def initialize(self,**hyper_params):
if(not "batch_size" in hyper_params.keys()):
batch_size = 20
if(not "layer_sizes" in hyper_params.keys()):
num_samples = [20, 10]
if(not "num_samples" in hyper_params.keys()):
layer_sizes = [10, 10 ]
if(not "bias" in hyper_params.keys()):
bias = True
if(not "dropout" in hyper_params.keys()):
dropout = 0.1
if(not "lr" in hyper_params.keys()):
lr = 1e-2
graph = sg.StellarGraph(nodes=self.nodes,edges=self.edges)
# Test split
edge_splitter_test = EdgeSplitter(graph)
self.graph_test, edge_ids_test, edge_labels_test = edge_splitter_test.train_test_split(
p=0.1, method="global", keep_connected=True, seed = 42
)
# Train split
edge_splitter_train = EdgeSplitter(self.graph_test)
self.graph_train, edge_ids_train, edge_labels_train = edge_splitter_train.train_test_split(
p=0.1, method="global", keep_connected=True, seed = 42
)
# Train iterators
train_gen = GraphSAGELinkGenerator(self.graph_train, batch_size, num_samples, seed = 42)
self.train_flow = train_gen.flow(edge_ids_train, edge_labels_train, shuffle=True)
# Test iterators
test_gen = GraphSAGELinkGenerator(self.graph_train, batch_size, num_samples, seed = 42)
self.test_flow = test_gen.flow(edge_ids_test, edge_labels_test, shuffle=True)
# Model defining - Keras functional API + Stellargraph layers
graphsage = GraphSAGE(
layer_sizes=layer_sizes, generator=train_gen, bias=bias, dropout=dropout
)
x_inp, x_out = graphsage.in_out_tensors()
prediction = link_classification(
output_dim=1, output_act="sigmoid", edge_embedding_method="ip"
)(x_out)
self.model = keras.Model(inputs=x_inp, outputs=prediction)
self.model.compile(
optimizer=keras.optimizers.Adam(lr=lr),
loss=keras.losses.binary_crossentropy,
metrics=[keras.metrics.BinaryAccuracy(),keras.metrics.Recall(),keras.metrics.AUC(),keras.metrics.Precision()],
)
# return number of training and testing examples
return edge_ids_train.shape[0],edge_ids_test.shape[0]
def inference(embeddings):
G = form_graph("data/cutted_edges.csv", "data/cutted_features.csv", "data/cutted_edges_to.csv", embeddings)
edge_splitter_full = EdgeSplitter(G)
import keras as keras
from stellargraph.layer import MeanAggregator, LinkEmbedding
model = keras.models.load_model('data/w_rev1.h5',
custom_objects={'MeanAggregator': MeanAggregator, 'LinkEmbedding': LinkEmbedding})
G_full, edge_ids_full, edge_labels_full = edge_splitter_full.train_test_split(
p=0.5, method="global", keep_connected=True
)
batch_size = 20
num_samples = [20, 10]
generator = GraphSAGELinkGenerator(G, batch_size, num_samples)
hold_out_gen = generator.flow(edge_ids_full, edge_labels_full)
hold_out_predictions_pr = model.predict(hold_out_gen)
ID = 111180
EDGE_results = []
for i in range(len(edge_ids_full)):
if edge_ids_full[i][0] == ID or edge_ids_full[i][1] == ID:
EDGE_results.append(i)
predictions = [[hold_out_predictions_pr[EDGE_results[i]][0], edge_ids_full[EDGE_results[i]][0]] for i in
range(len(hold_out_predictions_pr[EDGE_results]))]
a = sorted(predictions, reverse=True)[0:10]
sorted_ids = [a[i][1] for i in range(len(a))]
return sorted_ids
def create_graphSAGE_model(graph, link_prediction=False):
if link_prediction:
# We are going to train on the original graph
generator = GraphSAGELinkGenerator(graph,
batch_size=2,
num_samples=[2, 2])
edge_ids_train = np.array([[1, 2], [2, 3], [1, 3]])
train_gen = generator.flow(edge_ids_train, np.array([1, 1, 0]))
else:
generator = GraphSAGENodeGenerator(graph,
batch_size=2,
num_samples=[2, 2])
train_gen = generator.flow([1, 2], np.array([[1, 0], [0, 1]]))
# if link_prediction:
# edge_ids_train = np.array([[1, 2], [2, 3], [1, 3]])
# train_gen = generator.flow(edge_ids_train, np.array([1, 1, 0]))
# else:
# train_gen = generator.flow([1, 2], np.array([[1, 0], [0, 1]]))
base_model = GraphSAGE(layer_sizes=[8, 8],
generator=train_gen,
bias=True,
dropout=0.5)
if link_prediction:
# Expose input and output sockets of graphsage, for source and destination nodes:
x_inp_src, x_out_src = base_model.node_model()
x_inp_dst, x_out_dst = base_model.node_model()
# re-pack into a list where (source, destination) inputs alternate, for link inputs:
x_inp = [x for ab in zip(x_inp_src, x_inp_dst) for x in ab]
# same for outputs:
x_out = [x_out_src, x_out_dst]
prediction = link_classification(output_dim=1,
output_act="relu",
edge_embedding_method="ip")(x_out)
keras_model = Model(inputs=x_inp, outputs=prediction)
else:
x_inp, x_out = base_model.node_model()
prediction = layers.Dense(units=2, activation="softmax")(x_out)
keras_model = Model(inputs=x_inp, outputs=prediction)
return base_model, keras_model, generator, train_gen
def initialize(self,**hyper_params):
if(not "batch_size" in hyper_params.keys()):
batch_size = 16
if(not "layer_sizes" in hyper_params.keys()):
num_samples = [25, 10]
if(not "num_samples" in hyper_params.keys()):
layer_sizes = [256, 256]
if(not "bias" in hyper_params.keys()):
bias = True
if(not "dropout" in hyper_params.keys()):
dropout = 0.0
if(not "lr" in hyper_params.keys()):
lr = 1e-3
if(not "num_walks" in hyper_params.keys()):
num_walks = 1
if(not "length" in hyper_params.keys()):
length = 5
self.graph = sg.StellarGraph(nodes=self.nodes_df,edges=self.edges_df)
self.nodes = list(self.graph.nodes())
del self.nodes_df
del self.edges_df
unsupervised_samples = UnsupervisedSampler(
self.graph, nodes=self.nodes, length=length, number_of_walks=num_walks
)
# Train iterators
train_gen = GraphSAGELinkGenerator(self.graph, batch_size, num_samples)
self.train_flow = train_gen.flow(unsupervised_samples)
# Model defining - Keras functional API + Stellargraph layers
graphsage = GraphSAGE(
layer_sizes=layer_sizes, generator=train_gen, bias=bias, dropout=dropout, normalize="l2"
)
x_inp, x_out = graphsage.in_out_tensors()
prediction = link_classification(
output_dim=1, output_act="sigmoid", edge_embedding_method="ip"
)(x_out)
self.model = keras.Model(inputs=x_inp, outputs=prediction)
self.model.compile(
optimizer=keras.optimizers.Adam(lr=lr),
loss=keras.losses.binary_crossentropy,
metrics=[keras.metrics.binary_accuracy],
)
x_inp_src = x_inp[0::2]
x_out_src = x_out[0]
self.embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)
self.node_gen = GraphSAGENodeGenerator(self.graph, batch_size, num_samples).flow(self.nodes)
return self.model.get_weights()
def initialize(self, **hyper_params):
if (not "batch_size" in hyper_params.keys()):
batch_size = 20
if (not "layer_sizes" in hyper_params.keys()):
num_samples = [20, 10]
if (not "num_samples" in hyper_params.keys()):
layer_sizes = [20, 20]
if (not "bias" in hyper_params.keys()):
bias = True
if (not "dropout" in hyper_params.keys()):
dropout = 0.3
if (not "lr" in hyper_params.keys()):
lr = 1e-3
if (not "train_split" in hyper_params.keys()):
train_split = 0.2
self.graph = sg.StellarGraph(nodes=self.nodes, edges=self.edges)
# Train split
edge_splitter_train = EdgeSplitter(self.graph)
graph_train, edge_ids_train, edge_labels_train = edge_splitter_train.train_test_split(
p=train_split, method="global", keep_connected=True)
# Train iterators
train_gen = GraphSAGELinkGenerator(graph_train, batch_size,
num_samples)
self.train_flow = train_gen.flow(edge_ids_train,
edge_labels_train,
shuffle=True)
# Model defining - Keras functional API + Stellargraph layers
graphsage = GraphSAGE(layer_sizes=layer_sizes,
generator=train_gen,
bias=bias,
dropout=dropout)
x_inp, x_out = graphsage.in_out_tensors()
prediction = link_classification(output_dim=1,
output_act="relu",
edge_embedding_method="ip")(x_out)
self.model = keras.Model(inputs=x_inp, outputs=prediction)
self.model.compile(
optimizer=keras.optimizers.Adam(lr=lr),
loss=keras.losses.binary_crossentropy,
metrics=["acc"],
)
return self.model.get_weights()
# Train split
edge_splitter_train = EdgeSplitter(G)
G_train, edge_ids_train, edge_labels_train = edge_splitter_train.train_test_split(
p=0.2, method="global", keep_connected=True
)
# Hyperparams
batch_size = 20
epochs = 20
num_samples = [20, 10]
layer_sizes = [20, 20]
# Train iterators
train_gen = GraphSAGELinkGenerator(G_train, batch_size, num_samples)
train_flow = train_gen.flow(edge_ids_train, edge_labels_train, shuffle=True)
# Model defining - Keras functional API + Stellargraph layers
graphsage = GraphSAGE(
layer_sizes=layer_sizes, generator=train_gen, bias=True, dropout=0.3
)
x_inp, x_out = graphsage.in_out_tensors()
prediction = link_classification(
output_dim=1, output_act="relu", edge_embedding_method="ip"
)(x_out)
model = keras.Model(inputs=x_inp, outputs=prediction)
# ### Create the generators for training
# For training we create a generator on the `G_train` graph. The `shuffle=True` argument is given to the `flow` method to improve training.
# In[13]:
generator = GraphSAGELinkGenerator(G_train, batch_size, num_samples)
# In[14]:
train_gen = generator.flow(edge_ids_train, edge_labels_train, shuffle=True)
# At test time we use the `G_test` graph and don't specify the `shuffle` argument (it defaults to `False`).
# In[15]:
test_gen = GraphSAGELinkGenerator(G_test, batch_size, num_samples).flow(
edge_ids_test, edge_labels_test
)
# In[16]:
edgelist = [(start, end) for start, end in zip(link_ids_train[:,0], link_ids_train[:,1]) ]
g_test.remove_edges_from(edgelist)
G = StellarGraph.from_networkx(g, node_features="feature")
g_train = StellarGraph.from_networkx(g_train, node_features="feature")
g_test = StellarGraph.from_networkx(g_test, node_features="feature")
print(g_train.info())
print(g_test.info())
##
batch_size = 40
num_samples = [15, 10, 5]
train_gen = GraphSAGELinkGenerator(g_train, batch_size, num_samples)
train_flow = train_gen.flow(link_ids_train, link_labels_train, shuffle=True)
test_gen = GraphSAGELinkGenerator(g_test, batch_size, num_samples)
test_flow = test_gen.flow(link_ids_test, link_labels_test)
traintest_gen = GraphSAGELinkGenerator(G, batch_size, num_samples)
traintest_flow = traintest_gen.flow(link_ids, link_labels)
## =================== model -==============================
graphsage_model = GraphSAGE(layer_sizes=[64, 32, 16], generator= train_gen, activations=["relu","relu","linear"],
bias=True, aggregator = MaxPoolingAggregator, dropout=0.0)
x_inp, x_out = graphsage_model.in_out_tensors()
def custom_layer(x):
if isinstance(x, (list, tuple)):
def train(
G,
layer_size: List[int],
num_samples: List[int],
batch_size: int = 100,
num_epochs: int = 10,
learning_rate: float = 0.001,
dropout: float = 0.0,
):
"""
Train the GraphSAGE model on the specified graph G
with given parameters.
Args:
G: NetworkX graph file
layer_size: A list of number of hidden units in each layer of the GraphSAGE model
num_samples: Number of neighbours to sample at each layer of the GraphSAGE model
batch_size: Size of batch for inference
num_epochs: Number of epochs to train the model
learning_rate: Initial Learning rate
dropout: The dropout (0->1)
"""
# Split links into train/test
print("Using '{}' method to sample negative links".format(
args.edge_sampling_method))
# From the original graph, extract E_test and the reduced graph G_test:
edge_splitter_test = EdgeSplitter(G)
# Randomly sample a fraction p=0.1 of all positive links, and same number of negative links, from G, and obtain the
# reduced graph G_test with the sampled links removed:
G_test, edge_ids_test, edge_labels_test = edge_splitter_test.train_test_split(
p=0.1,
keep_connected=True,
method=args.edge_sampling_method,
probs=args.edge_sampling_probs,
)
# From G_test, extract E_train and the reduced graph G_train:
edge_splitter_train = EdgeSplitter(G_test, G)
# Randomly sample a fraction p=0.1 of all positive links, and same number of negative links, from G_test, and obtain the
# further reduced graph G_train with the sampled links removed:
G_train, edge_ids_train, edge_labels_train = edge_splitter_train.train_test_split(
p=0.1,
keep_connected=True,
method=args.edge_sampling_method,
probs=args.edge_sampling_probs,
)
# G_train, edge_ds_train, edge_labels_train will be used for model training
# G_test, edge_ds_test, edge_labels_test will be used for model testing
# Convert G_train and G_test to StellarGraph objects (undirected, as required by GraphSAGE) for ML:
G_train = sg.StellarGraph(G_train, node_features="feature")
G_test = sg.StellarGraph(G_test, node_features="feature")
# Mapper feeds link data from sampled subgraphs to GraphSAGE model
# We need to create two mappers: for training and testing of the model
train_gen = GraphSAGELinkGenerator(G_train, batch_size, num_samples)
train_flow = train_gen.flow(edge_ids_train,
edge_labels_train,
shuffle=True)
test_gen = GraphSAGELinkGenerator(G_test, batch_size, num_samples)
test_flow = test_gen.flow(edge_ids_test, edge_labels_test)
# GraphSAGE model
graphsage = GraphSAGE(layer_sizes=layer_size,
generator=train_gen,
bias=True,
dropout=dropout)
# Construct input and output tensors for the link prediction model
x_inp, x_out = graphsage.build()
# Final estimator layer
prediction = link_classification(
output_dim=1,
output_act="sigmoid",
edge_embedding_method=args.edge_embedding_method,
)(x_out)
# Stack the GraphSAGE and prediction layers into a Keras model, and specify the loss
model = keras.Model(inputs=x_inp, outputs=prediction)
model.compile(
optimizer=optimizers.Adam(lr=learning_rate),
loss=losses.binary_crossentropy,
metrics=[metrics.binary_accuracy],
)
# Evaluate the initial (untrained) model on the train and test set:
init_train_metrics = model.evaluate_generator(train_flow)
init_test_metrics = model.evaluate_generator(test_flow)
print("\nTrain Set Metrics of the initial (untrained) model:")
for name, val in zip(model.metrics_names, init_train_metrics):
print("\t{}: {:0.4f}".format(name, val))
print("\nTest Set Metrics of the initial (untrained) model:")
for name, val in zip(model.metrics_names, init_test_metrics):
print("\t{}: {:0.4f}".format(name, val))
# Train model
print("\nTraining the model for {} epochs...".format(num_epochs))
history = model.fit_generator(
train_flow,
epochs=num_epochs,
validation_data=test_flow,
verbose=2,
shuffle=False,
)
# Evaluate and print metrics
train_metrics = model.evaluate_generator(train_flow)
test_metrics = model.evaluate_generator(test_flow)
print("\nTrain Set Metrics of the trained model:")
for name, val in zip(model.metrics_names, train_metrics):
print("\t{}: {:0.4f}".format(name, val))
print("\nTest Set Metrics of the trained model:")
for name, val in zip(model.metrics_names, test_metrics):
print("\t{}: {:0.4f}".format(name, val))
# Save the trained model
save_str = "_n{}_l{}_d{}_r{}".format(
"_".join([str(x) for x in num_samples]),
"_".join([str(x) for x in layer_size]),
dropout,
learning_rate,
)
model.save("graphsage_link_pred" + save_str + ".h5")
display(HTML(dataset.description))
G, node_subjects = dataset.load()
nodes = list(G.nodes())
number_of_walks = 1
length = 5
unsupervised_samples = UnsupervisedSampler(
G, nodes=nodes, length=length, number_of_walks=number_of_walks
)
batch_size = 50
epochs = 4
num_samples = [10, 5]
generator = GraphSAGELinkGenerator(G, batch_size, num_samples)
train_gen = generator.flow(unsupervised_samples)
# +
# feature extractoring and preprocessing data
import librosa
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
import os
from PIL import Image
import pathlib
import csv
