diff --git a/README.md b/README.md index 8ab1239a..17e8239a 100644 --- a/README.md +++ b/README.md @@ -127,13 +127,13 @@ For information on installations of these additional libraries see [pyg-lib](htt To run our baseline static experimentation: ```sh -uv run tgrag/experiments/main.py +uv run tgrag/experiments/gnn_experiments/main.py ``` Alternatively, you can design your own configuration, updating the model parameters: ```sh -uv run tgrag/experiments/main.py --config configs/your_config.yaml +uv run tgrag/experiments/gnn_experiments/main.py --config configs/your_config.yaml ``` To learn more about making a contribution to CrediGraph see our [contribution guide](./.github/CONTRIBUTION.md) diff --git a/pyproject.toml b/pyproject.toml index 64d79b58..3b5fd6e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "tgrag" version = "0.1.0" description = "Temporal Graph Analysis on Large-Scale Web Data" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" authors = [ ] dependencies = [ @@ -19,14 +19,18 @@ dependencies = [ "scipy>=1.13.1", "sentence-transformers>=5.0.0", "tldextract>=5.3.0", - "torch>=2.7.1", - "torch-geometric>=2.6.1", + "torch==2.0.1", "torcheval>=0.0.7", "ujson>=5.10.0", "warcio>=1.7.5", "cloudscraper>=1.2.71", "BeautifulSoup4>=4.13.0", "jsonpath-ng>=1.7.0", + "kuzu==0.0.7", + "torch-geometric>=2.6.1", + "torchvision==0.15.2", + "torchaudio==2.0.2", + "numpy>=2.0.2", ] [tool.flit.module] @@ -112,3 +116,6 @@ exclude_lines = [ "register_parameter", "torch.cuda.is_available", ] + +[tool.uv.sources] +torch-geometric = { git = "https://github.com/pyg-team/pytorch_geometric.git" } diff --git a/tgrag/experiments/tgl_experiments/main.py b/tgrag/experiments/tgl_experiments/main.py new file mode 100644 index 00000000..6912c322 --- /dev/null +++ b/tgrag/experiments/tgl_experiments/main.py @@ -0,0 +1,332 @@ +import argparse +import faulthandler +import logging +import pickle +from multiprocessing import cpu_count +from pathlib import Path +from typing import Tuple, cast + +import kuzu +import numpy as np +import pandas as pd +import torch +from torch_geometric.data import FeatureStore, GraphStore +from torch_geometric.loader import NeighborLoader +from tqdm import tqdm + +from tgrag.utils.args import DataArguments, ModelArguments, parse_args +from tgrag.utils.logger import setup_logging +from tgrag.utils.path import get_root_dir, get_scratch +from tgrag.utils.seed import seed_everything + +parser = argparse.ArgumentParser( + description='TGL Experiments.', + formatter_class=argparse.ArgumentDefaultsHelpFormatter, +) +parser.add_argument( + '--config-file', + type=str, + default='configs/tgl/base.yaml', + help='Path to yaml configuration file to use', +) + + +def construct_kuzu_format( + db_path: Path, + node_csv: Path, + dqr_csv: Path, + seed: int = 42, + D: int = 128, + chunk_size: int = 1_000_000, +) -> None: + dqr = pd.read_csv(dqr_csv) + rng = np.random.default_rng(seed=seed) + + x_list = [] + y_list = [] + ts_list = [] + + x_path = db_path / 'x.npy' + y_path = db_path / 'y.npy' + ts_path = db_path / 'ts.npy' + + if x_path.exists() and y_path.exists() and ts_path.exists(): + y = np.load(y_path, mmap_mode='r') + x = np.load(x_path, mmap_mode='r') + ts = np.load(ts_path, mmap_mode='r') + logging.info( + f'x: {x.shape}, {x.dtype}; y: {y.shape}, {y.dtype}; ts: {ts.shape}, {ts.dtype}' + ) + + assert ( + np.load(x_path).dtype == np.float32 + ), f'x.npy has wrong dtype: {np.load(x_path).dtype}' + assert ( + np.load(y_path).dtype == np.float32 + ), f'y.npy has wrong dtype: {np.load(y_path).dtype}' + assert ( + np.load(ts_path).dtype == np.int64 + ), f'ts.npy has wrong dtype: {np.load(ts_path).dtype}' + logging.info(f'x.npy, y.npy and ts.npy at {db_path} already exists, returning.') + return + + logging.info(f'Processing {node_csv} in chunks of {chunk_size:,} rows...') + for chunk in tqdm( + pd.read_csv(node_csv, chunksize=chunk_size), + desc='Reading vertices', + unit='chunk', + ): + chunk = chunk.merge(dqr, on='domain', how='left') + chunk['pc1'].fillna(-1.0, inplace=True) + + x_chunk = rng.normal(size=(len(chunk), D)).astype(np.float32) + + x_list.append(x_chunk) + y_list.append(chunk['pc1'].astype(np.float32).values) + ts_list.append(chunk['ts'].astype(np.int64).values) + + x = np.vstack(x_list) + y = np.concatenate(y_list) + ts = np.concatenate(ts_list) + + logging.info(f'Saving arrays to {db_path}...') + np.save(x_path, x) + np.save(y_path, y) + np.save(ts_path, ts) + + logging.info(f'Saved x{list(x.shape)}, y[{y.shape[0]}]') + + +def build_domain_id_mapping( + node_csv: Path, edge_csv: Path, out_dir: Path, chunk_size: int = 1_000_000 +) -> None: + out_dir.mkdir(parents=True, exist_ok=True) + nid_map_path = out_dir / 'nid_map.pkl' + nid_array_path = out_dir / 'nid.npy' + edges_out_path = out_dir / 'edges_with_id.csv' + + if nid_array_path.exists() and nid_map_path.exists() and edges_out_path.exists(): + logging.info( + f'nid.npy, nid_map.pkl and edges with IDs already exists at {out_dir}, returning.' + ) + return + + logging.info(f'Building domain to id mapping from {node_csv}...') + domain_to_id = {} + next_id = 0 + domain_list = [] + + if not (nid_array_path.exists() and nid_map_path.exists()): + for chunk in tqdm( + pd.read_csv(node_csv, chunksize=chunk_size), + desc='Reading vertices', + unit='chunk', + ): + for domain in chunk['domain'].astype(str): + if domain not in domain_to_id: + domain_to_id[domain] = next_id + domain_list.append(domain) + next_id += 1 + + logging.info(f'Total unique domains: {len(domain_to_id):,}') + np.save(nid_array_path, np.arange(len(domain_list), dtype=np.int64)) + with open(nid_map_path, 'wb') as f: + pickle.dump(domain_to_id, f) + + if not edges_out_path.exists(): + logging.info(f'Rewriting {edge_csv} to {edges_out_path} with ID mapping...') + with open(edges_out_path, 'w') as fout: + fout.write('src_id,dst_id,ts\n') + + for chunk in tqdm( + pd.read_csv(edge_csv, chunksize=chunk_size), + desc='Rewriting edges', + unit='chunk', + ): + chunk['src_id'] = chunk['src'].map(domain_to_id) + chunk['dst_id'] = chunk['dst'].map(domain_to_id) + + chunk[['src_id', 'dst_id', 'ts']].astype( + {'src_id': 'int64', 'dst_id': 'int64'} + ).to_csv(fout, header=False, index=False) + + logging.info( + f'Finished. Saved nid_map.pkl, nid.npy, and edges_with_id.csv to {out_dir}' + ) + + +def initialize_graph_db( + db_path: Path, buffer: int = 40 +) -> Tuple[kuzu.Database, kuzu.Connection]: + logging.info('Connecting graph storage backend') + graph_db_path = db_path / 'graphdb' + edges_csv = db_path / 'edges_with_id.csv' + + if graph_db_path.exists(): + logging.info(f'Existing database found at {db_path}, skipping initalization.') + db = kuzu.Database(str(graph_db_path), buffer_pool_size=buffer * 1024**3) + conn = kuzu.Connection(db, num_threads=cpu_count()) + return db, conn + + db = kuzu.Database(str(graph_db_path), buffer_pool_size=buffer * 1024**3) + conn = kuzu.Connection(db, num_threads=cpu_count()) + + conn.execute( + 'CREATE NODE TABLE domain(nid INT64, x FLOAT[128], ts INT64, y FLOAT, PRIMARY KEY(nid));' + ) + conn.execute('CREATE REL TABLE link(FROM domain TO domain, ts INT64, MANY_MANY);') + conn.execute( + f'COPY domain FROM ("{db_path / "nid.npy"}", "{db_path / "x.npy"}", "{db_path / "ts.npy"}", "{db_path / "y.npy"}") BY COLUMN;' + ) + conn.execute(f'COPY link FROM "{edges_csv}" (HEADER=true);') + logging.info('Graph database initialized') + return db, conn + + +def construct_dataset(conn: kuzu.connection) -> Tuple[torch.Tensor, torch.Tensor]: + count_result = conn.execute( + """ + MATCH (d:domain) RETURN count(*); + """ + ) + count = count_result.get_next()[0] + logging.info(f'Match (d:domain) RETURN count(*) -> {count}') + + train_count = int(0.6 * count) + count - train_count + + indices = torch.randperm(count) + train_ids = indices[:train_count] + test_ids = indices[train_count:] + + train_mask = torch.zeros(count, dtype=torch.bool) + test_mask = torch.zeros(count, dtype=torch.bool) + + train_mask[train_ids] = True + test_mask[test_ids] = True + return train_mask, test_mask + + +def run_scalable_gnn( + data_arguments: DataArguments, + model_arguments: ModelArguments, + train_mask: torch.Tensor, + test_mask: torch.Tensor, + feature_store: FeatureStore, + graph_store: GraphStore, +) -> None: + loader = NeighborLoader( + data=(feature_store, graph_store), + input_nodes=('domain', train_mask), + num_neighbors={('domain', 'link', 'domain'): model_arguments.num_neighbors}, + batch_size=model_arguments.batch_size, + shuffle=True, + num_workers=4, + pin_memory=True, + persistent_workers=True, + ) + logging.info('Train loader created') + + next_data = next(iter(loader)) + logging.info(f'{next_data}') + + # logging.info('*** Training ***') + # for run in tqdm(range(model_arguments.runs), desc='Runs'): + # model = Model( + # model_name=model_arguments.model, + # normalization=model_arguments.normalization, + # in_channels=data.x.shape[1], + # hidden_channels=model_arguments.hidden_channels, + # out_channels=model_arguments.embedding_dimension, + # num_layers=model_arguments.num_layers, + # dropout=model_arguments.dropout, + # ).to(device) + # optimizer = torch.optim.AdamW( + # model.parameters(), lr=model_arguments.lr, weight_decay=5e-4 + # ) + # for _ in tqdm(range(1, 1 + model_arguments.epochs), desc='Epochs'): + # pass + # + + +def main() -> None: + faulthandler.enable() + root = get_root_dir() + scratch = get_scratch() + args = parser.parse_args() + config_file_path = root / args.config_file + meta_args, experiment_args = parse_args(config_file_path) + setup_logging(meta_args.log_file_path) + seed_everything(meta_args.global_seed) + + db_path = scratch / cast(str, meta_args.database_folder) + node_path = scratch / cast(str, meta_args.node_file) + edge_path = scratch / cast(str, meta_args.edge_file) + dqr_path = root / 'data' / 'dqr' / 'domain_pc1.csv' + + build_domain_id_mapping(node_csv=node_path, edge_csv=edge_path, out_dir=db_path) + construct_kuzu_format(db_path=db_path, node_csv=node_path, dqr_csv=dqr_path) + + db, conn = initialize_graph_db(db_path=db_path) + + # try: + # df = conn.execute('MATCH (n:domain) RETURN n LIMIT 5').get_as_df() + # print(df.head()) + # except RuntimeError as e: + # print('No domain table found:', e) + # + # node_df = conn.execute( + # """ + # MATCH (n:domain) + # RETURN n.nid AS domain, n.ts AS ts, n.x as RNI + # LIMIT 5 + # """ + # ).get_as_df() + # + # print('=== Example node records ===') + # print(node_df.head(), '\n') + # + # edge_df = conn.execute( + # """ + # MATCH (a:domain)-[r:link]->(b:domain) + # RETURN a.nid AS src, b.nid AS dst, r.ts AS ts + # LIMIT 5 + # """ + # ).get_as_df() + + # print('=== Example edge records ===') + # print(edge_df.head()) + + feature_store, graph_store = db.get_torch_geometric_remote_backend() + + logging.info(f'Feature store keys: {feature_store.get_all_tensor_attrs()}') + logging.info(f'Graph store keys: {graph_store.get_all_edge_attrs()}') + + logging.info('View of feature and graph store:') + try: + y_subset = feature_store['domain', 'x', 0] + logging.info(type(y_subset)) + except Exception as e: + logging.exception(f'Error accessing feature store {e}') + + train_mask, test_mask = construct_dataset(conn=conn) + logging.info(f'Train_mask size: {train_mask.size()}') + logging.info(f'Sample of train mask: {train_mask[0:5]}') + logging.info(f'Test_mask size: {test_mask.size()}') + logging.info(f'Sample of test mask: {test_mask[0:5]}') + for experiment, experiment_arg in experiment_args.exp_args.items(): + logging.info(f'\n**Running**: {experiment}') + run_scalable_gnn( + data_arguments=experiment_arg.data_args, + model_arguments=experiment_arg.model_args, + train_mask=train_mask, + test_mask=test_mask, + feature_store=feature_store, + graph_store=graph_store, + ) + + logging.info('Completed.') + + +if __name__ == '__main__': + main() diff --git a/tgrag/utils/args.py b/tgrag/utils/args.py index 20e8d213..18438b21 100644 --- a/tgrag/utils/args.py +++ b/tgrag/utils/args.py @@ -33,6 +33,9 @@ class MetaArguments: target_file: Union[str, List[str]] = field( metadata={'help': 'A csv or list of csv files containing the targets.'}, ) + database_folder: Union[str, List[str]] = field( + metadata={'help': 'A folder containing the graph database.'}, + ) processed_location: Union[str, List[str]] = field( metadata={'help': 'The location to save the processed feature matrix.'}, ) @@ -98,6 +101,7 @@ def resolve(f: str) -> str: self.node_file = resolve_paths(self.node_file) self.edge_file = resolve_paths(self.edge_file) self.target_file = resolve_paths(self.target_file) + self.database_folder = resolve_paths(self.database_folder) self.processed_location = resolve_paths(self.processed_location) if self.log_file_path is not None: diff --git a/tgrag/webScrape/scrape_home_pages.py b/tgrag/webscrape/scrape_home_pages.py similarity index 54% rename from tgrag/webScrape/scrape_home_pages.py rename to tgrag/webscrape/scrape_home_pages.py index f75e9d6c..bf33a333 100644 --- a/tgrag/webScrape/scrape_home_pages.py +++ b/tgrag/webscrape/scrape_home_pages.py @@ -1,41 +1,43 @@ -import pandas as pd +import argparse import os -from tqdm import tqdm import threading -import cloudscraper -from bs4 import BeautifulSoup import warnings -from bs4 import XMLParsedAsHTMLWarning -import argparse -warnings.filterwarnings("ignore", category=XMLParsedAsHTMLWarning) +import cloudscraper +import pandas as pd +from bs4 import BeautifulSoup, XMLParsedAsHTMLWarning +from tqdm import tqdm + +warnings.filterwarnings('ignore', category=XMLParsedAsHTMLWarning) results = [] headers = { 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/137.0.0.0 Safari/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', 'Content-Type': 'text/html; charset=UTF-8', - "Accept-Language": "en-US,en;q=0.9", - } + 'Accept-Language': 'en-US,en;q=0.9', +} results = [] scraper = cloudscraper.create_scraper() + + def scrape_multithread(url): global results results.append(scrape(url)) + + def scrape(url): - # start = time.time() scraper = cloudscraper.create_scraper() - # print(f"############URL({url})###################") try: - response = scraper.get("https://www." + url, headers=headers, timeout=5) + response = scraper.get('https://www.' + url, headers=headers, timeout=5) except: try: - response = scraper.get("https://" + url, headers=headers, timeout=5) + response = scraper.get('https://' + url, headers=headers, timeout=5) except: try: - response = scraper.get("http://www." + url, headers=headers, timeout=5) + response = scraper.get('http://www.' + url, headers=headers, timeout=5) except: try: - response = scraper.get("http://" + url, headers=headers, timeout=5) + response = scraper.get('http://' + url, headers=headers, timeout=5) except Exception as e: return [None, str(e), None, None, url] soup = BeautifulSoup(response.text, 'html.parser') @@ -47,13 +49,13 @@ def scrape(url): res.append(None) try: description_meta = soup.find('meta', attrs={'name': 'description'}) - description = description_meta['content'] if description_meta else 'No description found' + description = ( + description_meta['content'] if description_meta else 'No description found' + ) res.append(description) - # print(f"Description: {description}") except: res.append(None) - # Get Open Graph title try: og_title_meta = soup.find('meta', attrs={'property': 'og:title'}) og_title = og_title_meta['content'] if og_title_meta else 'No OG title found' @@ -65,31 +67,45 @@ def scrape(url): scraper.close() return res -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="WebSite Home Page Scraper") - parser.add_argument("--domains_path", type=str, default="../../data/dqr/domain_pc1.csv", help="dqr dataset path") - parser.add_argument("--output_path", type=str, default="../../data/scrape/",help="output path") - parser.add_argument("--batch_size", type=int, default=1000, help="scarp batch_size") + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='WebSite Home Page Scraper') + parser.add_argument( + '--domains_path', + type=str, + default='../../data/dqr/domain_pc1.csv', + help='dqr dataset path', + ) + parser.add_argument( + '--output_path', type=str, default='../../data/scrape/', help='output path' + ) + parser.add_argument('--batch_size', type=int, default=1000, help='scarp batch_size') args = parser.parse_args() - if not os.path.exists(f"../../data/scrapedContent/"): - os.mkdir(f"../../data/scrapedContent/") + if not os.path.exists(f'../../data/scrapedContent/'): + os.mkdir(f'../../data/scrapedContent/') domains_df = pd.read_csv(args.domains_path) - domains_lst = domains_df["domain"].tolist() + domains_lst = domains_df['domain'].tolist() del domains_df - print(f"len of domains={len(domains_lst)}") + print(f'len of domains={len(domains_lst)}') batch_size = args.batch_size for b_idx in tqdm(range(0, len(domains_lst) + batch_size, batch_size)): threads = [] results = [] - ############### MultiThreading ########### - for url in domains_lst[b_idx:b_idx + batch_size]: + for url in domains_lst[b_idx : b_idx + batch_size]: thread = threading.Thread(target=scrape_multithread, args=(url,)) threads.append(thread) for i in tqdm(range(0, len(threads), 1000)): - for thread in threads[i:i + 1000]: + for thread in threads[i : i + 1000]: thread.start() - for thread in threads[i:i + 1000]: + for thread in threads[i : i + 1000]: thread.join() - results_df = pd.DataFrame(results, columns=["title", "desc", "OG-Title", "text", "url"]) - results_df.to_csv(f"../../data/scrapedContent/{args.domains_path.split('/')[-1].split('.')[0]}_scraped_homepage_{b_idx}.csv", index=None, encoding='utf-8',errors='ignore') + results_df = pd.DataFrame( + results, columns=['title', 'desc', 'OG-Title', 'text', 'url'] + ) + results_df.to_csv( + f'../../data/scrapedContent/{args.domains_path.split("/")[-1].split(".")[0]}_scraped_homepage_{b_idx}.csv', + index=None, + encoding='utf-8', + errors='ignore', + ) diff --git a/uv.lock b/uv.lock index 0552f312..f0b7faba 100644 --- a/uv.lock +++ b/uv.lock @@ -1,8 +1,7 @@ version = 1 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