def available_datasets():
return gf.BunchDict(reddit="reddit dataset")
def fit(self, train_data, val_data=None, **kwargs):
cache = self.cache
cfg = self.cfg.fit
cfg.merge_from_dict(kwargs)
ckpt_cfg = cfg.ModelCheckpoint
es_cfg = cfg.EarlyStopping
pb_cfg = cfg.Progbar
log_cfg = cfg.Logger
if log_cfg.enabled:
log_cfg.name = log_cfg.name or self.name
logger = gg.utils.setup_logger(output=log_cfg.filepath,
name=log_cfg.name)
model = self.model
if model is None:
raise RuntimeError(
'You must compile your model before training/testing/predicting. Use `trainer.build()`.'
)
if not isinstance(train_data, (Sequence, DataLoader, Dataset)):
train_data = self.train_loader(train_data)
if cfg.cache_train_data:
cache.train_data = train_data
validation = val_data is not None
if validation:
if not isinstance(val_data, (Sequence, DataLoader, Dataset)):
val_data = self.test_loader(val_data)
if cfg.cache_val_data:
cache.val_data = val_data
# Setup callbacks
callbacks = callbacks_module.CallbackList()
history = History()
callbacks.append(history)
cfg, callbacks = setup_callbacks(cfg, callbacks, validation)
callbacks.set_model(model)
model.stop_training = False
verbose = cfg.verbose
assert not (
verbose and log_cfg.enabled
), "Progbar and Logger cannot be used together! You must set `verbose=0` when Logger is enabled."
if verbose:
if verbose <= 2:
progbar = Progbar(target=cfg.epochs,
width=pb_cfg.width,
verbose=verbose)
print("Training...")
elif log_cfg.enabled:
logger.info("Training...")
logs = gf.BunchDict()
callbacks.on_train_begin()
try:
for epoch in range(cfg.epochs):
if verbose > 2:
progbar = Progbar(target=len(train_data),
width=pb_cfg.width,
verbose=verbose - 2)
callbacks.on_epoch_begin(epoch)
callbacks.on_train_batch_begin(0)
train_logs = self.train_step(train_data)
if hasattr(train_data, 'on_epoch_end'):
train_data.on_epoch_end()
logs.update(train_logs)
if validation:
valid_logs = self.test_step(val_data)
logs.update({("val_" + k): v
for k, v in valid_logs.items()})
if hasattr(val_data, 'on_epoch_end'):
val_data.on_epoch_end()
callbacks.on_train_batch_end(len(train_data), logs)
callbacks.on_epoch_end(epoch, logs)
if verbose > 2:
print(f"Epoch {epoch+1}/{cfg.epochs}")
progbar.update(len(train_data), logs.items())
elif verbose:
progbar.update(epoch + 1, logs.items())
elif log_cfg.enabled:
logger.info(
f"Epoch {epoch+1}/{cfg.epochs}\n{gg.utils.create_table(logs)}"
)
if model.stop_training:
if log_cfg.enabled:
logger.info(f"Early Stopping at Epoch {epoch}")
else:
print(f"Early Stopping at Epoch {epoch}",
file=sys.stderr)
break
callbacks.on_train_end()
if ckpt_cfg.enabled:
if ckpt_cfg.save_weights_only:
model.load_weights(ckpt_cfg.path)
else:
self.model = model.load(ckpt_cfg.path)
finally:
# to avoid unexpected termination of the model
if ckpt_cfg.enabled and ckpt_cfg.remove_weights:
self.remove_weights()
return history
def test(self, index):
index = gf.asarray(index)
y_true = self.graph.node_label[index]
y_pred = self.classifier.predict(self.embeddings[index])
accuracy = accuracy_score(y_true, y_pred)
return gf.BunchDict(loss=None, accuracy=accuracy)
def empty_cache(self):
self.cache = gf.BunchDict()
import os
import os.path as osp
from typing import Optional, List
from graphgallery import functional as gf
from .in_memory_dataset import InMemoryDataset
from ..data.io import makedirs, download_file
from ..data.preprocess import process_planetoid_datasets
from ..data.graph import Graph
_DATASETS = gf.BunchDict({"citeseer": "citeseer citation dataset",
"cora": "cora citation dataset",
"pubmed": "pubmed citation dataset",
"nell.0.1": "NELL dataset",
"nell.0.01": "NELL dataset",
"nell.0.001": "NELL dataset", })
_DATASET_URL = "https://github.com/EdisonLeeeee/" + \
"GraphData/raw/master/datasets/planetoid"
class Planetoid(InMemoryDataset):
r"""The citation network datasets "Cora", "CiteSeer" and "PubMed" from the
`"Revisiting Semi-Supervised Learning with Graph Embeddings"
<https://arxiv.org/abs/1603.08861>`_ paper.
Nodes represent documents and edges represent citation links.
Training, validation and test splits are given by binary masks.
The original url is: <https://github.com/kimiyoung/planetoid/raw/master/data>
"""
def flips(self):
# TODO
return gf.BunchDict(edge_flips=self.edge_flips, nx_flips=self.nx_flips)
import os.path as osp
import pickle as pkl
from graphgallery import functional as gf
from ..data import Reader
from ..data.graph import Graph
from ..data.multi_graph import MultiGraph
from .in_memory_dataset import InMemoryDataset
_DATASET = gf.BunchDict(
deezer="deezer dataset (node-level)",
facebook="facebook dataset (node-level)",
github="github dataset (node-level)",
lastfm="lastfm dataset (node-level)",
twitch="twitch dataset (node-level)",
wikipedia="wikipedia dataset (node-level)",
reddit10k="reddit10k dataset (graph-level)",
)
class KarateClub(InMemoryDataset):
"""Datasets from `Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs`, CIKM 2020
<https://github.com/benedekrozemberczki/karateclub>
"""
__node_level_url__ = "https://github.com/EdisonLeeeee/GraphData/raw/master/datasets/karateclub/node_level"
__graph_level_url__ = "https://github.com/EdisonLeeeee/GraphData/raw/master/datasets/karateclub/graph_level"
def __init__(self,
name,
root=None,
def available_datasets():
return gf.BunchDict(ppi="ppi dataset")