 +149,6 @@ class DISNETConstructor:
pro_feat = torch.tensor([[1] * 100] * nsizes['protein'], dtype=torch.float32)
ddi_feat = torch.tensor([[1] * 100] * nsizes['drug-drug-interaction'], dtype=torch.float32)
feats = {'disorder': dis_feat, 'drug': dru_feat, 'pathway': pat_feat, 'protein': pro_feat,
'drug-drug-interaction': ddi_feat}
......@@ -163,7 +163,7 @@ class DISNETConstructor:
# DataFrames of each type of edges
if full:
dis_dru_the, dis_sym, dis_pat, dis_pro, dru_dru, dru_pro, dru_sym_ind, dru_sym_sef, pro_pat, \
pro_pro, ddi_phe, ddi_dru = self.getEdgeInfo(full)
pro_pro, ddi_phe, ddi_dru = self.getEdgeInfo(full)
dis_pat['disNID'] = dis_pat.dis.map(dis_dict)
dis_pat['patNID'] = dis_pat.pat.map(pat_dict)
......@@ -215,7 +215,7 @@ class DISNETConstructor:
dis_dru_the = pd.concat([dis_dru_the, dis_dru_the_repoDBAll])
dis_dru_the.drop_duplicates(keep=False, inplace=True, ignore_index=True)
dis_dru_the = dis_dru_the[:50355] # Those of RepoDB added for the concatenation are deleted.
dis_dru_the = dis_dru_the[:50355] # Those of RepoDB added for the concatenation are deleted.
dis_dru_the_repoDBAll = (
torch.tensor(dis_dru_the_repoDBAll['disNID'].astype(np.int32).to_numpy(), dtype=torch.int32,
......@@ -321,7 +321,8 @@ class DISNETConstructor:
# Add the edges to the graph.
for edge_t in edges_dict.keys():
for edge in edges[edge_t]: # If a relation between a phenotype and a drug is in more than one type. The final type will be the last one.
for edge in edges[
edge_t]: # If a relation between a phenotype and a drug is in more than one type. The final type will be the last one.
try:
G.add_edge(int(edge[0]), int(edge[1]), edge_feature=edge[2], edge_type=edge_t)
except IndexError:
......
testRepoDB.py
View file @ 6d263e1a
......@@ -15,7 +15,6 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # It defi
constructor = heterograph_construction.DISNETConstructor(device=device) # Graph constructor.
toStudy = 'dis_dru_the' # Graph edge type to study.
"""
It gets the predictions of the model and decodes them.
Input:
......@@ -50,6 +49,7 @@ def randomEids():
tensor2 = torch.randint(0, 3944, (5013,), device=torch.device(device))
return (tensor1, tensor2)
"""
It plots the metrics for the results of the real edge and the random edge set. It joins them vertically and horizontally.
Input:
......@@ -161,25 +161,25 @@ if __name__ == '__main__':
rocL, prcL = np.array([]), np.array([])
# Number of iterations.
k = 50
# Set of hyperparameters.
epochs = 2343
epochs = 2343
hidden_dim = 31
lr = 0.0010235455088934942
weight_decay = 0.005144745056173074
dropout = 0.5
# Train and test k models and obtain their metrics.
for i in range(k):
model = main(epochs, hidden_dim, lr, weight_decay, dropout)
roc1, prc1 = metrics(model)
rocL = np.append(rocL, roc1)
prcL = np.append(prcL, prc1)
# Average of the metrics of all the generated models.
rocM = sum(rocL) / k
prcM = sum(prcL) / k
# Obtain confidence intervals, if number of samples is under 30 t-distribution is used, if over 30 the normal
# distribution is used.
if k < 30:
......@@ -189,6 +189,5 @@ if __name__ == '__main__':
r = st.norm.interval(0.95, loc=np.mean(rocL), scale=st.sem(rocL))
p = st.norm.interval(0.95, loc=np.mean(prcL), scale=st.sem(prcL))
print("AUCROC: ", rocM, "+-", rocM-r[0])
print("AUCPR: ", prcM, "+-", prcM-p[0])
print("AUCROC: ", rocM, "+-", rocM - r[0])
print("AUCPR: ", prcM, "+-", prcM - p[0])
testRepoDBWeightsAndBiases.py
View file @ 6d263e1a
......@@ -13,6 +13,7 @@ import scipy.stats as st
# Import W&B
import wandb
wandb.init(project="dmsr", entity="ayusoupm")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # It defines whether to execute on cpu or gpu.
......@@ -29,8 +30,6 @@ Input:
Output:
Dataframe containing all the predictions ordered and decoded.
"""
def getDecode(model, eid, dataloader, random=''):
print(" Looking for new edges.")
for batch in dataloader:
......@@ -50,8 +49,6 @@ It generates random edges in the graph.
Output:
Randomly generated edges.
"""
def randomEids():
tensor1 = torch.randint(0, 30729, (5013,), device=torch.device(device))
tensor2 = torch.randint(0, 3944, (5013,), device=torch.device(device))
......@@ -68,8 +65,6 @@ Input:
precision: Precision.
label2: Area Under the PR curve.
"""
def plotMetrics(fpr, tpr, label1, recall, precision, label2):
# Vertical plotting.
fig, axs = plt.subplots(2, figsize=(6, 10))
......@@ -127,8 +122,6 @@ Input:
Output:
Area Under the ROC and PR curve.
"""
def metrics(model):
hetero, eids = constructor.DISNETHeterograph(full=False, withoutRepoDB=True)
dataset = GraphDataset(
......@@ -210,4 +203,3 @@ if __name__ == '__main__':
print("AUCROC: ", rocM, "+-", rocM - r[0])
print("AUCPR: ", prcM, "+-", prcM - p[0])
topN.py
View file @ 6d263e1a
......@@ -19,8 +19,6 @@ Input:
original: Graph.
pred: Edge predictions.
"""
def filterPreds(original, pred):
headsO = original.edge_index[edge][0, :].long() # Heads of the original edges of the graph.
......@@ -49,8 +47,6 @@ Input:
Output:
Dataframe containing the top n predictions ordered and decoded.
"""
def getTopN(model, dataloader, n):
print(" Looking for new edges.")
for batch in zip(dataloader):
......@@ -68,8 +64,6 @@ It gets the heterograph object and its conversion to dataloader.
Output:
The heterograph and its dataloader.
"""
def getOriginal():
hetero, _ = constructor.DISNETHeterograph(full=True, withoutRepoDB=False)
dataset = GraphDataset(
......@@ -88,7 +82,6 @@ def getOriginal():
It wraps all the necessary calls to get the top n predictions of the DMSR model.
"""
def dmsr():
# Necessary instantiations.
original, hetero = getOriginal()
......@@ -108,4 +101,3 @@ def dmsr():
if __name__ == '__main__':
dmsr()
utilities.py
View file @ 6d263e1a
......@@ -3,7 +3,6 @@ import pandas as pd
import sklearn.metrics as metrics
import matplotlib.pyplot as plt
"""
Calculate and plot ROC curve.
Input:
......@@ -16,12 +15,12 @@ Output:
"""
def plot_roc(y_test, preds, edge, extension=''):
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)
roc_auc = metrics.auc(fpr, tpr)
label = " ".join(edge) + " " + 'AUC = %0.2f' % roc_auc
fileName = 'metrics/aucroc' + extension+'.svg'
fileName = 'metrics/aucroc' + extension + '.svg'
random = [[0, 1], [0, 1], 'r--']
plotAndSaveFig(title='ROC Curve', x=fpr, y=tpr, label=label, path=fileName,
loc='lower right', xlim=[0, 1], ylim=[0, 1], xlabel='False Positive Rate',
ylabel='True Positive Rate', random=random)
......@@ -43,16 +42,17 @@ Output:
def plot_prc(y_true, y_pred, edge, extension=''):
precision, recall, trhesholds = metrics.precision_recall_curve(y_true, y_pred)
average_precision = metrics.average_precision_score(y_true, y_pred)
label = " ".join(edge) + " " + 'RPC = %0.2f' % average_precision
fileName = 'metrics/prc'+extension+'.svg'
fileName = 'metrics/prc' + extension + '.svg'
plotAndSaveFig(title='Precision-Recall Curve', x=recall, y=precision, label=label, path=fileName,
loc='lower right', xlim=[0, 1], ylim=[0, 1], xlabel='Recall', ylabel='Precision')
print("PRC:", average_precision)
return recall, precision, average_precision
"""
Plot distribution of predictions of the true and random edges. Plots the distribution function and histogram.
Input:
......@@ -66,17 +66,18 @@ def plot_dist(extension=''):
ax.set_xticks(np.arange(0, 1.1, 0.1))
ax.set_title('RepoDB & Random Prediction Histogram')
ax.set_xlabel('Prediction Score')
ax.figure.savefig('results/histogram'+extension+'.svg', format='svg', dpi=1200)
ax.figure.savefig('results/histogram' + extension + '.svg', format='svg', dpi=1200)
ax.figure.clf()
bx = df.plot.kde()
bx.set_title('RepoDB & Random Prediction')
bx.set_xlabel('Prediction Score')
bx.figure.savefig('results/distribution'+extension+'.svg', format='svg', dpi=1200)
bx.figure.savefig('results/distribution' + extension + '.svg', format='svg', dpi=1200)
bx.set_xlim([0, 1])
bx.figure.savefig('results/distribution01'+extension+'.svg', format='svg', dpi=1200)
bx.figure.savefig('results/distribution01' + extension + '.svg', format='svg', dpi=1200)
bx.figure.clf()
"""
It plots and saves the figure sent.
Input:
......@@ -95,29 +96,30 @@ Input:
def plotAndSaveFig(title, x, y, label, path, loc=None, xlim=None, ylim=None, xlabel=None, ylabel=None, random=None):
plt.title(title)
plt.plot(x, y, label=label)
if loc is not None:
plt.legend(loc=loc)
if xlim is not None:
plt.xlim(xlim)
if ylim is not None:
plt.ylim(ylim)
if xlabel is not None:
plt.xlabel(xlabel)
if ylabel is not None:
plt.ylabel(ylabel)
if random is not None:
plt.plot(random[0], random[1], random[2])
plt.show()
plt.savefig(path, format='svg', dpi=1200)
plt.clf()
"""
It plots and saves together the figures sent.
Input:
......@@ -136,31 +138,31 @@ Input:
def plotTogether(title, x, y, label, path, loc=None, xlim=None, ylim=None, xlabel=None, ylabel=None, random=None):
figH, axs = plt.subplots(2, figsize=(6, 10))
figV, axs2 = plt.subplots(1, 2, figsize=(12, 4))
for i, elem in enumerate(axs):
elem.plot(x[i], y[i], label=label[i])
elem.set_title(title[i])
if loc[i] is not None:
elem.legend(loc=loc[i])
if random[i] is not None:
elem.plot(random[i][0], random[i][1], random[i][2])
if xlim[i] is not None:
elem.set_xlim(xlim[i])
if ylim[i] is not None:
elem.set_ylim(ylim[i])
if xlabel[i] is not None:
elem.set_xlabel(xlabel)
if ylabel[i] is not None:
elem.set_ylabel(ylabel)
axs2[0] = axs[0]
axs2[1] = axs[1]
figH.savefig(path[0], format='svg', dpi=1200)
figV.savefig(path[1], format='svg', dpi=1200)
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